Multi-objective optimal design of a five-phase fault-tolerant axial flux PM motor

Electric motors used for traction purposes in electric vehicles (EVs) must meet several requirements, including high efficiency, high power density and faulttolerance. Among them, permanent magnet synchronous motors (PMSMs) highlight. Especially, five-phase axial flux permanent magnet (AFPM) synchronous motors are particularly suitable for in-wheel applications with enhanced fault-tolerant capabilities. This paper is devoted to optimally design an AFPM for in-wheel applications. The main geometric, electric and mechanical parameters of the designed AFPM are calculated by applying an iterative method based on a set of analytical equations, which is assisted by means of a reduced number of three-dimensional finite element method (3D-FEM) simulations to limit the computational burden. To optimally design the AFPM, a constrained multi-objective optimization process based on a genetic algorithm is applied, in which two objective functions are considered, i.e. the power density and the efficiency. Several fault-tolerance constraints are settled during the optimization process to ensure enhanced fault-tolerance in the resulting motor design. The accuracy of the best solution attained is validated by means of 3D-FEM simulations.Postprint (published version

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address  large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably

Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

A significant study area in cloud computing that still requires attention is how to distribute the workload among virtual machines and resources. Main goal of this research is to develop an efficient cloud load balancing approach, improve response time, decrease readiness time, maximise source utilisation, and decrease activity rejection time. This research propose novel technique in load balancing based network optimization using routing protocol for 5G wireless communication networks. the network load balancing has been carried out using cloud based software defined multi-objective optimization routing protocol. then the network security has been enhanced by data classification utilizing deep belief Boltzmann NN. Experimental analysis has been carried out based on load balancing and security data classification in terms of throughput, packet delivery ratio, energy efficiency, latency, accuracy, precision, recall

Fracturing and thermal extraction optimization methods in enhanced geothermal systems

Fracture networks, fluid flow and heat extraction within fractures constitute pivotal aspects of enhanced geothermal system advancement. Conventional hydraulic fracturing in dry hot rock reservoirs typically requires high breakdown pressure and only produces a single major fracture morphology. Thus, it is imperative to explore better fracturing methods and consider more reasonable coupling mechanisms to improve the prediction efficiency. Cyclic fracturing using liquid nitrogen instead of water can generate more complex fracture networks and improve the fracturing performance. The simulation of fluid flow and heat transfer processes in the fracture network is crucial for an enhanced geothermal system, which requires a more comprehensive coupled thermo-hydro-mechanical-chemical model for matching, especially the characterization of coupling mechanism between the chemical and mechanical field. Based on the results of field engineering, laboratory experiments and numerical simulation, the optimum engineering scheme can be obtained by a multi-objective optimization and decision-making method. Furthermore, combining it with the deep-learning-based proxy model to achieve dynamic optimization with time is a meaningful future research direction.Document Type: PerspectiveCited as: Yang, R., Wang, Y., Song, G., Shi, Y. Fracturing and thermal extraction optimization methods in enhanced geothermal systems. Advances in Geo-Energy Research, 2023, 9(2): 136-140. https://doi.org/10.46690/ager.2023.08.0

Parametric optimization of bio-inspired engineered sandwich core

The present study aims to design an efficient honeycomb cell structure for enhanced energy absorption. Elytra and bamboo bio-inspired parts were compared using a multi-criteria decision-making methodology (COPRAS) and finite element analysis (through Abaqus/CAE) to select the optimal candidate geometry for the study. A circular elytra-inspired geometry featuring four reinforcing cylinders was selected, demonstrating an increase in Specific Energy Absorption (SEA) of over 68% compared to a baseline geometry of the same mass. Structure optimization, aided by a genetic algorithm (NSGA-II), significantly improved crashworthiness parameters, presenting optimized values for design variables, This resulted in an increase in SEA by up to 94% and a 34% improvement in Crushing Force Efficiency (CFE) compared to a baseline geometry. The robust correlation between the algorithm and Finite Element Method (FEM) results highlights its usefulness for initial design, reducing computational demands. The research selects a circular elytra-inspired geometry featuring four reinforcing cylinders and showcasing the potential of multi-objective optimization algorithm in conjunction with FEM analysis in creating high-performance, lightweight structures for passive safety in aeronautics

Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively

Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications

Large-scale commercialization of electric vehicles (EVs) seeks to develop battery systems with higher energy efficiency and improved thermal performance. Integrating simulation-based design optimization in battery development process expands the possibilities for novel design exploration. This study presents a dual-stage multiphysics simulation optimization methodology for comprehensive concept design of Lithium-ion (Li-ion) battery packs for EV applications. At the first stage, multi-objective optimization of electrochemical thermally coupled cells is performed using genetic algorithm considering the specific energy and the maximum temperature of the cells as design objectives. At the second stage, the energy efficiency and the thermal performances of each optimally designed cell are evaluated under pack operation to account for cell-to-pack interactions under realistic working scenarios. When operating at 1.5 C discharge current, the battery pack comprising optimally designed cells for which the specific energy and the maximum temperature are equally weighted delivers the highest specific energy with enhanced thermal performance. The most favorable pack design shows 8% reduction in maximum pack temperature and 16.1% reduction in module-to-module temperature variations compared to commercially available pack. The methodology for design optimization presented in this work is generic, providing valuable knowledge for future cell and pack designs that employ different chemistries and configurations