International Journal of Thermofluids最新文献_第6页

Thermo-fluid analysis of bio-inspired fish scale fins and eco-friendly H2O/GAGNPs nanofluid for CPU cooling: A CFD-CCD hybrid approach 仿生鱼鳞鳍和环保H2O/GAGNPs纳米流体用于CPU冷却的热流体分析：一种CFD-CCD混合方法

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101474

M. Esmaeilisafa, M. Gholinia, D.D. Ganji, M.Gorji Bandpy

This research investigates the efficiency of a micro heat sink featuring bio-inspired flow-disturbing structures modeled after fish scales. The micro heat sink consists of 12 rows of fins with a fish scale design, placed on a heat source such as a central processing unit (CPU) to control the temperature. The channel flow is laminar, steady, and incompressible. In addition, a kind of nanoparticle-containing liquid called gallic acid combined with graphene nanoplatelets (GNPs) has been used as a new type of nanofluid (GAGNPs /H₂O). The governing equations were solved in three dimensions using the finite volume method in ANSYS-FLUENT, incorporating structure-fluid coupling. The results show: The micro heat sink geometry was modified from a simple model to designs inspired by gar, Cyprinus carpio, and salmon scales, the maximum temperature of the CPU is reduced about 3.5, 5.7, and 9.23 % respectively. The minimum acquired exergy is about 2.48W for simple model with GAGNP/H₂O nanofluid: 0.1 % wt and Reynolds number Re=1500. The maximum acquired exergy was also recorded for the geometry of the micro heat sink with the salmon scale design at about 11.44 W (about 4.6 times the minimum value). By modifying the micro heat sink geometry from the simple model to the salmon fish scales, the uniformity index decreases by about 17.78 % and the thermal resistance index decreases about 22.05 %. The second-order model of data analysis for predicting convective heat transfer coefficient and maximum surface temperature in different geometries of micro heat sink is reported based on the Response Surface Method (RSM).

本研究考察了以鱼鳞为模型的仿生流扰结构微型散热器的效率。微型散热器由12排鱼鳞设计的鳍片组成，放置在中央处理器（CPU）等热源上以控制温度。通道流动是层流的，稳定的，不可压缩的。此外，一种名为没食子酸与石墨烯纳米血小板（GNPs）结合的纳米颗粒液体已被用作新型纳米流体（GAGNPs /H2O）。在ANSYS-FLUENT中采用有限体积法对控制方程进行三维求解，并考虑结构-流体耦合。结果表明：将微散热器的几何形状从简单的模型改为以gar、Cyprinus carpio和salmon鳞片为灵感的设计，CPU的最高温度分别降低了约3.5%、5.7%和9.23%。对于GAGNP/H2O纳米流体：0.1% wt，雷诺数Re=1500的简单模型，获得的最小火能约为2.48W。采用鲑鱼鳞设计的微型散热器的几何形状也记录了最大获得的火能，约为11.44 W（约为最小值的4.6倍）。将微散热器几何形状由简单模型修改为鲑鱼鱼鳞后，均匀性指数降低约17.78%，热阻指数降低约22.05%。基于响应面法（RSM），建立了预测微散热器不同几何形状对流换热系数和最大表面温度的二阶数据分析模型。

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引用次数: 0

Editorial: Thermal power cycles and optimization 社论：热电循环和优化

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101420

Parth Prajapati , Bertrand Delpech , Vivek Patel

The special issue on Thermal Power Cycles and Optimization presents a collection of cutting-edge research and insightful reviews that advance the understanding and application of energy systems. This issue encompasses a broad spectrum of topics, including heat engines, combined heat and power systems, refrigeration and air conditioning, and the integration of renewable energy sources with conventional power cycles. Emphasis is placed on thermodynamic analysis, heat transfer mechanisms, and environmental and economic considerations in energy generation. The contributions collectively underscore the importance of optimizing thermal systems to enhance efficiency, reduce emissions, and support sustainable energy development. This editorial provides an overview of the thematic focus, key findings, and future directions highlighted by the articles in this issue.

