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

arXiv: https://arxiv.org/abs/1808.01859 · HTML: https://mlsanity.com/paper/1808.01859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks](https://mlsanity.com/paper/2211.08654/) (2022)
- [NEORL: NeuroEvolution Optimization with Reinforcement Learning](https://mlsanity.com/paper/2112.07057/) (2021)
- [An AI-Driven Thermal-Fluid Testbed for Advanced Small Modular Reactors: Integration of Digital Twin and Large Language Models](https://mlsanity.com/paper/2507.06399/) (2025)
- [Automating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents](https://mlsanity.com/paper/2510.01398/) (2025)
- [On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem](https://mlsanity.com/paper/2303.08455/) (2023)
- [Clustering and Uncertainty Analysis to Improve the Machine Learning-based Predictions of SAFARI-1 Control Follower Assembly Axial Neutron Flux Profiles](https://mlsanity.com/paper/2312.14193/) (2023)
- [Open datasets and machine learning for two-phase heat transfer: a review following a spatial-temporal taxonomy](https://mlsanity.com/paper/2605.23037/) (2026)

## Abstract (arXiv)

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.