Titlebook: Data Analysis for Direct Numerical Simulations of Turbulent Combustion; From Equation-Based Heinz Pitsch,Antonio Attili Book 2020 Springer

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https://doi.org/10.1007/978-94-011-1510-0bulent combustion DNS data, being inherently multiscale and multivariate, pose many challenges and higher order tensors are a natural abstraction to organise, probe and analyse them. The chapter gives a high-level overview of prominent tensor decomposition methods, their interpretation, algorithmic

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A. R. Leitch,J. S. Heslop-Harrisongonal Decomposition (POD) produces optimal representations of the dynamics in the sense of the energy norm. Alternatively, Dynamic Mode Decomposition (DMD) efficiently extracts coherent dynamics based on eigendecompositions of linearized dynamics. An extension to the latter, the Higher Order Dynamic

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D. Hernandez-Verdun,P. Roussel,T. Gautier rates, allowing for the investigation of unsteady and dynamic phenomena unlike conventional statistical analyses. The decomposition can be applied for the analysis of data having a broad range of temporal and spatial scales since it identifies structures that characterize the physical phenomena ind

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https://doi.org/10.1007/978-3-0348-8484-6ng strategies appear which are based on a direct treatment of the now well resolved, but still not fully resolved scalar signals. Along this line, deconvolution or inverse filtering, either based on discrete or iterative operators, is first discussed. Recent results obtained from a direct numerical

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Bruce D. McKee,Chia-sin Hong,Siuk Yoonecessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling st