Efficient Ensemble Methods for Predictive Fluid Flow Simulations Subject to Uncertainty

用于预测不确定性流体流动模拟的有效集成方法

基本信息

  • 批准号:
    1720001
  • 负责人:
  • 金额:
    $ 14.99万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-07-01 至 2021-02-28
  • 项目状态:
    已结题

项目摘要

Uncertainty quantification is a central topic in predictive science, where model predictions with quantified uncertainties are critical for understanding and predicting scientific phenomena and making informed decisions based upon these predictions. The applications include energy (nuclear, wind, solar, etc.) generation, control and manufacturing, atmosphere-ocean modeling, weather prediction, surface water and ground water contamination, and so on. For all of these applications, the model problem is subject to numerous sources of uncertainty that include uncertain model parameters, forcing functions, initial conditions, and boundary conditions. For instance, in numerical weather prediction, to deal with uncertain initial conditions the weather model needs to be run multiple times with different initial conditions to generate an ensemble of possible model outputs, which will be analyzed and predictions made according to these data. This process is called ensemble forecasting, which is commonly done at all major operational weather prediction facilities worldwide, including the U.S. National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts (ECMWF). One common problem faced in these calculations is the excessive cost in terms of both storage and computing time. For many complex systems, especially those that deal with large spatial scales, running the model once is already very expensive. Running the model multiple times within a given limited computational time is very challenging even with modern supercomputers, and is not feasible in most large-scale applications. An efficient ensemble simulation algorithm that can reduce the computing cost significantly is thus highly desirable. This project seeks to develop novel, efficient ensemble algorithms and their analytical foundation for fast calculation of flow ensembles that is required to account for uncertainties in predictive simulations of fluid flows.The inevitable conflict of high-resolution single realizations and computing ensembles is a central difficulty in many engineering and geophysical applications that are subject to uncertainties in both input data and model parameters. The development of efficient methods that allow for fast calculation of flow ensembles at a sufficiently fine spatial resolution is of great practical interest. This research is to develop novel, efficient ensemble algorithms for fast calculation of flow ensembles and conduct rigorous numerical analysis for the new algorithms and methods. The first research problem is to develop new efficient ensemble algorithms to compute multiple realizations for the Boussinesq equations. This includes the development of partitioned ensemble algorithms so that highly optimized Navier-Stokes-equation codes can be used to solve the problem. The second is to advance higher-order time discretizations for ensemble algorithms based on artificial compression. The third problem is the development of novel, efficient ensemble algorithms for the fast calculation of flow ensembles with varying model parameters. The methods studied will allow efficient determination of the multiple solutions corresponding to many parameter sets.
不确定性量化是预测科学中的一个中心话题,其中具有量化不确定性的模型预测对于理解和预测科学现象以及基于这些预测做出明智的决策至关重要。应用包括能源(核能、风能、太阳能等)对于所有这些应用,模型问题受到许多不确定性来源的影响,包括不确定的模型参数、强迫函数、初始条件和边界条件。例如,在数值天气预报中,为了处理不确定的初始条件,天气模型需要在不同的初始条件下运行多次,以生成可能的模型输出的集合,这些输出将根据这些数据进行分析和预测。这一过程被称为集合预报,通常在全球所有主要的天气预报机构进行,包括美国国家环境预报中心和欧洲中期天气预报中心(ECMWF)。这些计算中面临的一个常见问题是存储和计算时间方面的成本过高。对于许多复杂的系统,特别是那些处理大空间尺度的系统,运行一次模型已经非常昂贵了。在给定的有限计算时间内多次运行模型是非常具有挑战性的,即使是现代超级计算机,在大多数大规模应用中也是不可行的。因此,非常需要一种能够显著降低计算成本的高效集成仿真算法。该项目旨在开发新的,高效的集成算法和他们的分析基础,快速计算的流系综,需要考虑的不确定性预测模拟的流体flow.The高分辨率的单一实现和计算系综的不可避免的冲突是一个中心的困难,在许多工程和地球物理应用,是受输入数据和模型参数的不确定性。发展有效的方法,允许快速计算流系综在一个足够好的空间分辨率是非常实际的利益。本研究的目的是发展新的、高效的流系综快速计算算法,并对新的算法和方法进行严格的数值分析。第一个研究问题是开发新的有效的集成算法来计算Boussinesq方程的多个实现。这包括分区集成算法的发展,使高度优化的Navier-Stokes方程代码可以用来解决这个问题。二是提出基于人工压缩的集成算法的高阶时间离散化。第三个问题是开发新的,高效的集成算法的快速计算的流系综与不同的模型参数。所研究的方法将允许有效地确定对应于许多参数集的多个解决方案。

