ANN model to predict the performance of parabolic dish collector with tubular cavity receiver-Reference-Cited by-同舟云学术

ANN model to predict the performance of parabolic dish collector with tubular cavity receiver

Published:2017 Issue:4 Volume:18 Page:408
ISSN:2257-7777
Container-title:Mechanics & Industry
language:
Short-container-title:Mechanics & Industry

Author:

Loni Reyhaneh,Kasaeian Alibakhsh,Shahverdi Kazem,Askari Asli-Ardeh Ezzatollah,Ghobadian Barat,Ahmadi Mohammad H.

Publisher

EDP Sciences

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,General Materials Science

Link

http://www.mechanics-industry.org/10.1051/meca/2017016/pdf

Reference32 articles.

1. Solar thermal collectors and applications

2. Thermal performance of solar concentrator/cavity receiver systems

3. An analysis of convective losses from cavity solar central receivers

4. Le Quere P., Penot F., Mirenayat M., Experimental study of heat loss through natural convection from an isothermal cubic open cavity, Sandia Laboratory Report, 1981, SAN D81-8014

5. Prediction and optimization of the performance of parabolic solar dish concentrator with sphere receiver using analytical function

Cited by 49 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ORR Catalysts Based on Carbon Nanotubes and Metal Phthalocyanines Obtained by High-Temperature Synthesis;International Journal of Self-Propagating High-Temperature Synthesis;2024-03

2. Numerical investigation to optimize the modified cavity receiver for enhancement of thermal performance of solar parabolic dish collector system;Energy;2024-03

3. Integrated optical-thermal model and deep learning technique to estimate the performance of a conical cavity receiver coupled solar parabolic dish collector;Energy Conversion and Management;2024-02

4. Optical efficiency and convective heat loss of a cylindrical-hemispherical receiver used in parabolic dish concentrator;Journal of Thermal Analysis and Calorimetry;2024-01-30

5. Multi-objective optimizations of solar receiver based on deep learning strategy in different application scenarios;Solar Energy;2024-01