Influence of obstacle configuration on electrolyte flow in serpentine flow fields for redox flow batteries

Joseba Martínez-López; Koldo Portal-Porras; Unai Fernández-Gamiz; Eduardo Sánchez-Díez; Aitor Beloki-Arrondo; Íñigo Ortega-Fernández; Joseba Martínez-López; Koldo Portal-Porras; Unai Fernández-Gamiz; Eduardo Sánchez-Díez; Aitor Beloki-Arrondo; Íñigo Ortega-Fernández

doi:10.3934/era.2025234

Electronic Research Archive

2025, Volume 33, Issue 9: 5231-5251. doi: 10.3934/era.2025234

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Influence of obstacle configuration on electrolyte flow in serpentine flow fields for redox flow batteries

1.
Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain
2.
Centre for Cooperative Research on Alternative Energies (CIC EnergiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, Vitoria-Gasteiz 01510, Spain
3.
TECNALIA Research & Innovation, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 2, Donostia-San Sebastian 20009, Spain

Received: 18 June 2025 Revised: 01 August 2025 Accepted: 13 August 2025 Published: 04 September 2025

In this study, a three-dimensional numerical model was developed to investigate the influence of obstacles on the hydrodynamic behavior of a serpentine flow field. Various obstacle geometries (rectangular, trapezoidal, triangular, and cylindrical), quantities (1–3 blocks), and positions (straight vs. curved channel sections) were systematically analyzed. Results show that rectangular obstacles enhance mean velocity but significantly increase pressure drop and reduce flow uniformity. In contrast, trapezoidal and cylindrical shapes offer a more balanced tradeoff, achieving improved uniformity and flow enhancement with moderate hydraulic penalties. Increasing obstacle number improves electrolyte velocity uniformity across all cases, though diminishing returns are observed beyond two blocks. Importantly, placing obstacles in curved sections of the serpentine field yields up to 9% higher uniformity compared to straight placements, without increasing pressure loss—leveraging pre-existing low-velocity regions to enhance distribution. These findings align with previous literature and highlight that optimized obstacle shape, number, and positioning can significantly improve mass transport and flow distribution in vanadium redox flow batteries (VRFBs). To complement the computational fluid dynamics (CFD) analysis, an artificial neural network (ANN) was trained to predict pressure drop using key geometric and flow features as inputs. The ANN demonstrated excellent agreement with numerical results and reduced the computational time required to obtain the results by 6 orders of magnitude.
- vanadium redox flow battery,
- flow field,
- obstruction,
- numerical model,
- serpentine,
- pressure drop
Citation: Joseba Martínez-López, Koldo Portal-Porras, Unai Fernández-Gamiz, Eduardo Sánchez-Díez, Aitor Beloki-Arrondo, Íñigo Ortega-Fernández. Influence of obstacle configuration on electrolyte flow in serpentine flow fields for redox flow batteries[J]. Electronic Research Archive, 2025, 33(9): 5231-5251. doi: 10.3934/era.2025234

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Abstract

In this study, a three-dimensional numerical model was developed to investigate the influence of obstacles on the hydrodynamic behavior of a serpentine flow field. Various obstacle geometries (rectangular, trapezoidal, triangular, and cylindrical), quantities (1–3 blocks), and positions (straight vs. curved channel sections) were systematically analyzed. Results show that rectangular obstacles enhance mean velocity but significantly increase pressure drop and reduce flow uniformity. In contrast, trapezoidal and cylindrical shapes offer a more balanced tradeoff, achieving improved uniformity and flow enhancement with moderate hydraulic penalties. Increasing obstacle number improves electrolyte velocity uniformity across all cases, though diminishing returns are observed beyond two blocks. Importantly, placing obstacles in curved sections of the serpentine field yields up to 9% higher uniformity compared to straight placements, without increasing pressure loss—leveraging pre-existing low-velocity regions to enhance distribution. These findings align with previous literature and highlight that optimized obstacle shape, number, and positioning can significantly improve mass transport and flow distribution in vanadium redox flow batteries (VRFBs). To complement the computational fluid dynamics (CFD) analysis, an artificial neural network (ANN) was trained to predict pressure drop using key geometric and flow features as inputs. The ANN demonstrated excellent agreement with numerical results and reduced the computational time required to obtain the results by 6 orders of magnitude.

