Parameter Identification for HAPC System in Hot Tandem Mill

2010 ◽  
Vol 154-155 ◽  
pp. 781-786
Author(s):  
Xu Li ◽  
Wen Xue Zhang ◽  
Dian Hua Zhang ◽  
Dan Yan
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Actual Data ◽  
Automation System ◽  
Model Parameters ◽  
Simulation Data ◽  
Matlab Software ◽  
Identification Method ◽  
Curve Fit

Under condition that exact values of model parameters can not be calculated accurately in hot tandem mill system and change with the time passing, model parameters are identified by adopting identification method based on the parameter model and sampling the datum on site; Basic automation system is used for the sampling of actual data, MATLAB software is adopted for curve fit. By comparing the experimental data and simulation data, the consequence of simulation testifies the accuracy of identified mathematical model.

Research on Generator Excitation Parameter Identification - PSS Low-Pass Link Parameter Identification

2013 ◽  
Vol 805-806 ◽  
pp. 716-720
Author(s):  
Tao Xu ◽  
Tian Long Shao ◽  
Dong Fang Zhang
Keyword(s):  
Mathematical Model ◽  
Parameter Identification ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
National Standards ◽  
Matlab Software ◽  
Low Pass ◽  
Newton Iterations ◽  
The Mathematical Model

Combined with the contents of the study-PSS low-pass link parameter identification. Least-squares method is selected. Using least-square method for PSS low-pass link mathematical model are also deduced. For the results, because of the mathematical model is solving nonlinear equations, cannot used by the Newton method directly. So we choose to use Newton iterations, with this feature, choose to use MATLAB software to solve the equation. Identification of the use of MATLAB software lags after the PSS parameters obtained recognition results compared with national standards, identifying and verifying the practicability.

Nonlinear Optimization Methods for the Determination of Heat Source Model Parameters

2016 ◽  
Vol 879 ◽  
pp. 2008-2013
Author(s):  
Udo Hartel ◽  
Alexander Ilin ◽  
Steffen Sonntag ◽  
Vesselin Michailov
Keyword(s):  
Experimental Data ◽  
Heat Source ◽  
Parameter Identification ◽  
Nonlinear Optimization ◽  
Laser Beam Welding ◽  
Source Model ◽  
Transient Temperature ◽  
Surface Model ◽  
Model Parameters ◽  
Heat Source Model

In this paper the technique of parameter identification is investigated to reconstruct the 3D transient temperature field for the simulation of laser beam welding. The reconstruction bases on volume heat source models and makes use of experimental data. The parameter identification leads to an inverse heat conduction problem which cannot be solved exactly but in terms of an optimal alignment of the simulation and experimental data. To solve the inverse problem, methods of nonlinear optimization are applied to minimize a problem dependent objective function.In particular the objective function is generated based on the Response Surface Model (RSM) technique. Sampling points on the RSM are determined by means of Finite-Element-Analysis (FEA). The scope of this research paper is the evaluation and comparison of gradient based and stochastic optimization algorithms. The proposed parameter identification makes it possible to determine the heat source model parameters in an automated way. The methodology is applied on welds of dissimilar material joints.

Automatic Parameter Identification Applied to a Railroad Car Dynamic Draft Gear Model

1974 ◽  
Vol 96 (4) ◽  
pp. 460-465 ◽  
Author(s):  
E. D. Ward ◽  
R. G. Leonard
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Functional Relationship ◽  
Nonlinear Functional ◽  
Draft Gear ◽  
Identification Technique ◽  
Train Dynamics ◽  
Relationship Of ◽  
The Mathematical Model

One of the most important components in simulating track-train dynamics is the mathematical model of the connection between two cars, the draft gear-coupler combination. In this paper an automatic parameter identification technique is presented which can be used to generate a nonlinear functional relationship of dynamic draft gear characteristics using experimental data.

A mathematical model of metabolic insulin signaling pathways

2002 ◽  
Vol 283 (5) ◽  
pp. E1084-E1101 ◽  
Author(s):  
Ahmad R. Sedaghat ◽  
Arthur Sherman ◽  
Michael J. Quon
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Insulin Receptor ◽  
Signaling Pathways ◽  
Insulin Signaling ◽  
Molecular Mechanisms ◽  
Glucose Transporter ◽  
Insulin Dose ◽  
Model Parameters ◽  
Insight Into

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.

