Thermal Science and Engineering Progress最新文献_第4页

Conjugate heat transfer and fluid flow analysis on printable double-wall effusion cooling with internal topology-optimized TPMS structures 具有内部拓扑优化 TPMS 结构的可打印双壁喷流冷却器的共轭传热和流体流动分析

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102939

Double-wall effusion is a highly efficient cooling technique in modern gas turbine blades. This study uses topology optimization infilled with triply periodic minimal surface structures (TPMS) to design high-performance internal cooling structures, improving cooling effectiveness and mitigating thermal stress for the double-wall channel. The flow, heat transfer, and static structural characteristics of the topology-optimized TPMS model are compared with the results of the smooth and circular pin fin configurations. Results show that the optimized model provides a uniform flow inside the channel and the effusion holes, reducing the jet lift-off and keeping the coolant attached to the effusion wall. Within the blowing ratios of 0.5–1.7, the optimized model improves impingement heat transfer by 9.5 %–12.5 % compared to the pin fin configuration. The averaged overall cooling effectiveness is also 4.2 %–4.6 % with lower pressure loss. The thermal stress and total deformation are evenly distributed and show 22.9 % and 12.0 % lower than the pin fin model. Moreover, a 3D laser scanning microscope and high-resolution CT scan are used to evaluate the manufacturability of the optimized sample, printed by laser powder bed fusion with an actual gas turbine blade scale. The results benefit the fabrication improvement for next-generation gas turbine blades.

双壁喷流是现代燃气轮机叶片的一种高效冷却技术。本研究利用拓扑优化填充三周期最小表面结构（TPMS）来设计高性能的内部冷却结构，从而提高冷却效果并减轻双壁通道的热应力。拓扑优化 TPMS 模型的流动、传热和静态结构特征与光滑和圆形针形鳍片配置的结果进行了比较。结果表明，优化模型在通道和喷流孔内提供了均匀的流动，减少了射流升空，并使冷却剂附着在喷流壁上。在 0.5-1.7 的吹气比范围内，优化模型比针形翅片配置提高了 9.5%-12.5% 的撞击传热效果。平均整体冷却效果也提高了 4.2 %-4.6 %，压力损失更低。热应力和总变形分布均匀，分别比针翅模型低 22.9 % 和 12.0 %。此外，还利用三维激光扫描显微镜和高分辨率 CT 扫描评估了优化样品的可制造性，该样品是通过激光粉末床熔融技术与实际燃气轮机叶片尺度打印而成的。这些结果有利于改进下一代燃气轮机叶片的制造。

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引用次数: 0

Carbon dioxide evaporation heat transfer coefficient prediction in porous media using Machine learning algorithms based on experimental data 利用基于实验数据的机器学习算法预测多孔介质中的二氧化碳蒸发传热系数

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102929

The prediction of the internal heat transfer coefficient during evaporation is vital for vapor-compression refrigeration and closed-loop power cycles. Accurate measurements and understanding of CO₂ heat transfer in porous evaporators are essential for optimal system design across various operating conditions. This study utilizes a reference dataset derived from previous experiments that investigated the impact of porous evaporators on CO₂′s internal heat transfer coefficient under sub-critical conditions, employing gravel sand as the porous medium. The dataset encompasses key factors: gravel sand porosities ranging from 39.8 % to 44.5 %, evaporator inlet pressures between 3700 and 4300 kPa, CO₂ mass flow rates from 10.7x10^-5–18x10^-5kg.s⁻¹, and porous tube effective diameters spanning 1.53 x10^-3 –3.4x10^-3 m. Employing three machine learning techniques (SVM, GPR, OBEM), the study predicts the internal heat transfer coefficient using regression models. The models’ predictions are analyzed and compared to expected values for validation, evaluating their performance using four statistical criteria. Results indicate SVM, GPR, and OBEM models achieved RMSEs of 1.5471, 1.8212, and 3.6978, respectively, while MAE errors were 1.1479, 1.2418, and 2.9787, respectively. Comparison with the dimensional analysis method reveals the effectiveness of the proposed models in accurately predicting internal heat transfer coefficients. The models exhibit low uncertainty and maintain prediction quality on an extended dataset without overfitting concerns. Overall, this research contributes valuable insights for designing heat exchangers and systems in vapor-compression refrigeration and closed-loop power cycles.

