基于物理方程的高分辨率光场层析粒子图像测速技术  

High-Resolution Light Field Chromatography Particle Image Velocimetry Based on Physical Equation

作　　者：吴旗 朱效宇 许传龙[1] Wu Qi;Zhu Xiaoyu;Xu Chuanlong(National Engineering Research Center of Power Generation Control and Safety,Southeast University,Nanjing 210096,Jiangsu,China)

机构地区：[1]东南大学大型发电装备安全运行与智能测控国家工程研究中心,江苏南京210096

出　　处：《光学学报》2025年第1期138-149,共12页Acta Optica Sinica

基　　金：国家自然科学基金(52306211);中国博士后科学基金(2023M730558)。

摘　　要：提出一种基于物理信息神经网络(PINN)的高分辨率光场层析粒子图像测速(PIV)技术,在稀疏速度场观测数据基础上耦合Navier-Stokes方程作为先验物理信息,建立PINN-PIV融合模型,实现对致密流场信息的预测。对于所构建的模型,首先采用高斯涡环的仿真数据对其性能进行了评估,然后开展了圆柱绕流三维流场测量实验,以进一步验证PINN-PIV融合模型的有效性。结果表明,采用PINN-PIV模型所预测的高斯涡环u、v、w位移分量的全局均方根误差分别为0.2433、0.2105、0.2423 voxel,较传统互相关算法结果降低了52.36%、58.95%、75.84%。从圆柱绕流流场测试结果可以看出,PINN-PIV模型能够将光场PIV流场测量分辨率提高8倍,实现了流场小尺度漩涡结构的表征。Objective Combining light field imaging with particle image velocimetry(PIV),single-camera light field tomographic PIV technology allows for three-dimensional flow field measurements from a single viewpoint,particularly useful in narrow-channel applications where observation windows are limited.However,significant axial stretching of flow tracer particles and the averaging effects inherent in cross-correlation algorithms reduce spatial resolution,limiting the ability of this technology to resolve finer flow structures.While existing methods,including traditional algorithms,data assimilation techniques,and neural networks,attempt to address these challenges,none fully succeed.In this paper,we propose a high-resolution light field tomographic PIV technique based on physics-informed neural networks(PINNs),aimed at enhancing spatial resolution and accurately predicting dense flow field information.Methods To meet the practical demands of light field PIV,we first analyze the integration of the Navier-Stokes(N-S)equations as prior physical information with a neural network model,constructing a PINN-PIV model for high-resolution three-dimensional flow field prediction.The model is trained using experimental data.Prior to training,three-dimensional velocity fields are segmented into two-dimensional slices,which are then fed into the model for refined predictions.The model's performance is evaluated through numerical simulation and the reconstruction of Gaussian vortex displacement fields.We compare the results of PINN-PIV with those obtained using traditional cross-correlation methods to validate the effectiveness of the PINN-PIV approach.Finally,we conduct experiments on cylindrical flow using light field tomographic PIV to assess the model's predictive accuracy on real experimental data.Results and Discussions The numerical reconstruction shows that the global root mean square errors of the predicted u,v,and w displacement components of the Gaussian vortex using the PINN-PIV model are 0.2433,0.2105,and 0.2423 voxel,res

关 键 词：三维流场 光场层析粒子图像测速 物理信息神经网络 物理信息神经网络-粒子图像测速融合模型 空间分辨率 

分 类 号：O435[机械工程—光学工程]