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Today CFD is an important tool for engineers in the automotive industry who model and simulate fluid flow. For the complex field of Underhood Thermal Management, CFD has become a very important tool to engineer the cooling airflow process in the engine bay of vehicles. To model the cooling airflow process accurately in CFD, it is of utmost importance to model all components in the cooling airflow path accurately. These components are the heat exchangers, fan and engine bay blockage effect. This paper presents CFD simulations together with correlating measurements of a cooling airflow system placed in a test rig. The system contains a heavy duty truck louvered fin radiator core, fan shroud, fan ring and fan. Behind the cooling module and fan, a 1D engine silhouette is placed to mimic the blockage done by a truck engine. Furthermore, a simple hood is mounted over the module to mimic the guiding of air done by the hood shape in an engine bay.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.