An Innovative Approach to Race Track Simulations for Vehicle Thermal Management

Haehndel, Kristian; Frank, Torsten; Christel, Frieder; Abanteriba, Sylvester

doi:10.4271/2013-01-9121

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The calculation of the heat transfer coefficients was done based on 1-D Nusselt correlations. These HTC values were then applied to the inner surface of the pipe sections in a thermal solver [16,17].…”

Section: State Of the Artmentioning

confidence: 99%

“…The flow of the exhaust gas coming out of the exhaust valves is pulsating. The effect of pulsation on the heat transfer coefficients is highest in the components closest to the exhaust valves, i.e., the hot-end [15]. To accurately capture this effect, a submodel of the hot-end of the exhaust system was separately simulated under transient boundary conditions.…”

Section: Complete 3dmentioning

confidence: 99%

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Hybrid model for exhaust systems in vehicle thermal management simulations

Ahmed

Rottengruber

Full

2022

Automot. Engine Technol.

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Using Vehicle Thermal Management (VTM) simulations to predict the thermal load experienced by components is a popular method within the automotive industry. The VTM simulation approach is fast becoming equivalent to conducting thermal load tests with prototypes for vehicles powered by internal combustion engines. This is especially true in the early development phase of the vehicle. The accuracy of the VTM simulations plays a pivotal role at them being accepted as an eventual replacement for physical testing. The correct prediction of thermal loads in VTM simulations depends on a multitude of different parameters, but the modelling of the exhaust system plays a central role in it. This is because the exhaust gas, and with it the exhaust system, is the primary source of heat in a vehicle powered by an internal combustion engine. The developed approach not only needs to be accurate but also modular enough to allow for different exhaust configurations to be tested. It also needs to be capable of integration into any VTM simulation workflow while maintaining an industrially acceptable turnaround time. This paper explores a new methodology to achieve these requirements. A 1D/3D hybrid approach to exhaust system modelling is presented. In this, the components that have an enthalpy change of the exhaust gas, such as the turbocharger, have been modelled as 1D and simple components such as pipes have been modelled in 3D. This has the advantage of combining the speed of 1D simulations with the spatial accuracy of 3D simulations. The method uses a unique three-code co-simulation technique for full vehicle VTM simulations. The coupling is between a 3D CFD software, a 1D simulation tool, and a Finite Element based thermal solver. The methodology was validated against experimental data for multiple loadcases. The results show good agreement with experiment within acceptable tolerances.

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Section: State Of the Artmentioning

confidence: 99%

Section: Complete 3dmentioning

confidence: 99%

Hybrid model for exhaust systems in vehicle thermal management simulations

Ahmed

Rottengruber

Full

2022

Automot. Engine Technol.

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“…This particularly applies to the thermal safety of temperature-sensitive or safety-critical components. Temperature reduction on these components is mainly enforced through convective heat transfer by the surrounding air [1,2]. One established method to determine suitable component geometries is the simulation of the fluid flow and heat transfer inside the vehicles.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data

Heger¹,

Full²,

Hilger³

et al. 2022

Preprint

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The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles that these components are assessed for potential safety issues. However, with increasing number of design proposals, risk assessment quickly becomes expensive. We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation. In scope of this work, we could show that our nondimensional, multivariate scheme can be efficiently trained to predict the velocity and pressure distribution for different design scenarios and geometric scales. The proposed algorithm is based on a parametric minibatch training which enables the utilization of large datasets necessary for the three-dimensional flow modeling. Further, we introduce a continuous resampling algorithm that allows to operate on one static dataset. Every feature of our methodology is tested individually and verified against conventional CFD simulations. Finally, we apply our proposed method in context of an exemplary real-world automotive application.

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