预测蒸发过程中的内部传热系数对于蒸汽压缩制冷和闭环动力循环至关重要。精确测量和了解多孔蒸发器中的二氧化碳传热情况对于在各种运行条件下优化系统设计至关重要。本研究使用了一个参考数据集，该数据集来自以前的实验，这些实验采用砾石砂作为多孔介质，研究了亚临界状态下多孔蒸发器对二氧化碳内部传热系数的影响。数据集包含以下关键因素：砾石砂孔隙率在 39.8 % 到 44.5 % 之间，蒸发器入口压力在 3700 到 4300 kPa 之间，二氧化碳质量流量在 10.7x10-5-18x10-5kg.s-1 之间，多孔管有效直径在 1.53 x10-3.4x10-3 m 之间。对模型的预测结果进行分析，并与预期值进行比较验证，使用四种统计标准评估其性能。结果表明，SVM、GPR 和 OBEM 模型的 RMSE 分别为 1.5471、1.8212 和 3.6978，而 MAE 误差分别为 1.1479、1.2418 和 2.9787。与尺寸分析方法的比较显示，所提出的模型在准确预测内部传热系数方面非常有效。模型显示出较低的不确定性，并在扩展数据集上保持了预测质量，没有过拟合问题。总之，这项研究为设计蒸汽压缩制冷和闭环动力循环中的热交换器和系统提供了有价值的见解。

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引用次数: 0

Performance investigation of a thermoelectric generator for vehicle exhaust recovery using graded pore density foam metal 使用分级孔隙密度泡沫金属的汽车尾气回收热电发电机性能研究

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102935

Improving the efficiency of thermoelectric generators (TEGs) used to harness residual heat from automobile exhausts is crucial for their widespread adoption. To enhance fluid heat transfer, the Kelvin tetrahedron model is employed for metal foam, and a multiphysical field model of the thermoelectric generator based on metal foam is established. The effects of inserting metal foam with uniform and gradient pore densities into the heat exchanger on the performance of the TEG are investigated. Experimental verification is conducted by constructing a test bench with dimensions identical to those of the model. The findings suggest that inserting foam metal significantly enhances the output performance of the TEG, resulting in increases in both output power and efficiency as pore density rises. At T_a = 573 K and m_a = 30 g/s, the output power of the TEG with inserted 20 PPI foam metal is enhanced by 140.46 %, while the efficiency experiences a remarkable increase of 197.50 % compared to a smooth pipe. Compared to the performance metrics of uniform foam metal, the positive gradient foam metal exhibits a maximum power increase of 7.89 % and a maximum efficiency increase of 34.46 %, along with an average pressure drop reduction of 27.29 %.

提高用于利用汽车尾气余热的热电发生器（TEG）的效率对其广泛应用至关重要。为了增强流体传热，对金属泡沫采用了开尔文四面体模型，并建立了基于金属泡沫的热电发生器多物理场模型。研究了在热交换器中插入均匀孔密度和梯度孔密度的金属泡沫对 TEG 性能的影响。通过构建与模型尺寸相同的试验台进行了实验验证。研究结果表明，插入泡沫金属可显著提高 TEG 的输出性能，随着孔隙密度的增加，输出功率和效率都会提高。在 Ta = 573 K 和 ma = 30 g/s 条件下，与光滑管道相比，插入 20 PPI 泡沫金属的 TEG 输出功率提高了 140.46%，效率显著提高了 197.50%。与均匀泡沫金属的性能指标相比，正梯度泡沫金属的最大功率提高了 7.89%，最大效率提高了 34.46%，平均压降降低了 27.29%。

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引用次数: 0

A reconfigurable architecture for maximizing energy harvesting of thermoelectric generators in non-stationary conditions 在非稳态条件下最大化热电发电机能量收集的可重构架构

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102932

The use of Thermoelectric Generators (TEGs) has proliferated across a multitude of applications for energy harvesting. As more modules are employed to recover greater amounts of energy, the temperature mismatch between them increases. This results in each module operating at a distinct maximum power point, thereby reducing the overall system efficiency. Furthermore, in dynamic applications such as automotive scenarios, the temperatures of the thermoelectric generators are not constant, and the maximum power point accordingly shifts. A fixed architecture is unable to cope with these fluctuating situations. Therefore, this paper introduces a reconfigurable architecture capable of harnessing maximum energy at any given moment, improving energy recovery compared to a fixed architecture. Optimization techniques, lean methodologies, and clustering approaches are employed to efficiently design the reconfigurable TEG, which enables modification of the electrical connections inside the TEG modules and the number of Maximum Power Point Tracking (MPPT) modules. A use case is presented where the reconfigurable TEG is compared with fixed, yet optimized, TEG configurations under mixed driving modes. In this specific case, the results demonstrate that the reconfigurable TEG achieves enhanced performance in dynamic environments with two MPPTs under mixed scenarios, reaching an efficiency of 96.3% and a 0.29% improvement in energy recovery.

