Nam Dinh — Page 2 | Searcharxiv

arXiv2018

Validation and Uncertainty Quantification for Wall Boiling Closure Relations in Multiphase-CFD Solver

The two-fluid model based Multiphase Computational Fluid Dynamics (MCFD) has been considered as one of the most promising tools to investigate two-phase flow and boiling system for engineering purposes. The MCFD solver requires closure relations to make the conservation equations solvable. The wall boiling closure relations, for example, provide predictions on wall superheat and heat partitioning. The accuracy of these closure relations significantly influences the predictive capability of the solver. In this paper, a study of validation and uncertainty quantification (VUQ) for the wall boiling closure relations in MCFD solver is performed. The work has three purposes: i). identify influential parameters to the quantities of interest of the boiling system through sensitivity analysis; ii). evaluate the parameter uncertainty through Bayesian inference with the support of multiple datasets; iii). quantitatively measure the agreement between solver predictions and datasets. The widely used Kurul-Podowski wall boiling closure relation is studied in this paper. Several statistical methods are used, including Morris screening method for global sensitivity analysis, Markov Chain Monte Carlo (MCMC) for inverse Bayesian inference, and confidence interval as the validation metric. The VUQ results indicated that the current empirical correlations-based wall boiling closure relations achieved satisfactory agreement on wall superheat predictions. However, the closure relations also demonstrate intrinsic inconsistency and fail to give consistently accurate predictions for all quantities of interest over the well-developed nucleate boiling regime.

physics.flu-dyn↗

arXiv2018

Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results

Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchangers in power plants and nuclear reactors. Essentially, boiling is a complex physical process that involves interactions between heating surface, liquid, and vapor. For engineering applications, the boiling heat transfer is usually predicted by empirical correlations or semi-empirical models, which has relatively large uncertainty. In this paper, a data-driven approach based on deep feedforward neural networks is studied. The proposed networks use near wall local features to predict the boiling heat transfer. The inputs of networks include the local momentum and energy convective transport, pressure gradients, turbulent viscosity, and surface information. The outputs of the networks are the quantities of interest of a typical boiling system, including heat transfer components, wall superheat, and near wall void fraction. The networks are trained by the high-fidelity data processed from first principle simulation of pool boiling under varying input heat fluxes. State-of-the-art algorithms are applied to prevent the overfitting issue when training the deep networks. The trained networks are tested in interpolation cases and extrapolation cases which both demonstrate good agreement with the original high-fidelity simulation results.

cs.CE↗