Browse Publications Technical Papers 2017-01-0147
2017-03-28

Experimental and Numerical Investigation of Vehicle Drive and Thermal Soak Conditions in a Simplified Engine Bay 2017-01-0147

Driven by the demand to continuously reduce the development time of new vehicles, it is of critical importance to robustly develop design and packaging concepts early within a new vehicle program using CAE methods. As the underhood and underbody package is constantly getting tighter and the engine power increases, the development of a sophisticated heat protection concept requires much more attention. For many years, heat protection CAE is an integral part of the vehicle development at Ford. However, due to challenges related to transient analysis, e.g. high numerical effort, simulation of transient buoyancy driven airflow (thermal soak), and dependency on high quality thermal material properties, heat protection CAE was primarily focused on steady state vehicle operating conditions. Due to the constantly raising expectations towards CAE as a driver of vehicle design, an investigation has been carried out which aims to better understand the most influential factors impacting transient analysis, and in particular, thermal soak behavior. This investigation is expected to enable the development of an efficient numerical method to simulate this highly transient phenomenon. The present paper describes the design of a Soak Test Rig (STR) which mimics a vehicle front end but at the same time, provides a simplified and controlled environment for CAE method development. The tests carried out delivered a detailed view of the interior air and component temperature distribution for the steady-state preconditioning phase as well as throughout the transient thermal soak phase. Initial CAE analyses have been carried out and compared to test data. This overall investigation has led to an improved understanding of the underlying physics of thermal soak and led to the early phase development of a CAE process to predict thermal soak. The preliminary CAE method accurately predicts the trends in the measured data for three geometric configurations that were tested.

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