热电发生器 (TEG) 的使用已在能量收集的众多应用领域中激增。随着越来越多的模块被用于回收更多的能量，它们之间的温度失配也随之增加。这导致每个模块在不同的最大功率点上运行，从而降低了整个系统的效率。此外，在汽车等动态应用中，热电发电机的温度并不恒定，最大功率点也会相应变化。固定架构无法应对这些波动情况。因此，本文介绍了一种可重新配置的架构，能够在任何特定时刻利用最大能量，与固定架构相比，提高了能量回收率。本文采用了优化技术、精益方法和聚类方法来有效设计可重新配置的 TEG，从而能够修改 TEG 模块内部的电气连接和最大功率点跟踪 (MPPT) 模块的数量。本文介绍了一个使用案例，将可重新配置的 TEG 与固定但经过优化的 TEG 配置在混合驱动模式下进行比较。在这一具体案例中，结果表明可重构 TEG 在混合场景下使用两个 MPPT 的动态环境中实现了更高的性能，效率达到 96.3%，能量回收率提高了 0.29%。

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引用次数: 0

Fast-airflow tumble clothes dryer with small thermoelectric heat pump: Experimental evaluation 带小型热电热泵的快速气流滚筒式干衣机：实验评估

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102960

Residential clothes drying accounts for about 5 % of the total residential-sector energy consumption in the United States. Most dryers use electric resistance heaters to dry clothes and have low efficiencies. Higher-efficiency dryers that use vapor compression heat pumps are expensive and complex and have not gained a large market share in the United States. A novel tumble clothes dryer using a small thermoelectric heat pump with faster airflow than typical dryers is presented in this work. The benchtop performance of the thermoelectric heat pump and high-speed blower are presented, and the development of the prototype dryer is described. The dryer was tested for efficiency and dry time for a range of airflow rates and applied currents to the thermoelectric heat pump. The combined efficiency factor was 5.09–6.29 lb_BDW/kWh (specific moisture extraction rate of 1.23–1.53 kg_w/kWh) with 100–138 min dry times for these tests. The measured efficiency was 36 %–68 % greater than the minimum efficiency standard in the United States, and compared with vapor compression heat pump–based clothes dryers, the prototype dryer had less expensive, less complex components and did not use refrigerants. The performance of this small thermoelectric heat pump clothes dryer is also compared with previous iterations of the thermoelectric tumble clothes dryer described in the literature.

在美国，住宅烘干衣物的能耗约占住宅部门总能耗的 5%。大多数烘干机使用电阻电加热器烘干衣物，效率较低。使用蒸汽压缩热泵的高效烘干机既昂贵又复杂，在美国的市场份额不大。本文介绍了一种新型滚筒式干衣机，它使用小型热电热泵，气流速度比一般干衣机快。文中介绍了热电热泵和高速鼓风机的台式性能，并描述了干衣机原型的开发过程。在一系列气流速率和热电热泵应用电流条件下，对干燥机的效率和干燥时间进行了测试。在这些测试中，综合效率系数为 5.09-6.29 磅BDW/千瓦时（特定水分提取率为 1.23-1.53 公斤瓦/千瓦时），干燥时间为 100-138 分钟。测得的效率比美国的最低效率标准高出 36 %-68 %，与基于蒸汽压缩热泵的干衣机相比，原型干衣机的成本更低、组件更简单，而且不使用制冷剂。该小型热电热泵干衣机的性能还与文献中描述的热电滚筒式干衣机的前几代产品进行了比较。

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引用次数: 0

Simulation of metal fatigue crack analysis based on thermal environment numerical analysis in street metal sculpture design process 街头金属雕塑设计过程中基于热环境数值分析的金属疲劳裂纹模拟分析

