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Research article Special Issues

Innovation towards sustainable fresh-cut salad production: Are Italian consumers receptive?

  • Fresh-cut salads are ready-to-eat foodstuffs with a growing market share and increasingly popular with consumers. However, a significant part of public opinion considers that bagged salad production processes have an effect on sustainability. In parallel, fresh-cut salads producers implement high resources and innovation strategies to improve the production process and product sustainability, highlighting an increasing awareness of their responsibility. The objective of this study was to investigate whether a correspondence exists between consumer preferences and the fresh-cut salad sustainability attributes (environmental, economic and social), indicated by producers (on their packaging and/or company website). Consumer preference analysis of 12 attributes of fresh-cut salads was made using the Best-Worst scaling methodology. Among the selected attributes, 9 were related to sustainability issues and 3 to the intrinsic product characteristics. A paper questionnaire was developed and submitted directly to consumers (n = 216), at different points of sale of several large retail chains in the Turin metropolitan area (Northwest Italy). The analysis of the results highlights that no direct correspondence can be found between the companies’ communications regarding sustainability and the real interest of consumers of fresh-cut salads towards these attributes. Moreover, in contrast to the growing ‘green’ attitude among consumers, the lack of consumer interest in the attributes of environmental sustainability underlines the need to increase consumer awareness of the issue. Thus, this research could contribute to the development of more targeted and accessible communication strategies towards consumers.

    Citation: Valentina M. Merlino, Danielle Borra, Aurora Bargetto, Simone Blanc, Stefano Massaglia. Innovation towards sustainable fresh-cut salad production: Are Italian consumers receptive?[J]. AIMS Agriculture and Food, 2020, 5(3): 365-386. doi: 10.3934/agrfood.2020.3.365

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  • Fresh-cut salads are ready-to-eat foodstuffs with a growing market share and increasingly popular with consumers. However, a significant part of public opinion considers that bagged salad production processes have an effect on sustainability. In parallel, fresh-cut salads producers implement high resources and innovation strategies to improve the production process and product sustainability, highlighting an increasing awareness of their responsibility. The objective of this study was to investigate whether a correspondence exists between consumer preferences and the fresh-cut salad sustainability attributes (environmental, economic and social), indicated by producers (on their packaging and/or company website). Consumer preference analysis of 12 attributes of fresh-cut salads was made using the Best-Worst scaling methodology. Among the selected attributes, 9 were related to sustainability issues and 3 to the intrinsic product characteristics. A paper questionnaire was developed and submitted directly to consumers (n = 216), at different points of sale of several large retail chains in the Turin metropolitan area (Northwest Italy). The analysis of the results highlights that no direct correspondence can be found between the companies’ communications regarding sustainability and the real interest of consumers of fresh-cut salads towards these attributes. Moreover, in contrast to the growing ‘green’ attitude among consumers, the lack of consumer interest in the attributes of environmental sustainability underlines the need to increase consumer awareness of the issue. Thus, this research could contribute to the development of more targeted and accessible communication strategies towards consumers.


    Flocking is observed in large populations of social agents such as birds [2], fish [3] or insects [4]. It refers to the emergence of large scale spatio-temporal structures which are not directly encoded in the individual agents' behavior. Understanding how large scale structures appear from individual behavior has sparked a huge literature in the recent years concerned with both modelling [5,6,7,8,9,10] and experiments [11,12]. We refer the reader to the reviews [13,14,15]. Yet this phenomenon is still poorly understood. The modelling of individual behavior at the microscopic scale requires to resolve the motion of each individual in the course of time. This leads to so-called Individual-Based Models (IBM) (or particle models) consisting of a huge number of coupled ordinary or stochastic differential equations. By contrast, given the very large number of agents involved, the modelling of the macroscopic scale is best done through Continuum Models (CM). They describe the system as a fluid through average quantities such as the density or mean velocity of the agents. At the mesoscopic scale, kinetic models (KM) are intermediates ketween IBM and CM. They describe the agents dynamics through the statistical distribution of their positions and velocities. In the literature, IBM, KM or CM are often chosen according to the authors' preferences rather than following an explicit rationale. However, understanding flocking requires understanding how the individuals' microscale impacts on the system's macroscale and consequently demands the use of a consistent sequence of IBM, KM and CM models. This consistency can only be guaranteed if the passage from IBM to KM and from KM to CM can be systematically established. But this issue is seldom considered. One goal of this paper is to establish the consistency between a KM and a CM of flocking based on a variant of the celebrated Cucker-Smale model of consensus (Eqs (1.1) and (1.2) below). KM have been introduced in the last years for the mesoscopic description of collective behavior of agents/particles with applications in collective behavior of cell and animal populations, see [1,16,17,18] and the references therein for a general overview on this active field. These models usually include alignment, attraction and repulsion as basic bricks of interactions between individuals.

    In this paper, we will discard the microscopic scale, i.e. the IBM. We will directly consider the mescoscale, i.e. the KM and will focus on the derivation of the CM from the KM. Of course, our KM has an underlying IBM. However the techniques involved in the passage from IBM to KM are quite different from those needed to pass from KM to CM. For this reason, considering the IBM would have brought us beyond the scope of the present paper. We refer to [19,20,21,22,23,24,25,26,27] and references therein for a derivation of KM from IBM. The derivation of the CM from the KM requires a spatio-temporal rescaling. Indeed, the KM still describes-although in a statistical way compared to the IBM---the microscopic dynamics of the particles. In particular, it is written in space and time units that are of the order of the particle interaction distances and times. To describe the macroscopic scale, one needs to introduce a change of variables by which the space and time units become of the order of the system scale, which is much wider that the particle-related scales. This rescaling introduces a small parameter, the ratio of the microscopic to the macroscopic space units. The derivation of the CM from the KM consists in finding the limit of the KM when this small parameter tends to zero. This is the goal of this paper. We will see that the CM (Eqs (1.5) and (1.6) below) corresponding to the considered modified Cucker-Smale model is a system consisting of equations for the density and mean orientation of the particles as functions of space and time. This system, referred to as the `Self-Organized Hydrodynamics (SOH)' bears analogies with the Euler equations of isothermal compressible gas dynamics, with the important difference that the average velocity is replaced by the average orientation, i.e. a vector of norm one. The SOH appears in a variety of contexts related to alignment interactions, such as repulsion [28], nematic alignment [29], suspensions [30], solid orientation [31], and can be seen as a basic CM of collective dynamics.

    Here, we focus on the derivation of macroscopic equations (SOH) for the collective motion of self-propelled particles with alignment and noise when a cruise speed for individuals is imposed asymptotically for large times as in [32,33,34,35,36,37,38]. More precisely, in the presence of friction and self-propulsion and the absence of other interactions, individuals/particles accelerate or break to achieve a cruise speed exponentially fast in time. The alignment between particles is imposed via localized versions of the Cucker-Smale or Motsch-Tadmor reorientation procedure [17,39,40,41,42,43] leading to relaxation terms to the mean velocity modulated or not by the density of particles. By scaling the relaxation time towards the asymptotic cruise speed, or equivalently, penalizing the balance between friction and self-propulsion, this alignment interaction leads asymptotically to variations of the classical kinetic Vicsek-Fokker-Planck equation with velocities on the sphere, see [1,10,44,45,46,47,48]. It was shown in [37,38] that particular versions of the localized kinetic Cucker-Smale model can lead to phase transitions driven by noise. Moreover, these phase transitions are numerically stable in this asymptotic limit converging towards the phase transitions of the limiting versions of the corresponding kinetic Vicsek-Fokker-Planck equation.

