State space sensitivity to a prescribed probability density function shape in coal combustion systems: joint β-PDF versus clipped Gaussian PDF

Journal ArticleThe turbulent transport of three coal off-gas mixture fractions is coupled to a prescribed joint //-probability- density-function (//-PDF) mixing model. This physical transport and subgrid joint //-PDF mixing model is used to explore the incorporation of coal off-gas compositional disparities between the devolatilization and the char oxidation regime in detailed pulverized-coal combustion simulations. A simulation study of the University of Utah pulverized-coal research furnace is presented to evaluate the sensitivity of different mixing model assumptions. These simulation studies indicate that using a variable composition to characterize the process of coal combustion does not appreciably change the predicted gas-phase temperature field. Moreover, neglecting fluctuations in the char off-gas stream was found to change gas-phase temperature predictions by approximately 15%. State space variable sensitivity to the assumed shape of the PDF (clipped Gaussian vs. joint //) is presented. Simulation results indicate differences in temperature profiles of as much as 20% depending on the chosen shape of the PDF. Integration accuracy issues for the joint //-PDF arc presented and are found to be acceptable. A robust //-PDF function evaluation procedure is presented that accommodates arbitrarily high //-PDF distribution factors. This robust algorithm simply transforms the joint //-PDF function evaluation into a logarithmic form. The assumption that a joint PDF, as rigorously required within a prescribed subgrid mixing model, can be written as the product of N - 1 statistically independent probability density (unctions is quantified and shown to be less accurate

Validation and uncertainty quantification of Fuego simulations of calorimeter heating in a wind-driven hydrocarbon pool fire.

The objective of this work is to perform an uncertainty quantification (UQ) and model validation analysis of simulations of tests in the cross-wind test facility (XTF) at Sandia National Laboratories. In these tests, a calorimeter was subjected to a fire and the thermal response was measured via thermocouples. The UQ and validation analysis pertains to the experimental and predicted thermal response of the calorimeter. The calculations were performed using Sierra/Fuego/Syrinx/Calore, an Advanced Simulation and Computing (ASC) code capable of predicting object thermal response to a fire environment. Based on the validation results at eight diversely representative TC locations on the calorimeter the predicted calorimeter temperatures effectively bound the experimental temperatures. This post-validates Sandia's first integrated use of fire modeling with thermal response modeling and associated uncertainty estimates in an abnormal-thermal QMU analysis

State space sensitivity to a prescribed probability density function shape in coal combustion system: Joint B-PDF versus clipped Gaussian PDF

journal articleThe turbulent transport of three coal off-gas mixture fractions is coupled to a prescribed joint b-probability- density-function (b-PDF) mixing model. This physical transport and subgrid joint b-PDF mixing model is used to explore the incorporation of coal off-gas compositional disparities between the devolatilization and the char oxidation regime in detailed pulverized-coal combustion simulations. A simulation study of the University of Utah pulverized-coal research furnace is presented to evaluate the sensitivity of different mixing model assumptions. These simulation studies indicate that using a variable composition to characterize the process of coal combustion does not appreciably change the predicted gas-phase temperature field. Moreover, neglecting fluctuations in the char off-gas stream was found to change gas-phase temperature predictions by approximately 15%. State space variable sensitivity to the assumed shape of the PDF (clipped Gaussian vs. joint b) is presented. Simulation results indicate differences in temperature profiles of as much as 20% depending on the chosen shape of the PDF. Integration accuracy issues for the joint b-PDF are presented and are found to be acceptable. A robust b-PDF function evaluation procedure is presented that accommodates arbitrarily high b-PDF distribution factors. This robust algorithm simply transforms the joint b-PDF function evaluation into a logarithmic form. The assumption that a joint PDF, as rigorously required within a prescribed subgrid mixing model, can be written as the product of N - 1 statistically independent probability density functions is quantified and shown to be less accurate

Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges

We examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors