Thermal Science and Engineering Progress最新文献

英文中文

Experimental analysis of the thermophysical properties of polyethylene glycol with MgO nanoparticles: Insights into thermal analysis, density, viscosity and isobaric heat capacity MgO纳米颗粒聚乙二醇热物理性质的实验分析：热分析，密度，粘度和等压热容的见解

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2025.104450

Nicoleta Cojocariu , Andrei Cătălin Ţugui , Bogdan Pricop , Dana Bejan , Elena Ionela Cherecheş , Alina Adriana Minea

Polyethylene glycol can be explored as a possible heat transfer fluid and this study scrutinizes the thermophysical properties of two classes of nanofluids based on PEG 200 and a mixture of PEG 200 and PEG 400, enhanced with 0.5–2.5 wt% MgO nanoparticles. Firstly, some insights into base fluids properties, as well as thermal analysis is discussed in comparison with state of the art. Furthermore, key thermophysical properties, like viscosity, density, and isobaric heat capacity, were measured at ambient temperature, during heating, and after multiple heating–cooling cycles to assess these new fluids temperature dependence properties and their long-term stability. Experimental results were compared with theoretical models and state of the art literature data to validate findings and explain these nanofluids performance. The results offer insights for designing advanced heat transfer fluids with applications in electronics cooling, solar energy, and industrial processes. Concluding, PEG based nanofluids can be an option for medium temperature heat transfer fluids, while this study proposes a new PEG mixture as a possible candidate. We need to underlie here that these results are part of an ongoing complex experimental work.

聚乙二醇可以作为一种可能的传热流体进行探索，本研究仔细研究了两类基于PEG 200和PEG 200与PEG 400混合物的纳米流体的热物理性质，并添加了0.5-2.5 wt%的MgO纳米颗粒。首先，对基液性质和热分析的一些见解进行了讨论，并与现有技术进行了比较。此外，在环境温度下、加热过程中以及多次加热-冷却循环后，测量了关键的热物理性质，如粘度、密度和等压热容量，以评估这些新流体的温度依赖特性及其长期稳定性。实验结果与理论模型和最新文献数据进行了比较，以验证研究结果并解释这些纳米流体的性能。结果为设计先进的传热流体与应用于电子冷却，太阳能和工业过程提供了见解。总之，基于聚乙二醇的纳米流体可以作为中温传热流体的一种选择，而本研究提出了一种新的聚乙二醇混合物作为可能的候选流体。我们需要强调的是，这些结果是一项正在进行的复杂实验工作的一部分。

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引用次数: 0

Heat flux estimation in CSP receiver tubes using inverse heat transfer analysis 利用反传热分析估算CSP接收管内的热流密度

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2025.104468

Vahid Safari, María Fernandez-Torrijos, Antonio Acosta-Iborra, Celia Sobrino

Solar power tower (SPT) receivers operate under extreme thermal conditions, with absorbed heat fluxes on the order of 1 MW/m² and strongly non-uniform circumferential distributions, making direct flux measurement highly challenging. This study presents, for the first time, an inverse heat-transfer methodology to reconstruct the absorbed heat flux on concentrated solar power (CSP) receiver tubes under realistic operating conditions. A three-dimensional CFD model of a molten-salt receiver tube was validated against induction-heating experiments over a range of Reynolds numbers. Among the turbulence models evaluated, the SST k–ω model showed the best agreement with the experimental data, yielding a surface-temperature RMSE of 17.69 °C. The validated CFD framework was then employed to simulate a representative Gemasolar power-tower configuration using a realistic non-uniform absorbed heat-flux profile. Surface-temperature data extracted at the mid-height of the receiver tube were used as inputs to an inverse heat-conduction model. Accurate heat-flux reconstruction was achieved using a single optimally placed temperature measurement, with no additional benefit from multiple inputs. The minimum deviation of −6.82 % occurred at a circumferential measurement angle of 70°. The inverse method demonstrated strong robustness to synthetic noise, with a minimum average heat-flux RMSE of 0.21 % of the peak flux for white noise and a maximum error of 37.7 % for drift noise. Under well-conditioned conditions, heat-flux deviations remained below 2 %, confirming the suitability of the approach for real-time thermal monitoring of CSP receiver tubes.

