{"title":"低阶矩阵流形上湍流反应流的降阶建模","authors":"Aidyn Aitzhan , Arash G. Nouri , Peyman Givi , Hessam Babaee","doi":"10.1016/j.jcp.2024.113549","DOIUrl":null,"url":null,"abstract":"<div><div>A new low-rank approximation, referred to as time-dependent principal component analysis (t-PCA), is developed for reduced-order modeling (ROM) of scalar transport in turbulent reactive flows. In t-PCA, the evolution of the composition matrix is constrained to a low-rank matrix manifold, similar to that in standard PCA. Specifically, the t-PCA approximates the composition matrix through the multiplication of two thin, time-dependent matrices that represent spatial and composition subspaces. The evolution equations for these subspaces are derived by projecting the composition transport equation onto the tangent space of the low-rank matrix manifold. While the evolution equations for the spatial subspace in both PCA and t-PCA are similar, there are differences in how the composition subspace is obtained: (i) In t-PCA, the composition subspace is time-dependent, whereas in PCA, it is static. (ii) The t-PCA does not require any prior data, and an evolution equation for the composition subspace is derived. In PCA, the composition subspace is obtained from data. The t-PCA can be regarded as an on-the-fly low-rank approximation that can adapt to changes in the flow instantaneously. It is shown that the low-rank t-PCA approximations achieve residual levels lower than those obtained via PCA. For demonstrations and a comparative assessment of the ROMs, simulations are conducted of a non-premixed CO/H<sub>2</sub> flame in a temporally evolving jet. Two cases are considered, based on the mechanisms previously suggested for combustion kinetics of this flame: (i) the GRI-Mech 3.0 model involving 53 species for a two-dimensional flame, (ii) the skeletal syngas model involving 11 species for a three-dimensional turbulent flame. The results are appraised via <em>a posteriori</em> comparisons against data generated via full-rank direct numerical simulation (DNS) of the same flame, and also with the PCA-reduced data from the DNS. It is shown that t-PCA yields excellent predictions of various features of the thermo-chemistry field.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"521 ","pages":"Article 113549"},"PeriodicalIF":3.8000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reduced order modeling of turbulent reacting flows on low-rank matrix manifolds\",\"authors\":\"Aidyn Aitzhan , Arash G. Nouri , Peyman Givi , Hessam Babaee\",\"doi\":\"10.1016/j.jcp.2024.113549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>A new low-rank approximation, referred to as time-dependent principal component analysis (t-PCA), is developed for reduced-order modeling (ROM) of scalar transport in turbulent reactive flows. In t-PCA, the evolution of the composition matrix is constrained to a low-rank matrix manifold, similar to that in standard PCA. Specifically, the t-PCA approximates the composition matrix through the multiplication of two thin, time-dependent matrices that represent spatial and composition subspaces. The evolution equations for these subspaces are derived by projecting the composition transport equation onto the tangent space of the low-rank matrix manifold. While the evolution equations for the spatial subspace in both PCA and t-PCA are similar, there are differences in how the composition subspace is obtained: (i) In t-PCA, the composition subspace is time-dependent, whereas in PCA, it is static. (ii) The t-PCA does not require any prior data, and an evolution equation for the composition subspace is derived. In PCA, the composition subspace is obtained from data. The t-PCA can be regarded as an on-the-fly low-rank approximation that can adapt to changes in the flow instantaneously. It is shown that the low-rank t-PCA approximations achieve residual levels lower than those obtained via PCA. For demonstrations and a comparative assessment of the ROMs, simulations are conducted of a non-premixed CO/H<sub>2</sub> flame in a temporally evolving jet. Two cases are considered, based on the mechanisms previously suggested for combustion kinetics of this flame: (i) the GRI-Mech 3.0 model involving 53 species for a two-dimensional flame, (ii) the skeletal syngas model involving 11 species for a three-dimensional turbulent flame. The results are appraised via <em>a posteriori</em> comparisons against data generated via full-rank direct numerical simulation (DNS) of the same flame, and also with the PCA-reduced data from the DNS. It is shown that t-PCA yields excellent predictions of various features of the thermo-chemistry field.</div></div>\",\"PeriodicalId\":352,\"journal\":{\"name\":\"Journal of Computational Physics\",\"volume\":\"521 \",\"pages\":\"Article 113549\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0021999124007976\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Physics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0021999124007976","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Reduced order modeling of turbulent reacting flows on low-rank matrix manifolds
A new low-rank approximation, referred to as time-dependent principal component analysis (t-PCA), is developed for reduced-order modeling (ROM) of scalar transport in turbulent reactive flows. In t-PCA, the evolution of the composition matrix is constrained to a low-rank matrix manifold, similar to that in standard PCA. Specifically, the t-PCA approximates the composition matrix through the multiplication of two thin, time-dependent matrices that represent spatial and composition subspaces. The evolution equations for these subspaces are derived by projecting the composition transport equation onto the tangent space of the low-rank matrix manifold. While the evolution equations for the spatial subspace in both PCA and t-PCA are similar, there are differences in how the composition subspace is obtained: (i) In t-PCA, the composition subspace is time-dependent, whereas in PCA, it is static. (ii) The t-PCA does not require any prior data, and an evolution equation for the composition subspace is derived. In PCA, the composition subspace is obtained from data. The t-PCA can be regarded as an on-the-fly low-rank approximation that can adapt to changes in the flow instantaneously. It is shown that the low-rank t-PCA approximations achieve residual levels lower than those obtained via PCA. For demonstrations and a comparative assessment of the ROMs, simulations are conducted of a non-premixed CO/H2 flame in a temporally evolving jet. Two cases are considered, based on the mechanisms previously suggested for combustion kinetics of this flame: (i) the GRI-Mech 3.0 model involving 53 species for a two-dimensional flame, (ii) the skeletal syngas model involving 11 species for a three-dimensional turbulent flame. The results are appraised via a posteriori comparisons against data generated via full-rank direct numerical simulation (DNS) of the same flame, and also with the PCA-reduced data from the DNS. It is shown that t-PCA yields excellent predictions of various features of the thermo-chemistry field.
期刊介绍:
Journal of Computational Physics thoroughly treats the computational aspects of physical problems, presenting techniques for the numerical solution of mathematical equations arising in all areas of physics. The journal seeks to emphasize methods that cross disciplinary boundaries.
The Journal of Computational Physics also publishes short notes of 4 pages or less (including figures, tables, and references but excluding title pages). Letters to the Editor commenting on articles already published in this Journal will also be considered. Neither notes nor letters should have an abstract.