Design and Control of EMS Magnetic Levitation Train using Fuzzy MRAS and PID Controllers

In this paper, a Magnetic Levitation (MAGLEV) train is designed with a first degree of freedom electromagnetbased totally system that permits to levitate vertically up and down. Fuzzy logic, PID and MRAS controllers are used to improve the Magnetic Levitation train passenger comfort and road handling. A Matlab Simulink model is used to compare the performance of the three controllers using step input signals. The stability of the Magnetic Levitation train is analyzed using root locus technique. Controller output response for different time period and change of air gap with different time period is analyzed for the three controllers. Finally the comparative simulation and experimental results demonstrate the effectiveness of the presented fuzzy logic controller

Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria

Development of an inverse simulation method for the analysis of train performance

Conventional methods of computer-based simulation allow prediction of output variables, often as a function of time, for a given model of a physical system for a given set of initial conditions and input variables. In the case of train performance simulation models, the possible output variables include train speed or distance travelled, both expressed as functions of time. The corresponding input variables, also expressed as functions of time, are the tractive force or power levels for given train characteristics and route information such as gradients, track curvature and speed restrictions. Inverse simulation methods, on the other hand, allow selected model variables (such as the tractive force at any time instant) to be found from other specified model variables applied as input (such as the train speed or distance travelled versus time) for a given set of route conditions and train characteristics. The specific inverse simulation method presented in the paper is based on feedback principles. Illustrative results are used to verify this inverse simulation approach for train performance applications, and further cases are used to show that the inverse formulation provides an insight that is different from that obtained using more conventional forward simulation techniques

Studies on Heat Integration in Crude Preheat Train and the Effect of Fouling

This report is on the performance of heat exchanger in Crude Preheat Train and the effect of fouling. For years refinery had been struggling with various operational problem due to fouling. This project uses two different Crude Preheat Train design. The simulation work will study the difference in the performance of each heat exchanger in the preheat train both under clean and fouled condition. This project use Aspen Hysys 2006 software. The importance data in this project is the overall heat transfer coefficient and the duty of the heat exchangers. The result shows the advantage and disadvantage of both preheat train design. This project also looks into the economic impact of fouling on the preheat train. Results show the advantage and disadvantage both preheat train design. Simulation and calculation data are documented to provide a base for future work on possible reallocation of streams in the existing Crude Preheat Train

A H2 PEM fuel cell and high energy dense battery hybrid energy source for an urban electric vehicle

Electric vehicles are set to play a prominent role in addressing the energy and environmental impact of an increasing road transport population by offering a more energy efficient and less polluting drive-train alternative to conventional internal combustion engine (ICE) vehicles. Given the energy (and hence range) and performance limitations of electro-chemical battery storage systems, hybrid systems combining energy and power dense storage technologies have been proposed for vehicle applications. The paper discusses the application of a hydrogen fuel cell as a range extender for an urban electric vehicle for which the primary energy source is provided by a high energy dense battery. A review of fuel cell systems and automotive drive-train application issues are discussed, together with an overview of the battery technology. The prototype fuel cell and battery component simulation models are presented and their performance as a combined energy/power source assessed for typical urban and sub-urban driving scenario

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency

Using Gaussian process regression for efficient parameter reconstruction

Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. We examine how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.Comment: 8 pages, 4 figure