Browsing by Author "Igbudu, S. O."

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    Finite element modeling of an aluminum alloy automobile rim under static load
    (Obafemi Awolowo University, Ife, 2011) Fadare, D. A.; Odebunmi, O. O.; Igbudu, S. O.
    Rims are essential safety components for support, steering, mobility and break systems in automobile. Aluminum alloy rims are commonly used in the automobile industry due to their durability, light weight, high strength, good heat conductor, wear resistance and aesthetics characteristics. However, their structural integrity under diverse operating conditions is not well understood. In this study, the combined effects of static loads due to varying automobile weights and tyre air inflation pressures on the total displacement von Mises stress and principal strain of an aluminum alloy automobile rim (Toyota 6.0JXI5H2ET, CMS190CN604) was investignted using a commercially available 3-dimensional finite element code in FEMLAB 3.0. The effects of the loading condition were investigated at the point of contact of tyre with the ground; outboard and inboard bead seats; and the well. Results showed that maximum deflection occurred at the inboard bead seat, while the most stressed area occurred at the well. lncrease in automobile weight and tyre inflation pressure led to increased state of stress and strain. This study provides basic insights into the state of stress in aluminum alloy rims under diverse loading conditions.
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    Prediction of friction losses in spark-ignition engines: an artificial neural networks approach
    (2011) Fadare, D. A.; Idialu, E. E.; Igbudu, S. O.
    Artificial Neural Networks (ANNs) area prormsmg alternative to conventional tools in modeling and prediction of complex and non-linear parameters. However, the selection of appropriate network parameters for optimum performance pose application challenges. In this study, the modeling and predictive performances of six backpropagation learning algorithms: Levenberg-Marquardt (LM), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Fletcher-Powell Conjugate Gradient (CGF), Variable Learning Rate Backpropagation (GDX) and Bayesian Reglarization (BR) in solar radiation forecast were investigated. Multilayer perceptron (MPL) neural network with five, ten and one neuron(s) in the input, hidden and output layers, respectively was designed with MATLAB® neural network toolkit and trained with the six learning algorithms using the daily global solar radiation data of Ibadan (Lat. 7.4° N; Long. 3.90 E; Alt. 227.2m), Nigeria. The network performance was ranked based on the number of iterations required for convergence, and coefficient of correlation (r-value), mean square error (MSE) and mean absolute percentage error (MAPE) between the actual and predicted values of the training and testing datasets. Results showed that the LM and BR learning algorithms are the two best algorithms to be considered for use in modeling and forecasting of solar radiation data.