Joint probability density function models for multiscalar turbulent mixing

TL;DR: A review of emerging computational approaches for this heterogeneous data-driven environment is provided in this article, where a case is made that new but unconventional opportunities for physics-based combustion modeling exist in this realm.

TL;DR: A recurrent neural network-based approach for learning displacement fields in an end-to-end manner is applied to this technique and achieves state-of-the-art accuracy and allows generalization to new data, eliminating the need for traditional handcrafted models.

TL;DR: In this paper, the joint sub-filter PDF of mixture fraction and progress variable is modeled using various ML algorithms and commonly used analytical models, including traditional ensemble methods (random forests), deep learning (deep neural network), and generative learning (conditional variational autoencoder (CVAE)).

TL;DR: Morinishi et al. as mentioned in this paper extended the high order conservative finite difference scheme to simulate variable density flows in complex geometries with cylindrical or cartesian non-uniform meshes.

TL;DR: In this article, a new approach to chemistry modelling for large-eddy simulation of turbulent reacting flows is developed, whereby all of the detailed chemical processes are mapped to a reduced system of tracking scalars.

TL;DR: The overall numerical scheme obtained is highly suitable for the simulation of reactive turbulent flows in realistic geometries, for it combines arbitrarily high order of accuracy, discrete conservation of mass, momentum, and energy with consistent boundary conditions.

TL;DR: Probability density function (PDF) methods have been widely used for modeling chemically reacting turbulent flows as discussed by the authors, where one models and solves an equation that governs the evolution of the one-point, one-time PDF for a set of variables that determines the local thermochemical and/or hydrodynamic state of a reacting system.

TL;DR: In this article, the evolution of scalar fields, of different initial integral length scales, in statistically stationary, homogeneous, isotropic turbulence is studied, and it is shown that the pdf of the scalar tends to a Gaussian self-similar state.