Research article

Hedge asset for stock markets: Cryptocurrency, Cryptocurrency Volatility Index (CVI) or Commodity

  • In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the return–volatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession Ⅲ (a Russia-Ukraine war); next, part Ⅳ — the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.

    Citation: Rubaiyat Ahsan Bhuiyan, Tanusree Chakravarty Mukherjee, Kazi Md Tarique, Changyong Zhang. Hedge asset for stock markets: Cryptocurrency, Cryptocurrency Volatility Index (CVI) or Commodity[J]. Quantitative Finance and Economics, 2025, 9(1): 131-166. doi: 10.3934/QFE.2025005

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  • In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the return–volatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession Ⅲ (a Russia-Ukraine war); next, part Ⅳ — the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.



    Euler-Lagrange fluid-particle simulation has been extensively used as a surrogate for experiments in respiratory aerosol dynamics and inhalation drug delivery. This approach's popularity is rooted in its high accuracy in resolving fine-scale flow details and tracking individual droplets/particles in a time-efficient manner and thus providing an estimation of the dosimetry of released pharmaceuticals [1]. However, an important issue often neglected in numerical simulations of pulmonary drug delivery was the momentum exchange from the discrete phase to the carrier fluid or the reciprocal interactions between aerosols and flow (i.e., two-way coupling, or 2WC). One-way coupling (1WC) was adopted in most previous inhalation dosimetry simulations based on the assumption of dilute aerosol concentrations, where the presence of the aerosols did not affect the carrier flow [2]. This assumption was valid only when aerosols had a sufficiently low concentration and low exiting velocities, such as those from a mesh nebulizer. One-way coupling was also valid for aerosols from a valved holding chamber (VHC) or spacer connected to a metered-dose inhaler (MDI) due to the mixing of the spray droplets within it, which greatly reduced droplets' speed and concentration [3].

    However, the local concentration of MDI spray aerosols can be high upon discharge close to the actuator orifice, which has a small diameter of 0.2–0.5 mm [4,5,6]. The pressurized propellant generates a high-speed jet flow from the orifice and atomizes the drug solution into micro-scale droplets, which develop into a spray plume by advancing along the streamwise direction and dispersing in transverse directions. Because of the large density ratio between the droplet and fluid (i.e., three orders of magnitude), the inertia and response time of the droplets differ notably from those of the carrier flow, which will induce large droplet-flow slip velocities. As a result, the momentum exchange from the spray droplets to the co-flow can be significant downstream of the actuator orifice and modify the flow behaviors there, which in turn will modify aerosol motions. These reciprocal interactions kick in from the onset of the actuation and occur throughout the actuation process (usually 0.2 s). Spatially, the 2WC effect is more significant near the orifice and persists till either the local mass loading or aerosol velocities become sufficiently low. Due to their higher inertia, aerosols often exhibit an integrative feature, i.e., the current aerosol distribution (or droplet location) depends on not only the instantaneous velocity distribution but also the prior histories of the droplets. By contrast, airflow can quickly respond to external changes and is predominantly determined by the instantaneous pressure field. As a result, the motions of individual droplets will be constantly modified by the carrier flow, and these modifications will accumulate throughout the time course, yielding even more different spray plume morphology and velocity than in the 1WC case.

    Many experimental and numerical studies have considered the 2WC effects on particle-laden flows in general. Early observations by Torobin and Gauvin demonstrated that particles modified the drag force and heat transfer rate on the pipe, which couldn't be explained unless the flow turbulence had been modulated by the entrained particles [7]. Questions also arose about whether, at what conditions, and how the turbulence would be reduced or enhanced by the dispersed particles. It was known that when the aerosol droplets are comparable in size to the smallest fluid length scale or larger, unsteady flows can be important in affecting the aerosol motions [8,9]. Gore and Crowe reviewed a wide spectrum of experimental studies on particle-modulated turbulence in jets and pipes at varying orientations, with a Reynolds number of 8000–105, volume fraction of 10-6–0.2, and density ratio of 10-3–2500 [10]. They first observed that particles smaller than 200 µm suppressed turbulence while particles larger than 200 µm enhanced turbulence. A non-dimensional parameter dp/lt (particle diameter to turbulence length scale) was then proposed, with turbulence attenuation at dp/lt < 0.1 and enhancement at dp/lt > 0.1 [10,11]. The percentage change in turbulence intensity varied from -50% to 400%, and exhibited a nonlinear relation to dp/lt [10,11]. Later, Elghobashi and Truesdell updated the criterion to dp/η, with η being the smallest turbulence length scale or Kolmogorov length scale [12].

    Note that the particle-induced turbulence modulation can reversely modify particle motions at varying degrees, depending on the volume fraction, mass fraction, and/or diameter of the particles [13]. Using direct numerical simulations (DNS), Monchaux & Dejoan showed that 2WC had a weak effect on the global flow statistics when the particle loading is low (φ = 1.5×10-5). They also reported that 2WC enabled the particles to drag the fluid down with them, which further enhanced the particle settling [14]. In a classic review, Elghobashi categorized the particle-laden turbulent flows into three regimes based on the particle volume fraction φ: one-way coupling (1WC) regime when φ = 10-8–10-6, two-way coupling (2WC) regime when φ = 10-6–10-3, and particle-particle collision regime when φ > 10-3 [15]. The 1WC and 2WC regimes had dilute suspensions while the particle-particle collision regime had dense suspension. In the 2WC regime (φ = 10-6–10-3), a sub-division was made based on the time scale ratio, τpη where τp was the particle residence time, and τη was the Kolmogorov time scale. For particles with τpη > 100, 2WC enhanced turbulence production, and for particles with τpη < 100, 2WC enhanced dissipation [15].

    Till now, studies of the 2WC effects on MDI spray flow and aerosol dynamics have not been reported. Considering the high droplet concentration close to the actuator orifice and the high-speed jet flow that generates small turbulence length scales, 2WC effects are expected to be significant in the orifice vicinity and decay from the orifice. It is unknown yet to what extent the spray plume will be affected and at what level it is affected in different directions (i.e., spray centerline vs. transverse directions) and under various conditions (i.e., mass loading, jet flow speed, and aerosol diameter). This study seeks to answer the above questions related to three popular MDIs, i.e., Ventolin, ProAir, and Qvar, which have different droplet size distributions and co-flow velocities. Specific aims include:

    1) Develop an LES-Lagrangian computational model for MDI spray plume development in open space and validate it with complimentary high-speed imaging of Ventolin sprays.

    2) Compare the spatial and temporal evolutions of the Ventolin spray plumes between 2WC and 1WC predictions.

    3) Evaluate the 2WC effects with mass loadings, droplet sizes, and inhaler types.

    4) Understand diameter-dependent spray plume behaviors by correlating to flow Kolmogorov length/time scales and respective droplet-flow length/time ratios.

    The results of this study will shed light on whether 2WC effects should be considered in MDI spray simulations and provide quantitative guidance in estimating the differences between 2WC and 1WC cases.

    The 2WC effects on MDI airflow and aerosol dynamics were numerically evaluated in an MDI-open-space model, as shown in Figure 1(a). The MDI was actuated, and the generated spray droplets were released into the open space. A cone-shaped space was adopted to account for the expansion of the spray plume and, at the same time, minimize the computational mesh size. An airspace geometry had a length of 0.5 m based on the observations that the maximal spray plume lengths from all three inhalers considered hereof were equal to or smaller than 0.5 m.

    Figure 1.  Study design and computational model: (a) computational domain consisting of an MDI and an open space with a length of 0.5 m, (b) model for the Ventolin inhaler geometry and polydisperse spray aerosols, (c) aerosol size distribution for ProAir and Qvar, and (d) multidomain computational mesh, with a zoomed view of refined meshes in the mouthpiece and close to the actuator orifice.

    Figure 1(b) shows the model for Ventolin, including both the geometrical model and the model for the initial airflow and spray aerosols at actuation. The inhaler geometry was developed in SolidWorks (Dassault Systèmes, Concord, MA) based on the Ventolin inhaler with the most essential details included. Examples included the exact size of the mouthpiece and the actuator orifice indented in the cylindrical reservoir with an orifice diameter of 0.3 mm (Figure 1(b)). The orifice diameter of 0.3 mm followed Gabrio et al. [16], who evaluated the plume impact forces from several prototype MDIs (including Ventolin) with two different orifice diameters of 0.32 and 0.48 mm. They also measured the throat deposition from model actuators with orifice diameters ranging from 0.29 to 0.56 mm and reported that the throat deposition was sensitive to orifice diameters up to approximately 0.4 mm [16]. In this study, only the prototype actuator diameter of 0.32 mm (rounded to 0.3 mm) was considered. The exiting velocity of the airflow and aerosol was 40 m/s, which was based on previous measurements and computations [17,18,19]. A polydisperse aerosol size distribution was adopted (Figure 1(b)) based on previous measurements, with the mean droplet size being 9.1 µm and the standard geometrical derivation (GSD) being 1.32 [17,19]. The inhaler actuation started from 0.1 s and lasted for 0.2 s. After that, no further airflow and aerosols were released from the orifice, while the aerosols that had been released between 0.1–0.3 s would continue to be simulated till their complete decay.

    An MDI is supplied with a canister that contains 8.5 g of formulations intended for 200 puffs [20,21]. Each actuation delivers 90–120 μg medications (i.e., 0.2–0.3% w/w), such as albuterol from Ventolin, albuterol sulfate from ProAir, and beclomethasone dipropionate from Qvar [22]. In addition to the 0.1–1.0% w/w medication, a formulation also contains 3–13% w/w ethanol to increase the drug solubility and propellent to atomize the drug formulation into micron-scaled droplets [23,24]. In this study, it was assumed that the propellant evaporated immediately and completely at the orifice, leaving the liquid droplets that contained only the medication and ethanol. As a result, the aerosol droplet mass per actuation was approximately 3.0 mg (8500 mg, 200 puffs, 7% medication plus ethanol). The computational model was first applied to simulate the Ventolin spray plume development and was validated against complimentary high-speed images. This validated case with 2WC was then used as the baseline case for later comparisons. Various spray properties were considered regarding their relative contributions to the 2WC effects. These included the presence of the propellant jet flow, the spray mass loading, and individual droplet sizes. All cases were simulated with both 2WC and 1WC to characterize the 2WC effects under different scenarios.

    To evaluate the influences of the propellant flow, the spray plume behaviors were simulated with a 40-m/s velocity for aerosols but a zero velocity for the airflow from the orifice during actuation (0.1–0.3 s) under both two-way and one-way coupling. To evaluate the mass loading effects, three other drug masses were considered, which were one-tenth, ten times, and one hundred times (i.e., 0.30, 30, and 300 mg) that of the baseline (3.0 mg). To study the droplet diameter effects, seven monodisperse aerosols with a dose of 3.0 mg were considered, with the aerosol sizes being 2, 3.5, 5, 7.5, 10, 15, and 20 µm. Two other inhalers, ProAir and Qvar, were considered in addition to Ventolin, with their aerosol size distribution shown in Figure 1(c). Based on the measurements of Liu et al., the exiting spray velocity was 26 m/s for ProAir and 23 m/s for Qvar [17]. A summary of the test cases is listed in Table 1, with the corresponding figure numbers of the results.