太阳能发电塔（SPT）接收器在极端热条件下工作，其吸收的热通量约为1 MW/m2，且周向分布极不均匀，这使得直接通量测量极具挑战性。本研究首次提出了一种逆传热方法来重建现实运行条件下聚光太阳能（CSP）接收管的吸收热流密度。通过在一定雷诺数范围内的感应加热实验，验证了熔盐接收管的三维CFD模型。在评估的湍流模型中，海表温度k -ω模型与实验数据的一致性最好，得到的表面温度RMSE为17.69°C。然后，利用验证的CFD框架，采用真实的非均匀吸收热通量剖面，模拟具有代表性的Gemasolar发电塔结构。在接收管的中间高度提取的表面温度数据被用作反热传导模型的输入。精确的热通量重建使用一个单一的最佳放置的温度测量，没有额外的好处，从多个输入。最小偏差为- 6.82%，发生在70°的周向测量角。该方法对合成噪声具有较强的鲁棒性，对白噪声的平均热通量RMSE最小为峰值通量的0.21%，对漂移噪声的最大误差为37.7%。在良好的条件下，热流密度偏差保持在2%以下，证实了该方法对CSP接收管实时热监测的适用性。

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引用次数: 0

A plant-based resin composite containing microencapsulated phase change material for thermal management 一种植物基树脂复合材料，包含用于热管理的微胶囊相变材料

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2025.104459

I. Harris , O. MacNeill , E. De Obaldia , J. Yagoobi

The growing demand for sustainable materials for thermal management has driven the development of bio-based composites with enhanced functional properties. Hence, this study focuses on developing and characterizing a 3D-printable plant-based resin (PBR) composite incorporating microencapsulated phase change material (MPCM) with a melting temperature of

28^{\circ} C

. MPCM was systematically incorporated at different volume fractions (v/v%), 20%, 33%, and 50%, using commercial liquid crystal display (LCD) 3D printing technology, enabling precise geometric control without complex processing. Extensive characterization encompassed thermal, mechanical, and microstructural properties through experimental testing and numerical modeling validation. Differential scanning calorimetry confirmed effective phase change behavior with latent heat capacities ranging from 17.36 to

30.04 J / g

proportional to MPCM content. Mechanical testing demonstrated controlled trade-offs between thermal storage and structural properties, with tensile strengths of 21.26–

23.48 MPa

for MPCM composites versus

33.89 MPa

for PBR. Other measurements included thermal conductivity, thermal diffusivity, thermogravimetric analysis, and thermal cycling of these composites. Finite element modeling of thermal performance with composites’ thermal properties as inputs achieved high accuracy with coefficients of determination (

R^{2}

) between 0.97–0.98 and mean absolute error percentage of 2.29–3.13%. Experimental thermal regulation tests demonstrated that the 50% MPCM composite extended thermal protection duration by 4.5 times compared to the control.

对可持续热管理材料的需求不断增长，推动了具有增强功能特性的生物基复合材料的发展。因此，本研究的重点是开发一种含有微胶囊化相变材料（MPCM）的3d可打印植物基树脂（PBR）复合材料，其熔融温度为28°C。采用商用液晶显示器（LCD） 3D打印技术，系统地以不同的体积分数（v/v%）， 20%， 33%和50%加入MPCM，无需复杂的加工即可实现精确的几何控制。通过实验测试和数值模拟验证，广泛的表征包括热、机械和微观结构特性。差示扫描量热法证实了有效的相变行为，潜热容范围为17.36 ~ 30.04J/g，与MPCM含量成正比。力学测试表明，储热性能和结构性能之间的平衡得到了控制，MPCM复合材料的抗拉强度为21.26-23.48MPa，而PBR复合材料的抗拉强度为33.89MPa。其他测量包括导热系数、热扩散系数、热重分析和这些复合材料的热循环。以复合材料热性能为输入的热性能有限元建模精度较高，决定系数（R2）在0.97-0.98之间，平均绝对错误率为2.29-3.13%。实验热调节测试表明，与对照组相比，50% MPCM复合材料的热保护时间延长了4.5倍。

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引用次数: 0

Electro-osmosis flow of Oldroyd-B nanofluid: ANN-based prediction of heat and mass transfer characteristics in microfluidic systems Oldroyd-B纳米流体的电渗透流动：基于神经网络的微流体系统传热传质特性预测

