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Validation and Analysis of Heat Losses Prediction Using Conjugate Heat Transfer Simulation for an Internal Combustion Engine
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 09, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
New technologies are required to improve engine thermal efficiency. For this it is necessary to use all the tools available nowadays, in particular computational tools, which allow testing the viability of different solutions at reduced cost. In addition, numerical simulations often provide more detailed information than experimental tests. Such is the case for the study of the heat transfer through the walls of an engine. Conjugate Heat Transfer (CHT) simulations permit precise calculations of the heat transfer from gas to walls throughout the whole engine cycle, and thus it is possible to know such details as the instantaneous heat losses and wall temperature distribution on the walls, which no experiment can give. Nevertheless, it is important to validate CHT calculations, either with some experimental measurements or with some other reliable tool, such as 0D-1D modelling known to work well.
The proposed work is based on the CHT simulation of the heat transfer to the walls of an engine piston during an entire cycle to determine the parameters that permit obtaining good results. This will be ascertained by comparison with the results of a lumped model previously validated for many applications. Another objective of this work is also to determine if it is significant to take into account the spatial and temporal variations of the wall temperature for the prediction of the heat losses during the engine cycle, as generally a mean and constant wall temperature (isothermal walls) is assumed for CFD combustion calculations.
CitationBroatch, A., Margot, X., Garcia-Tiscar, J., and Escalona, J., "Validation and Analysis of Heat Losses Prediction Using Conjugate Heat Transfer Simulation for an Internal Combustion Engine," SAE Technical Paper 2019-24-0091, 2019, https://doi.org/10.4271/2019-24-0091.
Data Sets - Support Documents
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