CDS&E: Extracting Models from Data - A Novel Data-Driven Simulation Strategy for Reacting Flows

CDS

• 批准号: 1953350
• 负责人: James Sutherland
• 金额: $ 45.32万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953350&HistoricalAwards=false
• 关键词: CDS& Extracting; Models; Data; Novel

Numerical simulation of reacting flows of real systems is computationally challenging because of the large number variables involved. This research will develop a new data-driven modelling approach that directly incorporates information from canonical or reference test cases to extract simplified models with user-defined error limits. In particular, novel machine learning concepts will be used to generate simplified models that are suitable for use in practical, engineering-scale simulations. Such computationally efficient models can have a direct impact in addressing a range of relevant energy and environmental problems, for example oxy-fuel combustion (for easier CO2 capture and sequestration), pollutant and particulate formation in stationary and mobile combustion systems, etc. The techniques developed here are also applicable to other fields where many reaction manifolds or pathways exist such as in plasma physics or atmospheric chemistry.Many systems in nature evolve along manifolds, which are smooth and reduced complexity subspaces of a parameter space which satisfy, often unknown, physical constraints. However, developing models to describe this evolution is challenging. A key challenge to this modeling approach is dealing with the source terms that arise in the reduced order model. These are a reflection of the source terms in the full (high-fidelity) model, but must be well-parameterized by the reduced-order model parameters without causing unphysical behavior like divergence near manifold boundaries and spurious source/sink points. This will harness the power of data science to characterize the geometry of the low-dimensional manifold and use that information to improve the behavior of the derived models. Extracting the low-dimensional model is challenging because it requires identifying a moderate dimensional shape where gridding and meshing techniques which scale exponentially in dimension will fail. This work will instead be limited by properties such as boundary curvature and vector field acceleration which are well-controlled for most physics-defined systems. Moreover, these models will be learned in a robust manner consistent with a simple physical model, in spite of noise in training data which may otherwise result in spurious critical saddle points in the resulting vector field.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.

由于涉及大量变量，对真实系统反应流的数值模拟在计算上具有挑战性。这项研究将开发一种新的数据驱动建模方法，该方法将来自规范或参考测试用例的信息直接合并，以提取具有用户定义的误差限制的简化模型。特别是，新型的机器学习概念将用于生成适合在实用的工程尺度模拟中使用的简化模型。这种计算高效的模型在解决一系列相关的能源和环境问题方面可能会直接影响参数空间的平滑和降低的复杂性子空间满足（通常是未知的物理约束）。 但是，开发描述这一进化的模型具有挑战性。这种建模方法的一个关键挑战是处理减少订单模型中出现的源术语。这些是整个（高保真）模型中源术语的反映，但必须通过减少阶模型参数进行良好的参数，而不会引起非物理行为，例如差异界限附近的差异和虚假的源/下沉点。这将利用数据科学的力量来表征低维歧管的几何形状，并使用该信息来改善派生模型的行为。提取低维模型是具有挑战性的，因为它需要识别中等维的形状，在该形状中，网格和网格划分技术在维度上呈指数尺度会失败。相反，这项工作将受到诸如边界曲率和矢量场加速度之类的属性的限制，这些属性对大多数物理定义的系统得到了很好的控制。此外，尽管训练数据中有噪音，否则这些模型将以可靠的方式与简单的物理模型一致，否则可能会导致产生的矢量领域中的虚假关键鞍点。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的，这是值得的。

项目成果

Manifold-informed state vector subset for reduced-order modeling

用于降阶建模的流形通知状态向量子集

• DOI: 10.1016/j.proci.2022.06.019
• 发表时间: 2022
• 期刊: Proceedings of the Combustion Institute
• 影响因子: 3.4
• 作者: Zdybał, Kamila;Sutherland, James C.;Parente, Alessandro
• 通讯作者: Parente, Alessandro

Batch Multi-Fidelity Active Learning with Budget Constraints

具有预算约束的批量多保真主动学习

• 发表时间: 2022
• 期刊: Advances in neural information processing systems
• 作者: Shibo Li, Jeff Phillips

Local manifold learning and its link to domain-based physics knowledge

局部流形学习及其与基于领域的物理知识的联系

• DOI: 10.1016/j.jaecs.2023.100131
• 发表时间: 2023
• 期刊: Applications in Energy and Combustion Science
• 作者: Zdybał, Kamila;D’Alessio, Giuseppe;Attili, Antonio;Coussement, Axel;Sutherland, James C.;Parente, Alessandro
• 通讯作者: Parente, Alessandro

A technique for characterising feature size and quality of manifolds

• DOI: 10.1080/13647830.2021.1931715
• 发表时间: 2021-06-03
• 期刊: COMBUSTION THEORY AND MODELLING
• 影响因子: 1.3
• 作者: Armstrong, Elizabeth;Sutherland, James C.
• 通讯作者: Sutherland, James C.

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

PCAfold 2.0——用于低维流形评估和优化的新颖工具和算法

• DOI: 10.1016/j.softx.2023.101447
• 发表时间: 2023
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Zdybał, Kamila;Armstrong, Elizabeth;Parente, Alessandro;Sutherland, James C.
• 通讯作者: Sutherland, James C.