Study on Experiment Modeling of Human-Gun System

2014 ◽  
Vol 644-650 ◽  
pp. 2137-2142
Author(s):  
Jian Wang ◽  
Yan Liu ◽  
Zhi Guang Zhang ◽  
Zhan Jiang Yu ◽  
Bao Gui Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Dynamic Simulation ◽  
Test Data ◽  
Main Part ◽  
3D Model ◽  
Quality Test ◽  
High Quality ◽  
Model Parameter ◽  
Interaction System ◽  
Experiment Method

A new kind of human-imitate shooting platform is needed, so that the automation and standardization of small arm experiment could be realized. And the main part of shooting platform design is the modeling of human-gun interaction system. The main object of this paper is modeling human-gun interaction system by testing the model of the system. Firstly, the testing scheme is promoted for testing interaction between gun and human shoulder, and high quality test data is collected. Then, the model parameter of human-gun system is calculated by the method of model parameter identification. 3D model of human-gun system is built. At last, the dynamic simulation is made by ADAMS. And human-gun model built by experiment method is verified.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Meshing Stiffness and Tooth Root Stress for Internal Cylindrical Gears With Thin Rim

2003 ◽  
Author(s):  
Jean-Pierre de Vaujany ◽  
Miche`le Guingand ◽  
Didier Remond
Keyword(s):  
3D Model ◽  
Statistical Design ◽  
Tension Stress ◽  
Tooth Root ◽  
Meshing Stiffness ◽  
Cylindrical Gears ◽  
Maximal Tension ◽  
Experiment Method ◽  
Internal Gear ◽  
Root Stress

The main objective of this study is to quantify the influence of the deformation of the rim of an internal gear on the meshing stiffness and the stress distribution in tooth fillets. The 3D model used is based on a method derived from the Finite Prism Method. Tooth bending effects and contact deformations are processed simultaneously. Scientific use of the software has resulted in formulating an equation to calculate the maximal tension stress in the tooth root. This formula has been obtained by using the statistical design of experiment method.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

scholarly journals Diffusion models and scale-up

2005 ◽  
Vol 9 (1) ◽  
pp. 43-72 ◽  
Author(s):  
Christo Boyadjiev
Keyword(s):  
Mass Transfer ◽  
Parameter Identification ◽  
Fluidized Bed ◽  
Scale Up ◽  
Catalytic Reactions ◽  
Fluidized Bed Reactors ◽  
Hierarchical Approach ◽  
Model Parameter ◽  
Transfer Processes ◽  
Three Phase

A model for transfer processes in column apparatuses has been done. The model may be modified for different apparatuses as columns with (or without) packet bed, two (or three) phase airlift reactors and fluidized bed reactors. The mass transfer is result of different volume reactions as a chemical, photochemical, biochemical or catalytic, reactions, or interphase. mass transfer. The using of the average velocities and concentration permit to solve the scale-up problems. A hierarchical approach for model parameter identification has been proposed.

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