References

[1]	A. A. Kadhem, N. I. A. Wahab, I. Aris, J. Jasni, A. N. Abdalla, Y. Matsukawa, Reliability assessment of generating systems with wind power penetration via BPSO, Int. J. Adv. Sci., Eng. Inf. Technol., 7 (2017), 1248–1254. https://doi.org/10.18517/ijaseit.7.4.2311 doi: 10.18517/ijaseit.7.4.2311
[2]	O. Smith, O. Cattell, E. Farcot, R. D. O'Dea, K. I. Hopcraft, The effect of renewable energy incorporation on power grid stability and resilience, Sci. Adv., 8 (2022), eabj6734. https://doi.org/10.1126/sciadv.abj6734 doi: 10.1126/sciadv.abj6734
[3]	T. S. Le, T. N. Nguyen, D. K. Bui, T. D. Ngo, Optimal sizing of renewable energy storage: A techno-economic analysis of hydrogen, battery and hybrid systems considering degradation and seasonal storage, Appl. Energy, 336 (2023), 120817. https://doi.org/10.1016/j.apenergy.2023.120817 doi: 10.1016/j.apenergy.2023.120817
[4]	M. O. Bamgbopa, A. Fetyan, M. Vagin, A. A. Adelodun, Towards eco-friendly redox flow batteries with all bio-sourced cell components, J. Energy Storage, 50 (2022), 104352. https://doi.org/10.1016/j.est.2022.104352 doi: 10.1016/j.est.2022.104352
[5]	Z. Huang, A. Mu, L. Wu, B. Yang, Y. Qian, J. Wang, Comprehensive Analysis of Critical Issues in All-Vanadium Redox Flow Battery, ACS Sustainable Chem. Eng., 10 (2022), 7786–7810. https://doi.org/10.1021/acssuschemeng.2c01372 doi: 10.1021/acssuschemeng.2c01372
[6]	A. Aluko, A. Knight, A review on vanadium redox flow battery storage systems for large-scale power systems application, IEEE Access, 11 (2023), 13773–13793. https://doi.org/10.1109/ACCESS.2023.3243800 doi: 10.1109/ACCESS.2023.3243800
[7]	K. Lourenssen, J. Williams, F. Ahmadpour, R. Clemmer, S. Tasnim, Vanadium redox flow batteries: A comprehensive review, J. Energy Storage, 25 (2019), 100844. https://doi.org/10.1016/j.est.2019.100844 doi: 10.1016/j.est.2019.100844
[8]	K. Knehr, E. Agar, C. Dennison, A. Kalidindi, E. Kumbur, A transient vanadium flow battery model incorporating vanadium crossover and water transport through the membrane, J. Electrochem. Soc., 159 (2012), A1446–A1459. https://doi.org/10.1149/2.017209jes doi: 10.1149/2.017209jes
[9]	W. M. Carvalho, L. Cassayre, D. Quaranta, F. Chauvet, R. El-Hage, T. Tzedakis, et al., Stability of highly supersaturated vanadium electrolyte solution and characterization of precipitated phases for vanadium redox flow battery, J. Energy Chem., 61 (2021), 436–445. https://doi.org/10.1016/j.jechem.2021.01.040 doi: 10.1016/j.jechem.2021.01.040
[10]	F. Rahman, M. Skyllas-Kazacos, Vanadium redox battery: Positive half-cell electrolyte studies, J. Power Sources, 189 (2009), 1212–1219. https://doi.org/10.1016/j.jpowsour.2008.12.113 doi: 10.1016/j.jpowsour.2008.12.113
[11]	S. Maurya, P. T. Nguyen, Y. S. Kim, Q. Kang, R. Mukundan, Effect of flow field geometry on operating current density, capacity and performance of vanadium redox flow battery, J. Power Sources, 404 (2018), 20–27. https://doi.org/10.1016/j.jpowsour.2018.09.093 doi: 10.1016/j.jpowsour.2018.09.093
[12]	Z. Huang, A. Mu, Flow field design and performance analysis of vanadium redox flow battery, Ionics, 27 (2021), 5207–5218. https://doi.org/10.1007/s11581-021-04213-8 doi: 10.1007/s11581-021-04213-8
[13]	J. Wang, A. Mu, B. Yang, W. Wang, Y. Wang, Enhancing vanadium flow battery performance via trapezoidal channel design: An experimental validation and parametric investigation, J. Energy Storage, 119 (2025), 116346. https://doi.org/10.1016/j.est.2025.116346 doi: 10.1016/j.est.2025.116346
