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In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

In this paper, thermal models are developed based on experimental test data, and the physics of thermal systems. If experimental data is available, the data can be fitted to mathematical models that represent the system response to changes in its input parameters. Therefore, empirical models which are based on test data are developed. The concept of time constant is presented and applied to development of transient models. Mathematical models for component temperature changes during transient vehicle driving conditions are also presented. Mathematical models for climate control system warm up and cool-down are also discussed. The results show the significance of adopting this concept in analysis of vehicle test data, and in development of analytical models. The developed models can be applied to simulate the system or component response to variety of changes in input parameters. As a result, significant testing and simulation time can be saved during the vehicle development process.

Time-temperature analysis methods are usually applied to predict the useful life of automotive components. Components life is affected by exposure to heat during vehicle service life. The extent of reduction in component life, which may be caused by material thermal degradation, depends on the component temperature and the time duration at that temperature. The rate of material thermal degradation of automotive components varies widely depending on material thermal stability, vehicle duty cycle, and the thermal environment that the component is exposed to. Thermodynamic properties such as the activation energy of each material are used to determine the rate of thermal degradation [1,2]. In this approach, material thermal degradation models are used to predict component life during the service life of a vehicle. As the rate of thermal degradation increases with increasing material temperature, the useful life of a component will be reduced as the material temperature increases.

In this paper, a transient thermal analysis model for Diesel fuel systems is presented. The purpose of this work is to determine the fuel temperature at various locations along the system, especially inside the tank and at the returned fuel inlet to the tank. Due to the fact that the fuel level is continuously changing during any driving condition, the fuel mass inside the tank is also continuously changing. Consequently, the fuel temperature will change even under steady driving or idle conditions, therefore, this problem should be analyzed using transient thermal analysis models. Effective thermal management requires controlling the surface temperature of the fuel tank, fuel lines and the fuel temperature at the fuel return line as well as inside the tank [1, 2]. Based on the thermal analysis results, it is possible to determine the major source of heat input at several locations of the fuel system.

Computational tools have been extensively applied to predict component temperatures before an actual vehicle is built for testing [1, 2, 3, 4, and 5]. This approach provides an estimate of component temperatures during a specific driving condition. The predicted component temperature is compared against acceptable temperature limits. If violations of the temperature limits are predicted, corrective actions will be applied. These corrective actions may include adding heat shields to the heat source or to the receiving components. Therefore, design changes are implemented based on the simulation results. Sensitivity analysis is the formal technique of determining most influential parameters in a system that affects its performance. Uncertainty analysis is the process of evaluating the deviation of the design from its intended design target.