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CFD Modeling of Compact Heat Exchangers for I.C. Engine Oil Cooling
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 09, 2019 by SAE International in United States
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This work describes the development of a computational model for the CFD simulation of compact heat exchangers applied for the oil cooling in internal combustion engines. Among the different cooler types, the present modeling effort will be focused on liquid-cooled solutions based on offset strip fins turbulators. The design of this type of coolers represents an issue of extreme concern, which requires a compromise between different objectives: high compactness, low pressure drop, high heat-transfer efficiency. In this work, a computational framework for the CFD simulation of compact oil-to-liquid heat exchangers, including offset-strip fins as heat transfer enhancer, has been developed. The main problem is represented by the need of considering different scales in the simulation, ranging from the characteristic size of the turbulator geometry (tipically μm - mm) to the full scale of the overall device (typically cm - dm). Therefore, the developed modeling approach is based on a multi-scale concept, which applies: a) detailed simulations, performed on a refined grid on a representative elementary volume (REV), to characterize the fluid-dynamic and heat-transfer properties of the turbulator and b) full-scale simulations, performed on a coarse grid which describes the entire component, applying a porous media approach for the modeling of the turbulator, with properties upscaled from the detailed analysis. The model is firstly validated comparing with experimental data under different operating conditions. Then, it is adopted to investigate the details of the fluid-dynamics and heat-transfer process, providing guidelines for the optimization of the device.
CitationDella Torre, A., Montenegro, G., Onorati, A., Khadilkar, S. et al., "CFD Modeling of Compact Heat Exchangers for I.C. Engine Oil Cooling," SAE Technical Paper 2019-24-0179, 2019, https://doi.org/10.4271/2019-24-0179.
Data Sets - Support Documents
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