    In this work, we choose a localized and normalized version of the Cucker-Smale model not showing phase transition. More precisely, let us denote by f=f(t,x,v)0 the particle density in the phase space (x,v)Rd×Rd, with d2. The standard self-propulsion/friction mechanism leading to the cruise speed of the particles in the absence of alignment is given by the term divv{f(αβ|v|2)v} with α,β>0, and the relaxation toward the normalized mean velocity writes divv{f(vΩ[f])}. Here, for any particle density f(x,v), the notation Ω[f] stands for the orientation of the mean velocity

    Ω[f]:={Rdf(,v)vdv|Rdf(,v)vdv|, if Rdf(,v)vdvRd{0},0, if Rdf(,v)vdv=0.

    Notice that we always have

    ρu[f]:=Rdf(,v)vdv=|Rdf(,v)vdv|Ω[f]with ρ:=Rdf(,v)dv.

    Let us remark that the standard localized Cucker-Smale model would lead to ρdivv{f(vu[f])} while the localized Motsch-Tadmor model would lead to divv{f(vu[f])}. Our relaxation term towards the normalized local velocity Ω[f] does not give rise to phase transition in the homogeneous setting on the limiting Vicsek-Fokker-Planck-type model on the sphere according to [45] and it produces a competition to the cruise speed term comprising a tendency towards unit speed. Including random Brownian fluctuations in the velocity variable leads to the kinetic Fokker-Planck type equation

    tf+vxf+divv{f(αβ|v|2)v}=divv{σvf+f(vΩ[f])},(t,x,v)R+×Rd×Rd.

    We include this equation in a more general family of equations written in a compact form as

    tf+vxf=Q(f), (1.1)

    where

    Q(f)=divv{σvf+f(vΩ[f])+ηfvV}, (1.2)

    for any density distribution f with V a general confining potential in the velocity variables and η>0 (see Lemma 2.1 for more information on the type of potentials that we consider). In the particular example considered above we take V=Vα,β(|v|):=β|v|44α|v|22.

    We investigate the large time and space scale regimes of the kinetic tranport Eq (1.1) with collision operator given by (1.2). Namely, we study the asymptotic behavior when ε0 of

    tfε+vxfε=1εQ(fε), (1.3)

    supplemented with the initial condition

    fε(0,x,v)=fin(x,v),(x,v)Rd×Rd.

    The rescaling taken in the kinetic transport Eq (1.3) with confining potential Vα,β can be seen as an intermediate scaling between the ones proposed in [48] and [37]. The difference being that we have a relaxation towards the normalized mean velocity Ω[f] rather than the mean velocity u[f] as in [37,48]. This difference is important since in the first case there is no phase transition in the homogeneous limiting setting on the sphere as we mentioned above, while in the second there is, see [38,45,48]. In fact, in [48] the scaling corresponds to η=1/ε in (1.3), that is the relaxation to the cruise speed is penalized with a term of the order of 1/ε2. Whereas in [37] the scaling correponds to η=ε, that is the cruise speed is not penalized at all.

    The methodology followed in [48] lies within the context of measure solutions by introducing a projection operator onto the set of measures supported in the sphere whose radius is the critical speed r=α/β. These technicalities are needed because the zeroth order expansion of fε lives on the sphere. This construction followed closely the average method in gyro-kinetic theory [49,50,51].

    However, in our present case we will show in contrast to [37,48] that there are no phase transitions which is in accordance with the results obtained in [46] for the kinetic Vicsek-Fokker-Planck equation with analogous alignment operator on the sphere. A modified version of (1.1) and (1.2) in which phase-transitions occur was studied in [38] whose analysis is postponed to a future work to focus here on the mathematical difficulties of the asymptotic analysis. Another difference in the present case is that the zeroth order expansion of fε will be parameterized by Von Mises-Fisher distributions in the whole velocity space, that is f(t,x,v)=ρ(t,x)MΩ(t,x)(v), with ρ and Ω being, respectively, the density and the mean orientation of the particles. And where for any ΩSd1 we define (see section 2)

    MΩ(v)=1ZΩexp(ΦΩ(v)σ), with ZΩ=Rdexp(ΦΩ(v)σ)dv (1.4)

    and

    Φ(v)=|vΩ|22+V(|v|).

    The main result of this paper is the asymptotic analysis of the singularly perturbed kinetic transport equation of Cucker-Smale type (1.3). The particle density ρ and the orientation Ω obey the hydrodynamic type equations given in the following result.

    Theorem 1.1. Let fin0 be a smooth initial particle density with nonvanishing orientation at any xRd. For any ε>0 we consider the problem

    tfε+vxfε=1εdivv{σvfε+fε(vΩ[fε])+fεvV(|v|)},(t,x,v)R+×Rd×Rd,

    with initial condition

    fε(0,x,v)=fin(x,v),(x,v)Rd×Rd.

    At any (t,x)R+×Rd the leading order term in the Hilbert expansion fε=f+f1ε+ is an equilibrium distribution of Q, that is f(t,x,v)=ρ(t,x)MΩ(t,x)(v) with MΩ(t,x)(v) defined in (1.4), where the concentration ρ and the orientation Ω satisfy

    tρ+divx(ρc1Ω)=0,(t,x)R+×Rd, (1.5)
    tΩ+c2(Ωx)Ω+σ(IdΩΩ)xρρ=0,(t,x)R+×Rd, (1.6)

    with initial conditions

    ρ(0,x)=Rdfin(x,v)dv,Ω(0,x)=Rdfin(x,v)vdv|Rdfin(x,v)vdv|,xRd.

    The constants c1,c2 are given by

    c1=R+rdπ0cosθe(cosθ,r)sind2θdθdrR+rd1π0e(cosθ,r)sind2θdθdr,
    c2=R+rd+1π0cosθχ(cosθ,r)e(cosθ,r)sind1θdθdrR+rdπ0χ(cosθ,r)e(cosθ,r)sind1θdθdr,

    and the function χ solves

    σc[rd3(1c2)d12e(c,r)cχ]σr[rd1(1c2)d32e(c,r)rχ]+σ(d2)rd3(1c2)d52eχ=rd(1c2)d22e(c,r),

    where e(c,r)=exp(rc/σ)exp((r2+1)/(2σ)V(r)/σ).

    Our article is organized as follows. First, in section 2 we state auxiliary results allowing us to discuss the kernel of the collision operator. Then in section 3 we concentrate on the characterization of the collision invariants. We prove that the generalized collision invariants introduced in [1] coincide with the kernel of a suitable linearised collision operator. We explicitly describe the collision invariants in section 4 and investigate their symmetries. Finally, the limit fluid model is determined in section 5 and we analyse its main properties.

    Plugging into (1.3) the Hilbert expansion

    fε=f+εf1+,

    we obtain at the leading order

    Q(f)=0, (2.1)

    whereas to the next order we get

    tf+vxf=limε01ε{Q(fε)Q(f)}=dQf(f1)=:Lf(f1), (2.2)

    where dQf denotes the first variation of Q with respect to f. The constraint (2.1) leads immediately to the equilibrium

    MΩ(v)=1ZΩexp(ΦΩ(v)σ), with ZΩ=Rdpdvexp(ΦΩ(v)σ),

    where

    ΦΩ(v)=|vΩ|22+V(|v|). (2.3)

    Indeed, by using the identity

    vMΩ=MΩ(v)σvΦΩ=MΩ(v)σ(vΩ+vV(|v|)), (2.4)

    we can recast the operator Q as

    Q(f)=divv(σvf+fvΦΩ[f])=σdivv[MΩfv(fMΩf)].