热电循环与优化特刊介绍了一系列前沿研究和有见地的评论，这些评论促进了对能源系统的理解和应用。这一期涵盖了广泛的主题，包括热机，热电联产系统，制冷和空调，以及可再生能源与传统电力循环的整合。重点放在热力学分析，传热机制，以及能源生产中的环境和经济考虑。这些贡献共同强调了优化热力系统对提高效率、减少排放和支持可持续能源发展的重要性。这篇社论概述了本期文章的主题重点、主要发现和未来发展方向。

引用次数: 0

Investigations on heat transfer in turbulent free surface and submerged jets 湍流自由表面和浸没射流的传热研究

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101500

Vivek Mathew Jose

This paper details the outcomes derived from numerical studies carried out on submerged air jets and free surface water jets striking on a horizontal surface. The cooling capabilities of a water jet supplemented with Alumina nanoparticles are also examined. The normalized distance from the nozzle to the target (Z/D) ranges from 2 to 12, with the Reynolds number (Re) varying from 4000 to 23000 and the particle volume fraction spanning from 0 to 1 %. The optimal Z/D for a free surface water jet is 2, while for a submerged air jet, it is 6. Within the stagnation region of the free surface jet, the Nusselt number (Nu) increases radially, peaking at about r/D ≈ 0.7. However, this increase becomes trivial at higher Re. Compared to the submerged jet, the local Nu for the free surface jet is markedly greater. In free surface jet, the jet speed is sustained as it flows from the exit of the nozzle. The velocity gradient near the surface is greater at low Z/D, which accounts for the increased wall shear stress observed at lower Z/D. The maximum enhancement in Nuo is 15 % when the volume fraction is increased from 0 to 1 %.

本文详细介绍了水下空气射流和自由表面水射流撞击水平表面的数值研究结果。还研究了氧化铝纳米颗粒水射流的冷却能力。喷嘴到目标的归一化距离（Z/D）范围为2 ~ 12，雷诺数（Re）范围为4000 ~ 23000，颗粒体积分数范围为0 ~ 1%。自由表面水射流的最佳Z/D为2，而水下空气射流的最佳Z/D为6。在自由表面射流的停滞区，努塞尔数（Nu）呈径向增加，在r/D≈0.7处达到峰值。然而，在较高的Re下，这种增加变得微不足道。与淹没射流相比，自由表面射流的局部Nu明显更大。在自由表面射流中，当射流从喷嘴出口处流出时，射流速度保持不变。低Z/D时，地表附近的速度梯度较大，这是低Z/D时观察到的壁面剪应力增大的原因。当体积分数从0增加到1%时，诺的最大增益为15%。

引用次数: 0

Using computational fluid dynamics to explore new hydrokinetic fluid coupling design for industrial applications 利用计算流体动力学探索工业应用的新型流体动力学耦合设计

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101471

Deep Prajapati , Hasmukh G. Katariya , Vipul H. Chaudhari , Lalit Nakrani , Amit Prajapati , Pranav Mehta , Ravishankar Sathyamurthy

This study presents the initial design and computational investigation of a traction-type hydrokinetic fluid coupling for industrial applications. The problem addressed is the limited availability of validated mathematical and computational models for optimizing impeller–runner design and oil selection in fluid couplings. The objective is to establish an empirical–computational framework that combines dimensional analysis with CFD simulations to accurately predict operating behavior. The impeller and runner were designed using dimensional analysis–based empirical relations, supported by assumptions of incompressible flow, constant density, and steady-state operating conditions with a slip of 2–3 %. The theoretical oil mass required for power transmission was calculated using Rolfe’s hydrodynamic equations, and validated against actual industry data. For a 420-size coupling operating at 1500 rpm impeller speed and 1450 rpm runner speed, the predicted oil requirement was 9.33 L versus the actual 10.05 L. CFD analysis employing a moving mesh and k–ω turbulence model revealed a maximum dynamic pressure of 5.4 bar and tangential velocity of 34 m/s, which produced a torque of 507.3 Nm and transmitted power of 79.9 kW, matching the rated 80 kW within 0.13 %. These results confirm that the proposed empirical–CFD framework accurately captures pressure distribution, vortex dynamics, and slip characteristics, thereby validating the mathematical assumptions. The study establishes a robust base for optimizing hydrokinetic couplings to achieve 95–98 % efficiency with reduced slip, and provides insights for future improvements in rib geometry, oil filling strategies, and material selection

