HASTECS: Hybrid Aircraft: reSearch on Thermal and Electric Components and Systems

In 2019, transportation was the fastest growing sector, contributing to environmental degradation. Finding sustainable solutions that pollute less is a key element in solving this problem, particularly for the aviation sector, which accounts for around 2-3% of global CO2 emissions. With the advent of Covid-19, air traffic seems to have come to a fairly permanent halt, but this pandemic reinforces the need to move towards a "cleaner sky" and respect for the environment, which is the objective of the Clean Sky2 program (H2020 EU), the context in which the HASTECS project has been launched in September 2016

Conception optimale d’une gamme de moteurs synchrones à démarrage direct à haute performance énergétique

This work aims to develop a multi-physical generic model (and a pre-design software) for a range of LSPMSMs which would integrate the advantages of both technologies: self-start asynchronous technology and good energy performance of synchronous permanent magnet technology. The validation of this model is carried out by finite element commercial software ANSYS / Maxwell and by experimental tests using two 7.5kW.LSPMSM prototypes.Ce travail a pour objectif de développer un outil analytique multi-physiques de dimensionnement d’une gamme de moteurs « hybrides » à démarrage direct, intégrant les avantages des deux technologies : l’auto-démarrage de la technologie asynchrone et les bonnes performances énergétique en régime permanent de la technologie synchrone à aimants permanents en répondant aux nouveaux enjeux d’efficacité énergétique et en ajoutant à cela les aspects économiques.La validation de cet outil est effectuée par des modèles éléments finis créés avec un logiciel commercial ANSYS/Maxwell et par des essais expérimentaux réalisés à l’aide de deux prototypes LSPMSM 7.5kW

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Joint Dynamics and Adaptive Feedforward Control of Lightweight Industrial Robots

The use of lightweight strain-wave transmissions in collaborative industrial robots leads to structural compliance and a complex nonlinear behavior of the robot joints. Furthermore, wear and temperature changes lead to variations in the joint dynamics behavior over time. The immediate negative consequences are related to the performance of motion and force control, safety, and lead-through programming.This thesis introduces and investigates new methods to further increase the performance of collaborative industrial robots subject to complex nonlinear and time-varying joint dynamics behavior. Within this context, the techniques of mathematical modeling, system identification, and adaptive estimation and control are applied. The methods are experimentally validated using the collaborative industrial robots by Universal Robots.Mathematically, the robot and joint dynamics are considered as two coupled subsystems. The robot dynamics are derived and linearly parametrized to facilitate identification of the inertial parameters. Calibrating these parameters leads to improvements in torque prediction accuracy of 16.5 %-28.5 % depending on the motion.The joint dynamics are thoroughly analyzed and characterized. Based on a series of experiments, a comprehensive model of the robot joint is established taking into account the complex nonlinear dynamics of the strain-wave transmission, that is the nonlinear compliance, hysteresis, kinematic error, and friction. The steady-state friction is considered to depend on angular velocity, load torque, and temperature. The dynamic friction characteristics are described by an Extended Generalized Maxwell-Slip (E-GMS) model which describes in a combined framework; hysteresis characteristics that depend on angular position and Coulomb friction that depend on load torque. E-GMS model-based feedforward control improves the torque prediction accuracy by a factor 2.1 and improve the tracking error by a factor 1.5.An E-GMS model-based adaptive feedforward controller is developed to address the issue of friction changing with wear and temperature. The adaptive control strategy leads to improvements in torque prediction of 84 % and tracking error of 20 %

Advanced Testing of Soft Polymer Materials

Manufacturers of soft polymer products, as well as suppliers and processors of polymers, raw materials, and compounds or blends are compelled to use predictive and advanced laboratory testing in their search for high-performance soft polymer materials for future applications. The collection of 12 publications contained in this edition therefore presents different methods used to solve problems in the characterization of various phenomena in soft polymer materials, asks relevant questions and offers appropriate solutions

Hydrogen Research at Florida Universities

This final report describes the R&D activities and projects conducted for NASA under the 6-year NASA Hydrogen Research at Florida Universities grant program. Contained within this report are summaries of the overall activities, one-page description of all the reports funded under this program and all of the individual reports from each of the 29 projects supported by the effort. The R&D activities cover hydrogen technologies related to production, cryogenics, sensors, storage, separation processes, fuel cells, resource assessments and education. In the span of 6 years, the NASA Hydrogen Research at Florida Universities program funded a total of 44 individual university projects, and employed more than 100 faculty and over 100 graduate research students in the six participating universities. Researchers involved in this program have filed more than 20 patents in all hydrogen technology areas and put out over 220 technical publications in the last 2 years alone. This 6 year hydrogen research program was conducted by a consortium of six Florida universities: Florida International University (FIU) in Miami, Florida State University (FSU) and Florida A&M University (FAMU) in Tallahassee, University of Central Florida (UCF) in Orlando, University of South Florida (USF) in Tampa, and University of Florida (UF) in Gainesville. The Florida Solar Energy Center (FSEC) of the University of Central Florida managed the research activities of all consortium member universities except those at the University of Florida. This report does not include any of the programs or activities conducted at the University of Florida, but can be found in NASA/CR-2008-215440-PART 1-3

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms