A Predicting Method on Aerodynamic Performance of Flapping Wing Using Hybrid Coarse Data Driven Physical Informed Neural Network

HU Fujia; ZHOU Yilun; LIU Xiaomin

doi:10.7652/xjtuxb202311019

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A Predicting Method on Aerodynamic Performance of Flapping Wing Using Hybrid Coarse Data Driven Physical Informed Neural Network

更新时间：2025-12-08

- A Predicting Method on Aerodynamic Performance of Flapping Wing Using Hybrid Coarse Data Driven Physical Informed Neural Network
- Vol. 57, Issue 11, Pages: 194-205(2023)
- 作者机构：
  
  1. 西安交通大学能源与动力工程学院,西安,710049
  2. 西安陕鼓动力股份有限公司,西安,710075
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202311019
  CLC： TP18;V211
- Online First：10 November 2023，
  
  Published：2023
- 稿件说明：
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HU Fujia, ZHOU Yilun, LIU Xiaomin. A Predicting Method on Aerodynamic Performance of Flapping Wing Using Hybrid Coarse Data Driven Physical Informed Neural Network[J]. 2023, 57(11): 194-205.
DOI：

HU Fujia, ZHOU Yilun, LIU Xiaomin. A Predicting Method on Aerodynamic Performance of Flapping Wing Using Hybrid Coarse Data Driven Physical Informed Neural Network[J]. 2023, 57(11): 194-205. DOI： 10.7652/xjtuxb202311019.

摘要

为解决时空尺度上扑翼流动控制方程求解需要花费大量时间和计算资源的问题

基于强非线性曲线拟合能力的物理信息神经网络(PINN)深度学习方法

提出了一种混合粗糙数据驱动物理信息神经网络模型(HCDD-PINN)

研究了模型对涉及非定常流动特征和动边界二维俯仰扑翼问题的训练和预测性能。通过使用相较于传统计算流体动力学方法(CFD)更为粗糙的数据驱动模型训练

将扑翼流动控制方程嵌入神经网络损失中

并施加初始条件和边界条件约束

采用一阶自适应矩优化算法(ADAM)和二阶拟牛顿法优化算法(L-BFGS-B)

以前馈-反向传播方式最小化模型损失函数

从而提高模型预测控制方程数值解的准确性和可靠性。结果表明:与原始PINN模型相比

HCDD-PINN模型显著降低了流场的预测误差

能够准确地预测扑翼瞬时气动力和瞬时速度及压力场

训练时间缩短了75%。此外

训练完成的HCDD-PINN模型可以快速获得流场任意时刻的物理信息

而传统CFD方法则需要重新对流场进行计算。该研究为求解扑翼流动控制方程乃至流体非线性偏微分方程组(PDEs)提供了一种有效的替代方案。

Abstract

The solution of the governing equations of the flapping wing problem on a spatiotemporal scale takes a great amount of time and computational resources. To solve this challenge

this paper proposes a hybrid coarse data-driven with physics-informed neural network model(HCDD-PINN). The model leverages its strong nonlinear curve fitting ability to investigate the training and predicting performance of the model for two-dimensional pitching flapping wing problems

which involves unsteady flow characteristics and motion boundaries. The model is trained using an order-of-magnitude grid

coarser than the traditional computational fluid dynamic(CFD)required. The architecture is devised to enforce the initial and boundary conditions and incorporate the governing equations into the loss of the neural network. The optimization process is conducted by ADAM and L-BFGS-B methods with a feed forward-back propagation manner

to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions. The results show that the proposed HCDD-PINN framework exhibits improved stability and accuracy compared to the PINN models that lack internal data. It effectively reduces the prediction error of the flow field

and accurately predicts the instantaneous aerodynamic forces

velocity and pressure fields of flapping wings. Moreover

the training time has accelerated by approximately four times. Additionally

the interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model

which is superior to the traditional CFD method that usually needs to be re-run. This study provides an effective alternative for solving flapping wing governing equations and even nonlinear partial differential equations(PDEs).

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