[2I5-GS-10] AI application:

[2I5-GS-10-01] Study on the Generation of Pipeline Shapes through Path-Planning and Performance Prediction Using Thermal Fluid Surrogate Models for EV’s Thermal Management Products

〇Naoya Matsumura¹, Shoichiro Kisanuki¹, Takuya Sugiura¹ (1. AISIN CORPORATION)

Keywords:Product Design, Path-Planning, Fluid Dynamics

In the development of thermal management products for electric vehicles, a significant amount of effort is dedicated to thermal fluid analysis for temperature control performance evaluation. To reduce this effort, high-speed inference using surrogate models that train from past design shapes is useful. To improve the inference accuracy of surrogate models, a wide-ranging and dense dataset is required. However, components such as cooling pipelines have complex layout, which makes it difficult to prepare various shape data within realistic effort constraints. Therefore, in this study, we developed an automatic generation technology for piping layout diagrams based on path-planning methods. By training on the generated shape data, we obtained a surrogate model with applicable range and inference accuracy that contributes to practical design utilization.