基于CNN的流场预测用于公共汽车空气动力学分析.
Roberto Garcia-Fernandez1,2, Koldo Portal-Porras1, Oscar Irigaray1
1Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz, 01006, Araba, Spain.
Scientific reports
|December 1, 2023
概括
卷积神经网络 (CNN) 准确地预测重型车辆的空气动力学场,大大减少了模拟时间. 这种AI方法为传统的计算流体动力学 (CFD) 方法提供了更快,更可靠的替代方案.
科学领域:
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 人工智能 (AI) 是一种人工智能.
- 空气动力学是什么
背景情况:
- 预测重型车辆的空气动力学场对于设计优化至关重要.
- 传统的CFD模拟在计算上昂贵且耗时.
- 人工智能,特别是CNN,显示出加速这些预测的潜力.
研究的目的:
- 为了评估CNN能够预测重型车辆中的速度和压力空气动力学场的能力.
- 与CFD相比,评估CNN的准确性和计算效率.
- 为了调查隐私化策略对CNN业绩的影响.
主要方法:
- 开发和训练使用CFD模拟三个车辆几何形状 (SC7,SC5,艾哈迈德车身) 的CNN.
- 在CFD模拟中使用基于RANS的k-ω SST流模型.
- 基于速度梯度的离散场表示,以提高CNN的准确性.
主要成果:
- 美国有线电视新闻网准确地预测了速度和压力场的低误差,与数值结果有很好的一致性.
- 实现了实质性的计算时间缩短,大小为四个数量级.
- 证明了基于梯度的离散对提高CNN预测准确性的有效性.
结论:
- 对于重型车辆来说,CNN能够很好地预测空气动力学场.
- 与传统的CFD相比,开发的CNN可以节省大量的计算时间.
- 这种人工智能驱动的方法为空气动力学分析和车辆设计带来了有前途的进步.
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