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The cost of a flow battery system can be reduced by increasing its power density and thereby reducing its stack area. If per-pass utilizations are held constant, higher battery power densities can only be achieved using higher flow rates. Here, a 3D computational fluid dynamics model of a flow battery flow field and electrode is used to analyze the implications of increasing flow rates to high power density operating conditions. Interdigitated and serpentine designs, and cell sizes ranging from 10 cm 2 to 400 cm 2 , are simulated. The results quantify the dependence of pressure loss on cell size and design, demonstrating that the details of the passages that distribute flow between individual channels and the inlet and outlet have a major impact on pressure losses in larger cells. Additionally, in-cell flow behavior is analyzed as a function of cell size and design. Flow structures are interrogated to show how and where electrode parameters influence pressure drops, and how regions where transport is slow are correlated with the presence of experimentally observed cell degradation.

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Accurate prediction of nonpremixed turbulent combustion using large eddy simulation (LES) requires detailed modeling of the mixing between fuel and oxidizer at scales finer than the LES filter resolution. In conserved scalar combustion models, the small scale mixing process is quantified by two parameters, the subfilter scalar variance and the subfilter scalar dissipation rate. The most commonly used models for these quantities assume a local equilibrium exists between production and dissipation of variance. Such an assumption has limited validity in realistic, technically relevant flow configurations. However, nonequilibrium models for variance and dissipation rate typically contain a model coe cient whose optimal value is unknown a priori for a given simulation. Furthermore, conventional dynamic procedures are not useful for estimating the value of this coe cient. In this work, an alternative dynamic procedure based on the transport equation for subfilter scalar variance is presented, along with a robust conditional averaging approach for evaluation of the

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