[14]	J. Ramesh, M. Premalatha, D. R. Sudhakar, G. A. Pathanjali, M. Raja, A novel flow design to reduce pressure drop and enhance performance of Vanadium Redox Flow Battery, J. Energy Storage, 108 (2025), 115030. https://doi.org/10.1016/j.est.2024.115030 doi: 10.1016/j.est.2024.115030
[15]	Z. Huang, A. Mu, L. Wu, H. Wang, Vanadium redox flow batteries: Flow field design and flow rate optimization, J. Energy Storage, 45 (2022), 103526. https://doi.org/10.1016/j.est.2021.103526 doi: 10.1016/j.est.2021.103526
[16]	M. Messaggi, P. Canzi, R. Mereu, A. Baricci, F. Inzoli, A. Casalegno, et al., Analysis of flow field design on vanadium redox flow battery performance: Development of 3D computational fluid dynamic model and experimental validation, Appl. Energy, 228 (2018), 1057–1070. https://doi.org/10.1016/j.apenergy.2018.06.148 doi: 10.1016/j.apenergy.2018.06.148
[17]	Q. Xu, T. S. Zhao, C. Zhang, Performance of a vanadium redox flow battery with and without flow fields, Electrochim. Acta, 142 (2014), 61–67. https://doi.org/10.1016/j.electacta.2014.07.059 doi: 10.1016/j.electacta.2014.07.059
[18]	Y. Wang, M. Li, L. Hao, Three-dimensional modeling study of all-vanadium redox flow batteries with the serpentine and interdigitated flow fields, J. Electroanal. Chem., 918 (2022), 116460. https://doi.org/10.1016/j.jelechem.2022.116460 doi: 10.1016/j.jelechem.2022.116460
[19]	M. Yue, Q. Zheng, F. Xing, H. Zhang, X. Li, X. Ma, Flow field design and optimization of high power density vanadium flow batteries: A novel trapezoid flow battery, AIChE J., 64 (2018), 782–795. https://doi.org/10.1002/aic.15959 doi: 10.1002/aic.15959
[20]	J. Lee, J. Kim, H. Park, Numerical simulation of the power-based efficiency in vanadium redox flow battery with different serpentine channel size, Int. J. Hydrogen Energy, 44 (2019), 29483–29492. https://doi.org/10.1016/j.ijhydene.2019.05.013 doi: 10.1016/j.ijhydene.2019.05.013
[21]	E. Ali, J. Kim, H. Park, Numerical analysis of modified channel widths of serpentine and interdigitated channels for the discharge performance of vanadium redox flow batteries, J. Energy Storage, 53 (2022), 105099. https://doi.org/10.1016/j.est.2022.105099 doi: 10.1016/j.est.2022.105099
[22]	R. Gundlapalli, S. Jayanti, Effect of channel dimensions of serpentine flow fields on the performance of a vanadium redox flow battery, J. Energy Storage, 23 (2019), 148–158. https://doi.org/10.1016/j.est.2019.03.014 doi: 10.1016/j.est.2019.03.014
[23]	J. Sun, B. Liu, M. Zheng, Y. Luo, Z. Yu, Serpentine flow field with changing rib width for enhancing electrolyte penetration uniformity in redox flow batteries, J. Energy Storage, 49 (2022), 104135. https://doi.org/10.1016/j.est.2022.104135 doi: 10.1016/j.est.2022.104135
[24]	Z. Guo, J. Sun, X. Fan, T. Zhao, Numerical modeling of a convection-enhanced flow field for high-performance redox flow batteries, J. Power Sources, 583 (2023), 233540. https://doi.org/10.1016/j.jpowsour.2023.233540 doi: 10.1016/j.jpowsour.2023.233540
[25]	Z. Huang, A. Mu, L. Wu, H. Wang, Y. Zhang, Electrolyte flow optimization and performance metrics analysis of vanadium redox flow battery for large-scale stationary energy storage, Int. J. Hydrogen Energy, 46 (2021), 31952–31962. https://doi.org/10.1016/j.ijhydene.2021.06.220 doi: 10.1016/j.ijhydene.2021.06.220
