Parameters Calibration for Medium Hydraulic Excavator Simulation Model Based on Experimental Data

2013 ◽  
Vol 706-708 ◽  
pp. 1483-1491 ◽  
Author(s):  
An Lin Wang ◽  
Shi Ning Shi ◽  
Jun Huang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Simulation Model ◽  
Response Surface ◽  
Model Simulation ◽  
Error Function ◽  
Model Parameters ◽  
Hydraulic Excavator ◽  
Unknown Parameters ◽  
Model Based

The authenticity and reasonability of the medium hydraulic excavator simulation model parameters was the foundation to ensure the effectiveness of the simulation model. Based on the bond graph theory, a dynamic simulation model of a medium excavator was established. By the comparison of experimental data and simulation data, the response surface of unknown parameters and the error function of the system model were built. Subsequently, the genetic algorithm was employed to optimize the response surface and obtained optimal value. And then the calibration of the unknown parameters was automatically completed. It was proved that the model simulation curve and with experimental curve fitted better when response surface-genetic algorithm method was used for automatic optimization and calibration of unknown parameters. Furthermore, this method could also function to reduce effectively the number of trials of parameter calibration.

scholarly journals Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

2018 ◽  
Vol 9 (3) ◽  
pp. 696 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu ◽  
Tanmoy Roy Choudhury
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Adaptive Method ◽  
Dc Motor ◽  
Model Parameters ◽  
Current Response ◽  
Adaptive Model ◽  
Unknown Parameters ◽  
Dc Motors ◽  
Estimated Parameters

<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of  dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

2015 ◽  
Vol 740 ◽  
pp. 600-603
Author(s):  
You Jun Yue ◽  
Yan Fei Hu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
Model Parameters ◽  
Coke Rate ◽  
Rate Model ◽  
Iron And Steel ◽  
Model Based ◽  
Blast Furnace Coke

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

scholarly journals Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

2013 ◽  
Author(s):  
Mehdi Maasoumy ◽  
Barzin Moridian ◽  
Meysam Razmara ◽  
Mahdi Shahbakhti ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Convective Heat Transfer Coefficient ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Modeling Framework ◽  
Parameter Adaptation ◽  
Building Model ◽  
Model Based ◽  
On Line

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Coefficient Identification of Steam Network Model Based on Genetic Algorithm

2013 ◽  
Vol 658 ◽  
pp. 555-559
Author(s):  
Xiao Feng Zhu ◽  
Yong Zhang ◽  
Zhao Feng Lu ◽  
Yong Ma
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Network Model ◽  
Model Parameters ◽  
The Real ◽  
Coefficient Identification ◽  
Model Based ◽  
Test Result

This article establish a coupled thermo-hydraulic mathematical model for steam network by adopting a set of equations. Here, identification is defined as process in which a number of Steam Network model parameters are adjusted until the model mimics behavior of the real Steam Network as closely as possible. Test result indicates the advantage of genetic algorithm.

scholarly journals Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

2017 ◽  
Vol 17 (12) ◽  
pp. 8021-8029 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Kinetic Models ◽  
Reaction Rate ◽  
Rate Coefficients ◽  
Model Parameters ◽  
Data Sets ◽  
Model Input ◽  
Input Parameters

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.

FEM Simulation on Uniaxial Tension of Hyperelastic Elastomers

2014 ◽  
Vol 659 ◽  
pp. 57-62 ◽  
Author(s):  
Vlad Carlescu ◽  
Gheorghe Prisacaru ◽  
Dumitru Olaru
Keyword(s):  
Experimental Data ◽  
Uniaxial Tension ◽  
Smart Materials ◽  
Model Simulation ◽  
Fem Simulation ◽  
Tensile Tests ◽  
Material Behavior ◽  
Uniaxial Tensile ◽  
Model Parameters ◽  
Good Correspondence

Modeling large nonlinear elastic deformation of elastomers is an important issue for developing new materials. Particularly, this is very promising for design and performance analysis of dielectric elastomers (DEs). These “smart materials” are capable of responding to an external electric field by displaying significant change in shape and size. In this paper, finite element method (FEM) was used to simulate the mechanical behavior of soft elastomers on uniaxial tension. Experimental data from uniaxial tensile tests were used in order to calibrate hyperelastic constitutive models of the material behavior. The constitutive model parameters were evaluated in ABAQUS/CAE. The 3D-model simulation results of a dumbbell shaped specimen at uniaxial tension shows very good correspondence with experimental data.

scholarly journals Model-Based Design and Optimization of Blood Oxygenators

2020 ◽  
Vol 14 (4) ◽  
Author(s):  
Ge He ◽  
Tao Zhang ◽  
Jiafeng Zhang ◽  
Bartley P. Griffith ◽  
Zhongjun J. Wu
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Fiber Bundle ◽  
Cfd Modeling ◽  
Design Parameters ◽  
Model Parameters ◽  
Transfer Rates ◽  
Design And Optimization ◽  
Model Based ◽  
Model Predictions

Abstract Blood oxygenators, also known as artificial lungs, are widely used in cardiopulmonary bypass surgery to maintain physiologic oxygen (O2) and carbon dioxide (CO2) levels in blood, and also serve as respiratory assist devices to support patients with lung failure. The time- and cost-consuming method of trial and error is initially used to optimize the oxygenator design, and this method is followed by the introduction of the computational fluid dynamics (CFD) that is employed to reduce the number of prototypes that must be built as the design is optimized. The CFD modeling method, while having progress in recent years, still requires complex three-dimensional (3D) modeling and experimental data to identify the model parameters and validate the model. In this study, we sought to develop an easily implemented mathematical models to predict and optimize the performance (oxygen partial pressure/saturation, oxygen/carbon dioxide transfer rates, and pressure loss) of hollow fiber membrane-based oxygenators and this model can be then used in conjunction with CFD to reduce the number of 3D CFD iteration for further oxygenator design and optimization. The model parameters are first identified by fitting the model predictions to the experimental data obtained from a mock flow loop experimental test on a mini fiber bundle. The models are then validated through comparing the theoretical results with the experimental data of seven full-size oxygenators. The comparative analysis show that the model predictions and experimental results are in good agreement. Based on the verified models, the design curves showing the effects of parameters on the performance of oxygenators and the guidelines detailing the optimization process are established to determine the optimal design parameters (fiber bundle dimensions and its porosity) under specific system design requirements (blood pressure drop, oxygen pressure/saturation, oxygen/carbon dioxide transfer rates, and priming volume). The results show that the model-based optimization method is promising to derive the optimal parameters in an efficient way and to serve as an intermediate modeling approach prior to complex CFD modeling.

Research on Structural Optimal Design of a Car Plastic Back Door

2014 ◽  
Vol 971-973 ◽  
pp. 676-679
Author(s):  
Duo Nian Yu ◽  
Zhi Jia Wu ◽  
You Qun Zhao ◽  
Li Yang Gu ◽  
Jing Min Liu
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Glass Fiber ◽  
Experimental Method ◽  
Response Surface ◽  
Glass Fiber Reinforced ◽  
Back Door ◽  
Polynomial Response Surface ◽  
Rsm Model

This paper takes a passenger car back door as an example with the use of glass fiber reinforced PET to replace steel. The sampling space is sampled by the optimal Latin hypercube experimental method and according to the experimental data, it establishes the polynomial response surface (RSM) model. Select NSGA −∏ genetic algorithm to optimize the back door assembly thickness of multi-objection with the purpose of lightweight.

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