Scholarly work in the Department of Physics with Electronics

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Browsing Scholarly work in the Department of Physics with Electronics by Author "Akinnawo Olumide Olufemi"

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    Evaluation of Lateritic Soil Using 2-D Electrical Resistivity Methods at Alapoti, Southwestern Nigeria
    (Global Journal of Pure and Applied Sciences, 2021-04) Onaiwu Kingsley Nosakhare; Akinnawo Olumide Olufemi; Adeola Adewole John; Usifo Abel Giwa
    A 2-D resistivity survey was carried out in Alapoti, Ogun State, a sedimentary terrain of South-western Nigeria. This area lies between longitude 0060 34′0″N and 0060 40′0″N and latitude 00302′0″E and 00306′0″E. The wenner alpha electrode configuration was engaged through out in this study. Ten profiles were covered; five in the north-south direction, and the other five in the west-east direction. To obtain a good 2-D picture of the subsurface, the coverage of the measurements must be 2-D as well. The distance between adjacent traverse is 25 metres. The data from each 2-D survey line was inverted independently with RES2DINV to give 2-D cross-sections with averages of 4.8 iteration and RMS error of 8.15%. A contoured pseudosection conveys a qualitative two- dimensional resistivity variation with depth within the subsurface. The inversed model resistivity sections created models for the subsurface resistivity using an iterative smoothness constrained least square inversion and are interpreted to generate the subsurface geologic characteristics. Results from 2-D inversed resistivity section showed that the second layer with resistivity value of about 200 m to 600 m and thickness of about 4.0m is composed of lateritic clay. The third layer is made up of moderate laterite having a thickness of about 3.0m and apparent resistivity ranging from 600 m to 1,000 m, while the fourth layer of apparent resistivity value 1,000 m to 1,500 m is laterite but rich in sand and it is located at a deep of about 12.0m.
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    Fuzzy Model, Neural Network and Empirical Model for the Estimation of Global Solar Radiation for Port-Harcourt, Nigeria
    (Journal of Scientific Research & Reports, 2017) Akinnawo Olumide Olufemi; Adebayo Oluwaseun Caleb; Usifo Abel Giwa; Ogundele Abiodun Kazeem
    The invaluable role of the estimation of global solar radiation in solar engineering systems provides very useful direction for various solar applications. This paper employs the Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and regressive technique for the prediction of global solar radiation(GSR) on horizontal surface using temperature swing and relative humidity as input parameters covering years 1981 to 2005. The performance of the models was tested using statistical indicators such as mean bias error (MBE), root mean square error (RMSE), and correlation coefficient (CC). The results with ANFIS and ANN method provide a relatively better prediction with ANFIS the more preferable.
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    Models for Prediction of Global Solar Radiation On Horizontal Surface for Akure, Nigeria
    (Global Journal of Pure and Applied Sciences, 2016) Akinnawo Olumide Olufemi; Oludotun James Samuel Oladunjoye; Usifo Abel Giwa; Adejuwon Samuel Oluyemi
    The estimation of global solar radiation continues to play a fundamental role in solar engineering systems and applications. This paper compares various models for estimating the average monthly global solar radiation on horizontal surface for Akure, Nigeria, using solar radiation and sunshine duration data covering years 1981 to 1995. The analysis was performed using Angstrom models, two dimensional principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The performance of the models were tested using statistical indicators such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and correlation coefficient (CC). The results indicated that ANFIS and linear regression analysis provide relatively higher degree of prediction, with the performance of ANFIS slightly better.