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2026.104482

Nagaraju Gajjela , Mahesh Garvandha , Murali Krishna Boddu

The research on electro-osmosis in Oldroyd-B nanofluids aims to improve how fluids move in micro/nano-scale devices. The purpose is to explore the impact of electric fields on non-Newtonian nanofluid flow behaviour. This attempt requires understanding viscoelastic properties, nanoparticle dynamics, and electrokinetic effects for efficient energy and biomedical applications. The governing equations, which include electro-osmosis and activation energy effects, are solved numerically using the bvp4c method. A neural network-based approach is utilized for validation and predictive analysis of the flow and thermal profiles under varying physical parameters. Graphical and tabular results are provided for the distributions of skin friction coefficient, Nusselt number, and Sherwood number along the stretched surface. These results demonstrate substantial changes in temperature and velocity profiles attributable to magnetic field intensity, heat source/sink parameters, and nanoparticle characteristics. This research’s findings are quite like those of previous studies. The velocity profile exhibits an increase as the Deborah number (β₂) and the electro-osmosis parameter (m_e) are elevated. The thermal profile exhibits an upward trend as the thermophoresis parameter (Nt) and space-dependent parameter (A*) increase. The thermal transfer efficiency was also significantly improved. The concentration profile is noted to rise due to elevated values of the activation energy parameter, whereas a contrasting behaviour is displayed when the Brownian motion parameter (Nb) is heightened. This phenomenon highlights the impacts of enhanced nanoparticle dispersion and mass transfer. The model that uses neural networks accurately predicts how fluids will flow, making it a valuable tool for studying complex fluid dynamics problems. This study provides important information on improving thermal energy systems, advancing extrusion processes, and enhancing material processing, which are all essential for effective HMT in industrial engineering.

Oldroyd-B纳米流体的电渗透研究旨在改善流体在微/纳米级器件中的运动方式。目的是探讨电场对非牛顿纳米流体流动特性的影响。这一尝试需要理解粘弹性特性、纳米颗粒动力学以及高效能源和生物医学应用的电动效应。采用bvp4c方法对包括电渗透效应和活化能效应在内的控制方程进行了数值求解。利用基于神经网络的方法对不同物理参数下的流动和热分布进行验证和预测分析。给出了表面摩擦系数、努塞尔数和舍伍德数沿拉伸表面的分布的图形和表格结果。这些结果表明，温度和速度分布的实质性变化可归因于磁场强度、热源/汇参数和纳米颗粒特性。这项研究的结果与以前的研究结果非常相似。随着底波拉数（β2）和电渗透参数（me）的增加，速度分布呈增加趋势。随着热泳参数（Nt）和空间相关参数（A*）的增大，热剖面呈上升趋势。换热效率也显著提高。注意到由于活化能参数的升高，浓度曲线上升，而当布朗运动参数（Nb）升高时，则显示出相反的行为。这一现象突出了纳米颗粒分散和传质增强的影响。该模型使用神经网络准确地预测流体将如何流动，使其成为研究复杂流体动力学问题的宝贵工具。该研究为改进热能系统、改进挤压工艺和提高材料加工水平提供了重要信息，这些都是工业工程中有效的HMT所必需的。

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引用次数: 0

Biomimetic-driven electro-osmotic flow of a fractional second-grade fluid in a convergent–divergent channel considering the influence of hall current and ion slip under a periodic magnetic field 考虑周期磁场下霍尔电流和离子滑移影响的分数级二级流体在会聚-发散通道中的仿生驱动电渗透流动

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2026.104476

Asha Kotnurkar , Santosha Gowda , Sharanamma Aounti

This work investigates biomimetic peristaltic transport of a fractional second-grade fluid in a convergent/divergent channel under complex wave propagation, motivated by applications in microfluidic and biomedical systems. The objective is to analyze the combined effects of electro-osmosis, porous media, Hall current, ion-slip conditions, and periodic magnetic fields on flow, heat, and mass transfer characteristics. The governing equations are non-dimensionalized and extended using a modified Caputo fractional derivative, and analytical solutions are obtained via the Homotopy Perturbation Method under long-wavelength and low-Reynolds-number assumptions. The results reveal that ion slip and Hall current enhance axial velocity at the channel center while suppressing it near the walls, with stronger velocity retardation observed for non-periodic magnetic fields. Temperature decreases with Hall current but increases with ion-slip effects, whereas the Sherwood number increases with Brownian motion and decreases with thermophoresis. Comparisons with existing literature validate the accuracy of the solutions. The outcomes of this investigation offer valuable insights for applications in microfluidic and biomedical systems, where electro-osmotic transport, Hall current, and ion-slip effects play a crucial role in regulating fluid motion. The results are particularly relevant for understanding blood-flow behavior, optimizing targeted drug-delivery processes, and designing efficient lab-on-chip devices. Moreover, the analysis contributes to industrial applications involving heat-transfer enhancement and precise control of non-Newtonian fluids under oscillatory magnetic fields.

