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Batteries are the key elements for the massive electrification of the transport sector. With the rapidly growing popularity of electric vehicles, it is becoming increasingly important to characterize the behavior of battery packs through fast and accurate numerical models, in order to support experimental activities. A coupled electro-thermal simulation framework is required, as it is the only way to realistically represent the interactions between real world battery pack performances and the vehicle-level thermal management strategies. The purpose of this work is to pave the way for a comprehensive methodology for the development of a supporting modeling framework, to efficiently complement experiments in the optimal design and integration of battery packs.

Scale-resolving turbulence modeling for engine flow simulation has constantly increased its popularity in the last decade. In contrast to classical RANS modeling, LES-like approaches are able to resolve a larger number of unsteady flow features. In principle, this capability allows to accurately predict some of the key parameters involved in the development and optimization of modern engines such as cycle-to-cycle variations in a DI engine. However, since multiple simulated engine cycles are required to extract reliable flow statistics, the spatial and temporal resolution requirements of pure LES still represent a severe limit for its wider application on realistic engine geometries. In this context, Hybrid URANS-LES methodologies can therefore become a potentially attractive option. In fact, their task is to preserve the turbulence scale-resolving in the flow core regions but at a significantly lower computational cost compared to standard LES.