scholarly journals Hydraulic Model Calibration of Pressure Reduced Zones with Multiple Input Valves

10.29007/8vqn ◽  
2018 ◽  
Author(s):  
Attila Bibok ◽  
Roland Fülöp
Keyword(s):  
Model Calibration ◽  
Linear Optimization ◽  
Pressure Head ◽  
Optimization Methods ◽  
Hydraulic Model ◽  
Pipe Network ◽  
Multiple Input ◽  
Target Pressure ◽  
Background Leakage ◽  
Proper Setting

Pressure management is a widely adopted technique to decrease background leakage or to extend the lifespan of the pipe network [1]. In some cases, it is inevitable to deploy multiple pressure reducing valves to supply a particular zone. In order to supply water to the customers with optimal pressure head, the precise setting of the parallel pressure reducing valves’ (PRV) target pressure is required. Steady-state hydraulic models like EPANET has the functionality to simulate pressure loss of a pressure-reducing valve [1]. This can be simulated by adding minor-loss after the pipe, or by modifying the properties of the next link on the downstream side. Either way, the proper setting of the coefficients is essential to calibrate the hydraulic model. In this paper, two non-linear optimization methods were utilized to calibrate the hydraulic model with multiple input values.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

Multi-Field Characterization of Single Wall Nano-Tube Composites for Hydrogen Storage

2005 ◽  
Author(s):  
John G. Michopoulos ◽  
Sam G. Lambrakos ◽  
Nick E. Tran
Keyword(s):  
Hydrogen Storage ◽  
Objective Function ◽  
Storage Systems ◽  
Chemical Reactivity ◽  
Linear Optimization ◽  
Optimization Methods ◽  
Design Parameters ◽  
Spatio Temporal ◽  
Extensive Experimental Data

The goal of the present work is three fold. Firstly to create the forward continuum model of a multi-species diffusing system under simultaneous presence of chemical reactivity and temperature as the general case of all hydrogen storage systems. Secondly, cast the problem of hydrogen storage in a pragmatic product-design context where the appropriate design parameters of the system are determined via appropriate optimization methods that utilize extensive experimental data encoding the behavior of the system. Thirdly, demonstrate this methodology on characterizing certain systemic parameters. Thus, the context of the work presented is defined by a data-driven characterization of coupled heat and mass diffusion models of hydrogen storage systems from a multiphysics perspective at the macro length scale. In particular, a single wall nanotube (SWNT) based composite is modeled by coupled partial differential equations representing spatio-temporal evolution of distributions of temperature and hydrogen concentration. Analytical solutions of these equations are adopted for an inverse analysis that defines a non-linear optimization problem for determining the parameters of the model by objective function minimization. Experimentally acquired and model produced data are used to construct the system’s objective function. Simulations to demonstrate the applicability of the methodology and a discussion of its potential extension to multi-scale and manufacturing process optimization are also presented.

An Integrated Approach for the Teaching of Pipe Network Analysis Using Project Based Learning

2018 ◽  
Author(s):  
I. Ibrahim ◽  
M. W. B. Khalid ◽  
G. Shoukat ◽  
M. Sajid
Keyword(s):  
Network Analysis ◽  
Learning Experience ◽  
Pressure Head ◽  
Integrated Approach ◽  
Project Based Learning ◽  
Pipe Network ◽  
Actual Problem ◽  
The Subject ◽  
Complex Project ◽  
The Impact

This paper discusses the results of a study regarding the impact of using Project-Based Learning (PBL) to enhance the understanding of the concepts related to Pipe Network Analysis, a subtopic of Fluid Mechanics, studied by students enrolled in a mechanical engineering degree. It has been frequently reported by students and professors alike, that a lecture-only approach is not effective in terms of helping students grasp the fundamentals of a subject, nor does it help students in actual problem solving where different variables have to be catered to, which may have been ignored in a conventional lecture. Therefore, in this study, a more open-ended, complex project-based approach was used in addition to the lectures on the subject of Pipe Network Analysis. The project required students to design a pipe network for a scaled setup based on specified fluid flow and pressure head requirements at different nodes. An experimental setup that implemented these pipe networks was also developed in order to validate the theoretical results. The students’ grades and their documented responses were used as the criterion for compiling and analyzing the results. We also describe how we incorporated PBL into the classrooms in order to improve the learning experience, and evaluate the efficacy of the proposed method. The overall results show that the students were proactively engaged in the PBL activity, linking their knowledge to the real world, which ultimately led to improved concept development.

scholarly journals Hybrid 3D Dynamic Measurement by Particle Swarm Optimization and Photogrammetric Tracking

2013 ◽  
Vol 13 (6) ◽  
pp. 298-304 ◽  
Author(s):  
M. Shahbazi
Keyword(s):  
Particle Swarm Optimization ◽  
Image Registration ◽  
Linear Optimization ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Three Dimensions ◽  
Image Correlation ◽  
Motion Modeling ◽  
Swarm Optimization ◽  
Pixel Image

Abstract High-accuracy motion modeling in three dimensions via digital images has been increasingly the matter of interest in photogrammetry and computer vision communities. Although accurate sub-pixel image registration techniques are the key elements of measurement, they still demand enhanced intelligence, autonomy, and robustness. In this paper, a new correlationbased technique of stereovision is proposed to perform inter-frame feature tracking, inter-camera image registration, and to measure the 3D state vector of features simultaneously. The developed algorithm is founded on population-based intelligence (particle swarm optimization) and photogrammetric modeling. The proposed technique is mainly aimed at reducing the computational complexities of non-linear optimization methods of digital image registration for deformation measurement, and passing through 2D image correlation to 3D motion modeling. The preliminary results have illustrated the feasibility of this technique to detect and measure sub-millimeter deformations by performing accurate, sub-pixel image registration.

Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4241-4247 ◽  
Author(s):  
Hong Tao Sun ◽  
Yong Shou Dai ◽  
Fang Wang ◽  
Xing Peng
Keyword(s):  
Genetic Algorithm ◽  
Signal To Noise Ratio ◽  
Linear Optimization ◽  
Optimization Methods ◽  
High Signal ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Convergence Performance ◽  
High Order Statistics ◽  
Chaos Genetic Algorithm

Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.

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