Adaptive Physics-informed Machine Learning Strategies for Turbulent Combustion Modeling

用于湍流燃烧建模的自适应物理学机器学习策略

• 批准号: 2201297
• 负责人: Ope Owoyele
• 金额: $ 27.05万
• 依托单位: Louisiana State University
• 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: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201297&HistoricalAwards=false
• 关键词: Adaptive; Physics; informed; Machine; Learning

The design and optimization of combustion devices is crucial in the mission to combat climate change and achieve national security goals. Simulations can play a role in this mission by enabling rapid virtual testing of combustors at various design configurations, so that promising designs can be selected for physical prototyping. However, turbulent combustion simulations involve solving for a large number of molecular species that are produced and consumed as part of the combustion process. Due to this, combustion simulations require the use of many computer processors for several hours on supercomputers or compute clusters. This limits the usefulness of promising computer models for practical design and optimization endeavors. This work contributes to the ongoing quest to develop reduced combustion models that decrease the simulation times and required computing resources, yet preserve accuracy.In response to the excessive computational costs of turbulent combustion, physics-based reduced-order models have been introduced. These models often solve chemistry in an “offline” phase, store the solution in a table, and then interpolate the table’s entries to retrieve the chemical state during the “online” phase. The use of these lookup tables, however, suffers from the curse of dimensionality, wherein the size of the table and the interpolation complexities increase exponentially with the number of control variables. As a result, these lookup tables are limited to situations that employ a few control variables, thus preventing their application to many practical combustion devices. This work aims to address this problem by developing machine learning strategies to efficiently learn combustion physics within physically derived low-dimensional manifolds. This will be achieved by introducing machine learning models that are adaptive, consistent with the underlying physical laws, and suitable for high-dimensional combustion state spaces. This work will enable simulations at levels of fidelity that are currently impossible to perform using traditional tabulation techniques, and therefore, will aid in the design and development of clean and efficient combustion technologies. Furthermore, the tools developed in this study will be vital to the broad area of scientific machine learning, which continues to have an increasingly important impact on the modeling of many physical systems.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.

燃烧装置的设计和优化在应对气候变化和实现国家安全目标的使命中至关重要。模拟可以在这一使命中发挥作用，它可以在各种设计构型下对燃烧室进行快速虚拟试验，从而可以选择有前途的设计进行物理原型制作。然而，湍流燃烧模拟涉及求解作为燃烧过程的一部分产生和消耗的大量分子种类。因此，燃烧模拟需要在超级计算机或计算集群上使用许多计算机处理器数小时。这限制了有前途的计算机模型的实用性，实际的设计和优化工作。这项工作有助于正在进行的探索，以开发减少燃烧模型，减少模拟时间和所需的计算资源，但保持accuracy.In响应湍流燃烧的计算成本过高，基于物理的降阶模型已被引入。这些模型通常在“离线”阶段解决化学问题，将解决方案存储在表格中，然后在“在线”阶段插入表格的条目以检索化学状态。然而，这些查找表的使用受到维数灾难的影响，其中表的大小和插值复杂性随着控制变量的数量呈指数增加。结果，这些查找表仅限于采用少数控制变量的情况，从而阻止了它们应用于许多实际燃烧装置。这项工作旨在通过开发机器学习策略来解决这个问题，以有效地学习物理推导的低维流形内的燃烧物理。这将通过引入机器学习模型来实现，这些模型是自适应的，与潜在的物理定律一致，并且适合于高维燃烧状态空间。这项工作将使模拟的保真度水平，目前不可能使用传统的制表技术，因此，将有助于设计和开发清洁和高效的燃烧技术。此外，本研究中开发的工具对科学机器学习的广泛领域至关重要，该领域继续对许多物理系统的建模产生越来越重要的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。