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As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

The internal combustion engine’s performance is affected by in-cylinder combustion processes and heat transfer rates through the combustion chamber walls. Hot spots may affect the reliability and durability of the engine components. Design of efficient and effective coolant systems requires accurate accounting of the heat fluxes into and out of the solid parts during the engine operation. The need to assess the engine’s performance early in the design process has motivated the use of a computational approach to predict such data. A more accurate representation of the engine’s operation is obtained by coupling the thermal, flow, and combustion analysis of the various components, such as the combustion chamber, ports, engine block, and its cooling system. Typically, a stand-alone CFD simulation does not capture the complex nature of the problem, and the manual transfer of data between multiple analyses may lead to an onerous or error-prone workflow requiring multiple user interventions.

This paper presents a thermal electric two-way coupled li-ion battery pack model for an all-electric VW motorsport racer. It starts from the hybrid pulse power characterization (HPPC) test data at different state of charge (SoC) and temperature levels. Such information is used for cell level battery equivalent circuit model (ECM) parameter identification. Multiple cell ECMs are connected in series to create a module ECM. Battery thermal performance of the module is simulated first by computational fluid dynamics (CFD) for the module. Then, a thermal reduced order model (ROM) is created out of the CFD solution. The thermal ROM is then two-way coupled with the battery module ECM to form a complete battery module model. Multiple module models are connected to create a battery pack model. The complete pack model is then exported into Simulink for validation and simulation.

Advanced research in Spark-ignition (SI) engines has been focused on dilute-combustion concepts. For example, exhaust-gas recirculation is used to lower both fuel consumption and pollutant emissions while maintaining or enhancing engine performance, durability and reliability. These advancements achieve higher engine efficiency but may deteriorate combustion stability. One symptom of instability is a large cycle-to-cycle variation (CCV) in the in-cylinder flow and combustion metrics. Large-eddy simulation (LES) is a computational fluid dynamics (CFD) method that may be used to quantify CCV through numerical prediction of the turbulent flow and combustion processes in the engine over many engine cycles. In this study, we focus on evaluating the capability of LES to predict the in-cylinder flows and gas exchange processes in a motored SI engine installed with a transparent combustion chamber (TCC), comparing with recently published data.

Various 1D simulation tools (KULI & LMS Amesim) and 3D simulation tools (ANSYS FLUENT®) can be used to size and evaluate truck cooling system design. In this paper, ANSYS FLUENT is used to analyze and validate the design of medium duty truck cooling systems. LMS Amesim is used to verify the quality of heat exchanger input data. This paper discusses design and simulation of parent and derivative trucks. As a first step, the parent truck was modeled in FLUENT (using standard' k - ε model) with detailed fan and underhood geometry. The fan is modeled using Multiple Reference Frame (MRF) method. Detailed geometry of heat exchangers is skipped. The heat exchangers are represented by regular shape cell zones with porous medium and dual cell heat exchanger models to account for their contributions to the entire system in both flow and temperature distribution. Good agreement is observed between numerical and experimental engine out temperatures at different engine operating conditions.

Durability assessments of modern engines often require accurate modeling of thermal stresses in critical regions such as cylinder head firedecks under severe cyclic thermal loading conditions. A new methodology has been developed and experimentally validated in which transient temperature distributions on cylinder head, crankcase and other components are determined using a Conjugate Heat Transfer (CHT) CFD model and a thermal finite element analysis solution. In the first stage, cycle-averaged gas side boundary conditions are calculated from heat transfer modeling in a transient in-cylinder simulation. In the second stage, a steady-state CHT-CFD analysis of the full engine block is performed. Volume temperatures and surface heat transfer data are subsequently transferred to a thermal finite element model and steady state solutions are obtained which are validated against CFD and experimental results.

CFD simulations of an engine cooling system needs to resolve two aspects of the system; in-cylinder combustion and engine cooling. Underlying physics of an in-cylinder combustion process and heat transfer through engine cooling system requires very different time scales for resolution. This puts a limitation on practicality of solving the two problems simultaneously for any industrial case. Instead of solving the problem simultaneously, solution for an engine cooling system operating at a constant load can be derived using the coupled approach. This involves running two different CFD simulations: a transient in-cylinder simulation to model combustion in the engine, and a steady state CHT simulation using engine cooling system for heat transfer. These simulations are thermally coupled through boundary conditions and are performed in cyclic manner one after the other. Simulations are continued till the change in temperature with coupled cycles becomes insignificant.