多查询场景下基于集合的数据同化的增广协方差估计

IF 1.9 4区 工程技术 Q4 ENERGY & FUELS Combustion Theory and Modelling Pub Date : 2022-08-01 DOI:10.1080/13647830.2022.2105259
Andrew F. Ilersich, K. Schau, J. Oefelein, A. Steinberg, M. Yano
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引用次数: 0

摘要

我们提出并评估了一种方法,以降低在多个查询场景中执行基于集合的数据同化(DA)的计算成本,即在具有相似底层动力学的系统上执行多个模拟的场景。DA的准确性取决于样本协方差的准确性,随着集成大小的增加而提高,但导致计算成本的相应增加。为了在保持准确协方差的同时减小集合大小,我们提出了一种数据驱动的方法,以基于从以前的模型评估中学习到的统计行为来增加协方差。我们使用一维模型问题和二维合成反应流问题来评估我们的增广方法。我们表明,在所有这些情况下,在保持精度的同时,系综大小和计算成本可以减少三到四倍。
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Augmenting covariance estimation for ensemble-based data assimilation in multiple-query scenarios
We present and assess a method to reduce the computational cost of performing ensemble-based data assimilation (DA) for reacting flows in multiple-query scenarios, i.e. scenarios where multiple simulations are performed on systems with similar underlying dynamics. The accuracy of the DA, which depends on the accuracy of the sample covariance, improves with the ensemble size, but results in a commensurate increase to computational cost. To reduce the ensemble size while maintaining accurate covariance, we propose a data-driven approach to augment the covariance based on the statistical behaviour learned from previous model evaluations. We assess our augmentation method using one-dimensional model problems and a two-dimensional synthetic reacting flow problem. We show in all these cases that ensemble size, and thus computational cost, may be reduced by a factor of three to four while maintaining accuracy.
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来源期刊
Combustion Theory and Modelling
Combustion Theory and Modelling 工程技术-工程:化工
CiteScore
3.00
自引率
7.70%
发文量
38
审稿时长
6 months
期刊介绍: Combustion Theory and Modelling is a leading international journal devoted to the application of mathematical modelling, numerical simulation and experimental techniques to the study of combustion. Articles can cover a wide range of topics, such as: premixed laminar flames, laminar diffusion flames, turbulent combustion, fires, chemical kinetics, pollutant formation, microgravity, materials synthesis, chemical vapour deposition, catalysis, droplet and spray combustion, detonation dynamics, thermal explosions, ignition, energetic materials and propellants, burners and engine combustion. A diverse spectrum of mathematical methods may also be used, including large scale numerical simulation, hybrid computational schemes, front tracking, adaptive mesh refinement, optimized parallel computation, asymptotic methods and singular perturbation techniques, bifurcation theory, optimization methods, dynamical systems theory, cellular automata and discrete methods and probabilistic and statistical methods. Experimental studies that employ intrusive or nonintrusive diagnostics and are published in the Journal should be closely related to theoretical issues, by highlighting fundamental theoretical questions or by providing a sound basis for comparison with theory.
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