    We denote by Sd1 the set of unit vectors in Rd. For any ΩSd1, we consider the weighted spaces

    L2MΩ={χ:RdR measurable ,Rd(χ(v))2MΩ(v)dv<},

    and

    H1MΩ={χ:RdR measurable ,Rd[(χ(v))2+|vχ|2]MΩ(v)dv<}.

    The nonlinear operator Q should be understood in the distributional sense, and is defined for any particle density f=f(v) in the domain

    D(Q)={f:RdR+ measurable ,f/MΩfH1MΩf}={f:RdR+ measurable ,Rd{(fMΩf)2+|v(fMΩf)|2}MΩf(v)dv<}.

    We introduce the usual scalar products

    (χ,θ)MΩ=Rdχ(v)θ(v)MΩ(v)dv,χ,θL2MΩ,
    ((χ,θ))MΩ=Rd(χ(v)θ(v)+vχvθ)MΩ(v)dv,χ,θH1MΩ,

    and we denote by ||MΩ,MΩ the associated norms. We make the following hypotheses on the potential V. We assume that for any ΩSd1 we have

    ZΩ=Rdexp(1σ[|vΩ|22+V(|v|)])dv<. (2.5)

    Clearly (2.5) holds true for the potentials Vα,β. Notice that in that case 1L2MΩ and |1|MΩ=1 for any ΩSd1. Moreover, we need a Poincaré inequality, that is, for any ΩSd1 there is λΩ>0 such that for all χH1MΩ we have

    σRd|vχ|2MΩ(v)dvλΩRd|χ(v)Rdχ(v)MΩ(v)dv|2MΩ(v)dv. (2.6)

    A sufficient condition for (2.6) to hold comes from the well-known equivalence between the Fokker-Planck and Schrödinger operators (see for instance [52]). Namely, for any ΩSd1 we have

    σMΩdivv(MΩv(uMΩ))=σΔvu+[14σ|vΦΩ|212ΔvΦΩ]u.

    The operator HΩ=σΔv+[14σ|vΦΩ|212ΔvΦΩ] is defined in the domain

    D(HΩ)={uL2(Rd),[14σ|vΦΩ|212ΔvΦΩ]uL2(Rd)}.

    Using classical results for Schrödinger operators (see for instance Theorem ⅩⅢ.67 in [53]), we have a spectral decomposition of the operator HΩ under suitable confining assumptions.

    Lemma 2.1. Assume that for ΦΩ defined in (2.3) the function v14σ|vΦΩ|212ΔvΦΩ satisfies the following:

    a) it belongs to L1loc(Rd),

    b) it is bounded from below,

    c)

    lim|v|[14σ|vΦΩ|212ΔvΦΩ]=.

    Then H1Ω is a self adjoint compact operator in L2(Rd) and HΩ admits a spectral decomposition, that is a nondecreasing sequence of real numbers (λnΩ)nN, limnλnΩ=, and a L2(Rd)-orthonormal basis (ψnΩ)nN such that HΩψnΩ=λnΩψnΩ,nN, λ0Ω=0, λ1Ω>0.

    Let us note that the spectral gap of the Schrödinger operator HΩ is the Poincaré constant in the Poincaré inequality (2.6). Notice also that the hypotheses in Lemma 2.1 are satisfied by the potentials Vα,β, and therefore (2.6) holds true in that case. It is easily seen that the set of equilibrium distributions of Q is parametrized by d parameters as stated in the following result.

    Lemma 2.2. Let f=f(v)0 be a function in D(Q). Then f is an equilibrium for Q if and only if there are (ρ,Ω)R+×(Sd1{0}) such that f=ρMΩ. Moreover we have ρ=ρ[f]:=Rdf(v)dv and Ω=Ω[f].

    Proof. If f is an equilibrium for Q, we have

    σRd|v(fMΩf)|2MΩf(v)dv=0,

    and therefore there is ρR such that f=ρMΩf. Obviously ρ=Rdf(v)dv0 and Ω[f]Sd1{0}. Conversely, we claim that for any (ρ,Ω)R+×(Sd1{0}), the particle density f=ρMΩ is an equilibrium for Q. Indeed, we have

    σv(ρMΩ)+ρMΩ(vΩ+vV)=ρ(σvMΩ+MΩvΦΩ)=0.

    We are done if we prove that Ω[f]=Ω. If Ω=0, it is easily seen that

    Rdf(v)vdv=ρRd1Z0exp(Φ0(v)σ)vdv=ρZ0Rdexp(1σ(|v|22+V(|v|)))vdv=0,

    implying Ω[f]=0=Ω. Assume now that ΩSd1. For any ξSd1, ξΩ=0, we consider the orthogonal transformation Oξ=Id2ξξ. Thanks to the change of variable v=Oξv, we write

    Rd(vξ)f(v)dv=ρZΩRd(vξ)MΩ(v)dv=ρZΩRdpdv(Oξvξ)MΩ(Oξv)=ρZΩRdpdv(vξ)MΩ(v)=Rdpdv(vξ)f(v),

    where we have used the radial symmetry of V, Oξξ=ξ and OξΩ=Ω. We deduce that Rdf(v)vdv=Rd(vΩ)f(v)dvΩ. We claim that Rd(vΩ)f(v)dv>0. Indeed we have

    Rd(vΩ)f(v)dv=ρZΩvΩ>0(vΩ)MΩ(v)dv+ρZΩvΩ<0(vΩ)MΩ(v)dv=ρZΩvΩ>0(vΩ)[exp(ΦΩ(v)σ)exp(ΦΩ(v)σ)]dv.

    Obviously, we have for any vRd such that vΩ>0

    ΦΩ(v)σ+ΦΩ(v)σ=|vΩ|22σ+|vΩ|22σ=2vΩσ>0,

    implying that Rd(vΩ)f(v)dv>0 and

    Ω[f]=Rdf(v)vdv|Rdf(v)vdv|=Rd(vΩ)f(v)dvΩ|Rd(vΩ)f(v)dvΩ|=Ω.

    In [1], the following notion of generalized collision invariant (GCI) has been introduced.

    Definition 3.1. (GCI)

    Let ΩSd1 be a fixed orientation. A function ψ=ψ(v) is called a generalized collision invariant of Q associated to Ω, if and only if

    RdQ(f)(v)ψ(v)dv=0,

    for all f such that (IdΩΩ)Rdf(v)vdv=0, that is such that Rdf(v)vdvRΩ.

    In order to obtain the hydrodynamic limit of (1.3), for any fixed (t,x)R+×Rd, we multiply (2.2) by a function vψt,x(v) and integrate with respect to v yielding

    Rdtf(t,x,v)ψt,x(v)dv+Rdvxf(t,x,v)ψt,x(v)dv=RdLf(t,x,)(f1(t,x,))ψt,x(v)dv=Rdf1(t,x,v)(Lf(t,x,)ψt,x)(v)dv. (3.1)

    The above computation leads naturally to the following extension of the notion of collision invariant, see also [48].

