Development of a Reduced-Order Design/Optimization Tool for Automotive Engines Using Massively Parallel Computing
Design and optimization of automotive engines present unique challenges on account of the large design space and conflicting constraints. A notable example of such a problem is optimizing the fuel consumption and reducing emissions over the drive cycle of an automotive engine. There are over twenty design variables (including operating conditions and geometry) for the above-mentioned problem. Conducting design, analyses, and optimization studies over such a large parametric space presents a serious computational challenge. The large design parameter space precludes the use of detailed numerical or experimental investigations. Physics-based reduced-order models can be used effectively in the design and optimization of such problems.