{"title":"从城市环境中的中尺度模拟中获取高精细风场的逆向图像对图像模型","authors":"","doi":"10.1016/j.buildenv.2024.112123","DOIUrl":null,"url":null,"abstract":"<div><div>We propose a conditional Generative Adversarial Network (cGAN) that can produce detailed local wind fields in urban areas, comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, that are generated from coarser Numerical Weather Prediction (NWP) data.</div><div>In our approach, the cGAN is trained using NWP data as input and CFD as targets. Both CFD and NWP data are presented to the network as images, using an image-to-image model based on Pix2Pix to transform coarse meteorological conditions into detailed local wind fields.</div><div>The methodology is tested in a residential district in a large Spanish city, Zaragoza. The model predictions show significant agreement with the actual CFD results, while reducing the computational time from eight hours to seconds. Feature engineering of image channels effectively reduces the model error, especially in the wind direction, achieving a mean absolute error in the wind speed of <span><math><mrow><mn>0</mn><mo>.</mo><mn>35</mn><mspace></mspace><mi>m/s</mi></mrow></math></span> and a wind direction error of <span><math><mrow><mn>27</mn><mo>.</mo><mn>0</mn><mo>°</mo></mrow></math></span>.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in urban environments\",\"authors\":\"\",\"doi\":\"10.1016/j.buildenv.2024.112123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We propose a conditional Generative Adversarial Network (cGAN) that can produce detailed local wind fields in urban areas, comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, that are generated from coarser Numerical Weather Prediction (NWP) data.</div><div>In our approach, the cGAN is trained using NWP data as input and CFD as targets. Both CFD and NWP data are presented to the network as images, using an image-to-image model based on Pix2Pix to transform coarse meteorological conditions into detailed local wind fields.</div><div>The methodology is tested in a residential district in a large Spanish city, Zaragoza. The model predictions show significant agreement with the actual CFD results, while reducing the computational time from eight hours to seconds. Feature engineering of image channels effectively reduces the model error, especially in the wind direction, achieving a mean absolute error in the wind speed of <span><math><mrow><mn>0</mn><mo>.</mo><mn>35</mn><mspace></mspace><mi>m/s</mi></mrow></math></span> and a wind direction error of <span><math><mrow><mn>27</mn><mo>.</mo><mn>0</mn><mo>°</mo></mrow></math></span>.</div></div>\",\"PeriodicalId\":9273,\"journal\":{\"name\":\"Building and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S036013232400965X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S036013232400965X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in urban environments
We propose a conditional Generative Adversarial Network (cGAN) that can produce detailed local wind fields in urban areas, comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, that are generated from coarser Numerical Weather Prediction (NWP) data.
In our approach, the cGAN is trained using NWP data as input and CFD as targets. Both CFD and NWP data are presented to the network as images, using an image-to-image model based on Pix2Pix to transform coarse meteorological conditions into detailed local wind fields.
The methodology is tested in a residential district in a large Spanish city, Zaragoza. The model predictions show significant agreement with the actual CFD results, while reducing the computational time from eight hours to seconds. Feature engineering of image channels effectively reduces the model error, especially in the wind direction, achieving a mean absolute error in the wind speed of and a wind direction error of .
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.