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Flexible Water Distribution System Design under Future Demand Uncertainty

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第4期141卷 04014067-04014067页

作者： Basupi, Innocent Kapelan, Zoran Univ Exeter Coll Engn Math & Phys Sci Ctr Water Syst Exeter EX4 4QF Devon England

In order to address the issue of water demand uncertainty due to the effect of climate change and urbanization in the design and management of water distribution systems (WDSs), a flexible methodology that combines sampling techniques (Monte Carlo-MC or Latin Hypercube-LH simulations), decision tree analysis, and genetic algorithm optimization is presented. The methodology gives flexible and optimal decisions as future water demand unfolds. The problem of optimal WDS design under uncertain future water demand is formulated here as a multiobjective optimization problem. The two objectives are as follows: (1) minimisation of total intervention cost;and (2) maximization of WDS end resilience. The decision variables are the conventional design interventions (e.g., pipe duplication and/or replacement of existing pipes with new ones, addition of tanks and pumps, etc.) and the water demand threshold values. The output from the nondominated sorting genetic algorithm (NSGA2) optimisation process is the Pareto front containing staged (developmental) design solutions represented in a decision tree form with an optimal water demand threshold value that results due to the trade-off in terms of the two objectives analyzed over the planning horizon. This methodology was applied on the New York Tunnels and the Anytown network problems. The results show that there is value achieved by building flexibility in design when compared to the deterministic approach in the long-term planning of WDSs under uncertainty. (C) 2014 American Society of Civil Engineers.

关键词： Uncertainty Flexible design genetic algorithm optimization Water distribution systems

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Hybrid genetic algorithm optimization of Vehicle Major Reducer

Hybrid Genetic Algorithm Optimization of Vehicle Major Reduc...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Song Yandong Nanjing Inst Ind Technol Dept Mech Engn Nanjing 210046 Peoples R China

ISBN: (纸本)9780769538044

The function of vehicle major reducer is to increase the input torque and decrease rotational speed correspondingly. The performance parameters of major reducer can greatly affect the dynamics and economy of vehicle, therefore it is very important to optimize the vehicle major reducer. Considered the boundary and performance constraints, the objective function is specified to create optimization model of vehicle major reducer. Global algorithms are known for their slower convergence to the true global optimum once the optimum region is found. This drawback of the genetic algorithm can be overcome by combining it with local gradient-based algorithms, which are known for their faster convergence. The results demonstrate that the hybrid approach is an effective tool to deal with the uncertainties present in design optimization and can provide more realistic solutions. So that the optimization process is simplified and global optimum is acquired reliably.

关键词： genetic algorithm optimization major reducer neural network

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Research On Dynamics Modeling And genetic algorithm optimization Of Automobile Steer-By-Wire System

Research On Dynamics Modeling And Genetic Algorithm Optimiza...

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2nd International Conference on Information and Computing Science

作者： Yu Lei-yan Yun Ping-li Liu Feng China Univ Petr Coll Mech & Elect Engn Dongying Peoples R China

ISBN: (纸本)9780769536347

Automobile Steering by Wire (SBW) system is an advanced steering system, which cuts off the mechanical connection between steering wheel and steered wheels. It controls the steering motor to steer front wheels actively so as to improve handling and stabilities and active safety, and controls steering wheel motor to supply the driver with ideal road feel. Driver-vehicle-road closed loop models of SBW are built based on Matlab/Simulink software platform, including steering wheel assembly and driver model. Model parameters are optimized using genetic algorithm. Real car test proves that the model fits experiments well.

关键词： automobile Steer-by-Wire (SBW) system dynamics modeling genetic algorithm optimization

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Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Optimizing Online Sequential Extreme Learning Machine Parame...

