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Energies
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Heat Transfer Measurement and Modeling
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Heat Transfer Measurement and Modeling

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J1: Heat and Mass Transfer".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 2018

Special Issue Editors

Dr. Lingnan Lin

E-Mail Website
Guest Editor
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Interests: fluid flow and heat transfer; thermal energy storage; machine learning; molecular dynamics simulation; nanofluids
Dr. Mark Kedzierski

E-Mail Website
Guest Editor
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Interests: two-phase heat transfer; thermo energy storage; nanolubricants

Special Issue Information

Dear Colleagues,

Improved energy efficiency in the industrial, commercial, residential, and transportation sectors is an essential part of achieving a net zero emissions economy. Heat transfer plays a crucial role in almost every application for energy conversion and management, including HVAC, refrigeration, energy storage, chemical processing, and power generation. The equipment and system design for these applications relies on reliable measurement and modeling of heat transfer processes. Recent advances in instrumentation and computation have greatly improved the accuracy, reliability, and speed of measurement and modeling in all research fields, including heat transfer. The improved heat transfer measurement and modeling will undoubtedly enable the innovation in energy efficiency technologies and facilitate the decarbonization.

This special issue aims to highlight recent advances in heat transfer measurement and modeling. Prospective papers may develop or employ new techniques to measure or model heat transfer processes including (but not limited to) conduction, single-phase forced convection, natural convection, evaporation, condensation, boiling, solidification, melting, and radiation. New insights on experimental design, instrumentation, and uncertainty analysis are extremely welcome. The application of emerging computational methods like machine learning, lattice Boltzmann methods, and molecular simulation in heat transfer modeling are also of the interest. Both review and original research papers are welcome. All manuscripts will be peer reviewed.

Dr. Lingnan Lin
Dr. Mark Kedzierski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

14 pages, 5875 KiB  
Article
A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems
by Changxu Chen and Zhenhai Pan
Energies 2023, 16(23), 7819; https://doi.org/10.3390/en16237819 - 28 Nov 2023
Cited by 1 | Viewed by 562
Abstract
Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer [...] Read more.
Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems. Full article
(This article belongs to the Special Issue Heat Transfer Measurement and Modeling)
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16 pages, 6592 KiB  
Article
Effects of Numerical Schemes of Contact Angle on Simulating Condensation Heat Transfer in a Subcooled Microcavity by Pseudopotential Lattice Boltzmann Model
by Dongmin Wang, Gaoshuai Lin, Yugang Zhao and Ming Gao
Energies 2023, 16(6), 2622; https://doi.org/10.3390/en16062622 - 10 Mar 2023
Viewed by 1073
Abstract
Various numerical schemes of contact angle are widely used in pseudopotential lattice Boltzmann model to simulate substrate contact angle in condensation. In this study, effects of numerical schemes of contact angle on condensation nucleation and heat transfer simulation are clarified for the first [...] Read more.
Various numerical schemes of contact angle are widely used in pseudopotential lattice Boltzmann model to simulate substrate contact angle in condensation. In this study, effects of numerical schemes of contact angle on condensation nucleation and heat transfer simulation are clarified for the first time. The three numerical schemes are pseudopotential-based contact angle scheme, pseudopotential-based contact angle scheme with a ghost fluid layer constructed on the substrate with weighted average density of surrounding fluid nodes, and the geometric formulation scheme. It is found that the subcooling condition destabilizes algorithm of pseudopotential-based contact angle scheme. However, with a ghost fluid layer constructed on the substrate or using geometric formulation scheme, the algorithm becomes stable. The subcooling condition also decreases the simulated contact angle magnitude compared with that under an isothermal condition. The fluid density variation near a microcavity wall simulated by pseudopotential-based contact angle scheme plays the role of the condensation nucleus and triggers “condensation nucleation”. However, with a ghost fluid layer constructed on the substrate or using geometric formulation scheme, the simulated fluid density distribution near the wall is uniform so that no condensation nucleus appears in the microcavity. Thus, “condensation nucleation” cannot occur spontaneously in the microcavity unless a thin liquid film is initialized as a nucleus in the microcavity. The heat flux at the microcavity wall is unphysical during the “condensation nucleation” process, but it becomes reasonable with a liquid film formed in the microcavity. As a whole, it is recommended to use pseudopotential-based contact angle scheme with a ghost fluid layer constructed on the substrate or use the geometric formulation scheme to simulate condensation under subcooling conditions. This study provides guidelines for choosing the desirable numerical schemes of contact angle in condensation simulation by pseudopotential lattice Boltzmann model so that more efficient strategies for condensation heat transfer enhancement can be obtained from numerical simulations. Full article
(This article belongs to the Special Issue Heat Transfer Measurement and Modeling)
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