Parameter Identification for Nonlinear Dry-Friction Structure of Metallic Rubber

2011 ◽  
Vol 52-54 ◽  
pp. 494-499
Author(s):  
Yu Yan Li ◽  
Xie Qing Huang ◽  
Kai Song
Keyword(s):  
Parameter Identification ◽  
Dry Friction ◽  
Friction Angle ◽  
Element Model ◽  
Parameters Identification ◽  
Axial Stiffness ◽  
Model Parameters ◽  
Structural Element ◽  
Identification Method ◽  
Metallic Rubber

In order to reduce workload of parameter identification for nonlinear mechanical model of metallic rubber, in this paper, based on parameters identification method of static experimental curves, experiments were designed, and data were processed, further aimed at hollow cylindrical metallic rubber, nonlinear dry-friction structural element model’ parameters were identified, what’s more, friction coefficient, radial stiffness, axial stiffness, and friction angle of stainless wire under room temperature were obtained. It was proved by simulation that parameters identification method in this paper was effective and accurate. Based on this, errors of simulation were analyzed elaborately.

Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

scholarly journals Experimental studies of iron transformations kinetics and autocatalysis during its physicochemical removal from underground water

2021 ◽  
Author(s):  
S. Yu Martynov ◽  
V. L. Poliakov
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Experimental Studies ◽  
Underground Water ◽  
Model Parameters ◽  
Kinetic Coefficients ◽  
Transfer Equations ◽  
The Mathematical Model ◽  
Autocatalytic Effect ◽  
Forms Of Iron

Abstract The mathematical model of physicochemical iron removal from groundwater was developed. It consists of three interrelated compartments. The results of the experimental research provide information in support of the first two compartments of the mathematical model. The dependencies for the concentrations of the adsorbed ferrous iron and deposited hydroxide concentrations are obtained as a result of the exact solution of the system of the mass transfer equations for two forms of iron in relation to the inlet surface of the bed. An analysis of the experimental data of the dynamics of the deposit accumulation in a small bed sample was made, using a special application that allowed to select the values of the kinetic coefficients and other model parameters based on these dependencies. We evaluated the autocatalytic effect on the dynamics of iron ferrous and ferric forms. The verification of the mathematical model was carried out involving the experimental data obtained under laboratory and industrial conditions.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

scholarly journals Electromechanical Transient Modeling and LVRT Parameter Identification of Large Capacity Full Power Converter Wind Turbines Based on PSASP Program

2021 ◽  
Vol 2108 (1) ◽  
pp. 012002
Author(s):  
Guoqiang Lu ◽  
Xiangyu Tao ◽  
Chunmeng Chen ◽  
Jiatian Gan ◽  
Xufeng Zhao
Keyword(s):  
Parameter Identification ◽  
Wind Turbine ◽  
Low Voltage ◽  
Recovery Period ◽  
Power Converter ◽  
Control Mode ◽  
Model Parameters ◽  
Model Structure ◽  
Identification Method ◽  
Full Power

Abstract Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.

Parameter Identification of Inelastic Structures

1999 ◽  
Author(s):  
H. Zhang ◽  
Y. Yang ◽  
G. C. Foliente ◽  
F. Ma
Keyword(s):  
Parameter Identification ◽  
Gradient Methods ◽  
Model Parameters ◽  
Inelastic Behavior ◽  
Hysteretic Model ◽  
Differential Model ◽  
Inelastic Structures ◽  
Curve Fit ◽  
Successful Design ◽  
Strong Winds

Abstract Structures often exhibit nonlinear and inelastic behavior in the form of hysteresis loop under severe loads associated with earthquake, austere winds and waves. Hysteresis is particularly important in depicting the nonlinear response of wood buildings, braced steel frames, reinforced concrete, and structures with a high proportion of composite materials. A practical model of hysteresis that would match experimental observations on real structures is needed for the successful design of structures against earthquakes and strong winds. Two different time-domain system identification algorithms will be presented in this report to estimate the parameters of an extended Bouc-Wen hysteretic model. This version of the differential model of hysteresis can curve-fit practically any hysteresis trace with a suitable choice of the model parameters. Thirteen control parameters are included in the model. The parameter identification algorithms presented in this report include the constrained simplex and generalized reduced gradient methods. Noise filtering techniques and constraints will also be used in this study to assist in parameter identification. The effectiveness of the proposed algorithms will be demonstrated through simulations of nonlinear systems with pinching and degradation characteristics. Due to very modest computing requirements, the proposed identification algorithms can be acceptable as a basic tool for estimating hysteretic parameters in engineering design.

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