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

Deadline for manuscript submissions: 22 January 2025 | Viewed by 4907

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Special Issue Editor

Dr. Juan Carlos Ramos

E-Mail Website
Guest Editor

TECNUN—Escuela de Ingeniería, University of Navarra, Paseo de Manuel Lardizabal 13, 20018 Donostia-San Sebastian, Spain
Interests: heat transfer; thermal engineering; modeling of thermal systems; CFDs

Special Issue Information

Dear Colleagues,

Computational Fluid Dynamic (CFD) techniques have demonstrated their usefulness as an indispensable tool to analyze and optimize complex systems in which fluid flows with heat and mass transfer are involved. The global tendency to increase energy efficiency has fostered the use of more reliable and accurate tools, such as CFDs, to model the heat transfer processes present in different engineering systems. It is used widely in diverse engineering sectors, such as energy generation systems; energy storage systems; propulsion systems; electronics; and HVAC systems.

This Special Issue aims to present and disseminate the most recent advances in the use of CFD techniques for heat transfer modeling in engineering applications with the purpose of considering the analysis of and improvement in their operation and performance at the component or system level. Topics of interest for this Special Issue include, but are not limited to, the following:

Power generation systems;
Thermal management of electronics;
HVAC systems;
Heat exchangers;
Heat engines;
Thermal storage systems;
Chemical systems;
Thermal energy efficiency;
Building thermal systems;
Combustion systems: boilers and furnaces;
CHP systems.

Dr. Juan Ramos
Guest Editor

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Keywords

CFD
heat transfer
thermal engineering
thermal systems

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Published Papers (3 papers)

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Research

26 pages, 6201 KiB

Open AccessFeature PaperArticle

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

by Ahmed Mahmood, Timothy Cockerill, Greg de Boer, Jochen Voss and Harvey Thompson

Energies 2024, 17(18), 4575; https://doi.org/10.3390/en17184575 - 12 Sep 2024

Viewed by 1767

Abstract

Lithium ion (Li-ion) battery packs have become the most popular option for powering electric vehicles (EVs). However, they have certain drawbacks, such as high temperatures and potential safety concerns as a result of chemical reactions that occur during their charging and discharging processes. These can cause thermal runaway and sudden deterioration, and therefore, efficient thermal management systems are essential to boost battery life span and overall performance. An electrochemical-thermal (ECT) model for Li-ion batteries and a conjugate heat transfer model for three-dimensional (3D) fluid flow and heat transfer are developed using COMSOL Multiphysics^®. These are used within a novel computational fluid dynamics (CFD)-enabled multi-objective optimization approach, which is used to explore the effect of the mini-channel cold plates’ geometrical parameters on key performance metrics (battery maximum temperature (

T_{m a x}

), pressure drop (

∆ P

), and temperature standard deviation (

T_{σ}

)). The performance of two machine learning (ML) surrogate methods, radial basis functions (RBFs) and Gaussian process (GP), is compared. The results indicate that the GP ML approach is the most effective. Global minima for the maximum temperature, temperature standard deviation, and pressure drop (

T_{m a x}

T_{σ}

, and

∆ P

, respectively) are identified using single objective optimization. The third version of the generalized differential evaluation (GDE3) algorithm is then used along with the GP surrogate models to perform multi-objective design optimization (MODO). Pareto fronts are generated to demonstrate the potential trade-offs between

T_{m a x}

T_{σ}

, and

∆ P

. The obtained optimization results show that the maximum temperature dropped from 36.38 to 35.98 °C, the pressure drop dramatically decreased from 782.82 to 487.16 Pa, and the temperature standard deviation decreased from 2.14 to 2.12 K; the corresponding optimum design parameters are the channel width of 8 mm and the horizontal spacing near the cold plate margin of 5 mm. Full article

(This article belongs to the Special Issue Computational Fluid Dynamics (CFD) for Heat Transfer Modeling)

