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<div class="section abstract"><div class="htmlview paragraph">The development of efficient, reliable, and affordable Hybrid and Electric Vehicles (xEVs) relies on optimized Vehicle Thermal Management System (VTMS) architecture and control strategies. Compared to conventional vehicles, xEVs have more complex VTMS due to additional powertrain components and cooling circuits to meet distinct thermal requirements. The cooling circuits comprise a combination of hoses, straight, and bent pipes to route coolant flow around obstacles between powertrain components at distinct locations in the vehicle. The increased length and geometrical complexity of these piping systems, compared to conventional vehicles, results in increased pressure losses. Thus, accurate predictions of pressure drop within these piping systems is critical for component selection for an optimized VTMS. Numerical simulations are often used to study interactions between components from a system-level perspective allowing early stage rapid assessment of performance. In this work, an accurate 1D model for pressure drop in piping systems is developed based on literature review and validated using 3D Computational Fluid Dynamics (CFD) predictions. The 1D model is implemented in the software tool GT-SUITE which is used for integrated 0D/1D/3D multi-physics system simulation of the entire VTMS. The CFD calculations are performed using GT-CONVERGE. Overall, the pressure drop predictions of the 1D model are in good agreement with the 3D CFD for a range of Reynolds numbers including laminar, transition, and turbulent regimes and even when significant losses are present due to flow redevelopment after bends. Typically, commercial tools often ignore flow redevelopment and were originally developed for high Reynolds number flows. A typical battery electric vehicle is constructed in GT-SUITE and results indicate that deviations on pressure drop predictions lead to significant deviations on pump operating conditions and isentropic efficiency. The 1D model allows fast and accurate simulations over a broad range of complex piping configurations for an optimized VTMS.</div></div>
<div class="section abstract"><div class="htmlview paragraph">The development of efficient, reliable, and affordable Hybrid and Electric Vehicles (xEVs) relies on optimized Vehicle Thermal Management System (VTMS) architecture and control strategies. Compared to conventional vehicles, xEVs have more complex VTMS due to additional powertrain components and cooling circuits to meet distinct thermal requirements. The cooling circuits comprise a combination of hoses, straight, and bent pipes to route coolant flow around obstacles between powertrain components at distinct locations in the vehicle. The increased length and geometrical complexity of these piping systems, compared to conventional vehicles, results in increased pressure losses. Thus, accurate predictions of pressure drop within these piping systems is critical for component selection for an optimized VTMS. Numerical simulations are often used to study interactions between components from a system-level perspective allowing early stage rapid assessment of performance. In this work, an accurate 1D model for pressure drop in piping systems is developed based on literature review and validated using 3D Computational Fluid Dynamics (CFD) predictions. The 1D model is implemented in the software tool GT-SUITE which is used for integrated 0D/1D/3D multi-physics system simulation of the entire VTMS. The CFD calculations are performed using GT-CONVERGE. Overall, the pressure drop predictions of the 1D model are in good agreement with the 3D CFD for a range of Reynolds numbers including laminar, transition, and turbulent regimes and even when significant losses are present due to flow redevelopment after bends. Typically, commercial tools often ignore flow redevelopment and were originally developed for high Reynolds number flows. A typical battery electric vehicle is constructed in GT-SUITE and results indicate that deviations on pressure drop predictions lead to significant deviations on pump operating conditions and isentropic efficiency. The 1D model allows fast and accurate simulations over a broad range of complex piping configurations for an optimized VTMS.</div></div>
<div class="section abstract"><div class="htmlview paragraph">In recent years, swift changes in market demands toward achieving carbon neutrality have driven significant developments within the automotive industry. Consequently, employing computer simulations in the early stages of vehicle development has become imperative for a comprehensive understanding of performance characteristics. Of particular importance is the cooling performance of vehicles, which plays a vital role in ensuring safety and overall performance. It is crucial to predict optimal cooling performance, particularly about the heat generated by the powertrain during the initial phases of vehicle development. However, the utilization of thermal analysis models for assessing vehicle cooling performance demands substantial computational resources, rendering them less practical for evaluating performance associated with design changes in the planning phase. This paper introduces a method for constructing a low-dimensional model capable of predicting the time series response of cooling performance using a surrogate model based on thermal analysis. The thermal analysis model in this study is evaluated using design variables obtained through experiments, and a training data matrix is constructed from the corresponding time series responses. Unsupervised learning is employed to extract key features from these responses. For the regression model, a Gaussian process is applied to each latent variable derived from the unsupervised learning process. This transformation allows for a reduction in computational costs, shifting from high-dimensional calculations to a low-dimensional latent space for prediction. The proposed method is then applied to analyze the time series response of engine coolant temperature, obtained from the engine thermal analysis model, effectively demonstrating its utility.</div></div>
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