Development of heat rejection prediction methodology for selection of cooling elements in diesel engines

More stringent emissions regulations are forcing the automotive industry to focus on new technologies ensuring lower emissions. The declaration of the European Union Commission in May 2018 targets 30% lower CO 2 emissions compared with 2019 average fleet values in the heavy-duty vehicle market. To fulfill this requirement, conventional diesel engines must operate with maximum energy efficiency. Lowering the engine heat rejection to air and coolants is an obligation to increase energy efficiency and utilize the wasted energy. However, accurately predicting heat rejection during the early design phase remains a challenge due to the trade-off between the computational cost of 3D CFD simulations and the limited spatial resolution of 1D models. In this article, a robust and computationally efficient heat rejection prediction methodology is developed by integrating a predictive combustion model (DI-Pulse) with a conjugate heat transfer approach. First, a one-dimensional thermodynamical model is created, and thermal correlation is performed using 3D CFD outputs, including critical metal temperatures and rejected heat. Secondly, a comprehensive thermal survey is conducted in a dynamometer environment to validate the model under real-world conditions. Finally, the methodology is verified by comparing the 1D model with test outputs, achieving a prediction accuracy within approximately ±10 kW for total radiator heat rejection and ±5 kW for cylinder heat rejection. Unlike standard approaches, this methodology enables the optimization of cooling elements and combustion parameters simultaneously, significantly reducing the reliance on costly physical testing and accelerating the development of high-efficiency heavy-duty engines.