Combination of polynomial chaos and Kriging for reduced-order model of reacting flow applications
Tóm tắt
Từ khóa
Tài liệu tham khảo
Kabuba, 2020, Ion-exchange process for the removal of ni (ii) and co (ii) from wastewater using modified clinoptilolite: modeling by response surface methodology and artificial neural network, Results in Engineering, 8, 100189, 10.1016/j.rineng.2020.100189
Lang, 2011, Optimization of igcc processes with reduced order cfd models, Comput. Chem. Eng., 35, 1705, 10.1016/j.compchemeng.2011.01.018
Lang, 2009, Reduced order model based on principal component analysis for process simulation and optimization, Energy Fuels, 23, 1695, 10.1021/ef800984v
G. Aversano, A. Bellemans, Z. Li, A. Coussement, O. Gicquel, A. Parente, Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications, Comput. Chem. Eng. 121. doi:10.1016/j.compchemeng.2018.09.022.
G. Aversano, M. Ferrarotti, A. Parente, Digital twin of a combustion furnace operating in flameless conditions: reduced-order model development from CFD simulations, Proc. Combust. Inst.:10.1016/j.proci.2020.06.045.
Xiao, 2013, Constrained Proper Orthogonal Decomposition based on QR-factorization for aerodynamical shape optimization, Appl. Math. Comput., 223, 254, 10.1016/j.amc.2013.07.086
R. Schöbi, B. Sudret, J. Wiart, HAL Id : hal-01432195.
Schöbi, 2014, 1
Boukouvala, 2013, Reduced-order discrete element method modeling, Chem. Eng. Sci., 95, 12, 10.1016/j.ces.2013.01.053
Inazumi, 2020, Artificial intelligence system for supporting soil classification, Results in Engineering, 8, 100188, 10.1016/j.rineng.2020.100188
Klotz, 2021, Prediction of the business jet global 7500 wing deformed shape using fiber bragg gratings and neural network, Results in Engineering, 9, 100190, 10.1016/j.rineng.2020.100190
Cawley, 2010, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079
A. Parente, J. C. Sutherland, Principal component analysis of turbulent combustion data: data pre-processing and manifold sensitivity, Combust. Flame:10.1016/j.combustflame.2012.09.016.
Gillis, 2012, Sparse and unique nonnegative matrix factorization through data preprocessing, J. Mach. Learn. Res., 13, 3349
Frouzakis, 2000, Proper orthogonal decomposition of direct numerical simulation data: data reduction and observer, Construction, 28, 75
Paatero, 1994, Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, 5, 111, 10.1002/env.3170050203
Lebrun, 2009, An innovating analysis of the Nataf transformation from the copula viewpoint, Probabilist. Eng. Mech., 24, 312, 10.1016/j.probengmech.2008.08.001
Constantine, 2014, Active subspace methods in theory and practice: applications to kriging surfaces, SIAM J. Sci. Comput., 36, A1500, 10.1137/130916138
Lophaven, 2002, 1
Seeger, 2004, vol. 14
Goodfellow, 2016
Bishop, 2013, vol. 53
Gholamrezaei, 2007, Rotated general regression neural network, IEEE International Conference on Neural Networks - Conference Proceedings, 2, 1959
Cao, 2018, Effects of pressure and fuel dilution on coflow laminar methane-air diffusion flames: a computational and experimental study, Combust. Theor. Model., 22, 316, 10.1080/13647830.2017.1403051