    Definition 3.2. Let f=f(v)0 be an equilibrium of Q. A function ψ=ψ(v) is called a collision invariant for Q associated to the equilibrium f, if and only if Lfψ=0, that is

    Rd(Lfg)(v)ψ(v)dv=0for any function g=g(v).

    We are looking for a good characterization of the linearized collision operator Lf and its adjoint with respect to the leading order particle density f. Motivated by (2.1), we need to determine the structure of the equilibria of Q which are given by Lemma 2.2.

    By Lemma 2.2, we know that for any (t,x)R+×Rd, there are (ρ(t,x),Ω(t,x))R+×(Sd1{0}) such that f(t,x,)=ρ(t,x)MΩ(t,x), where

    ρ(t,x)=ρ[f(t,x,)] and Ω(t,x)=Ω[f(t,x,)].

    The evolution of the macroscopic quantities ρ and Ω follows from (2.2) and (3.1), by appealing to the moment method [54,55,56,57,58,59]. Next, we explicitly determine the linearization of the collision operator Q around its equilibrium distributions. For any orientation ΩSd1{0} we introduce the pressure tensor

    MΩ:=Rd(vΩ)(IdΩΩ)(vΩ)MΩ(v)dv,

    and the quantity

    c1:=Rd(vΩ)MΩ(v)dv>0.

    We will check later, see Lemma 3.2, that the pressure tensor MΩ is symmetric.

    Proposition 3.1. Let f=f(v)0 be an equlibrium distribution of Q with nonvanishing orientation, that is

    f=ρMΩ,whereρ=ρ[f],andΩ=Ω[f]Sd1.

    (1) The linearization Lf=dQf is given by

    Lfg=divv{σvg+gvΦΩfRd(vΩ)f(v)dvPfRdg(v)vdv},

    where Pf:=IdΩ[f]Ω[f] is the orthogonal projection onto {ξRd:ξΩ[f]=0}. In particular LρMΩ=LMΩ.

    (2) The formal adjoint of Lf is given by

    Lfψ=σdivv(MΩvψ)MΩ+PfvW[ψ],W[ψ]:=RdMΩ(v)vψdvRd(vΩ)MΩ(v)dv. (3.2)

    (3) We have the identity

    Lf(f(vΩ))=σvfdivv(fMΩc1).

    Note that divv refers to the divergence operator acting on matrices defined as applying the divergence operator over rows.

    Proof.

    (1) By standard computations we have

    Lfg=dds|s=0Q(f+sg)=divv{σvg+g(vΩ[f]+vV)fdds|s=0Ω[f+sg]},

    and

    dds|s=0Ω[f+sg]=(IdΩ[f]Ω[f])|Rdf(v)vdv|Rdg(v)vdv.

    Therefore we obtain

    Lfg=divv{σvg+gvΦΩfRd(vΩ)f(v)dvPfRdg(v)vdv}.

    (2) We have

    Rd(Lfg)(v)ψ(v)dv=Rd{σvg+gvΦΩfRd(vΩ)f(v)dvPfRdg(v)vdv}vψdv=Rdg[σdivvvψvψvΦΩ]dv+Rdpdvg(v)PfvRdf(v)vψ(v)dvRd(vΩ)f(v)dv,

    implying

    Lfψ=σdivv(MΩvψ)MΩ+PfvW[ψ].

    (3) For any i{1,...,d} we have

    Lf(f(vΩ)i)=divv{(vΩ)i(σvf+fvΦΩ)+σfeifRdpdvMΩ(v)(vΩ)iPfvRdpdv(vΩ)MΩ(v)},

    and therefore, since f=ρMΩ satisfies σvf+fvΦΩ=0, we get

    Lf(f(vΩ))=σvfdivv(fRdpdvMΩ(v)(vΩ)PfvRdpdv(vΩ)MΩ(v))=σvfdivv(fMΩc1).

    Notice that at any (t,x)R+×Rd, the function h=1 is a collision invariant for Q, associated to f(t,x,). Indeed, for any g=g(v) we have

    RdQ(f(t,x,)+sg)dv=0,

    implying that Rd(Lf(t,x,)g)(v)dv=0 and therefore Lf(t,x,)1=0, for all (t,x)R+×Rd. Once we have determined a collision invariant ψ=ψ(t,x,v) at any (t,x)R+×Rd, we deduce, thanks to (3.1), a balance for the macroscopic quantities ρ(t,x)=ρ[f(t,x,)] and Ω(t,x)=Ω[f(t,x,)], given by the relationship

    Rdt(ρMΩ(t,x))ψ(t,x,v)dv+Rdvx(ρMΩ(t,x))ψ(t,x,v)dv=0. (3.3)

    When taking as collision invariant the function h(t,x,v)=1, we obtain the local mass conservation equation

    tρ+divx(ρRd(vΩ(t,x))MΩ(t,x)(v)dvΩ)=0. (3.4)

    As usual, we are looking also for the conservation of the total momentum, however, the nonlinear operator Q does not preserve momentum. In other words, v is not a collision invariant. Indeed, if f=ρMΩ is an equilibrium with nonvanishing orientation, we have

    Lfv=σvMΩMΩ+PfvRdpdv(vΩ)MΩ(v)=vΦΩ+PfvRdpdv(vΩ)MΩ(v),

    and therefore v is not a collision invariant.

    We concentrate next on the resolution of (3.2). We will use the notation vξ=(ξivj) for the Jacobian matrix of a vector field ξ and divv for the divergence operator in v of both vectors and matrices with the convention of taking the divergence over the rows of the matrix. With this convention, we have

    RdgdivvAdv=RdAvgdvandRdξdivvηdv=Rdvξηdv (3.5)

    for all smooth functions g, vector fields ξ,η, and matrices A. We now focus in finding a parameterization of the kernel of the operator Lf.

    Lemma 3.1. Let f=ρMΩ be an equilibrium of Q with nonvanishing orientation. The following two statements are equivalent:

    (1) ψ=ψ(v) is a collision invariant for Q associated to f.

    (2) ψ satisfies

    σdivv(MΩvψ)MΩ+PfvW=0, (3.6)

    for some vector Wker(MΩσc1Id).

    Moreover, the linear map W:ker(Lf)ker(MΩσc1Id), with W[ψ]:=RdMΩ(v)vψdv/c1 induces an isomorphism between the vector spaces ker(Lf)/kerW and ker(MΩσc1Id), where kerW is the set of the constant functions.

    Proof.

    (1)(2) Since ψ is a collision invariant associated to f, i.e. Lfψ=0, and by the third statement in Proposition 3.1 we deduce (using also the first formula in (3.5) with fMΩ/c1 and ψ)

    0=RdLfψf(vΩ)dv=Rdψ(v)Lf(f(vΩ))dv=Rdψ(v)[σvfdivv(fMΩc1)]dv=σRdf(v)vψdv+MΩRdf(v)vψc1dv=ρσc1W[ψ]+ρMΩW[ψ].

    Note that if ρ=0 then f=0 and vfdv=0, implying that Ω[f]=0. Hence, since Ω0, we have ρ>0 and thus W[ψ]ker(MΩσc1Id), saying that (3.6) holds true with W=W[ψ]ker(MΩσc1Id).

