Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning.

Shixun Li, Wenchong Tian, Hexiang Yan, Wei Zeng, Tao Tao, Kunlun Xin

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Article in Water research X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

6 authors.

Shixun LiCollege of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Wenchong TianSchool of Energy and Environment, City University of Hong Kong, Hong Kong 999077, China.

Hexiang YanCollege of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Wei ZengSchool of Architecture and Civil Engineering, University of Adelaide, SA 5005, Australia.

Tao TaoCollege of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Kunlun XinCollege of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (

Indexed as

Data fusionHybrid modelHydraulic transientPhysics-informed neural networkUrban drainage system

Identifiers

PMID39497789

DOI10.1016/j.wroa.2024.100266

PMCPMC11533095

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.