    Table 1.  Different categories in comparing the predicted spray plumes using two-way coupling (2WC) vs. one-way coupling (1WC).
    Baseline (Ventolin) Mass loading Droplet size Inhaler type
    Aim Ventolin spray plume simulation Mass loading effect Size-dependent spray plumes Inhaler-specific spray plumes
    Inputs Ventolin
    Polydisperse
    dp: 9.1 µm, GSD: 1.32 V0: 40, Up: 40 m/s
    Mose mass: 6 mg
    Dose mass:
    0.6, 6, 60,600 mg
    Monodisperse
    Droplet size: 2, 3.5, 5, 7.5, 10, 15, 20 µm
    ProAir
    dp: 5.3 µm
    V0 = Up: 26 m/s
    Qvar, dp: 2.9 µm
    V0 = Up: 23 m/s
    Outputs Spray evolution;
    Vortex;
    Velocity contour;
    Streamlines;
    Droplet dynamics.
    Vortex;
    Vel contour;
    Streamlines;
    Droplet dynamics.
    Vel contour;
    Droplet dynamics
    Kolmogorov scales: η, τη;
    Ratio: dp/η, τp/τη;
    (V-Up);
    Spray velocity.
    Vel contour;
    Vortex;
    Droplet dynamics;
    Streamlines.
    Figures 3–5 6 7–12 13, 14

     | Show Table
    DownLoad: CSV

    The high-speed camera used was Phantom VEO, with an acquisition rate of up to 11,000 frames per second (fps). In this study, the temporal and spatial evolutions of the Ventolin spray plume after actuation were recorded at 4000 fps in order to achieve the optimal quality of acquired images. A laser sheet (OXlaser, 488 nm, 100 mW) was implemented to enhance the spray-environment contrast by aligning the sheet with the spray plume centerline. To understand the spray plume development at the very beginning of the actuation, the mouthpiece was partially removed to reveal the orifice. Images of the spray from the orifice were thus directly captured without the blockage by the mouthpiece.

    The LES-WALE model was used to simulate the multi-regime flows because of its ability to accurately capture turbulent-laminar transitions [25]. Droplet dynamics were simulated with a Lagrangian approach. An in-house MATLAB code was used to generate MDI droplets at the orifice, which specified the droplet diameter, size distribution, exit velocities, and plume angle [26,27]. The 2WC between the airflow and aerosol droplets were considered through the force below,

    F=(18μCDReρpd2p24(upu)+Fother)˙mpΔt (1)

    where µ is the fluid viscosity, ρp is the particle density, dp is the particle diameter, Re is the relative Reynolds number (Re = ρ(upu)l/µ), CD is the drag coefficient, up is the particle velocity, u is the fluid velocity, p is the aerosol mass flow rate, Δt is the time step, and Fother is a collection of other interaction forces, which are zero in this study. The mutual momentum transfer between the fluid and aerosols was numerically considered in each control volume for a given instant.

    ANSYS Fluent 19.1 (Canonsburg, PA) was utilized to solve flow-particle governing equations. ANSYS ICEM CFD was utilized to generate the computational mesh (Figure 1(d)). The characteristic length scales in the MDI-airspace system differed by three orders of magnitude, i.e., 0.3-mm diameter for the actuator orifice, 2.54-cm width for the mouthpiece opening, and a 0.5-m length for the open space. To adequately resolve the flow details in all regions, multidomain meshes were generated by dividing the system into many zones, with each zone being filled with varying-sized cells according to the anticipated flow complexities. For instance, ultrafine meshes were generated close to the orifice and within the shear layers around the spray centerline, with the mesh density gradually decreasing from the centerline, which eventually became coarse in the far open space (Figure 1(d)). Grid independent studies were conducted by testing six meshes from 3.2 million to 12.0 million (Figure 1(d)). The parameter of interest was the airflow 3 cm from the mouthpiece at 0.5 s. The grid-independent mass-based DF was reached at 10.1 million. A total 2×105 seed droplets were released for each actuation during 0.1–0.3 s. To approximate the continuous releasing of sprays during actuation, a group of 5000 droplets was released every 5 ms for 40 times, as opposed to a single injection in most previous numerical studies [28,29,30]. For an applied mass of 3.0 mg, the mass flow rate for each droplet parcel in the injection file was calculated to be 3.0×10-9 kg/s based on a droplet parcel number of 5000 and a time step of 0.005 s per release for 40 releases. The mass loading of 0.30, 30, and 300 mg was obtained by scaling the baseline particle mass flow rate per parcel by a factor of 1/10, 10, and 100, respectively. A time step size of 5 ms was used for a flow time of 1.5 s, or a complete spray decay, which came first. One test case took 60 hours or so in a Ryzen 9 3960x workstation with 3.79 GHz frequency and 256G RAM.

    A deeper understanding of the fluid-aerosol interactions involves those at small scales, especially when the length and time scales are equivalent between the fluid and individual droplets. The Kolmogorov scales are the smallest in turbulent flows and describe the microscopic airflow behaviors being dominated by viscosity, such as how much energy is contained in the smallest eddies, how energy is transferred between small and large eddies, and how energy is dissipated by eddies [31]. The Kolmogorov length scale, η and time scale, τη of the airflow are modeled as [32],

    η=(v3ε)1/4;τη=(vε)1/2 (2)

    where ν is the molecular viscosity and ɛ is the dissipation rate per unit mass, as shown below.

    ε=2<SijSij>=v{uixj+ujxiuixj+ujxi} (3)

    The length scale of a particle or droplet is obviously its diameter, and the time scale is described by the residence time, i.e., the duration required to react to a local flow change,

    τp=ρpd2pρf18v=ρpd2p18μ;Stk=ρpd2pu018μl0=τpt0 (4)

    The particle/droplet Stokes number was also presented above, which is the ratio of the particle/droplet residence time over the characteristic macroscopic time scale of the flow (l0/u0). Here l0 and u0 are the macroscopic characteristic length and velocity scale in a local region. Considering the multiscale dimensions (three orders of magnitude differences) of the system in this study, the local characteristic scales also varied drastically, which further led to drastic changes in the particle/droplet Stokes number.

    The aerosol-fluid length scale ratio (dp/η) and time scale ratio (τp/τη) have been suggested as indexes to evaluate the effects of aerosol-to-fluid forces on the flow turbulence [15]. It has been demonstrated that the presence of aerosols suppressed turbulence when dp/η < 0.1 and enhanced turbulence when dp/η > 0.1 [10,11,12]. Both ratios were used to evaluate the spray plume morphology variations for different aerosol sizes.

    Figure 2(a) shows the recorded images of the spray plume development from Ventolin at two instants using the Phantom VEO high-speed camera at an acquisition rate of 4000 fps. Intensive turbulence and eddies were observed at the front of the spray plume and, to a lesser degree, at the transverse interface with the ambient air. Considering the plume front, an aerosol stripe developed and moved downward (red arrow, second panel, Figure 2(a)), which subsequently separated from the main plume and started to settle downward from the gravity (yellow arrow, third panel, Figure 2(a)). Simultaneously, the eddies at the transverse interface continued to grow by mixing with the ambient air due to large shear forces. Because of the front and shear forces, as well as the entrained mass of the air, the overall velocity of the spray plume decreased quickly, from 40 m/s at the orifice to nearly zero at 0.5 m. The morphology of the spray plume exhibited a highly dynamic nature even after the spray plume reached its maximal length, driven both by the decaying vortices and gravity.

    Figure 2.  High-speed imaging: (a) temporal development of the spray plume from Ventolin after actuation, and (b) spray actuation from the orifice viewed from a cut-open mouthpiece.

    Considering that the orifice was blocked by the mouthpiece, parts of the mouthpiece were removed, and the emanation process of the spray from the orifice was captured using the high-speed camera at 4000 fps (Figure 2(b)). It can be clearly seen that the spray plume exited the orifice as a very thin jet with a cross-section diameter comparable to that of the orifice (first recorded image, Figure 2(b)). The plume grew quickly in the axial direction, and at a slower speed, in the transverse direction as well (second and third recorded images, Figure 2(b)). However, no apparent recirculation or vortices were noticed close to the orifice and within the mouthpiece. These observations were used to validate the complimentary computational results in the later section.

    Figure 3 shows the temporal and spatial evolution of the spray plume at different instants under 2WC. There were three stages during the plume development. The first stage occurs during 0.1–0.3 s (actuation, upper row of Figure 3), with different dynamics between the carrier flow and spray droplets. Due to the large density and high inertia, droplets traveled a much longer distance (~ 13 mm) in the first 5 ms than the airflow, with a vortex ring forming close to the mouthpiece exit (red arrow in Figure 2(a)). However, due to the drag force from the ambient air, the spray plume slowed down quickly, with further advancement of 7.5 mm at the next 15 ms (from 0.105 to 0.120 s) and 3 mm at the next 80 ms (from 0.120 to 0.200 s), as shown in Figure 2(a)(c). By contrast, the coherent flow structures developed steadily. The single vortex ring generated from the mouthpiece at 0.105 s (red arrow in Figure 2(a)) moved forward, while new vortex rings were constantly generated at the mouthpiece (black and green arrows in Figure 2(b)). The green ring caught up with the red ring at 0.200 s and merged together, while younger rings (pink and black arrows, Figure 2(b)) were continuously generated and moved forward. At 0.3 s, the vortex rings caught up with the droplet plume and disturbed the shear-shaped spray plume into a more irregular morphology (Figure 3(d)).

    Figure 3.  Temporal evolution of the spray plume from Ventolin predicted with 2WC: (a) 0.105 s, (b) 0.120 s, (c) 0.200 s, (d) 0.300 s, (e) 0.400 s, (f) 0.600 s (zoomed by 2), (g) 0.900 s, (h) 1.200 s, and (i) 1.500 s. The MDI was actuated at 0.100 s and the applied dose was 3.0 mg.

    In the second stage (0.3–0.8 s), vortex rings formed and moved forward, carrying the spray droplets with them. These vertex rings interacted with each other, as well as with the ambient air, leading to increasingly irregular patterns. Recirculating flows were induced from the interactions between the vortex and ambient air, which slowed down the advancement of the vortices and distributed the entrained droplets in transverse directions. For the aerosol droplets that fell below the spray centerline, the gravitational force became more dominant and started to settle in the negative y-direction (filled hollow arrow).

    The third stage occurred after 0.8 s when no apparent vortices were generated at the mouthpiece. The vortices progressively decayed while all aerosol droplets started settling down under gravity, with larger droplets falling faster, as illustrated by the hollow arrow in Figure 3(h), (i).

    The airflow development under the 2WC effects is further visualized in Figure 4 in terms of the velocity contour lines and streamlines. The jet flow was apparent in Figure 4(a) at the beginning of the actuation, with recirculating flows forming at the mouthpiece. The recirculating flow increased its size from 1.05 to 1.20 s by mixing with the surrounding air (red arrow, Figure 4(b)). It was also noted that jet flow pushed the ambient air at the front (green arrow) and sucked in the surrounding air due to low pressures induced by the high-velocity jet (pink arrow, Figure 4(b)). The recirculating flows above and below the jet centerline were approximately symmetrical, indicating an insignificant effect of the current mass loading (3.0 mg) on the airflow dynamics along the jet centerline (Figure 4(c)(e)). The recirculating flow zones advanced much slowly than the initial discharge velocity (i.e., ~0.4 vs. 40 m/s), indicating the quick decay of the spray plume energy due to the strong mixing with the ambient air. Also, note that the recirculating flow below the jet centerline slightly lagged behind the one above the centerline; this was because there were more large droplets in the lower region, which slowed down the recirculating flow (red arrow, Figure 4(f)). Moreover, the streamlines in the left lower region (filled arrow) bent downward because of the predominate gravitational settling in this region.

    Figure 4.  Airflow development in terms of velocity contour and streamlines under the effects of 2WC: (a) 0.105 s, (b) 0.200 s, (c) 0.400 s, (d) 0.600 s, (e) 0.900 s, and (f) 1.500 s. The MDI was actuated at 0.1 s and the applied dose was 3.0 mg.

    Remarkable differences in spray dynamics were observed with and without the consideration of momentum exchange from aerosols to airflow during the first 0.3 s after actuation, as shown in Figure 5. The MDI was actuated at 0.1 s, and the applied dose was 3.0 mg. One obvious difference was the backflow into the mouthpiece when neglecting the aerosol-to-flow effects (i.e., 1WC from flow to aerosol only). This was reasonable because the spray droplets moved faster than the airflow and thus imparted a forward momentum onto the airflow, which effectively prevented the occurrence of backflows. By contrast, the airflow experienced a large resistance from the upfront pressure, which would push a fraction of airflow backward when neglecting the aerosol-to-flow momentum transfer. These backflows would entrain some droplets into the mouthpiece, as demonstrated in Figure 5(b). The upfront pressure resistance also gave rise to the mushroom-shaped vortex rings at the mouthpiece exit (blue arrow in Figure 5(b)), a phenomenon often reported in jet flows [33,34,35].

    Figure 5.  Comparison of the predicted spray plume development between 2WC and 1WC during the 0.1–0.4 s. The MDI was actuated at 0.1 s with an applied dose of 3.0 mg.

    The second major difference in spray plume evolution between two-way and one-way coupling is the vortex generation and transportation along the centerline. With 2WC, the airflow had a higher momentum and, therefore, exhibited a higher rigidity as a bulk. A more coherent vortex formed (hollow arrow, 0.150 s, Figure 5(a)) than its counterpart with 1WC (hollow arrow, 0.15 s, Figure 5(b)). The vortex with 2WC moved faster, which caught up with or even surpassed the large droplets (red spheres) at 0.3 s (or 0.2 s after actuation), as denoted by the filled arrow in Figure 5(a). By comparison, the vortex under 1WC was still 26 mm short of the large droplets (0.3 s, Figure 5(b)). As alluded to in Figure 3, the vortex would persist for more than one second, which moved forward at a much-reduced speed (~0.4 m/s) than the discharge speed (40 m/s); it transported the polydisperse aerosol droplets along the axial direction, mixed with the surrounding air in transverse directions, and led to complex spray plume morphologies, as illustrated at 0.4 s in Figure 5(a), (b).

    Effects of the mass loading on the airflow and aerosol dynamics are shown in Figure 6 by varying the applied dose mass by three orders of magnitude (0.25–250 mg). Both the streamlines, vortex structures, and aerosol droplets are shown at 0.4 and 0.9 s (i.e., 0.3 and 0.8 s after the actuation), with the vortices being 60% transparent to avoid blocking the aerosol droplets. At a very low mass loading (i.e., 0.25 mg, or one-tenth of the typical MDI dose), the airflow pattern and aerosol distribution resembled those under 1WC. The vortices above and below the axial centerline kept relatively symmetric at both 0.4 and 0.9 s, reflecting the negligible effects from the discrete droplets (Figure 6(a)). By contrast, a high mass loading (i.e., 25 mg, or ten times of typical MDI dose) led to a considerable delay of the vortex below the centerline because of the larger mass that, in turn, resulted from increased gravitational settling at this higher mass loading. Particularly, due to the faster motion of the upper vortex, the core flows bent downward, further escalating the spray plume asymmetry (Figure 6(b)). In Figure 6(c), increasing the applied spray dose to 250 mg resulted in even a higher level of asymmetry, with the majority of airflow streamlines bending downward at 0.9 s. As a result, the mass loading could have a significant impact on the spray plume evolution for a spray dose typical of MDI inhalers or higher; 2WC should be implemented in simulations of MDI drug deliveries.

    Figure 6.  Comparison of the airflow and aerosol dynamics among varying applied doses: (a) 0.30 mg, (b) 30 mg, and (c) 300 mg.

    We further studied the airflow dynamics for monodisperse aerosols with varying droplet diameters, as shown in Figure 7. Significant differences were observed in the airflow pattern among different droplets (Figure 7(a)(d)). For a given dose of 3.0 mg, the airflow with the 2-µm aerosol advanced a much short distance in comparison to the 1WC case (Figure 7(a) vs. 7(e)). The advancement distance increased with the droplet diameter of the monodisperse aerosol from 2 to 20 µm (Figure 7(a)(d)). Compared to 1WC, where the airflow was not affected by the presence of aerosols, the spray plume length was shorter for 5-µm aerosols but longer for 10-µm aerosols. Moreover, the spray plume persistently expanded in transverse directions with the increasing aerosol droplet size, with the 20-µm spray aerosols exhibiting a much wider plume angle than other aerosols (Figure 7(d) vs. 7(a)(c)).

    Figure 7.  Droplet diameter effects on airflow dynamics for monodisperse aerosols under 2WC: (a) 2 µm, (b) 5 µm, (c) 10 µm, (d) 20 µm, vs. (e) 1WC. The MDI was actuated at 0.1 s.

    The corresponding monodisperse aerosol dynamics under 2WC is shown in Figure 8(a)(d) at 0.4 s after actuation for droplet diameter of 2, 5, 10 and 20 µm, all with a dose of 3.0 mg. For comparison purposes, the polydisperse aerosol dynamics under 1WC were also plotted, as shown in Figure 8(e). Very different patterns of aerosol distributions were noted for different droplet sizes, which, however, were generally consistent with the velocity contours in Figure 7. As expected, the largest differences occurred for the largest droplets hereof (i.e., 20 µm, Figure 8(d)).

    Figure 8.  Droplet diameter effects on aerosol dynamics at t = 0.5 s for monodisperse aerosols under 2WC: (a) 2 µm, (b) 5 µm, (c) 10 µm, (d) 20 µm, vs. (e) 1WC (for polydisperse aerosols).

    To understand the strikingly distinct behaviors of the airflow and aerosols for different droplet sizes in Figures 7 and 8, the theory of Kolmogorov scales and ratios were extracted from the spray simulations at 0.5 s under 1WC (Figure 9). Examining 1WC simulations had the advantage of evaluating the tendency of flow/aerosol behaviors without complications caused by the fluid-aerosol interweaving under 2WC. For the spray flow at 0.5 s, the smallest Kolmogorov length scale, η, was 2.72 µm, which led to the maximal magnitude of the aerosol-flow length ratio, dp/η, being 0.74 for 2-µm droplets, 3.68 for 10-µm droplets, and 7.35 for 20-µm droplets (Figure 9(a)). Note that both scales, at their minimal magnitudes, occurred in a very small region immediately downstream of the actuator orifice (first panel, Figure 9(a)). Both scales increased quickly in magnitude at other locations away from the orifice.

    Figure 9.  Kolmogorov scales and ratios for aerosol droplets of varying diameters: (a) Kolmogorov length scale, η and aerosol-flow length scale, dp/η for droplets of 2, 10, and 20 µm, and (b) Kolmogorov time scale, τη and aerosol-flow time scale, tpη for droplets of 2, 10, and 20 µm.

    The distribution of the length ratio dp/η is shown in the second to fourth panels in Figure 9(a) based on a color code range of 0–0.1. For 2-µm droplets, the dp/η magnitude was less than 0.1 for almost all regions except for a very slim region close to the orifice (second panel, Figure 9(a)). Accordingly, the turbulence development was noticeable suppressed, leading to weaker turbulence and vortex intensities, which in turn yielded a shorter penetration distance than when aerosol-to-flow forces were neglected, as previously described in Figure 8(a) vs. 8(e). By contrast, the 10-µm droplets had a much larger region with dp/η > 0.1 (red zone, third panel, Figure 9(a)). In this region, the forces from the aerosols would enhance the turbulent flows and increase the flow strength, as aforementioned in Figure 8(c) vs. 8(e). The dp/η distribution largely resembled the morphology of the orifice jet flow, with the flow-enhancing zones along the axial centerline, which decayed quickly in the transverse directions (third panel, Figure 9(a)). Considering 20-µm droplets, the red zones enlarged in both axial and transverse directions, reflecting a much larger impact from the spray droplets. This observation was consistent with the highly expanded spray cloud for 20-µm droplets, as exhibited in Figures 7(d) and 8(d).

    Figure 9(b) shows the Kolmogorov time scale and the droplet-specific time-scale-ratio, τp/τη for 2, 10, and 20-µm aerosols. For the spray flow at 0.5 s, the smallest Kolmogorov time scale, τη, was 5.1e-7 s, which was smaller than the residence time, τp, for all droplet sizes considered (2–20 µm), as listed in Table 2. It is also noted that the smallest Kolmogorov time scale (τη = 5.1e-7) existed in a very small region only; there was an appreciable region along the spray centerline with τη ranging from 1.0e-5 to 1.0e-3, which was equivalent to the residence time τp for droplets ranging from 2 to 20 µm. As a result, the airflow dynamics would be noticeably modified by the entrained droplets downstream of the actuator orifice, which was also demonstrated to modify the droplet behaviors and fates, as discussed in detail in Figures 7 and 8.

