Source term estimation in the unsteady flow with dynamic mode decomposition

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-09-28 DOI:10.1016/j.scs.2024.105843

Jianjie Zhu , Xuanyi Zhou , Hideki Kikumoto

{"title":"Source term estimation in the unsteady flow with dynamic mode decomposition","authors":"Jianjie Zhu , Xuanyi Zhou , Hideki Kikumoto","doi":"10.1016/j.scs.2024.105843","DOIUrl":null,"url":null,"abstract":"<div><div>When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-09-28","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/S221067072400667X","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

When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.

查看原文本刊更多论文

利用动态模式分解估算非稳定流中的源项

在估算非稳定流中的污染源参数时，污染扩散的流量信息是不可或缺的。通常的做法是预先将流场信息保存在计算机中，但这需要很大的存储空间。此外，当污染物在之前保存的流场一段时间后释放时，通过计算流体动力学（CFD）模型计算流场需要大量的计算成本。因此，我们提出了动态模式分解（DMD）来解决上述问题。首先，通过 DMD 对流场进行分解。然后，根据 DMD 合成的场，通过邻接方程获得模拟浓度。最后，将测量浓度和模拟浓度纳入贝叶斯推理，完成源项估计（STE）。结果表明，使用 DMD 获得的场，在重建阶段和预测阶段都能获得高精度的估计结果。同时，DMD 预测未来流量的效率远高于 CFD 模拟，表明在某些情况下 DMD 可以提高 STE 的效率。由于 DMD 使用少量主导模式来合成误差较小的近似场，因此减少了 STE 对流量信息的存储需求。为确保 STE 的精度，应适当选择采样范围和采样分辨率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

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;