51,384 research outputs found
Isogeometric Analysis and Harmonic Stator-Rotor Coupling for Simulating Electric Machines
This work proposes Isogeometric Analysis as an alternative to classical
finite elements for simulating electric machines. Through the spline-based
Isogeometric discretization it is possible to parametrize the circular arcs
exactly, thereby avoiding any geometrical error in the representation of the
air gap where a high accuracy is mandatory. To increase the generality of the
method, and to allow rotation, the rotor and the stator computational domains
are constructed independently as multipatch entities. The two subdomains are
then coupled using harmonic basis functions at the interface which gives rise
to a saddle-point problem. The properties of Isogeometric Analysis combined
with harmonic stator-rotor coupling are presented. The results and performance
of the new approach are compared to the ones for a classical finite element
method using a permanent magnet synchronous machine as an example
Application of Fuzzy control algorithms for electric vehicle antilock braking/traction control systems
Abstract—The application of fuzzy-based control strategies has recently gained enormous recognition as an approach for the rapid development of effective controllers for nonlinear time-variant systems. This paper describes the preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs).
As the dynamics of the braking systems are highly nonlinear and time variant, fuzzy control offers potential as an important tool for development of robust traction control. Simulation studies are employed to derive an initial rule base that is then tested on an experimental test facility representing the dynamics of a braking system. The test facility is composed of an induction machine load operating in the generating region. It is shown that the
torque-slip characteristics of an induction motor provides a convenient platform for simulating a variety of tire/road - driving conditions, negating the initial requirement for skid-pan trials when developing algorithms. The fuzzy membership functions were subsequently refined by analysis of the data acquired from the test facility while simulating operation at a high coefficient of friction. The robustness of the fuzzy-logic slip regulator is further
tested by applying the resulting controller over a wide range of operating conditions. The results indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions where classical ABS/traction control schemes are constrained to operate very conservatively
A Versatile workbench simulator: Five-phase inverter and PMa-SynRM performance evaluation
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Thispaperpresents the design and structure of aversatileworkbench simulator forevaluating the performance of a five-phase inverter andPermanent Magnet assisted Synchronous Reluctance Motor(PMa-SynRM). The simulatorallows for adding variations tothe modulationtechniques, changingthe inverter structure’s semiconductordevice, and calculatingtheinverter’spower losses. Itcanalso facilitate observingthe current, voltage,andthe jointtemperature ofthe semiconductors devices. Furthermore,wecanobtain a perform that is close to anactualPMa-SynRM, dependingon the desired conditionsof speed and torque. The workbench simulator wasdevelopedby combining three software: Matlab/Simulink, PLECSand Altair Flux.Postprint (author's final draft
Generalized, energy-conserving numerical simulations of particles in general relativity. II. Test particles in electromagnetic fields and GRMHD
Direct observations of compact objects, in the form of radiation spectra,
gravitational waves from VIRGO/LIGO, and forthcoming direct imaging, are
currently one of the primary source of information on the physics of plasmas in
extreme astrophysical environments. The modeling of such physical phenomena
requires numerical methods that allow for the simulation of microscopic plasma
dynamics in presence of both strong gravity and electromagnetic fields. In
Bacchini et al. (2018) we presented a detailed study on numerical techniques
for the integration of free geodesic motion. Here we extend the study by
introducing electromagnetic forces in the simulation of charged particles in
curved spacetimes. We extend the Hamiltonian energy-conserving method presented
in Bacchini et al. (2018) to include the Lorentz force and we test its
performance compared to that of standard explicit Runge-Kutta and implicit
midpoint rule schemes against analytic solutions. Then, we show the application
of the numerical schemes to the integration of test particle trajectories in
general relativistic magnetohydrodynamic (GRMHD) simulations, by modifying the
algorithms to handle grid-based electromagnetic fields. We test this approach
by simulating ensembles of charged particles in a static GRMHD configuration
obtained with the Black Hole Accretion Code (BHAC)
The use of real time digital simulation and hardware in the loop to de-risk novel control algorithms
Low power demonstrators are commonly used to validate novel control algorithms. However, the response of the demonstrator to network transients and faults is often unexplored. The importance of this work has, in the past, justified facilities such as the T45 Shore Integration Test Facility (SITF) at the Electric Ship Technology Demonstrator (ESTD). This paper presents the use of real time digital simulation and hardware in the loop to de-risk a innovative control algorithm with respect to network transients and faults. A novel feature of the study is the modelling of events at the power electronics level (time steps of circa 2 μs) and the system level (time steps of circa 50 μs)
An experimental laboratory bench setup to study electric vehicle antilock braking / traction systems and their control
This paper describes the preliminary research and implementation of an experimental test bench set up for an electric vehicle antilock braking system (ABS)/traction control system (TCS) representing the dry, wet and icy road surfaces. A fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking system is presented. The test facility comprised of an induction machine load operating in the generating region. The test facility was used to simulate a variety of tire/road μ-σ driving conditions, eliminating the initial requirement for skid-pan trials when developing algorithms. Simulation studies and results are provided
NASA automatic subject analysis technique for extracting retrievable multi-terms (NASA TERM) system
Current methods for information processing and retrieval used at the NASA Scientific and Technical Information Facility are reviewed. A more cost effective computer aided indexing system is proposed which automatically generates print terms (phrases) from the natural text. Satisfactory print terms can be generated in a primarily automatic manner to produce a thesaurus (NASA TERMS) which extends all the mappings presently applied by indexers, specifies the worth of each posting term in the thesaurus, and indicates the areas of use of the thesaurus entry phrase. These print terms enable the computer to determine which of several terms in a hierarchy is desirable and to differentiate ambiguous terms. Steps in the NASA TERMS algorithm are discussed and the processing of surrogate entry phrases is demonstrated using four previously manually indexed STAR abstracts for comparison. The simulation shows phrase isolation, text phrase reduction, NASA terms selection, and RECON display
Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability
Model simplification and optimization of a passive wind turbine generator
In this paper, the design of a "low cost full passive structure" of wind turbine system without active
electronic part (power and control) is investigated. The efficiency of such device can be obtained only if
the design parameters are mutually adapted through an optimization design approach. For this purpose,
sizing and simulating models are developed to characterize the behavior and the efficiency of the wind
turbine system. A model simplification approach is presented, allowing the reduction of computational
times and the investigation of multiple Pareto-optimal solutions with a multiobjective genetic algorithm.
Results show that the optimized wind turbine configurations are capable of matching very closely the
behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control
device
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Radio emission from air showers enables measurements of cosmic particle
kinematics and identity. The radio signals are detected in broadband Megahertz
antennas among continuous background noise. We present two deep learning
concepts and their performance when applied to simulated data. The first
network classifies time traces as signal or background. We achieve a true
positive rate of about 90% for signal-to-noise ratios larger than three with a
false positive rate below 0.2%. The other network is used to clean the time
trace from background and to recover the radio time trace originating from an
air shower. Here we achieve a resolution in the energy contained in the trace
of about 20% without a bias for of the traces with a signal. The
obtained frequency spectrum is cleaned from signals of radio frequency
interference and shows the expected shape.Comment: 20 pages, 13 figures, resubmitted to JINS
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