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A multi-objective optimized automatic design process is developed based on the airflow velocity required for cooling performance in the passenger cabin. This process considers the airflow performance on the driver's side and the passenger's side, focusing on the positioning of the grille vent blades. Based on CFD simulations and a multi-disciplinary optimization design platform, the Latin hypercube sampling method is used to generate sample points and construct the DOE matrix. A neural network-based proxy model is then built to predict the blow-face airflow velocity performance parameters. The NSGA-Ⅲ algorithm is used to obtain the Pareto frontier diagram for the multi-objective optimization problem. The optimized grille blade position increases the airflow speed by 109.1% on the driver's side and by 137.5% on the front passenger's side. The reliability of the optimization results is verified through unsteady CFD simulations and cooling performance tests before and after the optimization.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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