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Transforming Reduced-Order Models of Fluids with Data Assimilation

通过数据同化转换流体降阶模型

基本信息

批准号：
1953113
负责人：
Adrian Sandu
金额：
$ 20万
依托单位：
Virginia Polytechnic Institute and State University
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：
2020-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953113&HistoricalAwards=false
关键词：
Transforming Reduced Order Models Fluids

项目摘要

Computational models augment expensive physical experiments and play a significant role in many modern science and engineering fields such as automotive and aerospace industries, numerical weather prediction, and ocean and environmental modeling. However, computational models often require large computational resources, which limits their use in many practical applications. For example, designing an optimal shape for an automobile or an airplane requires a large number of simulations with complex computational models. This project is on reduced order models (ROMs), which are surrogate computational models of much lower complexity than traditional models, but which may suffer from lower fidelity. The proposed research takes advantage of data from observations within a data assimilation (DA) framework and fuses both observational and numerical data to develop a novel robust DA-ROM framework.Accuracy is one of the fundamental barriers that prevent current ROMs from being widely used on a large scale for fluid flows in industrial processes, uncertainty quantification, and ocean modeling. Modeling the interplay between the few resolved ROM modes and the many unresolved ROM modes (i.e., the ROM closure modeling) is critical for ROM accuracy. Furthermore, assimilating available physical observations, for example, data from measurements of the underlying physical system, is also needed in developing accurate ROMs. However, this insight is not available in today’s ROMs, which are constructed using exclusively numerical data. The proposed DA-ROM framework utilizes state-of-the-art DA algorithms and observational and numerical data to take a major leap toward the ROM simulation of realistic fluid flows. Accurate ROM closure models of two different types are constructed: (a) structural ROM closure models, in which the entire structure of the model is discovered from data; and (b) approximate deconvolution ROM closure models, in which ideas from image processing are used to build the ROM, and where the DA is used to infer the parameters. Furthermore, information from both observational and numerical data is fused in order to construct novel ROM closure models.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.

计算模型增强了昂贵的物理实验，并在许多现代科学和工程领域发挥着重要作用，例如汽车和航空航天工业、数值天气预报以及海洋和环境建模。然而，计算模型通常需要大量的计算资源，这限制了它们在许多实际应用中的使用。例如，设计汽车或飞机的最佳形状需要使用复杂的计算模型进行大量模拟。该项目基于降阶模型（ROM），它是比传统模型复杂性低得多的替代计算模型，但保真度可能较低。所提出的研究利用数据同化（DA）框架内的观测数据，并融合观测数据和数值数据，开发出一种新颖的稳健的 DA-ROM 框架。准确性是阻止当前 ROM 大规模广泛用于工业过程中的流体流动、不确定性量化和海洋建模的基本障碍之一。对少数已解析 ROM 模式和许多未解析 ROM 模式之间的相互作用进行建模（即 ROM 闭包建模）对于 ROM 准确性至关重要。此外，在开发精确的 ROM 时，还需要同化可用的物理观测结果，例如来自底层物理系统测量的数据。然而，这种洞察力在当今的 ROM 中不可用，因为这些 ROM 完全使用数字数据构建。所提出的 DA-ROM 框架利用最先进的 DA 算法以及观测和数值数据，在现实流体流动的 ROM 模拟方面取得了重大飞跃。构建了两种不同类型的精确ROM闭包模型：（a）结构ROM闭包模型，其中模型的整个结构是从数据中发现的； (b) 近似反卷积 ROM 闭包模型，其中使用图像处理的想法来构建 ROM，并使用 DA 来推断参数。此外，来自观测数据和数值数据的信息被融合，以构建新颖的 ROM 闭合模型。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。