    (2)(1) Let ψ be a function satisfying (3.6) for some vector Wker(MΩσc1Id). Multiplying (3.6) by f(vΩ) and integrating with respect to v yields (thanks to the second formula in (3.5))

    σρRdv(vΩ)vψMΩ(v)dv+ρMΩW=0,

    which implies W[ψ]=W since MΩW=σc1W by the assumption Wker(MΩσc1Id). Therefore ψ is a collision invariant for Q, associated to f

    Lfψ=σdivv(MΩvψ)MΩ+PfvW[ψ]=σdivv(MΩvψ)MΩ+PfvW=0.

    Remark 3.1. For any non negative measurable function χ=χ(c,r):]1,+1[×]0,[R and any ΩSd1, for d2, we have

    Rdχ(vΩ|v|,|v|)dv=|Sd2|R+π0χ(cosθ,r)rd1sind2θdθdr,

    where |Sd2| is the surface of the unit sphere in Rd1, for d3, and |S0|=2 for d=2. In particular we have the formula

    Rdχ(vΩ|v|,|v|)MΩ(v)dv=R+rd1π0χ(cosθ,r)e(cosθ,r)sind2θdθdrR+rd1π0e(cosθ,r)sind2θdθdr=R+rd1+11χ(c,r)e(c,r)(1c2)d32dcdrR+rd1+11e(c,r)(1c2)d32dcdr, (3.7)

    where e(c,r)=exp(rc/σ)exp((r2+1)/(2σ)V(r)/σ).

    Notice that thanks to (3.7) the coefficient c1 does not depend upon ΩSd1

    c1=Rd(vΩ)exp(|vΩ|22σV(|v|)σ)dvRdexp(|vΩ|22σV(|v|)σ)dv=R+rdπ0cosθe(cosθ,r)sind2θdθdrR+rd1π0e(cosθ,r)sind2θdθdr.

    In order to determine all the collision invariants, we focus on the spectral decomposition of the pressure tensor MΩ for any ΩSd1. In particular, the next lemma will imply the symmetry of the pressure tensor.

    Lemma 3.2. (Spectral decomposition of MΩ) For any ΩSd1 we have MΩ=σc1(IdΩΩ). In particular we have ker(MΩσc1Id)=(RΩ) and thus dim(ker(Lf)/kerW)=dimker(MΩσc1Id)=d1, cf. Lemma 3.1.

    Proof. Let us consider {E1,,Ed1} an orthonormal basis of (RΩ). By using the decomposition

    vΩ=(ΩΩ)(vΩ)+d1i=1(EiEi)(vΩ)=(ΩΩ)(vΩ)+d1i=1(EiEi)v,

    one gets

    MΩ=Rd[(ΩΩ)(vΩ)+d1i=1(EiEi)v][d1j=1(EjEj)v]MΩ(v)dv. (3.8)

    We claim that the following equalities hold true

    Rd[Ω(vΩ)](Ejv)MΩ(v)dv=0,1jd1, (3.9)
    Rd(Eiv)(Ejv)MΩ(v)dv=δijRd|v|2(vΩ)2d1MΩ(v)dv,1i,jd1. (3.10)

    Formula (3.9) is obtained by using the change of variable v=(Id2EjEj)v. It is easily seen that

    Ω(vΩ)=Ω(vΩ),Ejv=Ejv,MΩ(v)=MΩ(v),1jd1,

    and therefore we have

    Rd[Ω(vΩ)](Ejv)MΩ(v)dv=Rdpdv[Ω(vΩ)](Ejv)MΩ(v)

    which implies (3.9). For the formulae (3.10) with ij, we appeal to the orthogonal transformation

    v=Oijv,Oij=ΩΩ+k{i,j}EkEk+EiEjEjEi.

    Notice that Oijξ=ξ, for all ξ(span{Ei,Ej}), OijEi=Ej, OijEj=Ei and therefore

    (Eiv)(Ejv)=(Ejv)(Eiv).

    After this change of variable we deduce that

    Rd(Eiv)(Ejv)MΩ(v)dv=0,1i,jd1,ij,

    and also

    Rd(Eiv)2MΩ(v)dv=Rd(Ejv)2MΩ(v)dv,1i,jd1.

    Thanks to the equality d1i=1(Eiv)2=|v|2(vΩ)2, one gets

    Rd(Eiv)(Ejv)MΩ(v)dv=δijRd|v|2(vΩ)2d1MΩ(v)dv,1i,jd1.

    Coming back to (3.8) we obtain

    MΩ=d1i=1(Rd(Eiv)2MΩ(v)dv)EiEi=Rd|v|2(vΩ)2d1MΩ(v)dv(IdΩΩ).

    We are done if we prove that

    Rd|v|2(vΩ)2d1MΩ(v)dv=σc1.

    Notice that, using (2.4):

    ((|v|2Idvv)Ω)vMΩ=|v|2(vΩ)2σMΩ(v),

    and therefore

    Rd|v|2(vΩ)2σMΩ(v)dv=Rddivv[(|v|2Idvv)Ω]MΩ(v)dv=Rd[2(vΩ)d(vΩ)(vΩ)]MΩ(v)dv=(d1)c1.

    By Lemmas 3.1 and 3.2 the computation of the collision invariants is reduced to the resolution of (3.6) for any W(RΩ). Hence, if we denote by E1,E2,,Ed1 any orthonormal basis of (RΩ), we obtain a set of d1 collision invariants ψE1,ψE2,,ψEd1 for Q associated to the equilibrium distribution f such that Ei=W[ψEi], i=1,,d1. This set of collision invariants forms a basis for the ker(Lf). In the next section we will characterize this set of collision invariants and provide and easy manner to compute them (see Lemma 4.1).

    We conclude this section by showing that in our case the set of all GCIs of the operator Q coincide with the kernel of the operator Lf.

    Theorem 3.1. Let MΩ be an equilibrium of Q with nonvanishing orientation ΩSd1. The set of collision invariants of Q associated to MΩ coincides with the set of the generalized collision invariants of Q associated to Ω.

    Proof. Let ψ=ψ(v) be a generalized collision invariant of Q associated to Ω. We denote by {e1,,ed} the canonical basis of Rd. For any f=f(v) satisfying (IdΩΩ)eiRdf(v)vdv=0,1id, we have

    Rdf(σΔvψvΦΩvψ)dv=RdQ(f)(v)ψ(v)dv=0.

    Therefore the linear form fRdf(σΔvψvΦΩvψ)dv is a linear combination of the linear forms f(IdΩΩ)eiRdf(v)vdv. We deduce that there is a vector ˜W=(˜W1,,˜Wd)Rd such that

    Rdf(σΔvψvΦΩvψ)dv+(IdΩΩ)˜WRdf(v)vdv=0,

    for any f and thus

    σΔvψvΦΩvψ+(IdΩΩ)v˜W=0,

    implying that ψ satisfies (3.6) with the vector W=(IdΩΩ)˜W(RΩ), that is, ψ is a collision invariant of Q associated to MΩ.

    Conversely, let ψ=ψ(v) be a collision invariant of Q associated to MΩ. By Lemmas 3.1 and 3.2 we know that there is W(RΩ) such that

    σΔvψvΦΩvψ+vW=0.

    Multiplying by any function f such that (IdΩΩ)Rdf(v)vdv=0 one gets

    RdQ(f)(v)ψ(v)dv=Rdf(v)(σΔvψvΦΩvψ)dv=Rdf(v)vdvW=0,

    implying that ψ is a generalized collision invariant of Q associated to Ω.