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102965

With the popularity of urban public art, metal materials are easily affected by environmental factors in practical applications, especially the influence of thermal environment on metal fatigue, which often leads to cracks in sculptures, affecting their service life and safety. Through the numerical analysis of thermal environment, the fatigue crack generation mechanism of street metal sculpture in the process of use is deeply discussed in order to provide theoretical support and practical guidance for sculpture design. The finite element analysis method is used to simulate the stress distribution of metal sculpture under different thermal conditions. By setting a variety of conditions such as ambient temperature and humidity, the corresponding thermodynamic model is established, and the fatigue properties of metal materials are evaluated comprehensively. The numerical model is calibrated with experimental data to improve the accuracy of the simulation. It is found that the change of thermal environment significantly affects the internal stress distribution of metal sculpture, and then affects the generation of fatigue cracks. At high temperature, the fatigue limit of the material is reduced, which leads to more cracks. Under the condition of sudden temperature change, the stress concentration of the material is more obvious, and the fatigue life of the metal is significantly shortened.

随着城市公共艺术的普及，金属材料在实际应用中很容易受到环境因素的影响，特别是热环境对金属疲劳的影响，往往会导致雕塑出现裂纹，影响其使用寿命和安全性。通过对热环境的数值分析，深入探讨街头金属雕塑在使用过程中的疲劳裂纹产生机理，以期为雕塑设计提供理论支持和实践指导。采用有限元分析方法，模拟金属雕塑在不同热环境下的应力分布。通过设置环境温度、湿度等多种条件，建立相应的热力学模型，全面评价金属材料的疲劳性能。通过实验数据对数值模型进行校准，提高了模拟的准确性。研究发现，热环境的变化会显著影响金属雕塑的内应力分布，进而影响疲劳裂纹的产生。在高温条件下，材料的疲劳极限降低，从而产生更多裂纹。在温度骤变的情况下，材料的应力集中更加明显，金属的疲劳寿命明显缩短。

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引用次数: 0

Simulation of economic benefit prediction model for green energy manufacturing based on production process thermal energy cycle and data mining 基于生产过程热能循环和数据挖掘的绿色能源制造经济效益预测模型模拟

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102943

With the global focus on sustainable development and green manufacturing, there is an urgent need for companies to optimize their production processes to improve energy efficiency and reduce carbon emissions. An economic benefit prediction model based on thermal energy cycle and data mining in production process was developed to evaluate and optimize the economic benefit in green energy manufacturing process and provide theoretical support for enterprise decision-making. The thermal energy cycle model in the production process is studied and constructed, and its application in different production links is analyzed. Data mining technology is used to analyze historical production data to identify the key factors affecting the efficiency of thermal energy cycle. By constructing regression models and time series analysis, we predict the economic benefits under different optimization strategies. The simulation results show that by optimizing the thermal energy cycle, the energy utilization efficiency can be significantly improved, the production cost can be reduced, and the environmental impact can be reduced. Therefore, the combination of heat cycle optimization and data mining provides an effective economic benefit prediction tool for green energy manufacturing. Enterprises in the implementation of green manufacturing, should pay attention to the improvement of heat energy cycle, in order to achieve higher economic and environmental benefits, to contribute to sustainable development.

随着全球对可持续发展和绿色制造的关注，企业迫切需要优化生产流程，提高能源效率，减少碳排放。建立了基于生产过程热能循环和数据挖掘的经济效益预测模型，以评估和优化绿色能源生产过程中的经济效益，为企业决策提供理论支持。研究并构建了生产过程中的热能循环模型，分析了该模型在不同生产环节中的应用。利用数据挖掘技术分析历史生产数据，找出影响热能循环效率的关键因素。通过构建回归模型和时间序列分析，预测不同优化策略下的经济效益。仿真结果表明，通过优化热能循环，可以显著提高能源利用效率，降低生产成本，减少对环境的影响。因此，热能循环优化与数据挖掘相结合，为绿色能源制造提供了有效的经济效益预测工具。企业在实施绿色制造时，应重视热能循环的改善，以实现更高的经济效益和环境效益，为可持续发展做出贡献。

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引用次数: 0

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model 基于热能优化和模糊数学模型的复杂制造系统在线调度数学优化策略

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102949

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

随着制造业的快速发展和智能化进程的推进，复杂制造系统的调度问题变得越来越复杂，尤其是在能源管理和优化方面。本研究旨在提出一种基于热能优化和模糊数学模型的在线排产数学优化策略，以提高复杂制造系统在不同生产条件下的排产效率和资源配置合理性。利用模糊数学建立了一个考虑热能利用率、生产优先级和实时需求的调度模型。该模型通过模糊逻辑处理不确定因素，并通过遗传算法求解，实现了生产资源的动态调整和优化调度。通过对复杂制造系统的实验验证，调度效率的提高不仅降低了运营成本，还提高了系统的灵活性和响应速度。基于热能优化的模糊数学模型为复杂制造系统提供了有效的在线调度策略，可实现动态环境下的高效调度和能源管理。

