Thermal Science and Engineering Progress最新文献

This study examines the intricate dynamics surrounding the deposition of Al₂O₃ nanoparticles within a heat exchanger, with the aim of optimizing heat transfer efficiency and gaining insights into gas dynamics. A comprehensive investigation of various parameters is conducted, including nanoparticle diameter ranging from 10 to 100 nm, heat flux variations from 500 to 3000 W/m², Reynolds numbers spanning from 308 to 1540, and mass fractions ranging from 0.5 to 8 %. The methodology integrates machine learning algorithms with Eulerian and Lagrange methods, leveraging Python programming to deepen the understanding of complex deposition processes. Through the integration of random forest algorithms and SHAP values, the study achieves a model accuracy of 96.74 %, supported by minimal mean absolute error (6E^-06) and root mean square error (2.5E^-03). Key findings reveal the profound impact of heat flux, particularly at 3000 W/m², on enhancing nanoparticle deposition. Furthermore, a direct correlation is observed between mass fraction and sedimentation, peaking at a mass fraction of 8 %. In laminar flow regimes, the Reynolds number profoundly influences sedimentation, with the sedimentation rate reaching its apex as the Reynolds number decreases. The diameter of nanoparticles also emerges as a crucial factor, with larger diameters correlating with increased sedimentation.

With the intensification of global climate change, the reduction of building energy consumption has become an important goal of smart city development. By optimizing the thermal energy circulation system, low energy buildings can not only reduce energy consumption, but also improve living comfort. In recent years, with the help of environmental sensors and deep learning technology, the intelligent management of building heat energy cycle has become a research hotspot. The research constructs an intelligent thermal energy circulation system that integrates multiple environmental sensors for real-time monitoring of indoor and outdoor temperature, humidity and other key environmental parameters. A deep learning algorithm is used to analyze the collected data to optimize the control strategy of the thermal energy cycle. Through simulation, the energy efficiency performance of the scheme under different climatic conditions and building types was evaluated. The experimental results show that the control strategy based on environmental sensors and deep learning can significantly improve the thermal energy utilization efficiency of low-energy buildings, and the average energy consumption is greatly reduced compared with the traditional management mode. The system shows good adaptability and stability under different climate conditions. Therefore, the application of environmental sensors and deep learning technology in the thermal energy cycle of low-energy buildings can effectively promote the energy efficiency management of smart cities.

For a turbine blade endwall, the leakage (purge flow) exits through rim seal to prevent hot gas ingress and provide some cooling to the endwall. However, changes in the rim seal geometry will inevitably affect the leakage flow over the endwall. To obtain a clearer and quantitative understanding of the effect of rim seal variation on the cooling performance and flow structure, this paper investigated the effects of width and inclined angle of rim seal exit geometry, as well as the flow structure inside the gap and above the passage. A correlation fitting was constructed using the area-averaged cooling effectiveness to quantify the cooling performance on the suction side. The results indicate that the cooling performance of endwall is not sensitive to changes in width. However, a decrease in the inclined angle benefits the inhibition of the cavity vortex, prevents the gas ingress and improves the cooling performance over the endwall. The cooling performance of blade suction side is sensitive to the leakage mass flow ratio. As the mass flow rate increases from 0.5 % to 1.0 % and 1.5 %, the area-averaged cooling effectiveness on suction side for original case increases by 44.4 % and 30.7 %. The correlation established for the cooling of suction side shows a good fit, which provide a possible evaluation method for cooling design.

Phase Change Materials (PCMs) offer significant benefits for applications such as electronic cooling and Thermal Energy Storage (TES) due to their high energy storage capacity and stability. However, their limited thermal conductivity restricts widespread utilization. To address this limitation, the use of fins has become a common technique to enhance heat transfer. Optimizing fin lengths and locations is challenging, as they affect natural convection. This study aims to develop a model using a feed-forward back-propagation network, trained on simulation data, for designing a TES unit. A design matrix, incorporating factors such as fin lengths and heights, is obtained through response surface methodology. Single- and multi-objective Genetic Algorithms (GAs) are employed to seek optimal solutions, considering Complete Melting Time (CMT) and total fin length. The results demonstrate the excellent performance of the network model, with an error of less than 2.98%. The single-objective GA yields a minimum CMT. In a finless enclosure, the CMT is 338.5% higher compared to the single-objective configuration. The multi-objective GA, using Euclidean distance specified from the Pareto front to balance weight and material costs, results in a CMT that is 192% higher compared to the single-objective optimization but with a total fin length that is 286% shorter. While these improvements are significant, it is important to note that fins can increase material and manufacturing costs, as well as the overall unit weight. This artificial intelligence-computational fluid dynamics method not only predicts TES unit performance but also reduces computational costs in designing an optimal TES unit.