本文介绍了一种用于工业应用的牵引型流体动力联轴器的初步设计和计算研究。解决的问题是，用于优化叶轮流道设计和流体联轴器中油品选择的经过验证的数学和计算模型的可用性有限。目标是建立一个经验计算框架，将量纲分析与CFD模拟相结合，以准确预测操作行为。叶轮和流道的设计采用了基于尺寸分析的经验关系，并以不可压缩流动、密度恒定和滑移率为2 - 3%的稳态工况为假设。采用Rolfe流体动力学方程计算了动力传动所需的理论油量，并根据实际工业数据进行了验证。在叶轮转速为1500转/分、转轮转速为1450转/分的情况下，对于一个尺寸为420mm的联轴器，预测需油量为9.33 L，而实际需油量为10.05 L，采用移动网格和k -ω湍流模型的CFD分析显示，最大动压为5.4 bar，切向速度为34 m/s，产生的扭矩为507.3 Nm，传输功率为79.9 kW，与额定80 kW的匹配率在0.13%以内。这些结果证实了所提出的经验- cfd框架准确地捕获了压力分布、涡动力学和滑移特性，从而验证了数学假设。该研究为优化流体动力联轴器奠定了坚实的基础，可以在减少滑移的情况下实现95 - 98%的效率，并为未来改进肋板几何形状、充油策略和材料选择提供了见解

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引用次数: 0

Analyzing heat and mass transfer of nanofluid flow on a stenosed artery applying endo-exothermic chemical reaction and bioconvection using a model-agnostic meta-learner technique: A numerical approach 利用模型不可知的元学习器技术，应用内放热化学反应和生物对流分析纳米流体在狭窄动脉上的传热传质

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101470

Shivalila Hangaragi , Neelima N , G K Tejaswini , K Vinutha , Amal Abdulrahman , J K Madhukesh

In fluid flow applications, endothermic and exothermic chemical reactions are important, especially in the scientific and engineering fields. They make advanced modeling and optimization of complex systems possible when combined with artificial neural networks (ANNs). As a result, the present investigation uses ANNs to study the influences of heat source/sink, endothermic/exothermic chemical reactions, and Darcy-Forchheimer porous media on the two-dimensional, stable, incompressible flow of bio-convection nanofluids through a stenosed artery (cylinder). Using appropriate similarity equations, non-linear partial differential equations are transformed into ordinary differential equations, which are then resolved with RKF-45 and the shooting technique. Important engineering coefficients were also investigated. Outcomes show that in an endothermic chemical reaction, the temperature profile increases as the chemical reaction parameter rises, whereas in an exothermic chemical reaction, the reverse behaviour is observed. The

C f %

shows negligible variations with the addition of nanoparticles at about 8.2 % across distinct parameter values. The

N u %

is strongly influenced by nanoparticles, increasing significantly for

λ_{1} > 0

than

λ_{1} < 0

. Model-Agnostic-Meta-Learning relative studies show high convergence; it generalizes effectively on unknown data. Error histogram studies validate performance analysis, training is stable, and the predicted values almost equal the actual values, proving its effectiveness.

