Optimal Water Jacket Flow Distribution Using a New Group-Based Space-Filling Design of Experiments Algorithm

The availability of computational resources has enabled an increased utilization of Design of Experiments (DoE) and metamodeling (response surface generation) for large-scale optimization problems. Despite algorithmic advances however, the analysis of systems such as water jackets of an automotive engine, can be computationally demanding in part due to the required accuracy of metamodels. Because the metamodels may have many inputs, their accuracy depends on the number of training points and how well they cover the entire design (input) space. For this reason, the space-filling properties of the DoE are very important. This paper utilizes a new group-based DoE algorithm with space-filling groups of points to construct a metamodel. Points are added sequentially so that the space-filling properties of the entire group of points is preserved. The addition of points is continuous until a specified metamodel accuracy is met. The objective of this study is to first create a metamodel that accurately predicts a desired coolant distribution in the water jacket of a single cylinder engine, and then use it to optimize the temperature and flow distributions. The optimized flow and temperature distributions using a metamodel generated using the group-based DoE or an Optimal Latin Hypercube (OLH) DoE are compared using the same number of training points where an expensive CDF (Computational Fluid Dynamics) analysis is performed. We show, using the optimization of water jacket flow distribution, that the metamodels using the group-based DoE approach provide improved accuracy and efficiency compared to the metamodels using the OLH DoE.