    Table 2.  Droplet Residence time, τp and droplet Stokes number, Stk for different droplet sizes and with varying characteristic velocities and lengths (i.e., at different jet flow locations).
    Droplet size (µm) Residence time, τp (s) Stokes number, Stk
    Up: 40 m/s, L: 0.3 mm Up: 10 m/s, L = 0.3 mm Up: 10 m/s, L = 3 cm
    2 1.24E-05 1.66 0.41 0.004
    3.5 3.80E-05 5.07 1.27 0.01
    5 7.76E-05 10.35 2.59 0.03
    7.5 1.75E-04 23.28 5.82 0.06
    10 3.10E-04 41.39 10.35 0.10
    15 6.98E-04 93.12 23.28 0.23
    20 1.24E-03 165.55 41.39 0.41

     | Show Table
    DownLoad: CSV

    The Kolmogorov length scale, η, and ratio, dp/η, were further quantified in the axial and transverse directions downstream of the actuator orifice at 0.5 s (Figure 10(a)). As alluded to in Figure 9, the η magnitude increased with the distance from the orifice because of the decaying eddies or vortices away from the orifice (Figure 10(a)). Figure 10(b) shows the length scale ratio dp/η vs. the axial distance (i.e., Y-direction). As expected, in most regions, the dp/η magnitude for 2-µm droplets was lower than 0.1 (pink dashed line). By contrast, for 10-µm and 20-µm aerosols, the dp/η magnitude was larger than 0.1 for Y < 12 cm. A bump was observed at Y = 1–3 cm for 10-µm and 20-µm aerosols (Figure 10(b)) because the spray actuation had been completed at 0.5 s and the core jet flow started to decay thenceforth.

    Figure 10.  Kolmogorov length scale (η) and aerosol-flow ratio (dp/η): (a) η variation in the axial (Y) direction, (b) dp/η variation in the axial direction, (c) η variation in the transverse directions (Z, X) at Y = 3 and 6 cm, and (b) η variation in the transverse directions (Z, X) at Y = 9 and 12 cm.

    The distributions of the Kolmogorov length scale in the circumneutral directions (Z- and X- directions) are shown in Figure 10(c), (d) at different distances from the orifice. For all cross-sections considered, the Kolmogorov length scale η magnitude (the eddy scale) increased quickly from the axial centerline and became larger than 200 µm (10 times of the 20-µm droplets) when the radius from the centerline was larger than 0.5 cm. For a given radius, the η magnitude also decreased with the axial distance. At the cross-sections of Y = 3 and 6 cm, the η distributions were approximately symmetric upper-lower (Z) or left-right (X). This symmetry was also observed in the X-direction (left-right) at Y = 9 and 12 cm (two solid lines, Figure 10(d)). However, apparent asymmetry was observed in the gravitational Z-direction, with a much higher value above the axial centerline than below.

    The particle Reynolds number has been suggested as an index to differentiate the turbulence suppression and enhancement. In Figure 11, the flow and aerosol velocities were examined for the 2-µm aerosols under 2WC at t = 0.5 s. Figure 11(a) shows the airflow velocities at the locations that are coincident with the instantaneous particles. A wide range of velocities was observed, with low-speed flows in most of the regions downstream of the mouthpiece in contrast to a high-speed jet flow along the centerline. To better understand the velocity distributions, a slice view was presented in the lower panel of Figure 11(a), which only showed the aerosols within a 2-mm-thickness slice in the midplane. Without obscuring from surrounding droplets, the jet flow and vortex (pink square) were clearly displayed. The vortex was further visualized in a zoomed view, where flow recirculation was apparent.

    Figure 11.  Comparison of the airflow and aerosol droplet velocities for 2-µm aerosols with 2WC at t = 0.5 s: (a) airflow, (b) droplet velocities and relative velocities.

    The instantaneous velocities of the spray droplets within the midplane slice (2 mm thickness) are presented in Figure 11(b), which were much higher than the airflow velocities. Unlike the various directions of the airflow, most droplets exhibited a forward direction. The differences in the velocity and direction between airflow and aerosol droplets are shown in the right lower panel of Figure 11(a), where both the instantaneous velocity vectors of the flow and droplets are presented. Note that the flow and droplet velocity vectors were not to the scale in order to plot them in the same frame. It was observed that both the magnitude and direction could differ significantly between the airflow and an individual droplet, and that these differences varied among droplets. This made it challenging to use the particle Reynolds number as a practical criterion for turbulence-depression-enhancement.

    The effects of the monodisperse aerosol diameter on the spray plume evolution were evaluated by comparing the aerosol velocities at two points (i.e., 3 and 6 cm downstream of the mouthpiece). Both the maximal and average velocities were quantified and presented in Figure 12(a) as a function of the droplet diameter (2, 3.5, 5, 7.5, 10, 15, 20 µm). The aerosol velocity peaked around 5–7 µm for both the maximal and average values at both sampling points considered. The velocity decreased thereafter and then increased again from 15 µm (Figure 12(a)). The velocity decay from 3 to 6 cm had a similar magnitude for both the maximal and average velocities (Figure 12(a)).

    Figure 12.  Comparison of the airflow and aerosol velocities predicted using 2WC: (a) the maximal and average aerosol velocities vs. droplet size at two sampling points (3 and 6 cm downstream of the mouthpiece), and (b) airflow velocities vs. the distance from the mouthpiece for four monodisperse aerosols (i.e., 2, 5, 10, 20 µm).

    Figure 12(b) shows the velocities of the airflow along the axial direction (1–8 cm from the mouthpiece) for different aerosol diameters. Overall, the aerosol speeds are much higher than those of the carrier flow (Figure 12(a) vs. 12(b)). The airflow slowed down quickly after exiting the mouthpiece. However, the rate of decrease differed among aerosols, with 2-µm aerosols decaying most drastically and 5-µm aerosols decaying the slowest, which was consistent with the trend observed in Figure 12(a).

    The effects of the 2WC on different MDI types were evaluated by considering the other two inhalers: ProAir with a mean droplet size of 5.2 μm emitted at 26 m/s, and Qvar inhaler with a mean droplet size of 2.9 μm emitted at 23 m/s [17]. As expected, the predicted airflow dynamics without the aerosol impact were very similar regardless of the inhaler types (Figure 13(a) vs. 13(b)). The differences in the one-way-predicted spray morphologies at 0.5 s between ProAir and Qvar were noticeable but not significant. These slight differences were presumably attributed to the differences in the droplet sizes (5.2 vs. 2.9 µm) and initial velocities (26 vs. 23 m/s). Remarkable differences were predicted with 2WC in both the airflow and aerosol dynamics between Proair and Qvar (Figure 13(a) vs. 13(b)). Even though the airflow was suppressed by the entrained spray aerosols from both inhalers, that from Qvar was significantly more suppressed due to its smaller droplet sizes and lower dp/η ratio. As a result, the Qvar spray plume was much less dispersed and advanced a shorter distance (Figure 13(b)).

    Figure 13.  Comparison of the airflow and aerosol dynamics at t = 0.5 s between two-way and 1WC: (a) ProAir inhaler with a mean droplet size of 5.2 µm and an initial velocity of 26 m/s, and (b) Qvar inhaler with a mean droplet size of 2.9 µm and an initial velocity of 23 m/s.

    The airflow and aerosol dynamics under 2WC after 0.5 s are presented in Figure 14(a), (b) for ProAir and Qvar, respectively. For ProAir (with a mean aerosol diameter of 5.2 µm), a smaller vortex formed below the axial centerline, as denoted by the hollow red arrow in Figure 14(a). The airflow posterior to the vortex bent downward and became increasingly more apparent with time (hollow blue arrow in Figure 14(a)), indicating an increasing gravitational effect from large spray droplets. The bending-downward flow and the small vortex were not observed in Qvar. This was reasonable considering the much smaller aerosol mean diameter (2.9 µm) than that of ProAir (5.2 µm). Moreover, the spray plumes from Qvar were less dispersed than those from ProAir and advanced a shorter distance than their counterparts in ProAir. The dipole vortices below and above the axial centerline were slightly off asymmetric at t = 1.0 s, but overall, the bending-downward motions of airflow or aerosols were not observed (third panel, Figure 14(b)). The differences shown in this figure, together with Figure 7, indicated that the 2WC effects are sensitive to the spray droplet size, and thus it will be essential in numerical studies of MDI pulmonary drug delivery to consider the 2WC effects for different types of inhalers, which often had very different spray droplets and exit velocities.

    Figure 14.  Comparison of the airflow and aerosol dynamics under 2WC between: (a) ProAir and (b) Qvar at t = 0.6, 0.8, and 1.0 s.

    The magnitude of the 2WC force (Eq (1)) was proportional to both the slip velocity and local mass loading. It was demonstrated in this study (Figures 4 and 5) that a moderate mass loading typical of an MDI spray dose per puff (e.g., 3.0 mg) could alter the fluid flow and aerosol behaviors. For an applied dose ten times smaller (i.e., 0.30 mg), no significant differences were predicted between two-way and one-way coupling (Figure 7). However, for an applied dose ten times larger (i.e., 30 mg), both the flow and aerosols were remarkably modified under two-way-coupling. The jet flow became apparently asymmetric above and below the spray centerline while the front of the spray plume started to bend downward. In other words, the gravitational effect became more pronounced at higher mass loadings, which drove the droplet-laden flow toward the ground (Figure 7). These observations were in agreement with Tong and Wang, who reported that moderate loadings of particles could induce noticeable flow modulation [36]. Similarly, Monchaux and Dejoan reported that 2WC strongly enhanced particle settling than the 1WC case and that these particles dragged the fluid downward [14]. These also conformed to the three regimes of particle-laden flows proposed by Elghobashi et al. [15]. At very low volume fractions (φ < 10-6), the discrete phase did not affect the carrier flow and was regarded as the 1WC regime. In the second regime, the discrete phase had a low volume fraction but a moderate mass loading due to the high droplet-flow density ratio. In this case, droplets could perceivably modulate the co-flow (i.e., 2WC regime), but droplet-droplet interactions could be neglected. Note that the MDIs fell within this regime. In the third regime (i.e., dense flow regime), both the volume fraction and mass concentration were high, and collisions among droplets had to be included.

    To evaluate the droplet size effect on 2WC, monodisperse aerosols of varying sizes (2–20 μm) were simulated and compared for a given applied mass of 3.0 mg. Strikingly different spray plume morphologies were observed for different droplet sizes, with 2-μm aerosols significantly suppressed and 20-μm aerosols dispersed in comparison to their counterparts with 1WC simulations (Figures 8 and 9). To find out the underlying reasons for the sticky behavior of small particles and dispersing behavior of large parties, Kolmogorov scales for the airflow and scale ratios between droplets and airflow were quantified at t = 0.5 s. Turbulence modification in particle-laden flows has been acknowledged for several decades [7,12,37]. Early experimental observations by Torobin and Gauvin showed that the presence of particles changed the wall drag in pipes, as well as the heat transfer and chemical reaction rates, which could not be explained unless the fluid turbulence had been modified by particles [7]. In a comprehensive review, Gore and Crowe listed a spectrum of potential effects of the carrier flow depending on the particle size and volumetric loading. The ratio of the particle size to the turbulence integral length was proposed as an indicator of whether the turbulence was increased or decreased [10]. In this study, the ratio of the droplet diameter over the Kolmogorov length scale (dp/η) was quantified (Figures 10 and 11), which was found to be closely associated with the spray plume morphologies (i.e., advancement in the axial direction and dispersion in transverse directions) (Figures 8 and 9). For instance, the seemingly sticky aerosols of 2 μm had a dp/η < 0.1 in near all regions of the spray, while the highly dispersed and advanced aerosols of 20 μm had a dp/η < 0.1 only in the regions further than 15 cm (Figure 11(b)). This finding was consistent with Elghobashi and Truesdell, who reported that turbulence was suppressed when dp/η < 0.1 and enhanced when dp/η > 0.1 [12].