[26]	L. Pan, J. Sun, H. Qi, M. Han, L. Chen, J. Xu, et al., Along-flow-path gradient flow field enabling uniform distributions of reactants for redox flow batteries, J. Power Sources, 570 (2023), 233012. https://doi.org/10.1016/j.jpowsour.2023.233012 doi: 10.1016/j.jpowsour.2023.233012
[27]	B. Akuzum, Y. C. Alparslan, N. C. Robinson, E. Agar, E. C. Kumbur, Obstructed flow field designs for improved performance in vanadium redox flow batteries, J. Appl. Electrochem., 49 (2019), 551–561. https://doi.org/10.1007/s10800-019-01306-1 doi: 10.1007/s10800-019-01306-1
[28]	M. Messaggi, C. Gambaro, A. Casalegno, M. Zago, Development of innovative flow fields in a vanadium redox flow battery: Design of channel obstructions with the aid of 3D computational fluid dynamic model and experimental validation through locally-resolved polarization curves, J. Power Sources, 526 (2022), 231155. https://doi.org/10.1016/j.jpowsour.2022.231155 doi: 10.1016/j.jpowsour.2022.231155
[29]	Y. Liu, Z. Huang, X. Xie, Y. Liu, J. Wu, Z. Guo, et al., Redox flow battery: Flow field design based on bionic mechanism with different obstructions, Chem. Eng. J., 498 (2024), 155663. https://doi.org/10.1016/j.cej.2024.155663 doi: 10.1016/j.cej.2024.155663
[30]	J. Martínez-López, U. Fernández-Gamiz, E. Sánchez-Díez, A. Beloki-Arrondo, Í. Ortega-Fernández, Enhancing mass transport in organic redox flow batteries through electrode obstacle design, Batteries, 11 (2025). https://doi.org/10.3390/batteries11010029 doi: 10.3390/batteries11010029
[31]	Q. Wang, Z. G. Qu, Z. Y. Jiang, W. W. Yang, Experimental study on the performance of a vanadium redox flow battery with non-uniformly compressed carbon felt electrode, Appl. Energy, 213 (2018), 293–305. https://doi.org/10.1016/j.apenergy.2018.01.047 doi: 10.1016/j.apenergy.2018.01.047
[32]	M. Y. Lu, Y. H. Jiao, X. Y. Tang, W. W. Yang, M. Ye, Q. Xu, Blocked serpentine flow field with enhanced species transport and improved flow distribution for vanadium redox flow battery, J. Energy Storage, 35 (2021), 102284. https://doi.org/10.1016/j.est.2021.102284 doi: 10.1016/j.est.2021.102284
[33]	K. Portal-Porras, U. Fernandez-Gamiz, E. Zulueta, R. Garcia-Fernandez, O. Irigaray, Parametric study of vortex generators on a fin-and-tube heat exchanger, Energy Sources, Part A, 45 (2023), 10051–10072. https://doi.org/10.1080/15567036.2023.2242809 doi: 10.1080/15567036.2023.2242809
[34]	J. Martínez-López, K. Portal-Porras, U. Fernández-Gamiz, E. Sánchez-Díez, J. Olarte, I. Jonsson, Voltage and overpotential prediction of vanadium redox flow batteries with artificial neural networks, Batteries, 10 (2024). https://doi.org/10.3390/batteries10010023 doi: 10.3390/batteries10010023
[35]	M. T. Hagan, M. B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks, 5 (1994), 989–993. https://doi.org/10.1109/72.329697 doi: 10.1109/72.329697
[36]	B. G. Kermani, S. S. Schiffman, H. T. Nagle, Performance of the Levenberg–Marquardt neural network training method in electronic nose applications, Sens. Actuators, B, 110 (2005), 13–22. https://doi.org/10.1016/j.snb.2005.01.008 doi: 10.1016/j.snb.2005.01.008
[37]	MATLAB, 2025. Available from: https://www.mathworks.com/products/matlab.html.
[38]	Deep Learning Toolbox, 2025. Available from: https://www.mathworks.com/products/deep-learning.html.

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