Thermal Science and Engineering Progress最新文献_第10页

Thermodynamic evaluation of solar energy-based methanol and hydrogen production and power generation pathways: A comparative study 基于太阳能的甲醇和氢气生产及发电途径的热力学评估：比较研究

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-17 DOI: 10.1016/j.tsep.2024.102911

{"title":"Thermodynamic evaluation of solar energy-based methanol and hydrogen production and power generation pathways: A comparative study","authors":"","doi":"10.1016/j.tsep.2024.102911","DOIUrl":"10.1016/j.tsep.2024.102911","url":null,"abstract":"<div><div>This work presents a comparative evaluation of two distinct fuels, methanol and hydrogen, production and power generation routes via fuel cells. The first route includes the methanol production from direct partial oxidation of methane to methanol using solar energy, where the methanol is condensed, stored, and sent to a direct methanol fuel cell. The second route is hydrogen production from solar methane cracking (named as turquoise hydrogen), where heat is supplied from concentrated solar power, and hydrogen is stored and directed to a hydrogen fuel cell. This study aims to provide insights into these fuels' production conditions, storage methods, energy, and exergy efficiencies. The proposed system is simulated using the Engineering Equation Solver software, and a thermodynamic analysis of the entire system, including all the equipment and process streams, is performed. The methanol and hydrogen route’s overall energy and exergy efficiencies are 39.75 %, 38.35 %, 34.21 %, and 33 %, respectively. The highest exergy destruction rate of 1605 kW is observed for the partial oxidation of methane to methanol. The methanol and hydrogen routes generate 32.087 MWh and 11.582 MWh of electricity for 16-hour of fuel cell operation for the same amount of methane feedstock, respectively. Sensitivity analysis has been performed to observe the effects of different parameters, such as operating temperature and mass flow rate of fuels, on the electricity production and energy efficiencies of the systems.</div></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Validity of the quasi-2D optimal variable density lattice for effective liquid cooling based on Darcy–Forchheimer theory 基于达西-福克海默理论的有效液体冷却准二维最佳可变密度晶格的有效性

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-17 DOI: 10.1016/j.tsep.2024.102898

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Thermodynamic and economic analysis of a Kalina cycle-based combined heating and power system for low-temperature heat source utilization 基于卡利纳循环的低温热源利用供热发电联合系统的热力学和经济分析

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102904

{"title":"Thermodynamic and economic analysis of a Kalina cycle-based combined heating and power system for low-temperature heat source utilization","authors":"","doi":"10.1016/j.tsep.2024.102904","DOIUrl":"10.1016/j.tsep.2024.102904","url":null,"abstract":"<div><p>The Kalina cycle is effective for recovering and utilizing low-temperature heat sources, but it suffers from low efficiency. In order to enhance energy conversion efficiency, this paper proposes a novel combined heating and power system that integrates the Kalina cycle with an ammonia-water absorption refrigeration cycle. This system uniquely recovers all wasted heat to generate heating capacity, achieving 100 % thermal efficiency. Detailed thermodynamic and economic models are developed, based on which a performance optimization is conducted and shows that the new system generally outperforms the Kalina cycle with an exergy efficiency of 34.47 % and a payback period of 2.31 years. Further performance analysis reveals that turbine power output significantly impacts economic performance, and most exergy destructions occur in the heat exchanger-type equipment. Finally, a parameter sensitivity analysis explores the effects of seven key variables on system performance. Results indicate that increasing the ammonia concentration of the basic solution and turbine inlet temperature, while decreasing the turbine inlet pressure, improves the exergy efficiency of system. An optimal ammonia concentration of the ammonia-strong solution is identified for achieving the shortest payback period.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization 基于物联网技术和热能优化的工业 4.0 电力系统故障诊断与智能维护

