{"title":"基于压敏涂料数据的贝叶斯框架改进了带有优化稀疏传感器的 Ahmed 模型的偏航角和表面压力分布估计值","authors":"Ryoma Inoba, Kazuki Uchida, Yuto Iwasaki, Keigo Yamada, Ayoub Jebli, Takayuki Nagata, Yuta Ozawa, Taku Nonomura","doi":"10.1016/j.expthermflusci.2024.111210","DOIUrl":null,"url":null,"abstract":"<div><p>The present study provides a Bayesian framework for the estimation of the yaw angle and the pressure distribution on the surface of the vehicle from the spatially sparse pressure measurements obtained by optimized sensing locations and data-driven models. The framework is demonstrated on the Ahmed model which is the simplified car model. The yaw angle and the pressure distribution on the top surface of the Ahmed model are estimated based on the sparse pressure measurement on the top surface. The estimation models are constructed based on the time-averaged pressure distribution on the top surface of the car model with various yaw angles obtained by a pressure-sensitive paint technique. The estimation model for the yaw angle was constructed as the linear regression between the yaw angle and pressure at the sensing locations, and the estimation model for the pressure distribution was constructed from a POD-based reduced order model. The Bayesian estimation was newly adopted for the mode coefficient estimation of the reduced-order model of the pressure distribution, and the optimization method of the sensing locations for the Bayesian estimation was adopted. The performance of the present Bayesian method was compared with previously proposed methods, and the results showed that the Bayesian method provides the best performance under most conditions on the yaw angle estimation and the pressure distribution reconstruction. In addition, various combinations of the estimation method and sensing location optimization method were tested, and the impact of estimation and sensing locations was discussed.</p></div>","PeriodicalId":12294,"journal":{"name":"Experimental Thermal and Fluid Science","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved estimation of yaw angle and surface pressure distribution of Ahmed model with optimized sparse sensors by Bayesian framework based on pressure-sensitive paint data\",\"authors\":\"Ryoma Inoba, Kazuki Uchida, Yuto Iwasaki, Keigo Yamada, Ayoub Jebli, Takayuki Nagata, Yuta Ozawa, Taku Nonomura\",\"doi\":\"10.1016/j.expthermflusci.2024.111210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The present study provides a Bayesian framework for the estimation of the yaw angle and the pressure distribution on the surface of the vehicle from the spatially sparse pressure measurements obtained by optimized sensing locations and data-driven models. The framework is demonstrated on the Ahmed model which is the simplified car model. The yaw angle and the pressure distribution on the top surface of the Ahmed model are estimated based on the sparse pressure measurement on the top surface. The estimation models are constructed based on the time-averaged pressure distribution on the top surface of the car model with various yaw angles obtained by a pressure-sensitive paint technique. The estimation model for the yaw angle was constructed as the linear regression between the yaw angle and pressure at the sensing locations, and the estimation model for the pressure distribution was constructed from a POD-based reduced order model. The Bayesian estimation was newly adopted for the mode coefficient estimation of the reduced-order model of the pressure distribution, and the optimization method of the sensing locations for the Bayesian estimation was adopted. The performance of the present Bayesian method was compared with previously proposed methods, and the results showed that the Bayesian method provides the best performance under most conditions on the yaw angle estimation and the pressure distribution reconstruction. In addition, various combinations of the estimation method and sensing location optimization method were tested, and the impact of estimation and sensing locations was discussed.</p></div>\",\"PeriodicalId\":12294,\"journal\":{\"name\":\"Experimental Thermal and Fluid Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Experimental Thermal and Fluid Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0894177724000797\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Thermal and Fluid Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0894177724000797","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
摘要
本研究提供了一个贝叶斯框架,用于从通过优化传感位置和数据驱动模型获得的空间稀疏压力测量值中估计偏航角和车辆表面的压力分布。该框架在简化汽车模型 Ahmed 模型上进行了演示。根据对 Ahmed 模型顶表面的稀疏压力测量结果,估算出模型的偏航角和顶表面的压力分布。估算模型是根据压敏涂料技术获得的不同偏航角下汽车模型上表面的时间平均压力分布构建的。偏航角的估计模型由偏航角与传感位置压力之间的线性回归建立,压力分布的估计模型由基于 POD 的降阶模型建立。压力分布的降阶模型的模态系数估计采用了新的贝叶斯估计方法,并采用了贝叶斯估计的传感位置优化方法。将本贝叶斯方法的性能与之前提出的方法进行了比较,结果表明,在大多数条件下,贝叶斯方法在偏航角估计和压力分布重建方面的性能最佳。此外,还测试了估计方法和传感位置优化方法的各种组合,并讨论了估计和传感位置的影响。
Improved estimation of yaw angle and surface pressure distribution of Ahmed model with optimized sparse sensors by Bayesian framework based on pressure-sensitive paint data
The present study provides a Bayesian framework for the estimation of the yaw angle and the pressure distribution on the surface of the vehicle from the spatially sparse pressure measurements obtained by optimized sensing locations and data-driven models. The framework is demonstrated on the Ahmed model which is the simplified car model. The yaw angle and the pressure distribution on the top surface of the Ahmed model are estimated based on the sparse pressure measurement on the top surface. The estimation models are constructed based on the time-averaged pressure distribution on the top surface of the car model with various yaw angles obtained by a pressure-sensitive paint technique. The estimation model for the yaw angle was constructed as the linear regression between the yaw angle and pressure at the sensing locations, and the estimation model for the pressure distribution was constructed from a POD-based reduced order model. The Bayesian estimation was newly adopted for the mode coefficient estimation of the reduced-order model of the pressure distribution, and the optimization method of the sensing locations for the Bayesian estimation was adopted. The performance of the present Bayesian method was compared with previously proposed methods, and the results showed that the Bayesian method provides the best performance under most conditions on the yaw angle estimation and the pressure distribution reconstruction. In addition, various combinations of the estimation method and sensing location optimization method were tested, and the impact of estimation and sensing locations was discussed.
期刊介绍:
Experimental Thermal and Fluid Science provides a forum for research emphasizing experimental work that enhances fundamental understanding of heat transfer, thermodynamics, and fluid mechanics. In addition to the principal areas of research, the journal covers research results in related fields, including combined heat and mass transfer, flows with phase transition, micro- and nano-scale systems, multiphase flow, combustion, radiative transfer, porous media, cryogenics, turbulence, and novel experimental techniques.