CDS&E: Physics Guided Super-Resolution for Turbulent Transport
CDS
基本信息
- 批准号:2203581
- 负责人:
- 金额:$ 49.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-01 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Understanding turbulence phenomenon is the key to our comprehension of many natural and technological processes in diverse fields including aerodynamics, hydraulics, astrophysics, propulsion, atmospherics, oceanics, medicine, and many others. Direct numerical simulation (DNS) of the Navier-Stokes equations is widely recognized as the prime computational methodology with the highest fidelity in capturing the intricacies of turbulent transport. However, the wide range of flow's time and length scales makes DNS prohibitively expensive and time consuming even on the most advanced high-performance supercomputers. Large eddy simulation (LES), which filters out the very small-scale transport, provides an alternative with a much lower computational cost. However, the data generated by LES are of lower accuracy due to the associated filtering, and it is desirable to reconstruct the original true DNS results from the filtered LES data. This proposal describes a novel physics-guided machine learning methodology to perform this reconstruction with a systematic assessment for a variety of turbulent flows. This project aims to advance the restoration of high-fidelity turbulent flows via three innovations. First, a new physics-guided deep learning model will be developed to reconstruct fine-resolution turbulent flow data from low-resolution coarse LES data. Additional physical learning objectives and relationships will be incorporated to ensure that the proposed model meets specific physical constraints associated with the flow, and it is also generalizable for large scale simulations. Second, a novel deep learning scheme will be developed and utilized to construct high fidelity super-resolution fields from the most reliable LES that can be currently conducted. Finally, a wide variety of turbulent flows will be considered, ranging from passive incompressible, to chemically reactive compressible for comprehensive model assessments. This project has the potential to improve our capability to efficiently simulate high-resolution turbulent flows in many scientific and engineering domains. The research results will be used to develop materials for both undergraduate and graduate education, and for K-12 outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
理解湍流现象是我们理解不同领域中许多自然和技术过程的关键,包括空气动力学,水力学,天体物理学,推进,大气学,海洋学,医学等。Navier-Stokes方程的直接数值模拟(DNS)被广泛认为是捕捉湍流输运复杂性的最高保真度的主要计算方法。然而,流的时间和长度尺度的广泛范围使得DNS即使在最先进的高性能超级计算机上也非常昂贵和耗时。大涡模拟(LES),过滤掉非常小的尺度传输,提供了一个替代方案,具有更低的计算成本。然而,由于相关联的过滤,由LES生成的数据具有较低的准确性,并且期望从过滤的LES数据重建原始真实DNS结果。该提案描述了一种新的物理引导的机器学习方法,用于对各种湍流进行系统评估来执行这种重建。 该项目旨在通过三项创新推进高保真湍流的恢复。首先,将开发一种新的物理引导的深度学习模型,以从低分辨率的粗糙LES数据重建精细分辨率的湍流数据。额外的物理学习目标和关系将被纳入,以确保所提出的模型满足与流相关的特定物理约束,它也是大规模模拟的推广。其次,将开发一种新的深度学习方案,并利用该方案从目前可以进行的最可靠的LES构建高保真超分辨率场。最后,各种各样的湍流将被考虑,从被动不可压缩,化学反应可压缩的全面模型评估。该项目有可能提高我们在许多科学和工程领域有效模拟高分辨率湍流的能力。研究成果将用于为本科生和研究生教育以及K-12推广开发材料。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
- DOI:10.48550/arxiv.2304.12130
- 发表时间:2023-04
- 期刊:
- 影响因子:0
- 作者:Shengyu Chen;Tianshu Bao;P. Givi;Can Zheng-;Xiaowei Jia
- 通讯作者:Shengyu Chen;Tianshu Bao;P. Givi;Can Zheng-;Xiaowei Jia
Physics guided neural networks for spatio-temporal super-resolution of turbulent flows
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Tianshu Bao;Shengyu Chen;Taylor T. Johnson;P. Givi;S. Sammak;Xiaowei Jia
- 通讯作者:Tianshu Bao;Shengyu Chen;Taylor T. Johnson;P. Givi;S. Sammak;Xiaowei Jia
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Xiaowei Jia其他文献
Enhanced photoexcited carrier separation in Ta3N5/SrTaO2N (1D/0D) heterojunctions for highly efficient visible light-driven hydrogen evolution
增强 Ta3N5/SrTaO2N (1D/0D) 异质结中的光激发载流子分离,实现高效可见光驱动的析氢
- DOI:
10.1016/j.apsusc.2020.145915 - 发表时间:
2020-06 - 期刊:
- 影响因子:6.7
- 作者:
Xiaowei Jia;Wenjing Chen;Yunfeng Li;Xuanbo Zhou;Xiaodan Yu;Yan Xing - 通讯作者:
Yan Xing
Highly crystalline sulfur and oxygen co-doped g-C3N4 nanosheets as an advanced photocatalyst for efficient hydrogen generation
高结晶硫和氧共掺杂 g-C3N4 纳米片作为先进光催化剂用于高效制氢
- DOI:
10.1039/d2cy00824f - 发表时间:
2022 - 期刊:
- 影响因子:5
- 作者:
Xiaowei Jia;Yunfeng Li;Xianchun Liu;Xiaodan Yu;Cong Wang;Zhan Shi;Yan Xing - 通讯作者:
Yan Xing
Fe-doped perovskite-like oxide KCuTa3O9 for photocatalytic hydrogen evolution under visible light irradiation
- DOI:
10.1016/j.jallcom.2023.170635 - 发表时间:
2023 - 期刊:
- 影响因子:6.2
- 作者:
Xiaowei Jia;Xianchun Liu;Ruyu Zhang;Anqi Xie;Yueran Li;Xiaodan Yu;Min Yu;Yunfeng Li;Zhan Shi;Yan Xing - 通讯作者:
Yan Xing
Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery
地球图像洪水测绘的空间逻辑感知弱监督学习
- DOI:
10.1609/aaai.v38i20.30253 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Zelin Xu;Tingsong Xiao;Wenchong He;Yu Wang;Zhe Jiang;Shigang Chen;Yiqun Xie;Xiaowei Jia;Da Yan;Yang Zhou - 通讯作者:
Yang Zhou
Analysis of Energy Consumption Structure on CO2 Emission and Economic Sustainable Growth
能源消费结构对CO2排放与经济可持续增长的影响分析
- DOI:
10.1016/j.egyr.2022.02.296 - 发表时间:
2022 - 期刊:
- 影响因子:5.2
- 作者:
Zhiqiang Wang;Xiaowei Jia - 通讯作者:
Xiaowei Jia
Xiaowei Jia的其他文献
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{{ truncateString('Xiaowei Jia', 18)}}的其他基金
CAREER: Combining Machine Learning and Physics-based Modeling Approaches for Accelerating Scientific Discovery
职业:结合机器学习和基于物理的建模方法来加速科学发现
- 批准号:
2239175 - 财政年份:2023
- 资助金额:
$ 49.96万 - 项目类别:
Continuing Grant
Collaborative Research: III: Small: Physics Guided Graph Networks for Modeling Water Dynamics in Freshwater Ecosystems
合作研究:III:小型:用于模拟淡水生态系统中水动力学的物理引导图网络
- 批准号:
2316305 - 财政年份:2023
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
FAI: Advancing Deep Learning Towards Spatial Fairness
FAI:推进深度学习迈向空间公平
- 批准号:
2147195 - 财政年份:2022
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
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