Experimental and Numerical Investigation on High-Pressure Centrifugal Pumps:Ultimate Pressure Formulation,Fatigue Life Assessment and Topological Optimization of Discharge Section

A high percentage of failure in pump elements originates from fatigue.This study focuses on the discharge section behavior,made of ductile iron,under dynamic load.An experimental protocol is established to collect the strain under pressurization and depressurization tests at specific locations.These experimental results are used to formulate the ultimate pressure expression function of the strain and the lateral surface of the discharge section and to validate finite element modeling.Fe-Safe is then used to assess the fatigue life cycle using different types of fatigue criteria(Coffin-Manson,Morrow,Goodman,and Soderberg).When the pressure is under 3000 PSI,pumps have an unlimited service life of 107 cycles,regardless of the criterion.However,for a pressure of 3555 PSI,only the Morrow criterion denotes a significant decrease in fatigue life cycles,as it considers the average stress.The topological optimization is then applied to the most critical pump model(with the lowest fatigue life cycle)to increase its fatigue life.Using the solid isotropic material with a penalization approach,the Abaqus Topology OptimizationModule is employed.The goal is to reduce the strain energy density while keeping the volume within bounds.According to the findings,a 5%volume reduction causes the strain energy density to decrease from 1.06 to 0.66106 J/m^(3).According to Morrow,the fatigue life cycle at 3,555 PSI is 782,425 longer than the initial 309,742 cycles.

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are introduced.Since the COE depends on site specification constants and initialized parameters of wind turbine,the focus was on the design optimization of rotor radius,hub height and rated power.Based on literature,the COE is converted to the Saudi Arabia context.Thus,the constrained wind turbine optimization problem is developed.Then,non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 m.The results show that as altitude rises,the optimal rotor radius grows,but the optimal hub height and rated power drop,resulting in an increase in COE.Further,the non-Gaussian method display a faster convergence compared to the classical particle swarm optimization.These findings will be useful as a reference for wind turbine design at high altitudes.Thus,it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field.New algorithms are continually being designed on the basis of observations of nature,wildlife,and humanity.In this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems.Results from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance.However,this improvement has been difficult to achieve for high-reliability applications.Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.