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102902

{"title":"Fault diagnosis and intelligent maintenance of industry 4.0 power system based on internet of things technology and thermal energy optimization","authors":"","doi":"10.1016/j.tsep.2024.102902","DOIUrl":"10.1016/j.tsep.2024.102902","url":null,"abstract":"<div><p>The application of Internet of Things technology provides a new opportunity for the fault diagnosis and maintenance of power system. This study aims to explore how to improve the fault diagnosis ability of power system through Internet of Things technology, and realize the efficient utilization of heat energy, so as to achieve the goal of intelligent maintenance. Based on the analysis of current power system operation status, this paper determines the key role of thermal energy management in improving system operation efficiency and reducing energy consumption. It then uses iot sensors and data analytics to monitor heat flow and loss in the power system in real time, identifying potential points of failure through big data. In order to realize intelligent maintenance, this paper designs a fault prediction model based on thermal energy optimization, combined with machine learning algorithm, to further improve the accuracy of fault diagnosis. The experimental results show that the fault diagnosis method combined with the Internet of Things technology can significantly reduce the fault incidence and optimize the efficiency of heat energy use. By applying this model, the overall operating cost of the power system is reduced and the maintenance efficiency is improved.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Simulation of indoor thermal energy radiators and visualization design of indoor environment layout based on genetic algorithm and light sensor 基于遗传算法和光传感器的室内热能辐射器仿真与室内环境布局可视化设计

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102903

{"title":"Simulation of indoor thermal energy radiators and visualization design of indoor environment layout based on genetic algorithm and light sensor","authors":"","doi":"10.1016/j.tsep.2024.102903","DOIUrl":"10.1016/j.tsep.2024.102903","url":null,"abstract":"<div><p>With the increasing attention to comfortable living environment and energy efficiency, indoor thermal energy management has become an important task in architectural design. Reasonable heat dissipation system can not only improve indoor comfort, but also reduce energy consumption. The aim of this study is to optimize the layout of indoor heat radiator by genetic algorithm and visualization design of indoor environment combined with light sensor, so as to improve the efficiency and comfort of space heat utilization. The indoor heat dissipation model is established, and the changes of indoor temperature and light are monitored by light sensor in real time. Then genetic algorithm is used to optimize the layout of the radiator, considering the heat distribution and light intensity at different locations. The impact of different design schemes on indoor environment was evaluated through simulation experiments. After many iterations, the optimized radiator layout shows a more uniform heat distribution and significantly improved indoor comfort, and the data from the light sensor provides real-time feedback on the environmental layout, making the design solution more realistic. Therefore, the optimization method based on genetic algorithm can effectively improve the layout efficiency of indoor heat radiator, combined with the application of light sensor, can achieve a more scientific indoor environment design.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of thermal energy optimization based on improved neural network algorithm in green innovation capability evaluation of manufacturing enterprises 基于改进神经网络算法的热能优化在制造企业绿色创新能力评价中的应用

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102899

{"title":"Application of thermal energy optimization based on improved neural network algorithm in green innovation capability evaluation of manufacturing enterprises","authors":"","doi":"10.1016/j.tsep.2024.102899","DOIUrl":"10.1016/j.tsep.2024.102899","url":null,"abstract":"<div><p>As the global focus on sustainable development continues to increase, manufacturing companies face the dual challenges of energy efficiency and environmental impact in the process of enhancing green innovation capabilities. The purpose of this study is to explore the application of improved neural network algorithm in thermal energy optimization, so as to improve the green innovation ability of manufacturing enterprises and promote their sustainable development. By constructing an improved neural network model and using big data analysis technology, this paper conducts in-depth analysis of thermal energy use in manufacturing enterprises, so as to identify and optimize energy consumption patterns. The study considered a variety of influencing factors, including equipment efficiency, production process and environmental policy, and evaluated the optimization effect through model training and testing. The experimental results show that the improved neural network algorithm can effectively identify thermal energy waste points, and put forward the corresponding optimization measures. After optimization, the energy use efficiency of manufacturing enterprises has been improved, carbon emissions have been significantly reduced, and the comprehensive evaluation score of green innovation ability has been improved, providing effective technical support for promoting the sustainable development of enterprises.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of the thermal properties and operation temperature for metal–organic frameworks and amine-functionalized metal–organic frameworks/epoxy/novolac composites: A comparative study 评估金属有机框架和胺功能化金属有机框架/环氧树脂/Novolac 复合材料的热性能和操作温度：比较研究

