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Effect of the Skew on the Hawt Performances 偏度对发动机性能的影响
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023036916
A. Miloud
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引用次数: 0
Calophyllum Inophyllum (Punnai Seed oil) derived biofuel blends: Thermal performance and engine emissions 蒲奈籽油衍生的生物燃料混合物:热性能和发动机排放
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023045430
Narendranathan Srinivasan Kasinathan, Vijayananth Suyamburajan, Jayaseelan Veerasundaram, Sudhakar Kumarasamy
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引用次数: 0
Evaluation of the environmental impact of energy sources for a thermal power plant 火力发电厂能源对环境的影响评价
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023046656
D. Babatunde, A. Anozie, J. Omoleye, O. Odejobi, O. Babatunde, C. Ehi-Eromosele
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引用次数: 0
Neural Network Analogy over Numerical Analysis on Thermo-Hydraulic performance factors j & f correlations development for Compact Heat Exchangers 热工水力性能因素数值分析中的神经网络类比[j]f紧凑型换热器相关系数的发展
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023047835
Naveen Kumar S, Chennu Ranganayakulu, Vinayak B Hemadri
A Compact Heat Exchanger (CHE) is a device used to transfer energy from one fluid to another, vital role in the efficient energy transfer. The optimum design of these Compact heat exchangers is a challenging skill for making minimum pumping power in terms of minimum pressure drop and efficient heat transfer. The generation of thermo-hydraulic performance factors, Colburn factor ‘j’ and fanning friction factor ‘f’ correlations for CHE take a large number of simulations in numerical analysis and the same is very expensive in modelling the model for Experimental analysis. In the open literature, experimentally developed fins performance as a function of Reynolds number and geometric parameters, which is expensive. The Numerical model has been developed by simulating Reynolds number Re and geometric parameters, such as fin height h, fin spacing s and fin thickness t for generation of fanning friction factor f and Colburn factor j. The 144 fin geometric parameters are used in the numerical model to develop a correlation. The numerical model is analyzed using ANSYS Fluent®. This tremendous process of correlation development is faster by using Artificial Neural Networks (ANN). This Paper focused on developing design data requirements for rectangular plain fin compact heat exchangers using CFD and Neural Networks(NN). The Analogy of correlation developed by Neural Network Prediction and CFD Fluent are verified and validated using open literature. The correlations from Neural Networks using limited data are faster and has better agreement with open literature.
紧凑型热交换器(CHE)是一种用于将能量从一种流体传递到另一种流体的设备,在有效的能量传递中起着至关重要的作用。这些紧凑型热交换器的优化设计是一项具有挑战性的技术,以使泵送功率最小,压力降最小,传热效率最低。在数值分析中,热工性能因子、科尔伯恩因子“j”和扇形摩擦因子“f”相关性的产生需要大量的模拟,同样,在为实验分析建立模型时也是非常昂贵的。在公开的文献中,实验研究的翅片性能是雷诺数和几何参数的函数,这是昂贵的。通过模拟雷诺数Re和翅片高度h、翅片间距s、翅片厚度t等几何参数产生扇风摩擦系数f和科尔伯恩系数j,建立了数值模型。数值模型中采用144个翅片几何参数建立了相关关系。利用ANSYS Fluent®对数值模型进行了分析。使用人工神经网络(ANN)可以加快这一巨大的关联发展过程。本文主要研究利用CFD和神经网络(NN)建立矩形平面翅片紧凑型换热器的设计数据要求。利用公开文献对Neural Network Prediction和CFD Fluent开发的关联类比进行了验证和验证。使用有限数据的神经网络的相关性更快,并且与公开文献的一致性更好。
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引用次数: 0
Investigation of the Solid Oxide Fuel Cell (SOFC) by means of CFD simulation 固体氧化物燃料电池(SOFC)的CFD模拟研究
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023047868
T. Poojitganont, Adul Pinyolumlerd, Tucksaporn Ruanghiranwong, Boonchai Watchatrakul, H. Berg
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引用次数: 0
Preface for the Special Issue of Clearwater 2022 清水2022特刊前言
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023049308
Ashwani K. Gupta, R. Breault
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引用次数: 0
A Modeling Study on Ammonia and Ammonia/Hydrogen Kinetics for Gas Turbine Engines 燃气涡轮发动机氨和氨氢动力学建模研究
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023045330
C. Bedick, A. Tulgestke, P. Strakey
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引用次数: 0
Energy performance of insulated roofs for convenience store in hot-humid climate 湿热气候下便利店保温屋顶的能源性能研究
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023047395
P. Chaiwiwatworakul, Ranee Jan, S. Chirarattananon, Anurak Incham
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引用次数: 0
Forecasting of Thermal Energy Demand for Various Process Industries using Machine Learning Techniques 利用机器学习技术预测各种过程工业的热能需求
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023047190
Shivanand Gond, N. Krishnan, K. R. Kumar
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引用次数: 0
One-dimensional Transient Model of Product Temperature and Weight Loss Evolution in a Vacuum Cooler with Membrane Humidification 膜加湿真空冷却器中产品温度和失重演化的一维瞬态模型
Q2 Engineering Pub Date : 2023-01-01 DOI: 10.1615/interjenercleanenv.2023046935
Ronard Pana, M. Berana
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引用次数: 0
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International Journal of Energy for a Clean Environment
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