    In this section we investigate the structure of the collision invariants of Q associated to an equilibrium distribution f=ρMΩ. By Lemmas 3.1 and 3.2, we need to solve the elliptic problem

    σdivv(MΩvψ)=(vW)MΩ(v),vRd, (4.1)

    for any W(RΩ). We appeal to a variational formulation by considering the continuous bilinear symmetric form aΩ:H1MΩ×H1MΩR defined as

    aΩ(χ,θ)=σRdvχvθMΩ(v)dv,χ,θH1MΩ,

    and the linear form l:H1MΩR, l(θ)=Rdθ(v)(vW)MΩ(v)dv,θH1MΩ. Notice that l is well defined and bounded on H1MΩ provided that the additional hypothesis |v|L2MΩ holds true, that is

    Rd|v|2exp(|vΩ|22σV(|v|)σ)dv<.

    The above hypothesis is obviously satisfied by the potentials Vα,β. We say that ψH1MΩ is a variational solution of (4.1) if and only if

    aΩ(ψ,θ)=l(θ) for any θH1MΩ. (4.2)

    Proposition 4.1. A necessary and sufficient condition for the existence and uniqueness of variational solution to (4.1) is

    Rd(vW)MΩ(v)dv=0. (4.3)

    Proof. The necessary condition for the solvability of (4.1) is obtained by taking θ=1 (which belongs to H1MΩ thanks to (2.5)) in (4.2) leading to (4.3). This condition is satisfied for any W(RΩ) since we have

    Rd(vW)MΩ(v)dv=Rd(vΩ)MΩ(v)dv(ΩW)=0.

    The condition (4.3) also guarantees the solvability of (4.1). Indeed, under the hypotheses (2.5) and (2.6), the bilinear form aΩ is coercive on the Hilbert space ˜H1MΩ:={χH1MΩ:((χ,1))MΩ=0}, i.e. we have:

    aΩ(χ,χ)min{σ,λΩ}2χ2MΩ,χ˜H1MΩ.

    Applying the Lax-Milgram lemma to (4.2) with ψ, θ˜H1MΩ yields a unique function ψ˜H1MΩ such that

    aΩ(ψ,˜θ)=l(˜θ) for any ˜θ˜H1MΩ. (4.4)

    Actually, the compatibility condition l(1)=0 allows us to extend (4.4) to H1MΩ. This follows by applying (4.4) with ˜θ=θ((θ,1))MΩ, for θH1MΩ. Moreover, the uniqueness of the solution for the problem on ˜H1MΩ implies the uniqueness, up to a constant, of the solution for the problem on H1MΩ.

    As observed in (3.1), the fluid equations for ρ and Ω will follow by appealing to the collision invariants associated to the orientation ΩSd1, for any W(RΩ). When W=0, the solutions of (4.1) are all the constants, and we obtain the particle number balance (3.4). Consider now W(RΩ){0} and ψW a solution of (4.1). Obviously we have ψW=˜ψW+RdψW(v)MΩ(v)dv, where ˜ψW is the unique solution of (4.1) in ˜H1MΩ. It is easily seen, thanks to (3.4) and the linearity of (3.3), that the balances corresponding to ψW and ˜ψW are equivalent. Therefore for any W(RΩ) it is enough to consider only the solution of (4.1) in ˜H1MΩ. From now on, for any W(RΩ), we denote by ψW the unique variational solution of (4.1) verifying RdψW(v)MΩ(v)dv=0. The structure of the solutions ψW,W(RΩ){0} comes by the symmetry of the equilibrium MΩ. Analyzing the rotations leaving invariant the vector Ω, we prove as in [48] the following result.

    Proposition 4.2. Consider W(RΩ){0}. For any orthogonal transformation O of Rd leaving Ω invariant, that is OΩ=Ω, we have

    ψW(Ov)=ψtOW(v),vRd,

    where tO denotes the transpose of the matrix O.

    Proof. First of all notice that tOW(RΩ){0}. We know that ψW is the minimum point of the functional

    JW(z)=σ2Rd|vz|2MΩ(v)dvRd(vW)z(v)MΩ(v)dv,z˜H1MΩ.

    It is easily seen that, for any orthogonal transformation O of Rd leaving the orientation Ω invariant, and any function z˜H1MΩ, we have, by defining zO:=zO˜H1MΩ

    MΩO=MΩ,zO=tO(z)O.

    Moreover, we obtain with the change of variables v=Ov and using that MΩ(v)=MΩ(v):

    JtOW(zO)=σ2Rd|tO(z)(Ov)|2MΩ(v)dvRd(vtOW)z(Ov)MΩ(v)dv=σ2Rdpdv|(z)(v)|2MΩ(v)Rdpdv(vW)z(v)MΩ(v)=JW(z).

    Finally, we deduce that

    ψWO˜H1MΩ,JtOW(ψWO)=JW(ψW)JW(ztO)=JtOW(z),

    for any z˜H1MΩ, implying that ψWO=ψtOW.

    The computation of the collision invariants {ψW:W(RΩ){0}} can be reduced to the computation of one scalar function. For any orthonormal basis {E1,,Ed1} of (RΩ) we define the vector field F=d1i=1ψEiEi. This vector field does not depend upon the basis {E1,,Ed1} and has the following properties, see [48].

    Lemma 4.1. The vector field F does not depend on the orthonormal basis {E1,,Ed1} of (RΩ) and for any orthogonal transformation O of Rd, preserving Ω, we have FO=OF. There is a function χ such that

    F(v)=χ(vΩ|v|,|v|)(IdΩΩ)(v)|v|2(vΩ)2,vRd(RΩ),

    and thus, for any i{1,...,d1}, we have

    ψEi(v)=F(v)Ei=χ(vΩ|v|,|v|)vEi|v|2(vΩ)2,vRd(RΩ). (4.5)

    Proof. Let {F1,,Fd1} be another orthonormal basis of (RΩ). The following identities hold

    E1E1++Ed1Ed1+ΩΩ=Id,F1F1++Fd1Fd1+ΩΩ=Id,

    and therefore

    d1i=1ψEiEi=d1i=1ψd1j=1(EiFj)FjEi=d1i=1d1j=1(EiFj)ψFjEi=d1j=1ψFjd1i=1(EiFj)Ei=d1j=1ψFjFj.

    For any orthogonal transformation of Rd such that OΩ=Ω we obtain thanks to Proposition 4.2

    FO=d1i=1(ψEiO)Ei=d1i=1ψtOEiEi=Od1i=1ψtOEitOEi=OF,

    where, the last equality holds true since {tOE1,,tOEd1} is an orthonormal basis of (RΩ). Let vRd(RΩ) and consider

    E(v)=(IdΩΩ)v|v|2(Ωv)2.

    Notice that EΩ=0,|E|=1. When d=2, since the vector F(v) is orthogonal to Ω, there exists a function Λ=Λ(v) such that

    F(v)=Λ(v)E=Λ(v)(I2ΩΩ)v|v|2(Ωv)2,vR2(RΩ).

    If d3, let us denote by E, any unitary vector orthogonal to E and Ω. Introducing the orthogonal matrix O=Id2EE (which leaves Ω invariant), we obtain FO=OF. Observe that

    0=EE=Ev(vΩ)Ω|v|2(vΩ)2=Ev|v|2(vΩ)2,Ov=v,

    and thus

    F(v)=F(Ov)=OF(v)=(Id2EE)F(v)=F(v)2(EF(v))E,

    from which it follows that EF(v)=0, for any vector E orthogonal to E and Ω. Hence, there exists a function Λ(v) such that

    F(v)=Λ(v)E(v)=Λ(v)(IdΩΩ)v|v|2(vΩ)2,vRd(RΩ).

