Sukhatme S.P, 'Principles of Solar Energy Collection and Storage', Tata-McGraw Hill. Publications 1996, pp 1-20. Barney Parsons and John Westwood,.
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Name: SOLAR ENERGY BY S P SUKHATME PDF Downloads: 1469 Update: December 24, 2015 File size: 15 MB SOLAR ENERGY BY S P SUKHATME PDF ಉಡುಪಿ ರಾಮಚಂದ್ರ ರಾವ್ 10 March 1932-24 July 2017) was a space scientist and chairman of the Indian. Empirical-Based Approach for Prediction of Global Irradiance and Energy for Solar Photovoltaic Systems. One of the major concerns of many countries today is that there is a mismatch between graduates’ skills, acquired solar energy by s p sukhatme pdf from higher education institutions.
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Solar energy by s p sukhatme pdf free download - WordPress.com. Solar energy by s p sukhatme pdf free download The revision of this text hallmark text on Solar Energy has been done keeping in. S P Sukhatme is Professor Emeritus. SOLAR ENERGY BY S P SUKHATME PDF - Name: SOLAR ENERGY BY S P SUKHATME PDF Downloads: 1469 Update: December 24, 2015 File size: 15 MB SOLAR ENERGY BY S P SUKHATME PDF ಉಡುಪಿ ರಾಮಚಂದ್ರ ರಾವ್ 10 March 1932-24 July 2017) was a space scientist and chairman of the Indian. SOLAR ENERGY BY S P SUKHATME PDF - Name: SOLAR ENERGY BY S P SUKHATME PDF Downloads: 1469 Update: December 24, 2015 File size: 15 MB SOLAR ENERGY BY S P SUKHATME PDF ಉಡುಪಿ ರಾಮಚಂದ್ರ ರಾವ್ 10 March 1932-24 July 2017) was a space scientist and chairman of the Indian.
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,1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria2Department of Metallurgical and Materials Engineering, Enugu State University of Science & Technology, Enugu, Nigeria3Project Development Institute, Enugu4Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria5Department of Environmental Technology, Federal University of Technology, Owerri, Nigeria. 2015, 9-13 DOI: 10.12691/ajcrr-3-1-3 Copyright © 2015 Science and Education Publishing Cite this paper: C. A Computational Mechanism for Analysis of the Functional Dependence of Solar Energy Transmissivity on Collector’s Latitude Angle and Exposure Time. American Journal of Computing Research Repository. 2015; 3(1):9-13. Doi: 10.12691/ajcrr-3-1-3.
Correspondence to: C. Nwoye, Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria.
This paper presents a factorial analysis of energy transmissivity by solar collector based on the collector exposure time and latitude angle of its location. A two-factorial model was derived and validated for the predictive analysis. The model structure highlighted the dependency of solar energy transmissivity on the collector exposure time and latitude angle of its location. Ζ = - 0.0021θ - 5 x 10 -6ϑ + 0.9081. The validity of the derived model was rooted on the core model expression ζ - 0.9081 = - 0.0021θ - 5 x 10 -6 ϑ where both sides of the expression are correspondingly approximately equal. Regression model was used to generate results of transmissivty, and its trend of distribution was compared with that from derived model as a way of verifying its validity relative to experimental results. The results of this verification translated into very close alignment of curves and significantly similar trend of data point’s distribution for experimental (ExD), derived model (MoD) and regression model-predicted (ReG) results.
Evaluations from generated results indicated that transmissivity per unit exposure time of collector and latitude angle of its location as obtained from experiment, derived model & regression model were 1.0545 x 10 -5, 1.0545 x 10 -5 & 1.0909 x 10 -5 (day) -1 and 0.0040, 0.0040 & 0.0042 deg.1 respectively. Standard errors incurred in predicting transmissivity for each value of the solar collector exposure time & latitude angle considered as obtained from experiment, derived model & regression model were 0.0003, 0.0002 & 2.1422 x 10 -5% and 0.0001, 0.0005 & 2.8396 x10 -5% respectively. The maximum deviation of model-predicted transmissivity (from experimental results) was less than 0.03% which is insignificant. This implies a model operational confidence level above 99.9%.
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