Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.

Application of Metaheuristic Algorithms for Optimizing Longitudinal Square Porous Fins

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms.A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method.The temperature of the base surface is higher than the fin surface,and the fin tip is kept adiabatic or cooled by convection heat transfer.The other pertinent parameters include Rayleigh number(100≤Ra≤10^(4)),Darcy number,(10^(−4)≤Da≤10^(−2)),relative thermal conductivity ratio of solid phase to fluid(1000≤kr≤8000),Nusselt number(10≤Nu≤100),porosity(0.1≤φ≤0.9).The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic algorithms.In computer science,metaheuristic algorithms are one of the most widely used techniques for optimization problems.In this research,three metaheuristic algorithms,including the firefly algorithm(FFA),particle swarm algorithm(PSO),and hybrid algorithm(FFAPSO)are employed to examine the performance of square fins.It is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms.

Second Law Analysis and Optimization of Elliptical Pin Fin Heat Sinks Using Firefly Algorithm

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size.These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate.They are designed to optimize overall performance.Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power.In this study,the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins.The pin-fins are arranged in an inline fashion.The nature-inspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems.Based on mass,energy,and entropy balance,three models are developed for thermal resistance,hydraulic resistance,and entropy generation rate in the heat sink.The major axis is used as the characteristic length,and the maximum velocity is used as the reference velocity.The entropy generation rate comprises the combined effect of thermal resistance and pressure drop.The total EGR is minimized by utilizing the firefly algorithm.The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors.It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm.It is demonstrated that the performance of FFA is much better than PPA.

Fast Online Diagnosis of Open-circuit Switching Faults in Flying Capacitor Multilevel Inverters

Flying capacitor multilevel(FCML)inverter is an attractive power converter topology which provides high-quality staircase output voltage waveforms by cascading flying capacitor cells.However,the large number of semiconductor devices utilized in the FCML inverters degrades the hardware reliability,which may constrain such converters from being applied in safety-critical applications.Targeting at open-circuit switching faults,a fast online fault diagnostic method for FCML inverters is presented.Conventional phase-shifted PWM(PSPWM),which can naturally balance the voltage across flying capacitors,is used as the modulation method in this work.Hence,to retain the simplicity feature of the PSPWM,the proposed diagnostic method is developed so that it does not require any voltage measurements of flying capacitors.Only the output AC voltage and current data along with the switching PWM signals from the microcontroller are needed to detect an open-circuit switching fault,and all such sensory data is typically available in the inverter,requiring no additional sensors or hardware for the implementation of this diagnostic method.The detection process takes 5% of the fundamental period of the inverter output signals to diagnose the faulty switch.Simulation and experimental results are presented to verify the effectiveness of the proposed diagnostic method.