    It was also observed that the transverse dispersion of 20-μm aerosols in Figure 9 was much larger than the regions with dp/η > 0.1 (red zone) in Figure 10, indicating that the Kolmogorov scale ratio theory might not fully explain the spray suppression/expansion in the transverse directions. One salient feature of MDI sprays was mixing layers at the interface between the jet and ambient air, where intensive vortex formations occurred. Many experimental and numerical investigations have demonstrated that aerosol dynamics in the mixing layers differed significantly from that of the passive tracers if the particle/droplet response time was comparable to the characteristic time scale of the flow. Aerosols would accumulate in the outskirts of vortices, especially in the proximity of braid stagnation points where two counter-rotating vortices met [38]. Therefore, the strong mixing and vortex formations at the jet interfaces could have assisted the transverse dispersion in addition to the Kolmogorov-associated turbulence enhancement from the core flow of 20-μm aerosols.

    Turbulence modification by particles was also correlated to the particle Reynolds number Re,p based on the interphase slip velocity, with a low Re,p decreasing the fluid turbulence, and a high Re,p increasing the turbulence [39,40,41]. In this study, the Re,p was also sought by characterizing the slip velocity between an individual droplet and the local flow (Figure 12). However, both the airflow and droplets in the MDI sprays were highly transient in time and heterogeneous in space (Figure 12), making it impractical to quantify the Re,p for individual droplets based on their instantaneous slip velocities relative to the local flow. By contrast, the Kolmogorov length scale of the spray flow was more practical to quantify, and its ratio to droplet diameters has been demonstrated to be useful in predicting suppression or enhancement of the carrier flow and aerosol dispersion (Figures 8 and 9 vs. Figures 10 and 11).

    The high sensitivity of the MDI spray plume to its droplet sizes rested on several MDI-specific factors, such as the high-speed jet flow from the orifice that led to Kolmogorov length scales comparable to the droplet sizes (2–20 μm), and the applied mass loading (3.0 mg in this study) that was large enough to elicit perceivable aerosol-to-flow momentum exchanges. In this study, we demonstrated that reducing the mass loading by ten times (0.30 mg) led to negligible two-coupling effects while increasing the mass loading by ten times (30 mg) led to large variations in spray plume morphologies (Figure 7). It was reminded that the droplet-size-dependent spray behaviors were shown for monodisperse aerosols at a 3.0-mg mass loading (Figures 8 and 9). For polydisperse aerosols like an MDI spray, the transient morphology of the aerosol plume was an instantaneous collective manifestation of all droplets with varying diameters and probability densities. For Ventolin, 2WC provided a better approximation to the experimental aerosol behaviors close to the orifice (Figure 5 vs. Figure 2); it also predicted a more detached aerosol bolus from the mouthpiece and a slightly faster advancement than 1WC (Figure 5). By contrast, for ProAir and Qvar, 2WC predicted a spray plume advancing at lower speeds than 1WC (Figures 14 and 15). However, the Qvar spray development appeared more retarded in both axial advancement and transverse dispersion due to its smaller mean diameter (2.9 μm) and smaller length ratio, dp/η, than those of ProAir (5.2 μm).

    Even though only three types of inhalers were considered hereof, the spray behaviors of other MDIs can be roughly estimated based on the droplet-Kolmogorov length scale, which can be readily calculated from the aerosol size and discharge flow speed from the actuator orifice. Another implication was that previous one-way-coupled predictions of the delivery efficiencies from MDI delivery might have errors of varying degrees, especially for Qvar with small aerosol sizes (2.9 μm) and suppressed plume developments. Future studies are warranted to include the 2WC effects in predictive inhalation dosimetry in the respiratory tract for different inhalers.

    MDI actuation and the subsequent spray plume development are highly dynamic and involve multiscale and multi-physics. Multiple assumptions were made in this study to fill the gap of missing data in current literature or mitigate the prohibitive numerical requirements. These included the same discharge velocity for aerosol droplets and co-flow, a constant aerosol output during the 0.2-s actuation, a constant temperature, no evaporation, no electrostatic charge, and no droplet interactions. The MDI formulation contains approximately 80–90% w/w propellant, which vaporizes quickly to atomize the solution into micro-scale droplets; however, measurements of propellent flow speed at the actuator orifice are not yet available [24]. Liu et al. measured the spray (aerosol and co-flow) velocities at 3 and 6 cm from the mouthpiece of different inhalers, including Ventolin, ProAir, and Qvar [17]. These measurements were used to reversely estimate the discharge velocity at the orifice, as explained in Talaat et al. [18,30]. The spray output during actuation can also be time-dependent during the 0.2-s actuation, adding more flow fluctuations. Drastic temperature variation can occur during MDI actuation, leading to thermophoretic effects on droplets [42,43,44]. The ethanol droplets can evaporate and decrease in size, further altering the thermal environment and droplet behavior [45,46,47]. Particle-particle interactions can also be important at high mass loading, e.g., 300 mg, but should be insignificant due to the low volume fraction ratio of the liquid droplets in the open space. Electrostatic charges on the droplets can be another factor that complicates the trajectories of individual droplets and interactions among neighboring droplets [48,49,50,51,52]. An orifice diameter of 0.3 mm was used all three inhalers, which was larger than Qvar's actual diameter (0.25 mm) and smaller than Ventolin's diameter (0.48 mm). Its effects on aerosol dynamics were expected to be insignificant considering that the experimentally measured aerosol sizes and speeds were implemented for Qvar. For Ventolin, we acknowledged that our LES simulations could not be regarded as replicating the experimental observations, given the differences between the model and prototype orifice diameters. Rather, the general features of 2WC simulations seemed to be more representative of the Ventolin plume than 1WC simulations. Furthermore, this study considered only spray evolutions in the open space, which can be different from those in a closed space like the respiratory tract [53,54,55,56]. However, neglecting these compounding factors allowed us to isolate the 2WC effect and examine it vs. the 1WC counterpart systemically under controlled, parametrized conditions, i.e., with different mass loadings, droplet sizes, and inhaler types.

    In summary, spray plume evolutions in open space were simulated using the LES-Lagrangian approach under both 2WC and 1WC conditions. Differences in the temporal and spatial morphologies of the spray plumes were characterized, and the reasons underlying the differences were explored using Kolmogorov scale ratios relative to the spray droplets. Specific findings are listed below:

    1) Numerical simulations with 2WC seemed to provide better agreement with complimentary high-speed imaging of the spray from the orifice than 1WC.

    2) Increasing the mass loading altered the spray morphology, with both the airflow and aerosol droplets bending downward at the spray plume front.

    3) The droplet size had a significant impact on the spray evolution in both axial (advancement) and transverse (dispersion) directions, with suppressed sprays for 2-μm aerosols and more dispersed sprays for 20-μm aerosols.

    4) The Kolmogorov length scale, η of the spray jet flow were comparable to the MDI droplet sizes. The length scale ratio dp/η correlated well with spray suppression when less than 0.1 and with spray enhancement when larger than 0.1.

    5) Different levels of impact from 2WC were predicted on the spray plume evolution of Ventolin, ProAir, and Qvar in comparison to 1WC.

    Amr Seifelnasr was gratefully acknowledged for reviewing this manuscript.

    The authors declare there is no conflict of interest.