在流体流动应用中，吸热和放热化学反应是重要的，特别是在科学和工程领域。当与人工神经网络（ann）相结合时，它们使复杂系统的高级建模和优化成为可能。因此，本研究使用人工神经网络来研究热源/汇、吸热/放热化学反应和Darcy-Forchheimer多孔介质对生物对流纳米流体通过狭窄动脉（圆柱）的二维、稳定、不可压缩流动的影响。利用适当的相似方程，将非线性偏微分方程转化为常微分方程，利用RKF-45和射击技术进行求解。对重要的工程系数也进行了研究。结果表明，在吸热化学反应中，温度分布随着化学反应参数的增加而增加，而在放热化学反应中，观察到相反的行为。在不同的参数值中，Cf%随纳米粒子的加入而变化可忽略不计，约为8.2%。Nu%受纳米粒子的影响较大，λ1>；0时Nu%比λ1<；0时Nu%显著增加。模型-不可知论-元学习相关研究显示出高度收敛性；它对未知数据进行了有效的泛化。误差直方图研究验证了性能分析，训练结果稳定，预测值与实际值基本相等，证明了其有效性。

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引用次数: 0

Ternary hybrid-nanofluid magneto-convective flow inside the octagonal enclosure with an inner circular obstacle contribution to entropy generation 具有内圆障碍物的八角形外壳内三元混合纳米流体磁对流流对熵的产生有贡献

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101480

Nur Jahangir Moon , Bijan Krishna Saha , Jahidul Islam Jihan , Goutam Saha , Md.Nur Alam , Suvash.C. Saha

Natural convection (NC) plays a pivotal role in convective heat transfer (HT) and has been extensively studied. This research focuses on examining the impact of different parameters on HT and fluid flow behavior of a ternary hybrid nanofluid (Al₂O₃-Fe₃O₄Cu-H₂O) under the influence of a magnetic field within an octagonal enclosure containing a circular obstacle. This work investigates HT features of a buoyancy-driven NC flow that is laminar, steady and incompressible. The study also takes into account the entropy generation (E_gen) and the Bejan number (Be) in an octagonal enclosure with an inner circular obstacle for varying boundary conditions. The finite element method is used to numerically solve the governing equations and the associated boundary conditions. A variety of parameter values are employed in this study such as 0 % ≤ nanoparticles volume fraction (φ) ≤ 5 %, 10³ ≤ Rayleigh number (Ra) ≤ 10⁶, 0 ≤ Hartmann number (Ha) ≤ 60. The present analysis highlights that the rate of HT and E_gen can be improved by adding ternary hybrid nanoparticles within the cavity by 42.9 % and 14.89 % respectively. However, the average E_gen becomes higher for increasing Ra and decreasing Ha at φ_a = φ_b = φ_c = 5 %. Moreover, larger nanoparticle volumes result in improved thermal performance, especially when Ra is higher. The ternary hybrid nanofluids for this architecture has the potential to improve thermal management systems by efficiently reducing external heat loss.

自然对流（NC）在对流换热（HT）中起着至关重要的作用，并得到了广泛的研究。本研究主要研究了在含有圆形障碍物的八角形封闭环境中，不同参数对三元杂化纳米流体（Al2O3-Fe3O4Cu-H2O）在磁场影响下的高温和流体流动行为的影响。这项工作研究了浮力驱动的NC流的高温特征，该流是层流，稳定和不可压缩的。该研究还考虑了具有内圆障碍物的八角形围场在不同边界条件下的熵产（Egen）和贝尚数（Be）。采用有限元法对控制方程和相关边界条件进行了数值求解。本研究采用了0 %≤纳米颗粒体积分数(φ)≤5%、103≤瑞利数(Ra)≤106、0≤哈特曼数(Ha)≤60等参数值。分析结果表明，在空腔中加入三元杂化纳米颗粒可使HT和Egen的速率分别提高42.9%和14.89%。当φa = φb = φc = 5%时，Ra增大，Ha减小，平均Egen增大。此外，更大的纳米颗粒体积可以改善热性能，特别是当Ra较高时。这种结构的三元混合纳米流体具有通过有效减少外部热损失来改善热管理系统的潜力。

