Natural Convection and Irreversibility of Nanofluid Due to Inclined Magnetohydrodynamics(MHD)Filled in a Cavity with Y-Shape Heated Fin:FEM Computational

This study explains the entropy process of natural convective heating in the nanofluid-saturated cavity in a heated fin andmagnetic field.The temperature is constant on the Y-shaped fin,insulating the topwall while the remaining walls remain cold.All walls are subject to impermeability and non-slip conditions.The mathematical modeling of the problem is demonstrated by the continuity,momentum,and energy equations incorporating the inclined magnetic field.For elucidating the flow characteristics Finite ElementMethod(FEM)is implemented using stable FE pair.A hybrid fine mesh is used for discretizing the domain.Velocity and thermal plots concerning parameters are drawn.In addition,a detailed discussion regarding generation energy by monitoring changes in magnetic,viscous,total,and thermal irreversibility is provided.In addition,line graphs are created for the u and v components of the velocity profile to predict the flow behavior.Current simulations assume the dimensionless representative of magnetic field Hartmann number Ha between 0 and 100 and a magnetic field inclination between 0 and 90 degrees.A constant 4% volume proportion of nanoparticles is employed throughout all scenarios.

Evaluation of microstructure and mechanical properties of squeeze overcast Al7075-Cu composite joints

Al7075-Cu composite joints were prepared by the squeeze overcast process.The effects of melt temperature,die temperature,and squeeze pressure on hardness and ultimate tensile strength(UTS)of squeeze overcast Al7075-Cu composite joints were studied.The experimental results depict that squeeze pressure is the most significant process parameter affecting the hardness and UTS.The optimal values of UTS(48 MPa)and hardness(76 HRB)are achieved at a melt temperature of 800℃,a die temperature of 250℃,and a squeeze pressure of 90 MPa.Scanning electron microscopy(SEM)shows that fractured surfaces show flatfaced morphology at the optimal experimental condition.Energy-dispersive spectroscopy(EDS)analysis depicts that the atomic weight percentage of Zn decreases with an increase in melt temperature and squeeze pressure.The optimal mechanical properties of the Al7075-Cu overcast joint were achieved at the Al2Cu eutectic phase due to the large number of copper atoms that dispersed into the aluminum melt during the solidification process and the formation of strong intermetallic bonds.Gray relational analysis integrated with the Taguchi method was used to develop an optimal set of control variables for multi-response parametric optimization.Confirmatory tests were performed to validate the effectiveness of the employed technique.The manufacturing of squeeze overcast Al7075-Cu composite joints at optimal process parameters delivers a great indication to acknowledge a new method for foundry practitioners to manufacture materials with superior mechanical properties.

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Wind energy is featured by instability due to a number of factors,such as weather,season,time of the day,climatic area and so on.Furthermore,instability in the generation of wind energy brings new challenges to electric power grids,such as reliability,flexibility,and power quality.This transition requires a plethora of advanced techniques for accurate forecasting of wind energy.In this context,wind energy forecasting is closely tied to machine learning(ML)and deep learning(DL)as emerging technologies to create an intelligent energy management paradigm.This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set.Moreover,we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast.For the selection of model parameters,a detailed exploratory data analysis is conducted.All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h.The main objective of the study is to foster an efficient forecasting technique for Estonia.The comparative analysis of the results indicates that Support Vector Machine(SVM),Non-linear Autoregressive Neural Networks(NAR),and Recurrent Neural Network-Long-Term Short-Term Memory(RNNLSTM)are respectively 10%,25%,and 32%more efficient compared to TSO’s forecasting algorithm.Therefore,RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

Transmission dynamics of Hand-Foot-Mouth Disease with partial immunity through non-integer derivative

In this paper,we formulate the transmission phenomena of Hand-Foot-Mouth Disease(HFMD)through non-integer derivative.We interrogate the biological meaningful results of the recommended system of HFMD.The basic reproduction number is determined through next generation method and the impact of different parameters on the reproduction number is examined with the help of partial rank correlation coeficient(PRCC)technique.In addition,we concentrated on qualitative analysis and dynamical behavior of HFMD dynamics.Banach's and Schaefer's fixed-point theorems are used to analyze the uniqueness and existence of the solution of the proposed HFMD model.The HFMD system's Ulam-Hyers stability has been confirmed to be sufficient.To highlight the impact of the parameters on the dynamics of HFMD,we performed several simulations through numerical scheme to conceptualize the transmission route of the infection.To be more specific,numerical simulations are used to visualize the effect of input parameters on the systems dynamics.We have shown the key input parameters of the system for the control of infection in the society.