Low-concentrator solar cell (SC) experimentally cooled using a new composite thermal regulation system of heat dissipator (HD) and phase change material (PCM) is investigated. The impact of area ratio (AR; HD area/SC area) of 1.5 and 2 at different HD thicknesses (h) on the system performance is reported. An energy, exergy, economic, and enviroeconomic assessments for the different cooling systems used for the SC thermal regulation are presented. The results show that the SC temperature reduction increases with increasing the HD thickness and AR. Compared with reference SC, SC-PCM/HD (AR=2/h = 3) achieves a maximum temperature reduction of 24 °C compared with 9.1 °C and 21.8 °C for SC-PCM and SC-HD, respectively. Moreover, SC-PCM/HD cooling system achieves the maximum enhancement in the average electrical efficiency and power output of 11.88 % and 12 %, respectively, compared with reference SC. The PCM thermal energy storage rate and PV system efficiency increase with increasing area ratio and decreasing the HD thickness. Using HD of (AR=2/h = 1) in conjunction with PCM yields the most favorable PCM thermal performance where it enhances the thermal energy storage, rate of energy storage, and overall thermal system efficiency by 9.5 %, 40 %, and 20.36 %, respectively, compared with using PCM only. Moreover, the PCM/HD cooling system is more economical than using PCM or HD only, and reference SC, with a maximum cost saving of 41.7 % compared with reference SC. The SC-PCM/HD cooling system is the most eco-friendly option with net CO₂ mitigation and ${\psi }_{{\mathrm{CO}}_2}$ of 146.2 kg and 5.84$, respectively compared with 143.7 kg and 5.74$, respectively for SC-PCM.

This paper is to investigate the feasibility of a natural draft cooling system for PV panel cooling in a typical hot and dry region (Hami, Xinjiang, China). A 3-D model of PV panels cooled by a natural draft cooling system is established using Fluent 2020R2 software, and the cooling effect of the natural draft cooling system on PV panels is simulated and analyzed based on the meteorological data of Hami, China. Simulation finds that the natural draft cooling system can reduce the average temperature of the PV panels. The minimum temperature drops of the PV panels in January (represents winter season), April (represents spring season), July (represents summer season) and October (represents autumn season) of the year 2020 are 1.7 °C, 3.1 °C, 6.1 °C and 2.5 °C, respectively; while the maximum temperature drops of the PV panels are 7.0 °C, 11.2 °C, 15.5 °C and 6.3 °C, respectively. Besides, the natural draft cooling system is also compared with the PV panels without cooling system in six typical days, and finds that the PV panel with a natural draft cooling system increases the photoelectric conversion efficiency by up to 1.41 % (i.e., from 13.71 % to 15.12 %) when compared with the PV panel without cooling. Therefore, the proposed natural draft cooling system can effectively reduce the temperature of PV panels in Hami, Xinjiang.

As the global focus on sustainable development continues to grow, green manufacturing, as a way to optimize resource use and reduce environmental impact, needs to be combined with advanced technologies to improve its efficiency. This study aims to explore the application of energy sustainability model based on optical sensing technology in smart storage of green manufacturing industry, evaluate its impact on storage performance management, and further promote the development of green manufacturing industry. In this paper, an optical sensing system is constructed to monitor and analyze the energy consumption in storage environment in real time. In this study, a green manufacturing enterprise was selected as an example, and the optical sensor was used to monitor energy consumption, inventory status and environmental parameters in real time to ensure the real-time and accuracy of data. The collected data is analyzed to identify patterns and anomalies in energy use to optimize inventory management and logistics scheduling. Evaluate the energy efficiency of storage systems by setting energy efficiency indicators and compare them with traditional models to quantify improvements. On the basis of monitoring and analysis, the energy management strategy is continuously adjusted to enhance the adaptability and flexibility of the intelligent storage system. The research results show that the application of optical sensing technology can significantly improve the energy management performance in warehousing, through real-time monitoring and intelligent optimization, enterprises can achieve more efficient energy use, further promote the implementation of green manufacturing, and provide strong support for the realization of environmental and economic benefits.