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引用次数: 0

Viscous fluid flow past a permeable sphere- ANN approach 粘性流体流过可渗透球体-人工神经网络方法

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101475

P. Aparna , V.Ganesh Kumar , P. Padmaja , H. Niranjan

The current paper explores artificial neural networks (ANNs) approach to analyze flow of a viscous fluid through a spongy sphere. The aim of this study is to analyze viscous fluid flow past axisymmetric permeable sphere using Artificial Neural Networks (ANN). The specific objectives are to investigate velocity and pressure distributions. The proposed approach integrates Analytical methods with ANN modeling to achieve accurate predictions and deeper physical insight into viscous fluid flow behavior. The flow is regulated by non-linear PDE’s in terms of the stream function, subject to the relevant frontier conditions. The stream function is used to describe the flow pattern of the sphere's interior and exterior. The permeability parameter's limits are evaluated. The streamlining design is illustrated for various kinds of permeability parameter values. We investigate numerically how the permeability parameter affects drag, and findings are illustrated through graphs. The Feed-Forward Neural Network (FFNN) methods are employed to generate and study the solution for regulated fluid flow. A multilayer perceptron (MLP) neural network is employed in the sample functions. The adaptive moment estimation (ADAM) algorithm is employed to compute the adjustable parameters. The mathematical computations of both ANN and exact solutions are presented in tabular form and visually displayed for various physical parameter values. The effectiveness of the solution improves with the expansion of neurons and data points in neural networks. R-Squared values of 0.999 were achieved for the stream function of the fluid. Moreover, because to its diminished time and processing capacity demands for problem-solving, the current ANN framework can be used to more intricate models.

本文探讨了用人工神经网络（ANNs）方法来分析粘性流体在海绵体中的流动。利用人工神经网络（ANN）分析了轴对称可渗透球中粘性流体的流动。具体目标是研究速度和压力分布。所提出的方法将分析方法与人工神经网络建模相结合，以实现对粘性流体流动行为的准确预测和更深入的物理洞察。流动由非线性偏微分方程根据流函数进行调节，并受相关边界条件的约束。流函数用于描述球体内部和外部的流动模式。评估了渗透率参数的极限。给出了不同渗透率参数下的流线设计。我们用数值方法研究了渗透率参数对阻力的影响，并通过图表说明了结果。采用前馈神经网络（FFNN）方法生成并研究了调节流体流动的解。在样本函数中采用多层感知器神经网络。采用自适应矩估计（ADAM）算法计算可调参数。对于不同的物理参数值，人工神经网络的数学计算和精确解都以表格的形式显示出来。该方法的有效性随着神经网络中神经元和数据点的增加而提高。流体流函数的R-Squared值为0.999。此外，由于其减少了解决问题所需的时间和处理能力，目前的人工神经网络框架可以用于更复杂的模型。

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引用次数: 0

Heat transfer analysis of Al₂O₃–Cu/water nanofluid in a C-shaped wavy cavity under inclined magnetic effects 倾斜磁效应下c形波腔中Al₂O₃-Cu /水纳米流体的传热分析

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101510

M.M. Nour , Shaik Jakeer , M.A. Mansour , A.M. Rashad , T. Salah , Mohamed M. Awad , Abdallah Eldreham , Hossam A. Nabwey