这项工作研究了复杂波传播下分数级二级流体在收敛/发散通道中的仿生蠕动传输，其应用在微流体和生物医学系统中。目的是分析电渗透、多孔介质、霍尔电流、离子滑移条件和周期性磁场对流动、热和传质特性的综合影响。利用改进的Caputo分数阶导数对控制方程进行了无量纲化扩展，并在长波长和低雷诺数假设下，利用同伦摄动方法得到了解析解。结果表明，离子滑移和霍尔电流增强了通道中心的轴向速度，抑制了通道壁附近的轴向速度，在非周期磁场中观察到更强的速度延迟。温度随霍尔电流而降低，随离子滑移而升高，舍伍德数随布朗运动而升高，随热泳运动而降低。与现有文献的比较验证了解的准确性。这项研究的结果为微流体和生物医学系统的应用提供了有价值的见解，其中电渗透传输，霍尔电流和离子滑移效应在调节流体运动中起着至关重要的作用。这些结果对于理解血流行为、优化靶向给药过程和设计高效的芯片实验室设备尤为重要。此外，该分析有助于在振荡磁场下加强传热和精确控制非牛顿流体的工业应用。

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引用次数: 0

Improving the accuracy of machine learning models in predicting evacuated tube solar collector through Random Forest replacement 通过随机森林替换提高机器学习模型预测真空管太阳能集热器的准确性

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01 DOI: 10.1016/j.tsep.2026.104480

Bin Du

Data is a critical factor that determines the upper limit of machine learning models performance. The quality and quantity of data directly influence a machine learning algorithm’s ability to understand the data and learn patterns. During the operation of solar energy devices, uncertainties such as weather and environmental factors often lead abnormal data that deviate from normal conditions. These low-quality data reduce the prediction accuracy of models. In this work, operational data of a novel all-glass straight through evacuated tube collector were collected through experiments. A clustering algorithm was employed to identify outliers, which were then replaced and imputed using three methods: Random Forest, zero and mean values. Prediction test by machine learning algorithms illustrated that the dataset processed with Random Forest computed values had the highest quality. On this basis, several machine learning models were applied to predict the water outlet temperature and thermal efficiency of the evacuated tube collector. It is indicated that the eXtreme Gradient Boosting (XGBoost) and Random Forest methods achieved higher prediction accuracy, significantly outperforming Decision Tree and Multiple Linear Regression (MLR). Replacing outliers in the original dataset with values estimated by Random Forest algorithm increases the model’s generalization ability and offers a new approach to improve the accuracy of solar energy system performance analysis using machine learning techniques.

数据是决定机器学习模型性能上限的关键因素。数据的质量和数量直接影响机器学习算法理解数据和学习模式的能力。在太阳能装置的运行过程中，由于天气、环境等不确定因素的影响，经常会导致数据出现异常，偏离正常状态。这些低质量的数据降低了模型的预测精度。本文通过实验收集了一种新型全玻璃直通式真空管集热器的运行数据。采用聚类算法识别离群点，然后采用随机森林、零和均值三种方法替换和估算离群点。机器学习算法的预测测试表明，用随机森林计算值处理的数据集质量最高。在此基础上，应用多个机器学习模型对真空管集热器出水温度和热效率进行预测。结果表明，极端梯度增强（XGBoost）和随机森林方法具有更高的预测精度，显著优于决策树和多元线性回归（MLR）方法。将原始数据集中的异常值替换为随机森林算法估计的值，提高了模型的泛化能力，为利用机器学习技术提高太阳能系统性能分析的精度提供了一种新的途径。

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引用次数: 0

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01

引用次数: 0

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01

引用次数: 0

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01

引用次数: 0

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2026-01-01

引用次数: 0

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