Momentum conservation mesoscale

The DPD thermostat is the most straightforward and natural way to introduce Galilean invariance and momentum conservation. Whenever feasible, NEMD simulations with DPD and explicit solvent molecules would yield reasonable mesoscale hydrodynamics. Simulations far b ond static equilibrium [143,147, 149-151] and nonstationary flows [150] have been performed. However, the level of coarse-graining for the explicit solvent stUl is a relevant issue. 

How will the method develop in the future This is of course difficult to predict. However, it seems clear that there will be two main directions, a further development of the method itself, and its application to new problems in Soft Matter hydrodynamics. On the methodological front, there are several very recent developments, like angular-momentum conservation, multi-phase flows and viscoelastic fluids, which have to be explored in more detail. It will also be interesting to combine them to study, for example, multi-phase flows of viscoelastic fluids. On the application side, the trend will undoubtedly be towards more complex systems, in which thermal fluctuations are important. In such systems, the method can play out its strengths, because the interactions of colloids, polymers, and membranes with the mesoscale solvent can all be treated on the same basis. 

By resolving the structures and dominant mechanisms, it is possible to establish a stability condition reflecting the compromise between different dominant mechanisms for multiphase reaction systems, and the stability condition supplies a mesoscale constraint in addition to mass and momentum conservation equations. The calculation of EMMS model... 

Dissipative particle dynamics (DPD) is a technique for simulating the motion of mesoscale beads. The technique is superficially similar to a Brownian dynamics simulation in that it incorporates equations of motion, a dissipative (random) force, and a viscous drag between moving beads. However, the simulation uses a modified velocity Verlet algorithm to ensure that total momentum and force symmetries are conserved. This results in a simulation that obeys the Navier-Stokes equations and can thus predict flow. In order to set up these equations, there must be parameters to describe the interaction between beads, dissipative force, and drag. 

In the formulation of a mesoscale model, the number-density function (NDF) plays a key role. For this reason, we discuss the properties of the NDF in some detail in Chapter 2. In words, the NDF is the number of particles per unit volume with a given set of values for the mesoscale variables. Since at any time instant a microscale particle will have a unique set of microscale variables, the NDF is also referred to as the one-particle NDF. In general, the one-particle NDF is nonzero only for realizable values of the mesoscale variables. In other words, the realizable mesoscale values are the ones observed in the ensemble of all particles appearing in the microscale simulation. In contrast, sets of mesoscale values that are never observed in the microscale simulations are non-realizable. Realizability constraints may occur for a variety of reasons, e.g. due to conservation of mass, momentum, energy, etc., and are intrinsic properties of the microscale model. It is also important to note that the mesoscale values are usually strongly correlated. By this we mean that the NDF for any two mesoscale variables cannot be reconstructed from knowledge of the separate NDFs for each variable. Thus, by construction, the one-particle NDF contains all of the underlying correlations between the mesoscale variables for only one particle. 

Microscale fluid turbulence is, by deflnition, present only when the continuous fluid phase is present. The coefficients Bpv describe the interaction of the particle phase with the continuous phase. In contrast, Bpvf models rapid fluctuations in the fluid velocity seen by the particle that are not included in the mesoscale drag term Ap. In the mesoscale particle momentum balance, the term that generates Bpv will depend on the fluid-phase mass density and, hence, will be null when the fluid material density (pf) is null. In any case, Bpv models momentum transfer to/from the particle phase in fluid-particle systems for which the total momentum is conserved (see discussion leading to Eq. (5.17)). 

This formulation is particularly convenient when Euler-Lagrange simulations are used to approximate the disperse multiphase flow in terms of a fimte sample of particles. As discussed in Sections 5.2 and 5.3, although some of the mesoscale variables are intensive (i.e. independent of the particle mass), it is usually best to start with a conserved extensive variable (e.g. particle mass or particle momentum) when deriving the single-particle models. For example, in Chapter 4 we found that must have at least one component, corresponding to the fluid mass seen by a particle, in order to describe cases in which the disperse-phase volume fraction is not constant. 

When the velocity of the particle phase is different than that of the fluid phase, the transfer of mass between phases will also result in the transfer of momentum. For example, if we let be the mass of a particle and be the fluid mass seen by the particle, then conservation of mass at the mesoscale leads to... 

Invoking conservation of momentum at the mesoscale then leads to the following expression for the fluid velocity seen by the particle ... 

As can be seen from Eq. (5.100), the virtual-mass force reduces the drag and lift forces by a factor of 1 /y. The buoyancy force is not modified because we have chosen to define it in terms of the effective volume Vpy. We remind the reader that the mesoscale acceleration model for the fluid seen by the particle A j must be consistent with the mesoscale model for the particle phase A p in order to ensure that the overall system conserves momentum at the mesoscale. (See Section 4.3.8 for more details.) As discussed near Eq. (5.14) on page 144, this is accomplished in the single-particle model by constraining the model for Apf given the model for Afp (which is derived from the force terms introduced in this section). Thus, as in Eqs. (5.98) and (5.99), it is not necessary to derive separate models for the momentum-transfer terms appearing in Apf. 

Cell-level models solve the species [Eq. (26.1)], momentum [Eq. (26.5)], and energy [Eq. (26.7)] conservation equations using the effective properties of the electrodes and can include the electrochemistry using a continuum-scale (Section 26.2.4.1) or a mesoscale (Section 26.2.4.2) approach. Traditionally, cell-level models use a continuum-scale electrochemistry approach, which includes the electrochemistry as a boundary condition at the electrode-electrolyte interface [17, 51, 54] or over a specified reaction zone near the interface. The electrochemistry is modeled via the Nernst equation [Eq. (26.12)] using a prescribed current density and assumptions for the polarizations in the cell. The continuum-scale electrochemistry is then coupled to the species conservation equation [Eq. (26.1)] using Faraday s law ... 

Mesoscale modeling of SOFCs focuses on modeling the transport and reactions of gas species in the porous microstructures of the electrodes [3, 34, 56-59]. In these models, the porous microstructure is explicitly resolved, which negates the need for the effective parameters of macroscale models. The transport and reactions of species in mesoscale models are described by the species [Eq. (26.1)], momentum [Eq. (26.5)], and energy [Eq. (26.7)] conservation equations, which are solved at the pore scale. At the pore scale, the conservation equations are solved in two separate domains the solid domain of the tri-layer and the gas domain of the pore space within the tri-layer. Mesoscale models aim to understand the effects of microstructure and local conditions near the electrode-electrolyte interface on the SOEC physics and performance. These models have been used to investigate a number of design and degradation issues in the electrodes such as the effects of microstructure on the transport of species in the anode [19, 56] and the reactions of chromium contaminants in the cathode [34]. 

The first technique is known as the stochastic rotational dynamics (SRD) method or multiparticle collision dynamics, which is a particle-based algorithm suited to account for hydrodynamic interactions on the mesoscale. The coarse-grained solvent is described as ideal-gas particles that propagate via streaming and collision steps, which are constructed such that the dynamics conserves mass, momentum, and energy. 

---