Parameter Estimation Using a Gaussian Process Regression-Based Reduced-Order Model and Sparse Sensing: Application to a Methane/Air Lifted Jet Flame

被引:0
作者
Alberto Procacci
Laura Donato
Ruggero Amaduzzi
Chiara Galletti
Axel Coussement
Alessandro Parente
机构
[1] Université Libre de Bruxelles,Aero
[2] Université Libre de Bruxelles and Vrije Universiteit Brussel,Thermo
[3] University of Pisa,Mechanics Laboratory
来源
Flow, Turbulence and Combustion | 2024年 / 112卷
关键词
Parameter estimation; Machine-learning; PaSR; Gaussian process regression; Sparse sensing; Data assimilation;
D O I
暂无
中图分类号
学科分类号
摘要
The goal of this work is to perform parameter estimation by comparing a Reduced Order Model (ROM), built using Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR), with a Sparse Sensing (SpS) model. This framework is demonstrated by selecting the optimal set of the Partially Stirred Reactor (PaSR) coefficients used in the modelling of the Cabra flame. The Cabra flame is a methane flame in a vitiated coflow, consisting of the combustion products of hydrogen and air. The PaSR model necessitates the knowledge of 4 scalar coefficients, which are unknown a priori. To select the optimal set of coefficients, 57 simulations were performed with a different combination of PaSR coefficients. These simulations were used to build the ROM via POD and GPR. To compare the numerical solution with the experimental data, the SpS technique has been employed. SpS is a framework that leverages dimensionality reduction to predict the state of the system given few measurements. The optimal coefficients have been estimated by applying an optimization algorithm to the ROM, using the solution provided by SpS as target. Finally, the data assimilation framework has been used to provide a solution with lower uncertainty bounds. The results show that this framework is able to estimate the optimal set of coefficients, and it can be used to identify residual sources of uncertainty in the numerical model by highlighting the difference between the optimized model and the experimental values.
引用
收藏
页码:879 / 895
页数:16
相关论文
共 51 条
[1]  
Amaduzzi R(2022)Impact of scalar mixing uncertainty on the predictions of reactor-based closures: application to a lifted methane/air jet flame Proc. Combust. Inst. 121 422-441
[2]  
Bertolino A(2019)Application of reduced-order models based on pca & kriging for the development of digital twins of reacting flow applications Comput. Chem. Eng. 37 4461-4469
[3]  
Özden A(2019)Pca and kriging for the efficient exploration of consistency regions in uncertainty quantification Proc. Combust. Inst. 38 5373-5381
[4]  
Aversano G(2021)Digital twin of a combustion furnace operating in flameless conditions: reduced-order model development from cfd simulations Proc. Combust. Inst. 143 491-506
[5]  
Bellemans A(2005)Lifted methane-air jet flames in a vitiated coflow Combust. Flame 52 5406-5425
[6]  
Li Z(2006)Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52 5177-5196
[7]  
Aversano G(2006)The distributed karhunen-loève transform IEEE Trans. Inf. Theory 31 1559-1566
[8]  
Parra-Alvarez JC(2007)Transported pdf modelling with detailed chemistry of pre- and auto-ignition in ch4/air mixtures Proc. Combust. Inst. 151 495-511
[9]  
Isaac BJ(2007)Transport budgets in turbulent lifted flames of methane autoigniting in a vitiated co-flow Combust. Flame 82 35-45
[10]  
Aversano G(1960)A new approach to linear filtering and prediction problems J. Basic Eng. 38 63-86