The present work focusses on 4E analysis of a 50 kW cooling capacity cascade refrigeration cycle covering the aspects of energy, exergy, economic and environment analysis. The numerical investigation and the multi-objective optimization is carried out for the system using the refrigerant pair R170-R600a and R41-R600a. The refrigerant pair is selected based on the environmental implications in terms of GWP and ODP. Multi-objective optimization of the objective functions is carried out using a heat transfer search optimization algorithm to evaluate the optimal performance of the system. The effect of evaporator temperature, condenser temperature, LTC condenser temperature and LTC condenser temperature difference on the exergy efficiency and total cost of the system is studied. A set of multiple optimal solutions is presented using the Pareto optimal curve and TOPSIS criteria is employed to select the optimal operating condition. Compared to the refrigerant pair R170-R600a, the system with R41-R600a operates at better exergy efficiency and lower total cost. At the TOPSIS selected optimal condition, exergy efficiency and the total cost of the CRS is 63.5 % and 65,228 $/year for the refrigerant pair R41-R600a and 62.6 % and 67,690 $/year for R170-R600a, respectively. The distribution of variables shows that the effect of the evaporation temperature, condensation temperature and the LTC condenser temperature is profound in obtaining the optimal solution.

Large length–diameter ratio high-temperature heat pipes (large l/d ratio HTHPs) demonstrate wide-ranging application potential in areas such as nuclear energy and solar energy development. Distinct in their geometric configurations, large l/d ratio HTHPs’ performance attributes significantly diverge from traditional high-temperature heat pipes (HTHPs). In this paper, a large l/d ratio HTHP is engineered with dimensions of Φ16 × 2400 mm, achieving an aspect ratio (l/d) of 150. The wick structure incorporates dual layers of 80-mesh 316 stainless steel wire mesh. Employing liquid sodium as the working fluid, experiments are conducted with filling ratios set at 15 %, 25 %, and 35 %, to evaluate its efficacy in heat transfer processes. This research delineates the analysis of evaporator thermal resistance and the effective thermal conductivity of large l/d ratio HTHPs across varying filling ratios and angles (0°, 15°, 30°, 45°, 60°, 75°, 90°). HTHPs with filling ratio of 25 % are selected to investigate the steady-state thermal transfer characteristics at different powers (2.0 kW, 2.5 kW, 3.0 kW), along with the variation patterns of the effective thermal conductivity and thermal resistance. The findings reveal that large l/d ratio HTHPs, specifically those with a 25 % filling ratio, demonstrate superior heat transfer capabilities at a tilt angle of 15°. With a heating power set at 3.0 kW, the thermal transfer efficiency progressively diminishes as the tilt angle is increased beyond this optimum point. It is observed that the increase in thermal resistance, which adversely affects heat transfer performance, primarily emanates from the evaporator section and intensifies with an increase in the tilt angle.

As a traditional art form, jade carving has gradually combined with modern technology in recent years, introducing new technologies in the design and production process. Indoor thermal environment has an important influence on the creation process of jade carving, especially the influence of temperature and humidity on the physical characteristics of jade carving materials and its processing. In this study, the influence of indoor thermal environment on jade carving design process was simulated by light sensing texture image processing technology, so as to optimize the design scheme and ensure the quality and artistic expression of jade carving works. The temperature, humidity and illumination data of indoor environment are obtained by light sensing technology, and the thermal environment required in jade carving design is simulated by texture image processing method. The thermal environment model was constructed to analyze the performance of jade carving materials under different conditions and evaluate the possible changes in the processing process. The simulation results show that the temperature and humidity of indoor thermal environment have significant effects on the toughness, hardness and detail performance of jade carving materials. At temperatures above 25° C and relatively low humidity, jade carving materials show better cutting results and surface finish. Texture image processing technology can effectively capture and analyze the subtle changes of materials, which provides a reliable basis for design decisions.