Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model

Jerol Soibam,Achref Rabhi,Konstantinos Kyprianidis,Ioanna Aslanidou,Rebei Bel Fdhila

doi:10.3390/en13225987

Jerol Soibam, Achref Rabhi + Show 3 more

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https://doi.org/10.3390/en13225987

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Journal: Energies	Publication Date: Nov 16, 2020
Citations: 4	License type: CC BY 4.0

Affiliation: Mälardalen University, Hitachi (Sweden)

Abstract

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

Highlights

Engineering applications with high heat flux generally involve boiling heat transfer
The open-source deep-learning library Tensorflow 1.14 along with python 3.5 are used to build the architecture of the deep learning models (MLP, Monte Carlo (MC) Dropout, and Deep Ensemble)
The objective of this study is twofold: firstly, to measure the accuracy of the predictions of the deep learning models compared to the computational fluid dynamics (CFD) results and secondly to quantify the confidence level of the predictions

Summary

Introduction

Engineering applications with high heat flux generally involve boiling heat transfer. The high heat transfer coefficient reached by boiling flows makes boiling heat transfer relevant for research where thermal performance enhancement is needed. Boiling heat transfer can improve the cooling performance of a system, the underlying physics are not fully understood yet. It remains a major challenge to model the boiling heat transfer behavior for a boiling system. The two-fluid model-based computational fluid dynamics (CFD) has shown a good capability of dealing with boiling flow heat transfer problems. In such an approach, Energies 2020, 13, 5987; doi:10.3390/en13225987 www.mdpi.com/journal/energies

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