    [1] Aguiar-Conraria L, Soares MJ (2011) Oil and the macroeconomy: using wavelets to analyze old issues. Empir Econ 40: 645–655. https://doi.org/10.1007/s00181-010-0371-x doi: 10.1007/s00181-010-0371-x
    [2] Agyei-Ampomah S, Gounopoulos D, Mazouz K (2014) Does gold offer a better protection against losses in sovereign debt bonds than other metals? J Bank Fin 40: 507–521. https://doi.org/10.1016/j.jbankfin.2013.11.014 doi: 10.1016/j.jbankfin.2013.11.014
    [3] Agyei SK (2023) Emerging markets equities' response to geopolitical risk: Time-frequency evidence from the Russian-Ukrainian conflict era. Heliyon 9: e13319. https://doi.org/10.1016/j.heliyon.2023.e13319 doi: 10.1016/j.heliyon.2023.e13319
    [4] Akhtaruzzaman M, Boubaker S, Lucey BM, et al. (2021) Is gold a hedge or a safe-haven asset in the COVID–19 crisis? Econ Model 102: 105588. https://doi.org/10.1016/j.econmod.2021.105588 doi: 10.1016/j.econmod.2021.105588
    [5] Al-Nassar NS, Boubaker S, Chaibi A, et al. (2023) In search of hedges and safe havens during the COVID-19 pandemic: Gold versus Bitcoin, oil, and oil uncertainty. Q Rev Econ Financ 90: 318–332. https://doi.org/10.1016/j.qref.2022.10.010 doi: 10.1016/j.qref.2022.10.010
    [6] Alfaro L, Chari A, Greenland AN, et al. (2020) Aggregate and firm-level stock returns during pandemics, in real time (No. w26950) National Bureau of Economic Research. https://doi.org/10.3386/w26950
    [7] An D, Choi JH, Kim NH (2013) Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliab Eng Syst Saf 115: 161–169. https://doi.org/10.1016/j.ress.2013.02.019 doi: 10.1016/j.ress.2013.02.019
    [8] Antonini M, Barlaud M, Mathieu P, et al. (1992) Image coding using wavelet transform. IEEE Trans Image Processing 1: 205–220. https://doi.org/10.1109/83.136597 doi: 10.1109/83.136597
    [9] Ashfaq S, Tang Y, Maqbool R (2019) Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies' stock returns. Energy 188: 116002. https://doi.org/10.1016/j.energy.2019.116002 doi: 10.1016/j.energy.2019.116002
    [10] Aydoğan B, Vardar G, Taçoğlu C (2022) Volatility spillovers among G7, E7 stock markets and cryptocurrencies. J Econ Adm Sci 40: 364–387. https://doi.org/10.1108/jeas-09-2021-0190 doi: 10.1108/jeas-09-2021-0190
    [11] Babar M, Ahmad H, Yousaf I (2024) Returns and volatility spillover between agricultural commodities and emerging stock markets: new evidence from COVID-19 and Russian-Ukrainian war. Int J Emerg Mark 19: 4049–4072. https://doi.org/10.1108/ijoem-02-2022-0226 doi: 10.1108/ijoem-02-2022-0226
    [12] Baek C, Elbeck M (2015) Bitcoins as an investment or speculative vehicle? A first look. Appl Econ Lett 22: 30–34. https://doi.org/10.1080/13504851.2014.916379 doi: 10.1080/13504851.2014.916379
    [13] Balcilar M, Bouri E, Gupta R, et al. (2017) Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Econ Model 64: 74–81. https://doi.org/10.1016/j.econmod.2017.03.019 doi: 10.1016/j.econmod.2017.03.019
    [14] Balcilar M, Gupta R, Jooste C (2017) Long memory, economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach. Appl Econ 49: 1047–1054. https://doi.org/10.1080/00036846.2016.1210777 doi: 10.1080/00036846.2016.1210777
    [15] Baur DG, Dimpfl T (2021) The volatility of Bitcoin and its role as a medium of exchange and a store of value. Empir Econ 61: 2663–2683. https://doi.org/10.1007/s00181-020-01990-5 doi: 10.1007/s00181-020-01990-5
    [16] Baur DG, Dimpfl T, Kuck K (2018) Bitcoin, gold and the US dollar–A replication and extension. Financ Res Lett 25: 103–110. https://doi.org/10.1016/j.frl.2017.10.012 doi: 10.1016/j.frl.2017.10.012
    [17] Baur DG, Hoang LT, Hossain MZ (2022) Is Bitcoin a hedge? How extreme volatility can destroy the hedge property. Financ Res Lett 47: 102655. https://doi.org/10.1016/j.frl.2021.102655 doi: 10.1016/j.frl.2021.102655
    [18] Baur DG, Lucey BM (2010) Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financ Rev 45: 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x doi: 10.1111/j.1540-6288.2010.00244.x
    [19] Baur DG, McDermott TK (2010) Is gold a safe haven? International evidence. J Bank Financ 34: 1886–1898. https://doi.org/10.1016/j.jbankfin.2009.12.008 doi: 10.1016/j.jbankfin.2009.12.008
    [20] Baur DG. McDermott TK (2016) Why is gold a safe haven? J Behav Exp Financ 10: 63–71. https://doi.org/10.1016/j.jbef.2016.03.002 doi: 10.1016/j.jbef.2016.03.002
    [21] Baur DG, Smales LA (2020) Hedging geopolitical risk with precious metals. J Bank Financ 117: 105823. https://doi.org/10.1016/j.jbankfin.2020.105823 doi: 10.1016/j.jbankfin.2020.105823
    [22] Beckmann J, Berger T, Czudaj R (2015) Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Econ Model 48: 16–24. https://doi.org/10.1016/j.econmod.2014.10.044 doi: 10.1016/j.econmod.2014.10.044
    [23] Beckmann J, Czudaj R (2013) Gold as an inflation hedge in a time-varying coefficient framework. N Am J Econ Financ 24: 208–222. https://doi.org/10.1016/j.najef.2012.10.007 doi: 10.1016/j.najef.2012.10.007
    [24] Będowska-Sójka B, Demir E, Zaremba A (2022) Hedging geopolitical risks with different asset classes: A focus on the Russian invasion of Ukraine. Financ Res Lett 50: 103192. https://doi.org/10.1016/j.frl.2022.103192 doi: 10.1016/j.frl.2022.103192
    [25] Beer C, Maniora J, Pott C (2023) COVID-19 pandemic and capital markets: the role of government responses. J Bus Econ 93: 11–57. https://doi.org/10.1007/s11573-022-01103-x doi: 10.1007/s11573-022-01103-x
    [26] Beneki C, Koulis A, Kyriazis NA, et al. (2019) Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Res Int Bus Financ 48: 219–227. https://doi.org/10.1016/j.ribaf.2019.01.001 doi: 10.1016/j.ribaf.2019.01.001
    [27] Bhuiyan RA, Husain A, Zhang C (2021) A wavelet approach for causal relationship between bitcoin and conventional asset classes. Resour Policy 71: 101971. https://doi.org/10.1016/j.resourpol.2020.101971 doi: 10.1016/j.resourpol.2020.101971
    [28] Blau BM, Griffith TG, Whitby RJ (2021) Inflation and Bitcoin: A descriptive time-series analysis. Econ Lett 203: 109848. https://doi.org/10.1016/j.econlet.2021.109848 doi: 10.1016/j.econlet.2021.109848
    [29] Blose LE (2010) Gold prices, cost of carry, and expected inflation. J Econ Bus 62: 35–47. https://doi.org/10.1016/j.jeconbus.2009.07.001 doi: 10.1016/j.jeconbus.2009.07.001
    [30] Bouoiyour J, Selmi R. Wohar ME (2019) Bitcoin: competitor or complement to gold? Econ Bull 39: 186–191. https://hal.science/hal-01994187v1
    [31] Bouri E, Gupta R, Tiwari AK, et al. (2017) Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Financ Res Lett 23: 87–95. https://doi.org/10.1016/j.frl.2017.02.009 doi: 10.1016/j.frl.2017.02.009
    [32] Bouri E, Jalkh N, Molnár P, et al. (2017) Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven? Appl Econ 49: 5063–5073. https://doi.org/10.1080/00036846.2017.1299102 doi: 10.1080/00036846.2017.1299102
    [33] Bouri E, Shahzad SJH, Roubaud D, et al. (2020) Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. Q Rev Econ Financ 77: 156–164. https://doi.org/10.1016/j.qref.2020.03.004 doi: 10.1016/j.qref.2020.03.004
    [34] Brandvold M, Molnár P, Vagstad K, et al. (2015) Price discovery on Bitcoin exchanges. J Int Financ Mark Inst Money 36: 18–35. https://doi.org/10.1016/j.intfin.2015.02.010 doi: 10.1016/j.intfin.2015.02.010
    [35] Cai Y, Zhu Z, Xue Q, et al. (2022) Does bitcoin hedge against the economic policy uncertainty: based on the continuous wavelet analysis. J Appl Econ 25: 983–996. https://doi.org/10.1080/15140326.2022.2072674 doi: 10.1080/15140326.2022.2072674
    [36] Celeste V, Corbet S, Gurdgiev C (2020) Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple. Q Rev Econ Financ 76: 310–324. https://doi.org/10.1016/j.qref.2019.09.011 doi: 10.1016/j.qref.2019.09.011
    [37] Chaim P, Laurini MP (2018) Volatility and return jumps in bitcoin. Econs Lett 173: 158–163. https://doi.org/10.1016/j.econlet.2018.10.011 doi: 10.1016/j.econlet.2018.10.011
    [38] Chan WH, Le M, Wu YW (2019) Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin. Q Rev Econ Financ 71: 107–113. https://doi.org/10.1016/j.qref.2018.07.004 doi: 10.1016/j.qref.2018.07.004
    [39] Charfeddine L, Benlagha N, Maouchi Y (2020) Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Econ Model 85: 198–217. https://doi.org/10.1016/j.econmod.2019.05.016 doi: 10.1016/j.econmod.2019.05.016
    [40] Cheah ET, Fry J (2015) Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Econ Lett 130: 32–36. https://doi.org/10.1016/j.econlet.2015.02.029 doi: 10.1016/j.econlet.2015.02.029
    [41] Cheema MA, Faff R, Szulczyk KR (2022) The 2008 global financial crisis and COVID-19 pandemic: How safe are the safe haven assets? Int Rev Financ Anal 83: 102316. https://doi.org/10.1016/j.irfa.2022.102316 doi: 10.1016/j.irfa.2022.102316
    [42] Chkili W, Rejeb AB, Arfaoui M (2021) Does bitcoin provide hedge to Islamic stock markets for pre-and during COVID-19 outbreak? A comparative analysis with gold. Resour Policy 74: 102407. https://doi.org/10.1016/j.resourpol.2021.102407 doi: 10.1016/j.resourpol.2021.102407
    [43] Christou G, Zhang P, Zhao L (2021) The Impact of the Sino-US Trade War to the Global Economy. J Trans Chin Com Law, 7–25. Available from: https://openaccess.city.ac.uk/id/eprint/28117
    [44] Ciner C, Gurdgiev C, Lucey BM (2013) Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. Int Rev Financ Anal 29: 202–211. https://doi.org/10.1016/j.irfa.2012.12.001 doi: 10.1016/j.irfa.2012.12.001
    [45] Conlon T, Corbet S, Hou YG, et al. (2024) Seeking a shock haven: Hedging extreme upward oil price changes. Int Rev Financ Anal 94: 103245. https://doi.org/10.1016/j.irfa.2024.103245 doi: 10.1016/j.irfa.2024.103245
    [46] Conlon T, Corbet S, McGee RJ (2021) Inflation and cryptocurrencies revisited: A time-scale analysis. Econ Lett 206: 109996. https://doi.org/10.1016/j.econlet.2021.109996 doi: 10.1016/j.econlet.2021.109996
    [47] Corbet S, Hou YG, Hu Y, et al. (2021) Pandemic-related financial market volatility spillovers: Evidence from the Chinese COVID-19 epicentre. Int Rev Econ Financ 71: 55–81. https://doi.org/10.1016/j.iref.2020.06.022 doi: 10.1016/j.iref.2020.06.022
    [48] Diniz R, de Prince D, Maciel L (2022) Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes. J Econ Stud 50: 429–447. https://doi.org/10.1108/jes-09-2021-0452 doi: 10.1108/jes-09-2021-0452
    [49] Drake PP (2022) The gold-stock market relationship during COVID-19. Financ Res Lett 44: 102111. https://doi.org/10.1016/j.frl.2021.102111 doi: 10.1016/j.frl.2021.102111
    [50] Dwyer GP (2015) The economics of Bitcoin and similar private digital currencies. J Financ Stab 17: 81–91. https://doi.org/10.1016/j.jfs.2014.11.006 doi: 10.1016/j.jfs.2014.11.006
    [51] Dyhrberg AH (2016) Bitcoin, gold and the dollar-A GARCH volatility analysis. Financ Res Lett 16: 85–92. https://doi.org/10.1016/j.frl.2015.10.008 doi: 10.1016/j.frl.2015.10.008
    [52] Dyhrberg AH (2016) Hedging capabilities of bitcoin. Is it the virtual gold? Financ Res Lett 16: 139–144. https://doi.org/10.1016/j.frl.2015.10.025 doi: 10.1016/j.frl.2015.10.025
    [53] Engle R (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20: 339–350. https://doi.org/10.1198/073500102288618487 doi: 10.1198/073500102288618487
    [54] Erdin E, Cebe M, Akkaya K, et al. (2020) A Bitcoin payment network with reduced transaction fees and confirmation times. Comput Netw 172: 107098. https://doi.org/10.1016/j.comnet.2020.107098 doi: 10.1016/j.comnet.2020.107098
    [55] Gajardo G, Kristjanpoller WD, Minutolo M (2018) Does Bitcoin exhibit the same asymmetric multifractal cross-correlations with crude oil, gold and DJIA as the Euro, Great British Pound and Yen? Chaos Solit Fractals 109: 195–205. https://doi.org/10.1016/j.chaos.2018.02.029 doi: 10.1016/j.chaos.2018.02.029
    [56] Gallegati M (2008) Wavelet analysis of stock returns and aggregate economic activity. Comput Stat Data Anal 52: 3061–3074. https://doi.org/10.1016/j.csda.2007.07.019 doi: 10.1016/j.csda.2007.07.019
    [57] Gandal N, Hamrick JT, Moore T, et al. (2018) Price manipulation in the Bitcoin ecosystem. J Monet Econ 95: 86–96. https://doi.org/10.1016/j.jmoneco.2017.12.004 doi: 10.1016/j.jmoneco.2017.12.004
    [58] Ghazali MF, Lean HH, Bahari Z (2013) Is gold a hedge or a safe haven? An empirical evidence of gold and stocks in Malaysia. Int J Bus Soc 14: 428. Available from: https://api.semanticscholar.org/CorpusID: 189858310
    [59] Glosten LR, Jagannathan R, Runkle DE (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J Financ 48: 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x doi: 10.1111/j.1540-6261.1993.tb05128.x
    [60] Gürgün G, Ünalmış İ (2014) Is gold a safe haven against equity market investment in emerging and developing countries? Financ Res Lett 11: 341–348. https://doi.org/10.1016/j.frl.2014.07.003 doi: 10.1016/j.frl.2014.07.003
    [61] Guru BK, Pradhan AK, Bandaru R (2023) Volatility contagion between oil and the stock markets of G7 countries plus India and China. Resour Policy 81: 103377. https://doi.org/10.1016/j.resourpol.2023.103377 doi: 10.1016/j.resourpol.2023.103377
    [62] Hasan MB, Hassan MK, Rashid MM, et al. (2021) Are safe haven assets really safe during the 2008 global financial crisis and COVID-19 pandemic? Glob Financ J 50: 100668. https://doi.org/10.1016/j.gfj.2021.100668 doi: 10.1016/j.gfj.2021.100668
    [63] Hillier D, Draper P, Faff R (2006) Do precious metals shine? An investment perspective. Financ Anal J 62: 98–106. https://doi.org/10.2469/faj.v62.n2.4085 doi: 10.2469/faj.v62.n2.4085
    [64] Huang W, Chang MS (2021) Gold and government bonds as safe-haven assets against stock market turbulence in China. Sage Open 11: 2158244021990655. https://doi.org/10.1177/2158244021990655 doi: 10.1177/2158244021990655
    [65] Hung NT, Vo XV (2021) Directional spillover effects and time-frequency nexus between oil, gold and stock markets: evidence from pre and during COVID-19 outbreak. Int Rev Financ Anal 76: 101730. https://doi.org/10.1016/j.irfa.2021.101730 doi: 10.1016/j.irfa.2021.101730
    [66] Iglesias EM, Rivera-Alonso D (2022) Brent and WTI oil prices volatility during major crises and Covid-19. J Pet Sci Eng 211: 110182. https://doi.org/10.1016/j.petrol.2022.110182 doi: 10.1016/j.petrol.2022.110182
    [67] Jareño F, de la O González M, Tolentino M, Sierra K (2020) Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resour Policy 67: 101666. https://doi.org/10.1016/j.resourpol.2020.101666 doi: 10.1016/j.resourpol.2020.101666
    [68] Ji Q, Zhang D, Zhao Y (2020) Searching for safe-haven assets during the COVID-19 pandemic. Int Rev Financ Anal 71: 101526. https://doi.org/10.1016/j.irfa.2020.101526 doi: 10.1016/j.irfa.2020.101526
    [69] Jiang S, Li Y, Lu Q, et al. (2022) Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets. Res Int Bus Financ 59: 101543. https://doi.org/10.1016/j.ribaf.2021.101543 doi: 10.1016/j.ribaf.2021.101543
    [70] Jiang Z, Yoon SM (2020) Dynamic co-movement between oil and stock markets in oil-importing and oil-exporting countries: Two types of wavelet analysis. Energy Econ 90: 104835. https://doi.org/10.1016/j.eneco.2020.104835 doi: 10.1016/j.eneco.2020.104835
    [71] Junttila J, Pesonen J, Raatikainen J (2018) Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold. J Int Financ Mark Inst Money 56: 255–280. https://doi.org/10.1016/j.intfin.2018.01.002 doi: 10.1016/j.intfin.2018.01.002
    [72] Kamal JB, Wohar M, Kamal KB (2022) Do gold, oil, equities, and currencies hedge economic policy uncertainty and geopolitical risks during the COVID crisis? Resour Policy 78: 102920. https://doi.org/10.1016/j.resourpol.2022.102920 doi: 10.1016/j.resourpol.2022.102920
    [73] Kang SH, Yoon SM, Bekiros S, et al. (2020) Bitcoin as hedge or safe haven: evidence from stock, currency, bond and derivatives markets. Comput Econ 56: 529–545. https://doi.org/10.1007/s10614-019-09935-6 doi: 10.1007/s10614-019-09935-6
    [74] Kanjilal K, Ghosh S (2017) Dynamics of crude oil and gold price post 2008 global financial crisis–New evidence from threshold vector error-correction model. Resour Policy 52: 358–365. https://doi.org/10.1016/j.resourpol.2017.04.001 doi: 10.1016/j.resourpol.2017.04.001
    [75] Kassamany T, Harb E, Baz R (2022) Hedging and safe haven properties of Ethereum: evidence around crises. J Decis Syst 32: 761–779. https://doi.org/10.1080/12460125.2022.2133281 doi: 10.1080/12460125.2022.2133281
    [76] Katsiampa P (2017) Volatility estimation for Bitcoin: A comparison of GARCH models. Econ Lett 158: 3–6. https://doi.org/10.1016/j.econlet.2017.06.023 doi: 10.1016/j.econlet.2017.06.023
    [77] Khalfaoui R, Gozgor G, Goodell JW (2023) Impact of Russia-Ukraine war attention on cryptocurrency: Evidence from quantile dependence analysis. Financ Res Lett 52: 103365. https://doi.org/10.1016/j.frl.2022.103365 doi: 10.1016/j.frl.2022.103365
    [78] Kim T (2017) On the transaction cost of Bitcoin. Financ Res Lett 23: 300–305. https://doi.org/10.1016/j.frl.2017.07.014 doi: 10.1016/j.frl.2017.07.014
    [79] Klein T, Thu HP, Walther T (2018) Bitcoin is not the New Gold-A comparison of volatility, correlation, and portfolio performance. Int Rev Financ Anal 59: 105–116. https://doi.org/10.1016/j.irfa.2018.07.010 doi: 10.1016/j.irfa.2018.07.010
    [80] Kumar AS, Anandarao S (2019) Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis. Physica A 524: 448–458. https://doi.org/10.1016/j.physa.2019.04.154 doi: 10.1016/j.physa.2019.04.154
    [81] Kumar AS, Padakandla SR (2022) Testing the safe-haven properties of gold and bitcoin in the backdrop of COVID-19: A wavelet quantile correlation approach. Financ Res Lett 47: 102707. https://doi.org/10.1016/j.frl.2022.102707 doi: 10.1016/j.frl.2022.102707
    [82] Li D, Hong Y, Wang L, et al. (2022) Extreme risk transmission among bitcoin and crude oil markets. Resour Policy 77: 102761. https://doi.org/10.1016/j.resourpol.2022.102761 doi: 10.1016/j.resourpol.2022.102761
    [83] Liu F, Xu J, Ai C (2023) Heterogeneous impacts of oil prices on China's stock market: Based on a new decomposition method. Energy 268: 126644. https://doi.org/10.1016/j.energy.2023.126644 doi: 10.1016/j.energy.2023.126644
    [84] Liu J, Wan Y, Qu S, et al. (2022) Dynamic correlation between the Chinese and the US financial markets: From global financial crisis to covid-19 pandemic. Axioms 12: 14. https://doi.org/10.3390/axioms12010014 doi: 10.3390/axioms12010014
    [85] Liu M, Lee CC (2022) Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS. Resour Policy 76: 102703. https://doi.org/10.1016/j.resourpol.2022.102703 doi: 10.1016/j.resourpol.2022.102703
    [86] Long S, Pei H, Tian H, et al. (2021) Can both Bitcoin and gold serve as safe-haven assets?—A comparative analysis based on the NARDL model. Int Rev Financ Anal 78: 101914. https://doi.org/10.1016/j.irfa.2021.101914 doi: 10.1016/j.irfa.2021.101914
    [87] Macartney H, Montgomerie J, Tepe D (2022) The Fault Lines of Inequality: COVID 19 and the Politics of Financialization. Springer Nature. https://doi.org/10.1007/978-3-030-96914-1
    [88] Mariana CD, Ekaputra IA, Husodo ZA (2021) Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic? Financ Res Lett 38: 101798. https://doi.org/10.1016/j.frl.2020.101798 doi: 10.1016/j.frl.2020.101798
    [89] Marobhe MI (2022) Cryptocurrency as a safe haven for investment portfolios amid COVID-19 panic cases of Bitcoin, Ethereum and Litecoin. China Financ Rev Int 12: 51–68. https://doi.org/10.1108/cfri-09-2021-0187 doi: 10.1108/cfri-09-2021-0187
    [90] Mensi W, Maitra D, Selmi R, et al. (2023) Extreme dependencies and spillovers between gold and stock markets: evidence from MENA countries. Financ Innov 9: 47. https://doi.org/10.1186/s40854-023-00451-z doi: 10.1186/s40854-023-00451-z
    [91] Mensi W, Vo XV, Kang SH (2022) COVID-19 pandemic's impact on intraday volatility spillover between oil, gold, and stock markets. Econ Anal Policy 74: 702–715. https://doi.org/10.1016/j.eap.2022.04.001 doi: 10.1016/j.eap.2022.04.001
    [92] Metz M, Kruikemeier S, Lecheler S (2020) Personalization of politics on Facebook: Examining the content and effects of professional, emotional and private self-personalization. Inf Commun Soc 23: 1481–1498. https://doi.org/10.1080/1369118x.2019.1581244 doi: 10.1080/1369118x.2019.1581244