    We will show that Λ(v) depends only on vΩ/|v| and |v|. Indeed, for any d2, and any orthogonal transformation O such that OΩ=Ω we have F(Ov)=OF(v), E(Ov)=OE(v) because

    (IdΩΩ)Ov=Ov(ΩOv)Ω=O(IdΩΩ)v,
    |Ov|2(OvΩ)2=|v|2(vΩ)2,

    implying that Λ(Ov)=Λ(v), for any vRd(RΩ). We are done if we prove that Λ(v)=Λ(v) for any v,vRd(RΩ) such that vΩ/|v|=vΩ/|v|,|v|=|v|,vv. It is enough to consider the rotation O such that

    OE=E,(OId)span{E,E}=0,E=(IdΩΩ)v|v|2(vΩ)2,E=(IdΩΩ)v|v|2(vΩ)2.

    The equality OE=E implies that Ov=v and therefore Λ(v)=Λ(Ov)=Λ(v), showing that there exists a function χ such that Λ(v)=χ(vΩ/|v|,|v|),vRd{0}.

    In the last part of this section we concentrate on the elliptic problem satisfied by the function (c,r)χ(c,r) introduced in Lemma 4.1. Even if ψEi are eventually singular on RΩ, it will be no difficulty to define a Hilbert space on which solving for the profile χ. We again proceed using the minimization of quadratic functionals.

    Proposition 4.3. The function χ constructed in Lemma 4.1 solves the problem

    σc{rd3(1c2)d12e(c,r)cχ}σr{rd1(1c2)d32e(c,r)rχ}+σ(d2)rd3(1c2)d52eχ=rd(1c2)d22e(c,r),(c,r)]1,+1[×]0,[. (4.6)

    Proof. For any i{1,,d1}, let us consider ψEi,h(v)=h(vΩ/|v|,|v|)vEi|v|2(vΩ)2 where vh(vΩ/|v|,|v|) is a function such that ψEi,hH1MΩ. Observe that if h=χ, then ψEi,h coincides with ψEi. Note that generally ψEi,h are not collision invariants, but perturbations of them, corresponding to profiles h. In this way, the minimization problem (4.7) will lead to a minimization problem on h, whose solution will be χ. Notice that once that ψEi,hH1MΩ, then RdψEi,hMΩ(v)dv=0, saying that ψEi,h˜H1MΩ. We know that ψEi is the minimum point of JEi on ˜H1MΩ and therefore

    JEi(ψEi)JEi(ψEi,h). (4.7)

    A straightforward computation shows that

    vψEi,h=vEi|v|2(vΩ)2[chIdvv|v|2|v|Ω+rhv|v|]+h(vΩ|v|,|v|)[Id(v(vΩ)Ω)v|v|2(vΩ)2]Ei|v|2(vΩ)2,

    and

    |vψEi,h|2=(vEi)2|v|4(ch)2+(vEi)2|v|2(vΩ)2(rh)2+|v|2(vΩ)2(vEi)2(|v|2(vΩ)2)2h2(vΩ|v|,|v|).

    The condition ψEi,hH1MΩ writes

    Rd(ψEi,h)2MΩ(v)dv<,Rd|vψEi,h|2MΩ(v)dv<,

    which is equivalent, thanks to the Poincaré inequality (2.6) to

    Rd|vψEi,h|2MΩ(v)dv<.

    Based on formula (3.7), we have

    Rd|vψEi,h|2MΩ(v)dv=Rd[|v|2(vΩ)2(d1)|v|4(ch)2+(rh)2d1+(d2)h2(d1)(|v|2(vΩ)2)]MΩdv=R+rd1+11[1c2r2(ch)2+(rh)2+(d2)h2r2(1c2)]e(c,r)(1c2)d32dcdr(d1)R+rd1+11e(c,r)(1c2)d32dcdr}

    and therefore we consider the Hilbert space Hd={h:]1,+1[×]0,[R,h2d<}, endowed with the scalar product

    (g,h)d=R+rd1+11[1c2r2cgch+rgrh+(d2)ghr2(1c2)]e(c,r)(1c2)d32dcdr

    for g and h in Hd and the norm given by

    hd=(h,h)d.

    The expression JEi(ψEi,h) writes as functional of h

    JEi(ψEi,h)=σ2Rd|vψEi,h|2MΩ(v)dvRd(vEi)2h(vΩ|v|,|v|)|v|2(vΩ)2MΩ(v)dv=σ2Rd|vψEi,h|2MΩ(v)dvRdh(vΩ|v|,|v|)|v|2(vΩ)2d1MΩ(v)dv=J(h)(d1)R+rd1+11e(c,r)(1c2)d32dcdr,

    where

    J(h)=σ2R+rd1+11[1c2r2(ch)2+(rh)2+(d2)h2r2(1c2)]e(c,r)(1c2)d32dcdrR+rd1+11h(c,r)r1c2e(c,r)(1c2)d32dcdr.

    Coming back to (4.7) and using (4.5), we deduce that

    χHd and J(χ)J(h) for any hHd.

    Therefore, by the Lax-Milgram lemma, we deduce that χ solves the problem (4.6).

    After identifying the collision invariants, we determine the fluid equations satisfied by the macroscopic quantities entering the dominant particle density f(t,x,v)=ρ(t,x)MΩ(t,x)(v). As seen before the balance for the particle density follows thanks to the collision invariant ψ=1. The other balances follow by appealing to the vector field F (cf. Lemma 4.1) and the details are given in section 5.1.

    Applying (3.1) with ψ=1 leads to (3.4). For any (t,x)R+×Rd we consider the vector field

    vF(t,x,v)=χ(vΩ(t,x)|v|,|v|)(IdΩ(t,x)Ω(t,x))v|v|2(vΩ(t,x))2.

    By the definition of F(t,x,), we know that

    Lf(t,x,)F(t,x,)=0,(t,x)R+×Rd,

    and therefore (3.1) implies

    RdtfF(t,x,v)dv+RdvxfF(t,x,v)dv=0,(t,x)R+×Rd. (5.1)

    It remains to compute RdtfF(t,x,v)dv and RdvxfF(t,x,v)dv in terms of ρ(t,x) and Ω(t,x). By a direct computation we obtain

    tf=tρMΩ+ρMΩσ(vΩ)tΩ,

    implying, thanks to the equalities tΩΩ=0 and vtΩ=PfvtΩ,

    RdtfFdv=Rd(tρ+ρσ(vΩ)tΩ)χ(vΩ|v|,|v|)MΩ(v)Pfv|Pfv|dv=tρRdχ(vΩ|v|,|v|)MΩ(v)Pfv|Pfv|dv+ρσRdχ(vΩ|v|,|v|)MΩ(v)PfvPfv|Pfv|dvtΩ. (5.2)

    It is an easy exercise to show that the integral Rdχ(vΩ|v|,|v|)MΩ(v)Pfv|Pfv|dv vanishes and that the following relationship holds

    Rdχ(vΩ|v|,|v|)MΩ(v)PfvPfv|Pfv|dv=Rdχ(vΩ|v|,|v|)|v|2(vΩ)2d1MΩ(v)dv(IdΩΩ).

