Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2505.00823v1
• Date: Thu, 01 May 2025 19:26:01 GMT
• Title: Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks
• Authors: Qianxi Fu, Youngjoon Suh, Xiaojing Zhang, Yoonjin Won,
• Abstract summary: We present a data-driven framework that infers temperature fields from geometric phase in a canonical pool boiling configuration.<n>Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport.
• Score: 1.0499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phase change plays a critical role in thermal management systems, yet quantitative characterization of multiphase heat transfer remains limited by the challenges of measuring temperature fields in chaotic, rapidly evolving flow regimes. While computational methods offer spatiotemporal resolution in idealized cases, replicating complex experimental conditions remains prohibitively difficult. Here, we present a data-driven framework that leverages a conditional generative adversarial network (CGAN) to infer temperature fields from geometric phase contours in a canonical pool boiling configuration where advanced data collection techniques are restricted. Using high-speed imaging data and simulation-informed training, our model demonstrates the ability to reconstruct temperature fields with errors below 6%. We further show that standard data augmentation strategies are effective in enhancing both accuracy and physical plausibility of the predicted maps across both simulation and experimental datasets when precise physical constraints are not applicable. Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport, offering a powerful approach to augment and interpret experimental measurements in complex two-phase systems.

Related papers

• Data Collection with Non-Uniform Axial Power for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark [0.0]
  Critical heat flux (CHF) marks the onset of boiling crisis in light-water reactors.<n>This work compiles and digitizes a broad CHF dataset covering both uniform and non-uniform axial heating conditions.
  arXiv  Detail & Related papers  (2025-06-18T16:01:44Z)
• Decrypting the temperature field in flow boiling with latent diffusion models [1.9190568044682759]
  This paper presents an innovative method using Latent Diffusion Models (LDMs) to generate temperature fields from phase indicator maps.<n>By leveraging the BubbleML dataset from numerical simulations, the LDM phase field data translates into corresponding temperature distributions.<n>The resulting model effectively reconstructs complex temperature fields at interfaces.
  arXiv  Detail & Related papers  (2025-01-27T21:18:05Z)
• Bayesian inference of composition-dependent phase diagrams [47.79947989845143]
  We develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.
  arXiv  Detail & Related papers  (2023-09-03T20:57:10Z)
• Photoinduced prethermal order parameter dynamics in the two-dimensional large-$N$ Hubbard-Heisenberg model [77.34726150561087]
  We study the microscopic dynamics of competing ordered phases in a two-dimensional correlated electron model. We simulate the light-induced transition between two competing phases.
  arXiv  Detail & Related papers  (2022-05-13T13:13:31Z)
• Accessing the topological Mott insulator in cold atom quantum simulators with realistic Rydberg dressing [58.720142291102135]
  We investigate a realistic scenario for the quantum simulation of such systems using cold Rydberg-dressed atoms in optical lattices. We perform a detailed analysis of the phase diagram at half- and incommensurate fillings, in the mean-field approximation. We furthermore study the stability of the phases with respect to temperature within the mean-field approximation.
  arXiv  Detail & Related papers  (2022-03-28T14:55:28Z)
• Accurate simulation and thermal tuning by temperature-adaptive boundary interactions on quantum many-body systems [2.13230439190003]
  We propose the temperature-adaptive entanglement simulator (TAES) that mimics and tunes the thermodynamics of the one-dimensional (1D) many-body system. With the benchmark on 1D spin chains, TAES surpasses the state-of-the-art accuracy compared with the existing finite-temperature approaches.
  arXiv  Detail & Related papers  (2021-04-30T15:21:06Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)
• Unsupervised machine learning of topological phase transitions from experimental data [52.77024349608834]
  We apply unsupervised machine learning techniques to experimental data from ultracold atoms. We obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. Our work provides a benchmark for unsupervised detection of new exotic phases in complex many-body systems.
  arXiv  Detail & Related papers  (2021-01-14T16:38:21Z)
• Adiabatic Sensing Technique for Optimal Temperature Estimation using Trapped Ions [64.31011847952006]
  We propose an adiabatic method for optimal phonon temperature estimation using trapped ions. The relevant information of the phonon thermal distributions can be transferred to the collective spin-degree of freedom. We show that each of the thermal state probabilities is adiabatically mapped onto the respective collective spin-excitation configuration.
  arXiv  Detail & Related papers  (2020-12-16T12:58:08Z)
• Uncertainty estimation for molecular dynamics and sampling [0.0]
  Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations. It is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the training of the model. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium.
  arXiv  Detail & Related papers  (2020-11-10T00:07:50Z)
• Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation [4.350727579753697]
  Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM, to overcome these difficulties. Deep learning is applied to construct and explore the relationship between the local physical features and simulation errors.
  arXiv  Detail & Related papers  (2020-01-06T20:14:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.