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

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.
计算模型增加了昂贵的物理实验,并在许多现代科学和工程领域发挥着重要作用,如汽车和航空航天工业,数值天气预报,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,为汽车或飞机设计最佳形状需要使用复杂的计算模型进行大量模拟。 这个项目是关于降阶模型(ROM),它是比传统模型复杂得多的替代计算模型,但可能会受到较低的保真度。 拟议的研究利用数据同化(DA)框架内的观测数据,并融合观测和数值数据,开发一种新的强大的DA-ROM framework.Accuracy是一个根本的障碍,阻止当前ROM被广泛用于大规模的流体流动在工业过程中,不确定性量化,海洋建模。 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闭合建模)对于ROM准确性至关重要。 此外,在开发准确的只读存储器时,还需要吸收现有的物理观测数据,例如从底层物理系统的测量数据。然而,这种见解在今天的ROM中是不可用的,ROM完全使用数字数据构建。拟议的DA-ROM框架利用国家的最先进的DA算法和观测和数值数据,采取了重大飞跃的ROM模拟现实的流体流动。构造两种不同类型的精确ROM闭合模型:(a)结构ROM闭合模型,其中从数据发现模型的整个结构;以及(B)近似去卷积ROM闭合模型,其中来自图像处理的想法用于构建ROM,并且其中DA用于推断参数。 此外,观测和数值数据的信息融合,以构建新的ROM closure models.This奖项反映了NSF的法定使命,并已被认为是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的支持。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates
  • DOI:
    10.1137/20m1349965
  • 发表时间:
    2020-07
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Popov;Changhong Mou;T. Iliescu;Adrian Sandu
  • 通讯作者:
    A. Popov;Changhong Mou;T. Iliescu;Adrian Sandu
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
  • DOI:
    10.16993/tellusa.214
  • 发表时间:
    2020-02
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Popov;Adrian Sandu;E. Niño;G. Evensen
  • 通讯作者:
    A. Popov;Adrian Sandu;E. Niño;G. Evensen
A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter
  • DOI:
    10.5194/npg-29-241-2022
  • 发表时间:
    2021-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Popov;Amit N. Subrahmanya;Adrian Sandu
  • 通讯作者:
    A. Popov;Amit N. Subrahmanya;Adrian Sandu
Machine learning based algorithms for uncertainty quantification in numerical weather prediction models
  • DOI:
    10.1016/j.jocs.2020.101295
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Moosavi;Vishwas Rao;Adrian Sandu
  • 通讯作者:
    A. Moosavi;Vishwas Rao;Adrian Sandu
An energy-based lengthscale for reduced order models of turbulent flows
基于能量的湍流降阶模型的长度尺度
  • DOI:
    10.1016/j.nucengdes.2023.112454
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Mou, Changhong;Merzari, Elia;San, Omer;Iliescu, Traian
  • 通讯作者:
    Iliescu, Traian
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Adrian Sandu其他文献

Computing Sensitivity Analysis of Vehicle Dynamics Based on Multibody Models
基于多体模型的车辆动力学计算灵敏度分析
  • DOI:
    10.1115/detc2013-13212
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Yitao Zhu;D. Dopico;C. Sandu;Adrian Sandu
  • 通讯作者:
    Adrian Sandu
Chemical Data Assimilation with CMAQ: Continuous vs. Discrete Advection Adjoints
使用 CMAQ 进行化学数据同化:连续与离散平流伴随词
  • DOI:
    10.1007/978-3-642-01973-9_35
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tianyi Gou;Kumaresh Singh;Adrian Sandu
  • 通讯作者:
    Adrian Sandu
Alternating Directions Implicit Integration in a General Linear Method Framework
通用线性方法框架中的交替方向隐式积分
  • DOI:
    10.1016/j.cam.2019.112619
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Sarshar;Adrian Sandu
  • 通讯作者:
    Adrian Sandu
Multirate generalized additive Runge Kutta methods
多速率广义加性龙格库塔方法
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    M. Günther;Adrian Sandu
  • 通讯作者:
    Adrian Sandu
Discrete adjoint variable method for the sensitivity analysis of ALI3-P formulations
ALI3-P 制剂敏感性分析的离散伴随变量法
  • DOI:
    10.1007/s11044-023-09911-x
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Álvaro López Varela;C. Sandu;Adrian Sandu;Daniel Dopico Dopico
  • 通讯作者:
    Daniel Dopico Dopico

Adrian Sandu的其他文献

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{{ truncateString('Adrian Sandu', 18)}}的其他基金