    [93] Miyazaki T, Hamori S (2016) Asymmetric correlations in gold and other financial markets. Appl Econ 48: 4419–4425. https://doi.org/10.1080/00036846.2016.1158919 doi: 10.1080/00036846.2016.1158919
    [94] Moussa W, Mgadmi N, Regaieg R, et al. (2021) The relationship between gold price and the American financial market. Int J Finance Econ 26: 6149–6155. https://doi.org/10.1002/ijfe.2113 doi: 10.1002/ijfe.2113
    [95] Naeem MA, Hasan M, Arif M, et al. (2020) Can bitcoin glitter more than gold for investment styles? Sage Open 10: 2158244020926508. https://doi.org/10.1177/2158244020926508 doi: 10.1177/2158244020926508
    [96] Nguyen APN, Crane M, Bezbradica M (2022) Cryptocurrency volatility index: an efficient way to predict the future CVI. In: Irish Conference on Artificial Intelligence and Cognitive Science, Cham: Springer Nature Switzerland, 355–367. https://doi.org/10.1007/978-3-031-26438-2_28
    [97] Patel R, Gubareva M, Chishti MZ (2024) Assessing the connectedness between cryptocurrency environment attention index and green cryptos, energy cryptos, and green financial assets. Res Int Bus Financ 70: 102339. https://doi.org/10.1016/j.ribaf.2024.102339 doi: 10.1016/j.ribaf.2024.102339
    [98] Platanakis E, Urquhart A (2020) Should investors include bitcoin in their portfolios? A portfolio theory approach. Bri Account Rev 52: 100837. https://doi.org/10.1016/j.bar.2019.100837 doi: 10.1016/j.bar.2019.100837
    [99] Polat O, Kabakçı Günay E (2021) Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains. Stud Econ Financ 38: 946–963. https://doi.org/10.1108/sef-01-2021-0011 doi: 10.1108/sef-01-2021-0011
    [100] Popper N (2015) Digital gold: The untold story of Bitcoin. Penguin UK.
    [101] Qiu LD, Zhan C, Wei X (2019) An analysis of the China–US trade war through the lens of the trade literature. Econ Polit Stud 7: 148–168. https://doi.org/10.1080/20954816.2019.1595329 doi: 10.1080/20954816.2019.1595329
    [102] Raheem ID (2021) COVID-19 pandemic and the safe haven property of Bitcoin. Q Rev Econ Financ 81: 370–375. https://doi.org/10.1016/j.qref.2021.06.004 doi: 10.1016/j.qref.2021.06.004
    [103] Reboredo JC (2013) Is gold a safe haven or a hedge for the US dollar? Implications for risk management. J Bank Financ 37: 2665–2676. https://doi.org/10.1016/j.jbankfin.2013.03.020 doi: 10.1016/j.jbankfin.2013.03.020
    [104] Rehman MU, Kang SH (2021) A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets. Glob Financ J 49: 100576. https://doi.org/10.1016/j.gfj.2020.100576 doi: 10.1016/j.gfj.2020.100576
    [105] Rizvi A, Masih M (2014) Oil price shocks and GCC capital markets: who drives whom? MPRA paper 56993. University Library of Munich, Germany. Available from: https://mpra.ub.uni-muenchen.de/56993/
    [106] Salisu AA, Adediran I (2020) Gold as a hedge against oil shocks: Evidence from new datasets for oil shocks. Resour Policy 66: 101606. https://doi.org/10.1016/j.resourpol.2020.101606 doi: 10.1016/j.resourpol.2020.101606
    [107] Salisu AA, Ebuh GU, Usman N (2020) Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results. Int Rev Econ Financ 69: 280–294. https://doi.org/10.1016/j.iref.2020.06.023 doi: 10.1016/j.iref.2020.06.023
    [108] Salisu AA, Ndako UB, Vo XV (2023) Oil price and the Bitcoin market. Resour Policy 82: 103437. https://doi.org/10.1016/j.resourpol.2023.103437 doi: 10.1016/j.resourpol.2023.103437
    [109] Salisu AA, Raheem ID, Vo XV (2021) Assessing the safe haven property of the gold market during COVID-19 pandemic. Int Rev Financ Anal 74: 101666. https://doi.org/10.1016/j.irfa.2021.101666 doi: 10.1016/j.irfa.2021.101666
    [110] Sauer B (2016) Virtual currencies, the money market, and monetary policy. Int Adv Econ Res 22: 117–130. https://doi.org/10.1007/s11294-016-9576-x doi: 10.1007/s11294-016-9576-x
    [111] Selmi R, Bouoiyour J, Wohar ME (2022) "Digital Gold" and geopolitics. Res Int Bus Financ 59: 101512. https://doi.org/10.1016/j.ribaf.2021.101512 doi: 10.1016/j.ribaf.2021.101512
    [112] Selmi R, Mensi W, Hammoudeh S, et al. (2018) Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Econ 74: 787–801. https://doi.org/10.1016/j.eneco.2018.07.007 doi: 10.1016/j.eneco.2018.07.007
    [113] Shahzad SJH, Bouri E, Rehman MU, et al. (2022) The hedge asset for BRICS stock markets: Bitcoin, gold or VIX. World Econ 45: 292–316. https://doi.org/10.1111/twec.13138 doi: 10.1111/twec.13138
    [114] Shahzad SJH, Bouri E, Roubaud D, et al. (2020) Safe haven, hedge and diversification for G7 stock markets: Gold versus bitcoin. Econ Model 87: 212–224. https://doi.org/10.1016/j.econmod.2019.07.023 doi: 10.1016/j.econmod.2019.07.023
    [115] Shen D, Urquhart A, Wang P (2020) Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks. Eur Financ Manage 26: 1294–1323. https://doi.org/10.1111/eufm.12254 doi: 10.1111/eufm.12254
    [116] Shi Y, Wang L, Ke J (2021) Does the US-China trade war affect co-movements between US and Chinese stock markets? Res Int Bus Financ 58: 101477. https://doi.org/10.1016/j.ribaf.2021.101477 doi: 10.1016/j.ribaf.2021.101477
    [117] Shiva A, Sethi M (2015) Understanding dynamic relationship among gold price, exchange rate and stock markets: Evidence in Indian context. Glob Bus Rev 16: 93S-111S. https://doi.org/10.1177/0972150915601257 doi: 10.1177/0972150915601257
    [118] Sifat IM, Mohamad A, Shariff MSBM (2019) Lead-lag relationship between bitcoin and ethereum: Evidence from hourly and daily data. Res Int Bus Financ 50: 306–321. https://doi.org/10.1016/j.ribaf.2019.06.012 doi: 10.1016/j.ribaf.2019.06.012
    [119] Smales LA (2019) Bitcoin as a safe haven: Is it even worth considering? Financ Res Lett 30: 385–393. https://doi.org/10.1016/j.frl.2018.11.002 doi: 10.1016/j.frl.2018.11.002
    [120] Sun P, Lu X, Xu C, et al. (2020) Understanding of COVID‐19 based on current evidence. J Med Virol 92: 548–551. https://doi.org/10.1002/jmv.25722 doi: 10.1002/jmv.25722
    [121] Theiri S, Nekhili R, Sultan J (2023) Cryptocurrency liquidity during the Russia-Ukraine war: the case of Bitcoin and Ethereum. J Risk Financ 24: 59–71. https://doi.org/10.1108/jrf-05-2022-0103 doi: 10.1108/jrf-05-2022-0103
    [122] Tiwari AK, Aye GC, Gupta R, et al. (2020) Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model. Energy Econ 88: 104748. https://doi.org/10.1016/j.eneco.2020.104748 doi: 10.1016/j.eneco.2020.104748
    [123] Triki MB, Maatoug AB (2021) The GOLD market as a safe haven against the stock market uncertainty: Evidence from geopolitical risk. Resour Policy 70: 101872. https://doi.org/10.1016/j.resourpol.2020.101872 doi: 10.1016/j.resourpol.2020.101872
    [124] Tse YK, Tsui AKC (2002) A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. J Bus Econ Stat 20: 351–362. https://doi.org/10.1198/073500102288618496 doi: 10.1198/073500102288618496
    [125] Uddin GS, Hernandez JA, Shahzad SJH, et al. (2020) Characteristics of spillovers between the US stock market and precious metals and oil. Resour Policy 66: 101601. https://doi.org/10.1016/j.resourpol.2020.101601 doi: 10.1016/j.resourpol.2020.101601
    [126] Umar Z, Gubareva M (2020) A time–frequency analysis of the impact of the Covid-19 induced panic on the volatility of currency and cryptocurrency markets. J Behav Exp Financ 28: 100404. https://doi.org/10.1016/j.jbef.2020.100404 doi: 10.1016/j.jbef.2020.100404
    [127] Umar Z, Polat O, Choi SY, et al. (2022) The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Financ Res Lett 48: 102976. https://doi.org/10.1016/j.frl.2022.102976 doi: 10.1016/j.frl.2022.102976
    [128] Urquhart A, Zhang H (2019) Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. Int Rev Financ Anal 63: 49–57. https://doi.org/10.1016/j.irfa.2019.02.009 doi: 10.1016/j.irfa.2019.02.009
    [129] Vacha L, Barunik J (2012) Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Econ 34: 241–247. https://doi.org/10.1016/j.eneco.2011.10.007 doi: 10.1016/j.eneco.2011.10.007
    [130] Valadkhani A, Nguyen J, Chiah M (2022) When is gold an effective hedge against inflation? Resour Policy 79: 103009. https://doi.org/10.1016/j.resourpol.2022.103009 doi: 10.1016/j.resourpol.2022.103009
    [131] Wang GJ, Xie C, Jiang ZQ, et al. (2016) Extreme risk spillover effects in world gold markets and the global financial crisis. Int Rev Econ Financ 46: 55–77. https://doi.org/10.1016/j.iref.2016.08.004 doi: 10.1016/j.iref.2016.08.004
    [132] Wang J, Xue Y, Liu M (2016) An analysis of bitcoin price based on VEC model. In: 2016 international conference on economics and management innovations, Atlantis Press, 180–186. https://doi.org/10.2991/icemi-16.2016.36
    [133] Wang Y, Cao X, Sui X, et al. (2019) How do black swan events go global?-Evidence from US reserves effects on TOCOM gold futures prices. Financ Res Lett 31. https://doi.org/10.1016/j.frl.2019.09.001 doi: 10.1016/j.frl.2019.09.001
    [134] Wen X, Cheng H (2018) Which is the safe haven for emerging stock markets, gold or the US dollar? Emerg Mark Rev 35: 69–90. https://doi.org/10.1016/j.ememar.2017.12.006 doi: 10.1016/j.ememar.2017.12.006
    [135] Wu S, Tong M, Yang Z, et al. (2019) Does gold or Bitcoin hedge economic policy uncertainty? Financ Res Lett 31: 171–178. https://doi.org/10.1016/j.frl.2019.04.001 doi: 10.1016/j.frl.2019.04.001
    [136] Yousaf I, Plakandaras V, Bouri E, et al. (2023) Hedge and safe-haven properties of FAANA against gold, US Treasury, bitcoin, and US Dollar/CHF during the pandemic period. N Am J Econ Financ 64: 101844. https://doi.org/10.1016/j.najef.2022.101844 doi: 10.1016/j.najef.2022.101844
    [137] Zhang S, Mani G (2021) Popular cryptoassets (Bitcoin, Ethereum, and Dogecoin), Gold, and their relationships: Volatility and correlation modeling. Data Sci Manag 4: 30–39. https://doi.org/10.1016/j.dsm.2021.11.001 doi: 10.1016/j.dsm.2021.11.001
    [138] Zhang Y, Wang M, Xiong X, et al. (2021) Volatility spillovers between stock, bond, oil, and gold with portfolio implications: Evidence from China. Financ Res Lett 40: 101786. https://doi.org/10.1016/j.frl.2020.101786 doi: 10.1016/j.frl.2020.101786
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