    Therefore, by taking into account that tΩΩ=0, the equality (5.2) becomes

    RdtfFdv=˜c1ρσtΩ,˜c1=Rdχ(vΩ|v|,|v|)|v|2(vΩ)2d1MΩ(v)dv. (5.3)

    Similarly we write (for any smooth vector field ξ(x), the notation xξ stands for the Jacobian matrix of ξ, i.e. (xξ)i,j=xjξi)

    vxf=(vxρ)MΩ+ρσv(txΩ(vΩ))MΩ,

    implying

    Rd(vxf)Fdv=Rdχ(vΩ|v|,|v|)MΩ(v)PfvPfv|Pfv|dvxρ+ρσRd(vΩ)χ(vΩ|v|,|v|)MΩ(v)PfvPfv|Pfv|dv(xΩ)Ω=˜c1(IdΩΩ)xρ+ρσ˜c2xΩΩ, (5.4)

    where

    ˜c2=Rd(vΩ)χ(vΩ|v|,|v|)|v|2(vΩ)2d1MΩ(v)dv.

    Notice that in the above computations we have used (txΩ)Ω=0 and

    Rd(vEi)(vEj)(vEk)χ(vΩ|v|,|v|)MΩ(v)dv=0,

    for any i,j,k{1,,d1}. Combining (5.1), (5.3) and (5.4) yields

    ˜c1ρσtΩ+ρσ˜c2xΩΩ+~c1(IdΩΩ)xρ=0,

    or equivalently

    tΩ+c2xΩΩ+σ(IdΩΩ)xρρ=0, (5.5)

    where

    c2=˜c2˜c1=Rd(vΩ)χ(vΩ|v|,|v|)|v|2(vΩ)2MΩ(v)dvRdχ(vΩ|v|,|v|)|v|2(vΩ)2MΩ(v)dv=R+rd+1π0cosθχ(cosθ,r)e(cosθ,r)sind1θdθdrR+rdπ0χ(cosθ,r)e(cosθ,r)sind1θdθdr.

    Let us start by noticing that the system (1.5) and (1.6) is hyperbolic as a consequence of Theorem 4.1 in [60]. On the other hand, the orientation balance Eq (5.5) propagates the constraint |Ω|=1. Indeed, let Ω=Ω(t,x) be a smooth solution of (5.5), satisfying |Ω(0,)|=1. Multiplying by Ω(t,x) we obtain

    12t|Ω|2+c22(Ω(t,x)x)|Ω|2=0,

    implying that |Ω| is constant along the characteristics of the vector field c2Ω(t,x)x and thus |Ω(t,)|=|Ω(0,)|=1, for all t0.

    The rescaled Eq (1.3) can be considered as an intermediate model between the equations introduced in [37] and [48] when there are no phase transitions. In [48], the authors considered a strong relaxation towards the `terminal speed' (or cruise speed). Whereas in [37] the authors do not impose a penalization on the self-propelled/friction term. Our result could be applied to obtain the results in [48] without resorting to measures supported on the sphere by doing a double passage to the limit. First, passing to the limit ε0 in (1.3), taking V=Vα,β, we obtain (1.5) and (1.6). Afterwards, we rescale V to λ˜V in the system (1.5) and (1.6) and study the limit when λ. This amounts to study the behavior of the coefficients

    c1,λ=R+rdπ0cosθeλ(cosθ,r)sind2θdθdrR+rd1π0eλ(cosθ,r)sind2θdθdr,

    and

    c2,λ=R+rd+1π0cosθχλ(cosθ,r)eλ(cosθ,r)sind1θdθdrR+rdπ0χλ(cosθ,r)eλ(cosθ,r)sind1θdθdr,

    when λ, where the function χλ solves the elliptic problem

    σc[rd3(1c2)d12eλ(c,r)cχλ]σr[rd1(1c2)d32eλ(c,r)rχλ]+σ(d2)rd3(1c2)d52eλχλ=rd(1c2)d22eλ(c,r),

    and eλ(c,r)=exp(rc/σ)exp((r2+1)/(2σ)λ˜V(r)/σ).

    In order to analyse the asymptotic behavior of c1,λ we introduce the following result.

    Lemma 5.1. Let φC2((0,);R) and gC0((0,)×(0,π);R). Let us assume that

    i) R+π0exp(λφ(r))|g(r,θ)|dθdr<,

    ii) The function φ has a unique global maximum at an interior point r0,

    iii) π0g(r0,θ)dθ0.

    Then the function G(λ) defined as

    G(λ)=R+π0exp(λφ(r))g(r,θ)dθdr,

    has the the following asymptotic behavior

    G(λ)2π|φ(r0)|exp(λφ(r0))λπ0g(r0,θ)dθ,

    as λ.

    The proof of this result is a direct application of the Laplace method, see for instance [61]. As an immediate consequence of Lemma 5.1 we obtain

    limλc1,λ=r0π0cosθexp(r0cosθ/σ)sind2θdθπ0exp(r0cosθ/σ)sind2θdθ,

    where r0 is the minimum of the potential function Vα,β(r). Let us note that the asymptotic study of the coefficient c1,λ when λ can also be performed using Lemma 5.1 for more general potentials than Vα,β(r). In particular, we could also consider smooth potentials V(|v|) having a unique global minimum r0 such that V(r)<0, for 0<r<r0, and V(r)>0, for r>r0. On the other hand, the asymptotic study of c2,λ could be addressed following similar techniques as in [60], however, we do not dwell upon this matter here and leave it for a future work.

    In this paper, we have considered a flocking model consisting of a modified Cucker-Smale involving noise, alignment and self-propulsion. We have investigated its macroscopic limit when the time and space variables are rescaled to the macroscopic scale. In this limit, the velocity distribution converges to an equilibrium which depends on the local density and local mean orientation of the particles. The density and mean orientation evolve in space and time according to a hyperbolic system of equations named the Self-Organized Hydrodynamics. This system is akin to the system of isothermal compressible gas dynamics with the important difference that the velocity is a vector of unit norm. It is structurally identical with that obtained from the Vicsek dynamics, with differences only in the values of the coefficients. An interesting open problem would be to investigate whether the coefficients of the two models coincide in the limit where the Cucker-Smale model converges to the Vicsek model. Future work will investigate different variants in the Cucker-Smale model, possibly involving phase transitions (which have been avoided in the present work by an appropriate expression of the alignment force).

    PAS and PD acknowledge support by the Engineering and Physical Sciences Research Council (EPSRC) under grants no. EP/M006883/1 and EP/P013651/1. This work was accomplished during the visit of MB to Imperial College London with an ICL-CNRS Fellowship. JAC was partially supported by the EPSRC grant number EP/P031587/1. PD also acknowledges support by the Royal Society and the Wolfson Foundation through a Royal Society Wolfson Research Merit Award no. WM130048 and by the National Science Foundation (NSF) under grant no. RNMS11-07444 (KI-Net). PD is on leave from CNRS, Institut de Mathématiques de Toulouse, France. PAS and PD would like to thank the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the programme ``Growth form and self-organisation'' when part of this work was carried over which was supported by: EPSRC grant numbers EP/K032208/1 and EP/R014604/1.

    Data statement: No new data were collected in the course of this research.

    The authors declare there is no conflict of interest.



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