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102905

{"title":"Assessment of the thermal properties and operation temperature for metal–organic frameworks and amine-functionalized metal–organic frameworks/epoxy/novolac composites: A comparative study","authors":"","doi":"10.1016/j.tsep.2024.102905","DOIUrl":"10.1016/j.tsep.2024.102905","url":null,"abstract":"<div><p>Metal–organic frameworks (MOFs) are known for their excellent physical and thermal properties. In the current paper, the use of thermally stable MOFs in the preparation of epoxy composites were studied and their tolerance to high temperatures was investigated in terms of degradation kinetics and operating temperature. UiO-66 and UiO-66-NH<sub>2</sub> were used to prepare a series of novel composites from epoxy resin and Novolac (EU and EUN samples, respectively). The effect of the amine groups presented in the UiO-66-NH<sub>2</sub> structure on the thermal stability was studied using decomposition activation energy (E<sub>a</sub>). The Flynn–Wall–Ozawa (FWO), Kissinger-Akahira-Sunose (KAS) and Ozawa models were used to study the E<sub>a</sub>, where it was increased from 166.7 kJ·mol<sup>−1</sup> in neat epoxy samples to 238.58 kJ·mol<sup>−1</sup> in EUN samples by using only 0.5 Phr of the UiO-66-NH<sub>2</sub>. Moreover, the operating temperature of the prepared composites was calculated and compared for four sets of heating rates. Up to 10 % mass loss, the mean operating temperature for using the neat epoxy, EU, and EUN composites for 20,000 h, was found to be 184.17 ℃, 246.26 ℃, and 247.73 ℃, respectively. This approach can pave the way for using MOFs as fillers in preparing innovative thermoset composites.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Mechanistic impact of Stern bilayer-based electrolysis on the enhancement of pool boiling heat transfer 基于斯特恩双电层的电解对增强池沸腾传热的机理影响

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102908

{"title":"Mechanistic impact of Stern bilayer-based electrolysis on the enhancement of pool boiling heat transfer","authors":"","doi":"10.1016/j.tsep.2024.102908","DOIUrl":"10.1016/j.tsep.2024.102908","url":null,"abstract":"<div><p>Pool boiling is a heat transfer method that utilizes phase change for efficient heat transfer, and enhancing its heat transfer intensity and understanding the mechanism is of great practical significance for refrigeration and microelectronics thermal management. This study employs experimental methods to innovatively use ionic surfactant modification on heating surfaces, building on the basis of strengthening heat transfer by generating hydrogen bubbles through water electrolysis to increase nucleation sites on the heating surface. The Stern double layer formed on the heating surface is utilized to control the growth rate and quantity of hydrogen bubbles, thus achieving controllable nucleation density on the heating surface. The study examines its impact on heat transfer efficiency and the onset of nucleate boiling (ONB). Results indicate that electrolysis can increase nucleation sites at low heat fluxes, thereby enhancing heat transfer. Using a CTAB solution at a concentration of 3200 ppm with an electrolytic current of 0.08A, under the Stern potential at saturated adsorption, the heat transfer coefficient increased by up to 3.16 times. Additionally, the superheat at ONB decreased from 12.6 K to 4.4 K under boiling heat flux. Therefore, utilizing electrolysis with the addition of surfactants to enhance rapid cooling of high-temperature surfaces provides a novel engineering application approach.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization 基于人工智能算法和热能优化的电动汽车智能能源管理和运行效率

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102900

{"title":"Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization","authors":"","doi":"10.1016/j.tsep.2024.102900","DOIUrl":"10.1016/j.tsep.2024.102900","url":null,"abstract":"<div><p>As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design 室内设计中基于机器视觉模拟的热能资源利用和样本图像修复技术

IF 5.1 3区工程技术

Thermal Science and Engineering Progress Pub Date : 2024-09-16 DOI: 10.1016/j.tsep.2024.102897

{"title":"Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design","authors":"","doi":"10.1016/j.tsep.2024.102897","DOIUrl":"10.1016/j.tsep.2024.102897","url":null,"abstract":"<div><p>With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.</p></div>","PeriodicalId":23062,"journal":{"name":"Thermal Science and Engineering Progress","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0