Вестник Национальной инженерной академии Республики Казахстан最新文献

筛选

英文中文

Nonlinear inverse problem of finding thermophysical characteristics 寻找热物理特征的非线性反问题

Вестник Национальной инженерной академии Республики Казахстан Pub Date : 2023-06-30 DOI: 10.47533/2023.1606-146x.7

B. Rysbaiuly, S. D Alpar

{"title":"Nonlinear inverse problem of finding thermophysical characteristics","authors":"B. Rysbaiuly, S. D Alpar","doi":"10.47533/2023.1606-146x.7","DOIUrl":"https://doi.org/10.47533/2023.1606-146x.7","url":null,"abstract":"In this paper, a method has been developed for determining the nonlinear heat-conducting characteristics of the soil. Two-layer container complexes were created, the side faces of which are thermally insulated, so the 1D thermal conductivity equation is used. The temperature sensor is placed at the junction of two media, and a mixed boundary value problem is solved in each region. In order to provide the inverse coefficient problem with initial data, two temperature sensors are used: one sensor was placed at the open boundary of the container and recorded the soil temperature at this boundary, and the second sensor was placed a short distance from the boundary, which recorded the air temperature. The measurements were carried out in the time interval (0,4tmax). First, the initial-boundary problem of heat conduction with nonlinear coefficients is studied by the finite difference method. Two types of difference schemes are constructed: linearized and nonlinear. The linearized difference scheme is implemented numerically by the scalar Thomas method, and the nonlinear difference problem is solved by the Newton method. The solution of a linearized difference problem was taken as the initial approximation of Newton’s method. To find the thermophysical parameters, the corresponding functional is minimized using the gradient descent method. In addition, all thermophysical characteristics (8 coefficients) were found for a two-layer container with sand and chernozem.","PeriodicalId":478237,"journal":{"name":"Вестник Национальной инженерной академии Республики Казахстан","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136184934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The classification system of vehicles on road structures using artificial intelligence 基于人工智能的道路结构车辆分类系统

Вестник Национальной инженерной академии Республики Казахстан Pub Date : 2023-03-30 DOI: 10.47533/2020.1606-146x.219

B. E. Resnik, M. E. Mansurova, Zh. E. Baigarayeva, G. A. Amirkhanova, S. Sh. Ixanov

{"title":"The classification system of vehicles on road structures using artificial intelligence","authors":"B. E. Resnik, M. E. Mansurova, Zh. E. Baigarayeva, G. A. Amirkhanova, S. Sh. Ixanov","doi":"10.47533/2020.1606-146x.219","DOIUrl":"https://doi.org/10.47533/2020.1606-146x.219","url":null,"abstract":"This study proposes a method based on an artificial neural network for classifying truck systems for monitoring road structures. As a rule, in order to guarantee structural and operational safety and provide early warning of damage or wear before expensive repairs or even catastrophic collapse of road structures, it is necessary to provide a high-quality monitoring system based on innovative methods. Starting from the selection of suitable sensors and ending with the design of a structural condition assessment system, the development of an intelligent monitoring system for a large bridge that is really capable of providing information for assessing the integrity of the structure. This article explores the latest innovative achievements in the field of vehicle registration on road structures. Recognition and classification of each individual type of cargo vehicle is very difficult, because there are different types of them, there are different numbers of axles and their location, which cause ambiguity in the recognition and classification process. To cope with these classification ambiguities, the authors propose a method of recognition and classification based on an artificial neural network. Cargo vehicles are classified by applying an artificial neural network to the signals of truck systems received in the frequency domain. Once the neural network is trained on the received signals, it is used to determine the type of cargo vehicles using the received signals. The effectiveness of the classification was evaluated by experiments on the Freybrücke Bridge in Berlin, Germany. Model training was applied using the CNN method for vehicle classification, which resulted in an overall classification accuracy of about 95%.","PeriodicalId":478237,"journal":{"name":"Вестник Национальной инженерной академии Республики Казахстан","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135418316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0