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21 pages, 6577 KiB

Open AccessFeature PaperArticle

Enhancing Heat Transfer in Mini-Scale Liquid-Cooled Heat Sinks by Flow Oscillation—A Numerical Analysis

by James Hockaday and Richard Law

Energies 2024, 17(11), 2459; https://doi.org/10.3390/en17112459 - 21 May 2024

Viewed by 900

Abstract

Oscillatory baffled flows (OBFs) provide a combined active and passive means of achieving convective heat transfer enhancement, and previous studies at large scale have demonstrated the heat transfer benefits of OBFs. To date, however, this technology has not been scaled down for the purpose of heat sink performance enhancement. Presented in this study is a numerical investigation of a single baffled channel with a hydraulic diameter of 2.8 mm, containing gate baffles, with a 50% open area, which are spaced 7.5 mm apart. Three net-flow rates were investigated while varying the oscillation conditions by varying the oscillation amplitude (3 mm to 7 mm) and by varying the oscillation frequency (0 to 8 Hz). Increasing the oscillation intensity had a greater impact on the Nusselt number compared to simply increasing the net-flow rate, with Nu enhancements of up to 330% observed when imposing oscillatory flow on a purely steady flow. Ideal operating conditions were identified by grouping the data by velocity ratio (

Ψ

) and graphing the theoretical pumping power against the thermal resistance of the channel. The highest Nu enhancement of 330% was achieved for a net-flow Reynolds number (

{R e}_{n}

) of 165, oscillatory amplitude of 5 mm and a frequency of 8 Hz. Ideal operating conditions can be predicted by selecting conditions with

Ψ

> 1. A flow with a

{R e}_{n}

of 46,

Ψ

of 7 and Nu = 12 required the same pumping power as a flow with a

{R e}_{n}

of 165,

Ψ

of 0.65 and Nu = 6. Full article

(This article belongs to the Special Issue Computational Fluid Dynamics (CFD) for Heat Transfer Modeling)

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25 pages, 5985 KiB

Open AccessArticle

The Cut-Cell Method for the Conjugate Heat Transfer Topology Optimization of Turbulent Flows Using the “Think Discrete–Do Continuous” Adjoint

by Nikolaos Galanos, Evangelos M. Papoutsis-Kiachagias and Kyriakos C. Giannakoglou

Energies 2024, 17(8), 1817; https://doi.org/10.3390/en17081817 - 10 Apr 2024

Cited by 2 | Viewed by 1452

Abstract

This paper presents a topology optimization (TopO) method for conjugate heat transfer (CHT), with turbulent flows. Topological changes are controlled by an artificial material distribution field (design variables), defined at the cells of a background grid and used to distinguish a fluid from a solid material. To effectively solve the CHT problem, it is crucial to impose exact boundary conditions at the computed fluid–solid interface (FSI); this is the purpose of introducing the cut-cell method. On the grid, including also cut cells, the incompressible Navier–Stokes equations, coupled with the Spalart–Allmaras turbulence model with wall functions, and the temperature equation are solved. The continuous adjoint method computes the derivatives of the objective function(s) and constraints with respect to the material distribution field, starting from the computation of derivatives with respect to the positions of nodes on the FSI and then applying the chain rule of differentiation. In this work, the continuous adjoint PDEs are discretized using schemes that are consistent with the primal discretization, and this will be referred to as the “Think Discrete–Do Continuous” (TDDC) adjoint. The accuracy of the gradient computed by the TDDC adjoint is verified and the proposed method is assessed in the optimization of two 2D cases, both in turbulent flow conditions. The performance of the TopO designs is investigated in terms of the number of required refinement steps per optimization cycle, the Reynolds number of the flow, and the maximum allowed power dissipation. To illustrate the benefits of the proposed method, the first case is also optimized using a density-based TopO that imposes Brinkman penalization terms in solid areas, and comparisons are made. Full article

(This article belongs to the Special Issue Computational Fluid Dynamics (CFD) for Heat Transfer Modeling)

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