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The objective of this work is to perform a computer model based sensitivity analysis of parameters of an automotive air conditioning system to identify the critical parameters. Design of Experiment (DOE) and Analysis of Variance (ANOVA) techniques have been used to identify the critical parameters and their relative effects on the air conditioning system performance. The sensitivity analysis has been verified by running similar tests on an air conditioning system test stand (AC Test Stand).

This paper explores the trade-off between reliability-based design and robustness for an automotive under-hood thermal system using the iSIGHT-FD environment. The interaction between the engine cooling system and the heating, ventilating, and air-conditioning (HVAC) system is described. The engine cooling system performance is modeled using Flowmaster and a metamodel is developed in iSIGHT. The actual HVAC system performance is characterized using test bench data. A design of experiment procedure determines the dominant factors and the statistics of the HVAC performance is obtained using Monte Carlo simulation (MCS). The MCS results are used to build an overall system response metamodel in order to reduce the computational effort. A multi-objective optimization in iSIGHT maximizes the system mean performance and simultaneously minimizes its standard deviation subject to probabilistic constraints.

A 1-Dimensional model was developed to simulate the performance of an automotive Air Conditioning (AC) system. Its architecture and validation with vehicle test data (over a wide range of environmental and engine load conditions) are presented in this paper. This study demonstrates the ability of a simplified AC model to capture real system phenomena. Its sensitivity and limitations are evaluated, along with its potential as a system design tool.

This paper describes the use of robust engineering in engine cooling system design. 1-D thermal-fluid network simulation software has been used in conjunction with statistical variation analysis software. It shows how we can increase the reliability of an automotive engine cooling system by considering the variations of all factors of design. Design of experiment (DOE) and Monte Carlo simulation techniques have been used to optimize the engine cooling system design.