This study investigates the thermal dynamics of a C-shaped, wavy, porous cavity filled with Al₂O₃-Cu/H₂O hybrid nanofluids, influenced by an inclined magnetic field and a heat source/sink. The governing equations are non-dimensionalized and resolved using the finite difference method in a proprietary MATLAB solver. The study investigates the influence of numerous dimensionless parameters such as length of heat position(B = 0.2, 0.4, 0.8), heat source/sink (Q = −4, 0, 1), Porosity ( ∈ =0.1, 0.3, 0.9), Rayleigh number(Ra = 10, 100, 10000), Hartmann number(Ha = 0, 25, 50),length of a cavity (H = 0.5, 10, 100), length of ED/H (L2 = 0.2, 0.4, 0.6) and distance of AD/H (L1 = 0.2, 0.4, 0.6)are analyzed. The findings demonstrate that an elevated Rayleigh number augments convection, whilst increased porosity promotes heat transfer efficiency. The Al₂O₃-Cu/H₂O hybrid nanofluids markedly improve heat transfer owing to their exceptional thermal conductivity. The average Nusselt number validates the efficacy of hybrid nanofluids in enhancing thermal performance. The results indicate that hybrid nanofluids enhance heat transfer, while magnetic fields hinder convection, and the cavity shape influences flow patterns. By limiting convective flow, an increased Hartmann number leads to heat transport that is dominated by conduction. Additionally, the length of the heater has a direct influence on the generation of vortices and the enhancement of localized heat and heat transfer.

本文研究了在倾斜磁场和热源/汇的影响下，填充Al₂O₃-Cu/H₂O混合纳米流体的c形波状多孔腔的热动力学。控制方程是无量纲化的，在专用的MATLAB求解器中使用有限差分法求解。研究分析了热源位置长度（B = 0.2、0.4、0.8）、热源/热源（Q =−4、0,1）、孔隙度（∈=0.1、0.3、0.9）、瑞利数（Ra = 10、100、10000）、哈特曼数（Ha =0、25、50）、空腔长度（H = 0.5、10、100）、ED/H长度（L2 = 0.2、0.4、0.6）、AD/H距离（L1 = 0.2、0.4、0.6）等无因次参数的影响。研究结果表明，瑞利数的增加增加了对流，而孔隙率的增加提高了传热效率。Al₂O₃-Cu/H₂O混合纳米流体由于其优异的导热性而显著改善了传热。平均努塞尔数验证了混合纳米流体在提高热性能方面的有效性。结果表明，混合纳米流体增强了传热，而磁场阻碍了对流，空腔形状影响了流动模式。通过限制对流流动，哈特曼数的增加导致以传导为主的热传递。此外，加热器的长度对涡的产生和局部热量和传热的增强有直接影响。

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引用次数: 0

Artificial neural network-based study of unsteady MHD radiative hybrid nanofluid flow with activation energy in a porous medium 基于人工神经网络的多孔介质中带活化能的非定常MHD辐射混合纳米流体流动研究

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101460

Vakapalli Ramu , Paramsetti Sri Ramachandra Murty

This study investigates the unsteady heat and mass transfer characteristics of water-based hybrid nanofluid flow over a stretching surface embedded in a porous medium under mixed convection conditions. The hybrid nanofluid consists of titanium dioxide (TiO₂) and copper (Cu) nanoparticles dispersed in water. The analysis incorporates the effects of activation energy, thermal radiation, Brownian motion, and thermophoresis. The governing partial differential equations are transformed into ordinary differential equations using similarity transformations and solved numerically with a fourth-order Runge-Kutta method coupled with the shooting technique. The influence of key dimensionless parameters including magnetic field strength, Prandtl number, buoyancy ratio, unsteadiness parameter, thermal radiation, chemical reaction rate, and activation energy on velocity, temperature, and concentration profiles is examined. To improve predictive capability, an artificial neural network (ANN) is employed to estimate the skin friction coefficient using SCG, LM, CGB, and CGF algorithms. Performance evaluation through mean square error (MSE), regression plots, and error analysis indicates that the Levenberg–Marquardt (LM) algorithm provides superior accuracy, making it the most efficient method for predicting heat transfer characteristics.

This hybrid numerical AI framework demonstrates strong potential for accurately modeling magnetohydrodynamic hybrid nanofluid transport phenomena. The findings contribute to improved design and thermal management strategies in advanced energy and engineering systems.