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2015 4th International Conference on Mechatronics,Materials,Chemistry and Computer Engineering(ICMMCCE 2015)

作者： Wenrong Kang Wenyan Chen School of electrical And engineering control Xi`an University of Science and Technology Graduate School

In order to solve the problem that the(OS-ELM) is used in the fault diagnosis of the transformer, the genetic algorithm(algorithm genetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the algorithm genetic optimization algorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.

关键词： Online Sequential extreme learning machine genetic algorithm optimization powers transformer fault diagnosis parameter optimization

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Ovarian Cancer Classification Diagnostic Model of BP Networks and genetic algorithms

Ovarian Cancer Classification Diagnostic Model of BP Network...

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2015 SSR International Conference on Social Sciences and Information(SSR-SSI 2015)

作者： Hongjin Li Zunyi Medical College

genetic algorithm optimization BP neural network to establish a diagnosis of ovarian cancer classification model of ovarian cancer cells to extract images from Zunyi Medical College affiliated hospital clinical, using Mat lab image processing toolbox and programmed to select BP algorithm eigenvectors, compared to forecast data by using BP neural network and genetic algorithm optimization BP network prediction error curve, results showed that GA-BP algorithm enables network model predictions and experimental values after training error is small, to improve the simulation model the prediction accuracy of the classification system for the diagnosis of ovarian cancer patients with positive clinical significance of grading and staging.

关键词： genetic algorithm optimization BP Neural Network Classification Diagnostic Model

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Battery Characterization and State-of-Charge Prediction for Different Journey Conditions with the help of the "Journey Mapping" Concept

Battery Characterization and State-of-Charge Prediction for ...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Kavya Prabha Divakarla Shamsuddeen Nalakath Martin Drennan Ryan Ahmed Ali Emadi Saiedeh Razavi Electrical and Computer Engineering Department McMaster University Hamilton Canada

ISBN: (纸本)9781479917631

Electric vehicle battery modeling and state-of-charge prediction has gained a lot of importance with the growing range anxiety among electric vehicle users. The future of battery implementation in electric vehicles might be in their customized design according to specific journey conditions. As such, this paper highlights the application of a novel concept - Journey Mapping for a Ford Focus Electric 2012's battery characterization and SOC prediction with the help of genetic algorithm and the Recursive Least Squares techniques respectively. The Journey Mapping concept, which re-defines driving cycles in order to better capture the journey of a vehicle by including various external conditions such as weather, terrain, traffic, driver behavior, road, aerodynamic and vehicle proved to be a a more accurate testing bed for electric vehicle battery modeling.

关键词： Automotive drive cycles data acquisition genetic algorithm optimization modeling simulation and state-of-charge (SOC) Data Collection Methods Electric vehicles Batteries Modeling CHARGE STATES algorithm optimization Driving cycle vehicle

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Parametric optimization for Improved Tool Life and Surface Finish in Micro Turning Using genetic algorithm

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Procedia Engineering 2013年 64卷 878-887页

作者： M. Durairaj S. Gowri Sr. Asst. Professor Department of Mechanical Engineering Tagore Engineering College Chennai - 127 Director EMMRC Anna University Chennai - 25

Micro turning is a process by which micro components can be *** paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Machining is done in DT-110 integrated multiprocessor micro machine *** turning is carried out with full factorial experiments with various combinations of cutting parameters such as speed (25,31, and 37 m/min), feed (5,10, and 15 μm/rev) and depth of cut (30,50 and 70 μm). For every set of experiments, the output parameters such as the tool wear and the surface roughness are measured. Non-linear regression model is used to represent relationship between input and output variables and a multi-objective optimization method based genetic algorithm is used to optimize the cutting parameters in turning process such as cutting speed, feed and depth of cut. Two conflicting objectives such as tool wear and surface roughness are simultaneously optimized.