项目成果

期刊论文数量(19)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Stabilized scalar auxiliary variable ensemble algorithms for parameterized flow problems
参数化流问题的稳定标量辅助变量集成算法
Second Order, Unconditionally Stable, Linear Ensemble Algorithms for the Magnetohydrodynamics Equations
  • DOI:
    10.1007/s10915-022-02091-4
  • 发表时间:
    2022-09
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    J. Carter;Daozhi Han;N. Jiang
  • 通讯作者:
    J. Carter;Daozhi Han;N. Jiang
A Pressure-Correction Ensemble Scheme for Computing Evolutionary Boussinesq Equations
Unconditionally stable, second order, decoupled ensemble schemes for computing evolutionary Boussinesq equations
  • DOI:
    10.1016/j.apnum.2023.06.011
  • 发表时间:
    2023-10
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    N. Jiang;Huanhuan Yang
  • 通讯作者:
    N. Jiang;Huanhuan Yang
Numerical investigation of two second-order, stabilized SAV ensemble methods for the Navier–Stokes equations
  • DOI:
    10.1007/s10444-022-09977-9
  • 发表时间:
    2022-10
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    N. Jiang;Huanhuan Yang
  • 通讯作者:
    N. Jiang;Huanhuan Yang
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Nan Jiang其他文献

Multi-objective optimization of an innovative power-cooling integrated system based on gas turbine cycle with compressor inlet air precooling, Kalina cycle and ejector refrigeration cycle
基于压气机进风预冷、卡林纳循环和喷射制冷循环的燃气轮机循环创新电冷集成系统多目标优化
  • DOI:
    10.1016/j.enconman.2021.114473
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yang Du;Nan Jiang;Yicen Zhang;Xu Wang;Pan Zhao;Jiangfeng Wang;Yiping Dai
  • 通讯作者:
    Yiping Dai
Optimization investigation on geometrical parameters of a multistage asymmetric fin-type DBD reactor for improved degradation of toluene
多级非对称翅片式 DBD 反应器几何参数优化研究以改善甲苯降解
Semantics-Aware Remote Estimation via Information Bottleneck-Inspired Type Based Multiple Access
通过信息瓶颈启发的基于类型的多路访问进行语义感知远程估计
  • DOI:
    10.48550/arxiv.2212.09337
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Meiyi Zhu;Chunyan Feng;Caili Guo;Zhe Liu;Nan Jiang;O. Simeone
  • 通讯作者:
    O. Simeone
A shaking table real-time substructure experiment of an equipment–structure–soil interaction system
设备—结构—土壤相互作用系统振动台实时子结构实验
  • DOI:
    10.1177/1687814017724090
  • 发表时间:
    2017-10
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    Chongxiang Zhang;Nan Jiang
  • 通讯作者:
    Nan Jiang
Hierarchical Automatic Curriculum Learning: Converting a Sparse Reward Navigation Task into Dense Reward
分层自动课程学习:将稀疏奖励导航任务转化为密集奖励
  • DOI:
    10.1016/j.neucom.2019.06.024
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    6
  • 作者:
    Nan Jiang;Sheng Jin;Changshui Zhang
  • 通讯作者:
    Changshui Zhang

Nan Jiang的其他文献

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{{ truncateString('Nan Jiang', 18)}}的其他基金

CAREER: New Algorithms and Models for Turbulence in Incompressible Fluids
职业:不可压缩流体湍流的新算法和模型
  • 批准号:
    2143331
  • 财政年份:
    2022
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Continuing Grant
CAREER: Theoretical Foundations of Offline Reinforcement Learning
职业:离线强化学习的理论基础
  • 批准号:
    2141781
  • 财政年份:
    2022
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Continuing Grant
Probing Local Structural and Chemical Properties of Atomically Thin Two-Dimensional Materials by Optical Scanning Tunneling Microscopy
通过光学扫描隧道显微镜探测原子薄二维材料的局部结构和化学性质
  • 批准号:
    2211474
  • 财政年份:
    2022
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Continuing Grant
Efficient Ensemble Methods for Predictive Fluid Flow Simulations Subject to Uncertainty
用于预测不确定性流体流动模拟的有效集成方法
  • 批准号:
    2120413
  • 财政年份:
    2021
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Standard Grant
CAREER: Probing Chemistry of Surface-Supported Nanostructures at the Angstrom-Scale
职业:埃级表面支撑纳米结构的化学探索
  • 批准号:
    1944796
  • 财政年份:
    2020
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Continuing Grant
Collaborative Research: Integrated Experimental and Computational Studies for Understanding the Interplay of Photoreactive Materials and Persistent Contaminants
合作研究:用于了解光反应材料和持久性污染物相互作用的综合实验和计算研究
  • 批准号:
    1807465
  • 财政年份:
    2018
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Standard Grant
Time-Resolved EELS of Photonic Crystals and Glasses
光子晶体和玻璃的时间分辨 EELS
  • 批准号:
    0603993
  • 财政年份:
    2006
  • 资助金额:
    $ 14.99万
  • 项目类别:
    Continuing Grant

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职业:反问题和数据同化中的集成卡尔曼方法和贝叶斯优化
  • 批准号:
    2237628
  • 财政年份:
    2023
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  • 批准号:
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低维和高维数据的惩罚、非惩罚收缩和集成方法
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  • 财政年份:
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大规模应用中非线性、非高斯和数据驱动的集合数据同化方法
  • 批准号:
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使用集成方法改进睡眠阶段预测
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  • 财政年份:
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低维和高维数据的惩罚、非惩罚收缩和集成方法
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