本文研究了混合对流条件下水基混合纳米流体在多孔介质拉伸表面上的非定常传热传质特性。混合纳米流体由分散在水中的二氧化钛（TiO₂）和铜（Cu）纳米颗粒组成。该分析综合了活化能、热辐射、布朗运动和热泳的影响。利用相似变换将控制偏微分方程转化为常微分方程，并用四阶龙格-库塔法结合射击技术进行数值求解。考察了磁场强度、普朗特数、浮力比、非定常参数、热辐射、化学反应速率和活化能等关键无量纲参数对速度、温度和浓度分布的影响。为了提高预测能力，采用SCG、LM、CGB和CGF算法，采用人工神经网络（ANN）对皮肤摩擦系数进行估计。通过均方误差（MSE）、回归图和误差分析进行的性能评估表明，Levenberg-Marquardt （LM）算法具有优越的精度，是预测传热特性最有效的方法。这种混合数值AI框架显示了精确模拟磁流体动力混合纳米流体输运现象的强大潜力。研究结果有助于改进先进能源和工程系统的设计和热管理策略。

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引用次数: 0

Colloidal Er₂O₃ nanofluids for enhanced thermal and exergy performance of flat plate solar collectors: Interfacial insights and energy sustainability implications 用于增强平板太阳能集热器的热和火用性能的胶体Er₂O₃纳米流体：界面洞察和能源可持续性影响

Q1 Chemical Engineering

International Journal of Thermofluids

Pub Date : 2025-11-01 DOI: 10.1016/j.ijft.2025.101478

T. Sathish , A. Johnson Santhosh

The increasing demand for sustainable and long-lasting energy sources has highlighted the limitations of conventional flat plate collectors (FPCs), particularly their reduced efficiency under low solar radiation and weak fluid heat transfer capabilities. The research aims at investigating how thermal and exergy performance of FPC can be optimized by incorporating Erbium (III) oxide (Er₂O₃) nanofluids. The test of nanofluids in a split FPC system (outdoor absorber and indoor storage), 0.3 wt.%, 0.6 wt.% and 0.9 wt.% of nanofluid, dispersed by ultrasonication. At a solar radiation of <1200 W/m², the nanofluid (0.9 wt.% nanofluid) had a maximum exit temperature of 84.1 °C, which was considerably higher than that of water (70.9 °C), 1136.7 W heat gain, 78.2 % thermal efficiency and 21.3 % exergy efficiency. It was projected that the annual energy production will be 1678.5 kWh with a CO₂ reduction potential of 20.7 tons. These findings confirm that Er₂O₃ nanofluid is a valid additive to enhance the performance of FPC and aid in greener energy transformations. The paper indicates that the incorporation of nanotechnology into solar thermal systems presents a viable way of efficiency enhancement and mitigation of climate change.

对可持续和持久能源的需求日益增长，凸显了传统平板集热器（fpc）的局限性，特别是它们在低太阳辐射和弱流体传热能力下的效率降低。该研究旨在研究如何通过加入氧化铒（Er2O3）纳米流体来优化FPC的热学和火用性能。纳米流体在分体式FPC系统（室外吸收器和室内存储器）中的测试，用超声波分散0.3 wt.%、0.6 wt.%和0.9 wt.%的纳米流体。在1200 W/m2的太阳辐射下，纳米流体（0.9 wt.%）的最大出口温度为84.1°C，远高于水（70.9°C），热增益为1136.7 W，热效率为78.2%，火用效率为21.3%。预计年发电量将达到1678.5千瓦时，二氧化碳减排潜力为20.7吨。这些发现证实了Er2O3纳米流体是一种有效的添加剂，可以提高FPC的性能，并有助于绿色能源的转化。本文指出，将纳米技术纳入太阳能热系统是提高效率和减缓气候变化的可行途径。

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引用次数: 0