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引用次数: 0

Adaptive optimization of thermal energy and information management in intelligent green manufacturing process based on neural network 基于神经网络的智能绿色制造过程中热能与信息管理的自适应优化

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102953

The study of thermal energy adaptive optimization has important theoretical and practical significance. The deep learning neural network model is used to monitor and analyze the thermal energy data in the manufacturing process in real time. Through the construction of adaptive optimization algorithm, the heat input and output are systematically evaluated and adjusted, and the heat distribution scheme is dynamically optimized according to environmental changes and production needs. At the same time, information management system is introduced to realize data summary, feedback and decision support. The experimental results show that the proposed method can effectively reduce the heat energy loss, optimize the heat energy utilization, and significantly reduce the overall energy consumption compared with traditional management methods. With real-time data updates, the system improves the flexibility and responsiveness of the production process, significantly improving manufacturing efficiency. The thermal energy adaptive optimization method based on neural network provides an effective solution for intelligent green manufacturing, which can not only optimize thermal energy management, but also provide a reference for other resource conservation and environmental protection.

热能自适应优化研究具有重要的理论和实践意义。利用深度学习神经网络模型，对生产过程中的热能数据进行实时监测和分析。通过构建自适应优化算法，对热能输入和输出进行系统评估和调整，并根据环境变化和生产需求动态优化热能分配方案。同时，引入信息管理系统，实现数据汇总、反馈和决策支持。实验结果表明，与传统管理方法相比，所提出的方法能有效减少热能损耗，优化热能利用率，大幅降低综合能耗。通过实时数据更新，该系统提高了生产过程的灵活性和响应速度，显著提高了生产效率。基于神经网络的热能自适应优化方法为智能绿色制造提供了有效的解决方案，不仅能优化热能管理，还能为其他资源节约和环境保护提供参考。

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引用次数: 0

A comparative study on thermal behavior of PEG 400 and two oxide nanocolloids PEG 400 和两种氧化物纳米胶体热行为的比较研究

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress

Pub Date : 2024-10-01 DOI: 10.1016/j.tsep.2024.102968

Fluids plays a significant role in all heating and cooling processes, while their thermal conductivity is extremely relevant for a large number of heat exchange applications. This paper mainly discusses the results of an experimental study on thermal conductivity of PEG 400 and two oxide nanocolloids with MgO and TiO₂, together with an analysis on Prandtl number, thermal diffusivity and performance evaluation criteria. Several samples (i.e. 11), with different nanoparticle concentration, were manufactured, analyzed and all the experimental data were discussed and further compared with current findings on new heat transfer fluids. Collected experimental data outlined that the thermal conductivity increases with nanoparticle addition and highly depends on the nanoparticle type. Plus, temperature variation does not affect the thermal conductivity of studied samples, while Pr number for all samples follows the behavior of fluids viscosity. For example, Pr number is decreasing with MgO concentration, while the diffusivity increases by 33.86 % for PEG + 2.50 MgO. Concluding, the combined analysis on Prandtl number and thermal diffusivity revealed that the suspensions with MgO have a better behavior at heat transfer if compared with nanocolloids with TiO₂.

流体在所有加热和冷却过程中都发挥着重要作用，而流体的导热性则与大量热交换应用息息相关。本文主要讨论了 PEG 400 和两种氧化物纳米胶体（氧化镁和二氧化钛）的导热性实验研究结果，以及对普朗特尔数、热扩散率和性能评估标准的分析。对不同纳米粒子浓度的多个样品（即 11 个）进行了制造和分析，并对所有实验数据进行了讨论，还进一步与当前新型导热流体的研究结果进行了比较。收集到的实验数据表明，热导率会随着纳米粒子的添加而增加，并且在很大程度上取决于纳米粒子的类型。此外，温度变化不会影响所研究样品的热导率，而所有样品的 Pr 值都与流体粘度有关。例如，Pr 数随氧化镁浓度的增加而降低，而 PEG + 2.50 氧化镁的扩散率则增加了 33.86%。总之，对普朗特尔数和热扩散率的综合分析表明，与含有二氧化钛的纳米胶体相比，含有氧化镁的悬浮液具有更好的传热性能。

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引用次数: 0