Enhanced modeling of vehicle-induced turbulence and pollutant dispersion in urban street canyon: Large-eddy simulation via dynamic overset mesh approach
IF 10.5 1区 工程技术Q1 CONSTRUCTION & BUILDING TECHNOLOGY
{"title":"Enhanced modeling of vehicle-induced turbulence and pollutant dispersion in urban street canyon: Large-eddy simulation via dynamic overset mesh approach","authors":"","doi":"10.1016/j.scs.2024.105939","DOIUrl":null,"url":null,"abstract":"<div><div>This study presented a novel application of large-eddy simulation (LES) with a dynamic overset mesh approach to simulate vehicle-induced turbulence in a two-dimensional street canyon. The simulation incorporated moving vehicle entities to emulate two-way traffic, with each vehicle equipped with a pollutant source to simulate pollutant dispersion. Comprehensive long-term statistical analyses were conducted to compare the simulated turbulence with those produced by the conventional approach (where vehicle-induced momentum was not considered) and the quasi-steady method (where vehicle motion was simplified as momentum sources). The results revealed that the presence of moving vehicle entities significantly distorted the primary circulation within the canyon, altering the transport pathways of both lateral momentum and air pollutants. The motion of vehicle entities also induced a substantial amount of turbulence, resulting in different pollutant removal mechanisms at the top of the canyon. The ensemble-average analysis revealed a downwash followed by an upwash within a cycle of vehicle movement, which largely contributed to momentum and pollutant transport. These findings underscored the need for considering the moving entities in LES approaches to enhance vehicle-induced turbulence modeling. Other factors influencing the simulation were discussed, aiming to guide more accurate and reliable turbulence modeling in urban environments.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007637","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}
引用次数: 0
Abstract
This study presented a novel application of large-eddy simulation (LES) with a dynamic overset mesh approach to simulate vehicle-induced turbulence in a two-dimensional street canyon. The simulation incorporated moving vehicle entities to emulate two-way traffic, with each vehicle equipped with a pollutant source to simulate pollutant dispersion. Comprehensive long-term statistical analyses were conducted to compare the simulated turbulence with those produced by the conventional approach (where vehicle-induced momentum was not considered) and the quasi-steady method (where vehicle motion was simplified as momentum sources). The results revealed that the presence of moving vehicle entities significantly distorted the primary circulation within the canyon, altering the transport pathways of both lateral momentum and air pollutants. The motion of vehicle entities also induced a substantial amount of turbulence, resulting in different pollutant removal mechanisms at the top of the canyon. The ensemble-average analysis revealed a downwash followed by an upwash within a cycle of vehicle movement, which largely contributed to momentum and pollutant transport. These findings underscored the need for considering the moving entities in LES approaches to enhance vehicle-induced turbulence modeling. Other factors influencing the simulation were discussed, aiming to guide more accurate and reliable turbulence modeling in urban environments.
本研究提出了一种新颖的大涡度模拟(LES)应用,采用动态超集网格方法来模拟二维街道峡谷中由车辆引起的湍流。模拟结合了移动的车辆实体来模拟双向交通,每辆车都配备了一个污染源来模拟污染物的扩散。进行了全面的长期统计分析,将模拟湍流与传统方法(不考虑车辆引起的动量)和准稳定方法(将车辆运动简化为动量源)产生的湍流进行了比较。结果表明,移动车辆的存在极大地扭曲了峡谷内的主要环流,改变了横向动量和空气污染物的传输路径。车辆实体的运动还引发了大量湍流,导致峡谷顶部污染物清除机制的不同。集合平均分析显示,在车辆运动的一个周期内,先是下冲,然后是上冲,这在很大程度上促进了动量和污染物的传输。这些发现强调了在 LES 方法中考虑运动实体的必要性,以加强车辆引起的湍流建模。此外,还讨论了影响模拟的其他因素,旨在指导在城市环境中建立更准确、更可靠的湍流模型。
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;