关键词： Micro Turning Surface Roughness Tool Wear Mathematical Modelling genetic algorithm optimization

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Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2014年第3期2卷 659-677页

作者： Ahmed, Ryan El Sayed, Mohammed Arasaratnam, Ienkaran Tjong, Jimi Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada Powertrain Engn Res & Dev Ctr Windsor ON N8Y 1W2 Canada

The current phase in our transportation system represents a paradigm shift from conventional, fossil-fuel-based vehicles into the second-generation electric and hybrid vehicles. Electric vehicles (EVs) provide numerous advantages compared with conventional vehicles because they are more efficient, sustainable, greener, and cleaner. The commercial market penetration and success of EVs depend on the efficiency, safety, cost, and lifetime of the traction battery pack. One of the current key electrification challenges is to accurately estimate the battery pack state of charge (SOC) and state of health (SOH), and therefore provide an estimate of the remaining driving range at various battery states of life. To estimate the battery SOC, a high-fidelity battery model along with a robust, accurate estimation strategy is necessary. This paper provides three main contributions: 1) introducing a new SOC parameterization strategy and employing it in setting up optimizer constraints to estimate battery parameters;2) identification of the full-set of the reduced-order electrochemical battery model parameters by using noninvasive genetic algorithm optimization on a fresh battery;and 3) model validation by using real-world driving cycles. Extensive tests have been conducted on lithium iron phosphate-based cells widely used in high-power automotive applications. Models can be effectively used onboard of battery management system.

关键词： Battery parameters identification electric vehicles (EVs) electrochemical battery model (ECM) genetic algorithm optimization lithium-ion batteries

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Parameters Identification for Nonlinear Dynamic Systems Via genetic algorithm optimization

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2009年第4期4卷 41002-41002页

作者： Gondhalekar, A. C. Petrov, E. P. Imregun, M. Univ London Imperial Coll Sci Technol & Med Dept Mech Engn London SW7 2AZ England

This paper presents a frequency domain method for the location, characterization, and identification of localized nonlinearities in mechanical systems. The nonlinearities are determined by recovering nonlinear restoring forces, computed at each degree-of-freedom (DOF). Nonzero values of the nonlinear force indicate nonlinearity at the corresponding DOFs and the variation in the nonlinear force with frequency (force footprint) characterizes the type of nonlinearity. A library of nonlinear force footprints is obtained for various types of individual and combined nonlinearities. Once the location and the type of nonlinearity are determined, a genetic algorithm based optimization is used to extract the actual values of the nonlinear parameters. The method developed allows simultaneous identification of one or more types of nonlinearity at any given DOF. Parametric identification is possible even if the type of nonlinearity is not known in advance, a very useful feature when the type characterization is difficult. The proposed method is tested on simulated response data. Different combinations of localized cubic stiffness nonlinearity, clearance nonlinearity, and frictional nonlinearity are considered to explore the method's capabilities. Finally, the response data are polluted with random noise to examine the performance of the method in the presence of measurement noise. [DOI: 10.1115/1.3187213]

关键词： nonlinear parametric identification genetic algorithm optimization nonlinearity location and characterization nonlinearity footprint

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Aerodynamic Shape optimization of Wind Turbine Blades Using a Parallel genetic algorithm

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Procedia Engineering 2013年 61卷 28-31页

作者： Ozge Polat Ismail H. Tuncer Middle East Technical University 06800 Ankara TURKEY

An aerodynamic shape optimization methodology based on genetic algorithm and Blade Element Momentum theory is developed for rotor blades of horizontal axis wind turbines optimization studies are performed for the maximization of power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver with a boundary layer model, XFOIL, provides sectional aerodynamic loads. The sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade are taken as design variables. The blade sections may be defined by the NACA four digit airfoil series or by arbitrary airfoil profiles defined by a Bezier curve. The sectional flow computations required by genetic algorithm, which is inherently parallel, are performed in a parallel computing environment with 512 cores. Message Passage Interface (openMPI), is employed in parallel computations. Validation studies are first performed.

关键词： Horizontal Axis Wind Turbine Aerodynamic Design genetic algorithm optimization Blade Element Momentum Theory Panel Method

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