CDS&E: Space-Time Parallel Algorithms for Solving PDE-Constrained Optimization Problems
CDS
  • 批准号:
    1709727
  • 财政年份:
    2017
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
AF: Small: General Linear Multimethods for the Time Integration of Multiscale Multiphysics Problems
AF:小:多尺度多物理问题时间积分的通用线性多方法
  • 批准号:
    1613905
  • 财政年份:
    2016
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Collaborative Research: Construction, Analysis, Implementation and Application of New Efficient Exponential Integrators
合作研究:新型高效指数积分器的构建、分析、实现和应用
  • 批准号:
    1419003
  • 财政年份:
    2014
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
A Fully Discrete Framework for the Adaptive Solution of Inverse Problems
逆问题自适应求解的完全离散框架
  • 批准号:
    1218454
  • 财政年份:
    2012
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Collaborative Research: A multiscale unified simulation environment for geoscientific applications
协作研究:地球科学应用的多尺度统一模拟环境
  • 批准号:
    0904397
  • 财政年份:
    2009
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Collaborative Research: A Computational Framework for Assessing the Observation Impact in Air Quality Forecasting
合作研究:评估空气质量预测观测影响的计算框架
  • 批准号:
    0915047
  • 财政年份:
    2009
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
CIF:Small: General Linear Time-stepping Methods for Large-Scale Simulations
CIF:Small:用于大规模仿真的通用线性时间步进方法
  • 批准号:
    0916493
  • 财政年份:
    2009
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Solution of Inverse Problems with Adaptive Models
自适应模型反问题的求解
  • 批准号:
    0635194
  • 财政年份:
    2006
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Multirate Time Integration Algorithms for Adaptive Simulations of PDEs
用于偏微分方程自适应模拟的多速率时间积分算法
  • 批准号:
    0515170
  • 财政年份:
    2005
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant
CAREER: Development of Computational Methods for the New Generation of Air Quality Models
职业:新一代空气质量模型计算方法的开发
  • 批准号:
    0413872
  • 财政年份:
    2003
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant

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2C型蛋白磷酸酶REDUCED DORMANCY 5通过激酶-磷酸酶蛋白复合体调控种子休眠的分子机制
  • 批准号:
    32000250
  • 批准年份:
    2020
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    24.0 万元
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CAREER: Multiscale Reduced Order Modeling and Design to Elucidate the Microstructure-Property-Performance Relationship of Hybrid Composite Materials
职业:通过多尺度降阶建模和设计来阐明混合复合材料的微观结构-性能-性能关系
  • 批准号:
    2341000
  • 财政年份:
    2024
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    $ 20万
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CAREER: Physics-Infused Reduced-Order Modeling for Control Co-Design of Morphing Aerial Autonomous Systems
职业:用于变形空中自主系统控制协同设计的物理降阶建模
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    2340266
  • 财政年份:
    2024
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Collaborative Research: Data-Driven Variational Multiscale Reduced Order Models for Biomedical and Engineering Applications
协作研究:用于生物医学和工程应用的数据驱动的变分多尺度降阶模型
  • 批准号:
    2345048
  • 财政年份:
    2023
  • 资助金额:
    $ 20万
  • 项目类别:
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Tensorial Reduced Order Models: Development, Analysis, and Applications
张量降阶模型:开发、分析和应用
  • 批准号:
    2309197
  • 财政年份:
    2023
  • 资助金额:
    $ 20万
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Numerical Simulation of Hypersonic Turbulent Flow by Spatiotemporal Multi-Scale Reduced Order Model
时空多尺度降阶模型高超声速湍流数值模拟
  • 批准号:
    23KJ0127
  • 财政年份:
    2023
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    $ 20万
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Development of cluster-based reduced-order model for optimal feedback control of dynamic stall flow
开发基于集群的动态失速流最优反馈控制降阶模型
  • 批准号:
    22KJ0183
  • 财政年份:
    2023
  • 资助金额:
    $ 20万
  • 项目类别:
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Granular Bioink Made-To-Order from Macroporous Collagen Particles Offers Excellent Printability, Shape Fidelity, and Reduced Tissue Contraction
由大孔胶原颗粒定制的颗粒生物墨水具有出色的印刷适性、形状保真度和减少的组织收缩
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Collaborative Research: Understanding Urban Resilience to Pluvial Floods Using Reduced-Order Modeling
合作研究:使用降阶模型了解城市对洪涝灾害的抵御能力
  • 批准号:
    2053358
  • 财政年份:
    2022
  • 资助金额:
    $ 20万
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    Standard Grant
CAREER: Goal-Oriented Variable Transformations for Efficient Reduced-Order and Data-Driven Modeling
职业:面向目标的变量转换,用于高效的降阶和数据驱动建模
  • 批准号:
    2144023
  • 财政年份:
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  • 资助金额:
    $ 20万
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Deep learning for reduced order modelling of wall bounded, turbulent flows
用于壁面湍流降阶建模的深度学习
  • 批准号:
    2753788
  • 财政年份:
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  • 资助金额:
    $ 20万
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