scholarly journals Uncertainty Analysis of Ship Model Propulsion Test on Actual Seas Based on Monte Carlo Method

2020 ◽  
Vol 8 (6) ◽  
pp. 398 ◽  
Author(s):  
Guangli Zhou ◽  
Yuwei Wang ◽  
Dagang Zhao ◽  
Jianfeng Lin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Large Scale ◽  
Performance Test ◽  
The Yellow Sea ◽  
Test Accuracy ◽  
Northwestern Part ◽  
Test Environment ◽  
Ship Model ◽  
Propulsion Performance

As a new testing technology, large-scale ship model tests on the sea are advantageous in addressing the scale effect in ship models and in simulating ship navigation conditions. In this study, the uncertainty of a ship model propulsion test on the sea was analyzed using the Monte Carlo method, and the influence of the test environment was quantified. We used a 25 m-long ship model for the propulsion performance test. Based on the procedure recommended by the International Standardization Organization (ISO), several tests were conducted on the Yellow Sea (the northwestern part of the East China Sea). The results demonstrate that the wind and waves in the environment are the two factors that have the greatest influence on the test accuracy. This study will aid the development of sea trials, and the analysis method used in the propulsion test is also suitable for many complex ship tests.

Fabrication and Experiment of an Automatic Continuum Robot System Using Image Recognition

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yeong-min Na ◽  
Hyun-seok Lee ◽  
Jong-kyu Park
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Image Recognition ◽  
Performance Test ◽  
Skill Level ◽  
Experimental Results ◽  
Robot System ◽  
The Monte Carlo Method ◽  
Continuum Robot ◽  
Two Stages

Abstract This paper proposes a continuum robot that can be controlled automatically using image recognition. The proposed robot can operate in narrower spaces than the existing robots composed of links and joints. In addition, because it is automatically controlled through image recognition, the robot can be operated irrespective of the human controller's skill level. The manipulator is divided into two stages, with three wires connected to each stage to minimize the energy used to control the manipulator posture. The manipulator's posture is controlled by adjusting the length of the wire, similar to the relaxation and contraction of the muscles. Denavit–Hartenberg transformation and the Monte Carlo method were used to analyze the robot's kinematics and workspace. In a performance test, an experimental plate with nine targets was fabricated and the manipulator speed was adjusted to 5, 10, and 20 mm/s. Experimental results show that the manipulator was automatically controlled and reached all targets, with errors of 2.58, 3.28, and 9.18 mm.

Experimental simulation of large-scale bioreaetor environments using a Monte Carlo method

1991 ◽  
Vol 69 (2) ◽  
pp. 513-519 ◽  
Author(s):  
Pradyumna K. Namdev ◽  
Pallavur K. Yegneswaran ◽  
Murray R. Gray ◽  
B. G. Thompson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Large Scale ◽  
Experimental Simulation

Propulsion performance of large-scale ship model in real sea environment

2020 ◽  
Vol 210 ◽  
pp. 107440 ◽  
Author(s):  
Chun-yu Guo ◽  
Xiang-hai Zhong ◽  
Da-gang Zhao ◽  
Chao Wang ◽  
Jian-feng Lin ◽  
...  
Keyword(s):  
Large Scale ◽  
Ship Model ◽  
Propulsion Performance

scholarly journals Research on Region Division of Large Workpiece Based on Dexterous Workspace of Robot

2021 ◽  
Vol 2095 (1) ◽  
pp. 012059
Author(s):  
Yonggui Zhang ◽  
Gan Zhang ◽  
Xin Xu ◽  
Qianqiu Zhao
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Coordinate System ◽  
Large Scale ◽  
Forward Kinematics ◽  
Geometric Features ◽  
Task Space ◽  
Region Division ◽  
And Task

Abstract In order to complete the processing of large-scale workpieces, a region division of large-scale workpieces based on robot dexterous workspace is studied. The linkage coordinate system of the robot is established by D-H method, and the forward kinematics equation of the robot is obtained; Monte Carlo method is used to analyze the workspace of the robot, and MATLAB is used to program to draw the workspace and task space of the robot; Taking an example part as the object, the feature of the part is studied, and the process of determining the task space area of the workpiece in the dexterous workspace of the robot is given, and the region of the workpiece is divided based on the size of the task space and the geometric features of the workpiece.

Combined Cycle Performance Test Uncertainty Validation by Comparing Monte Carlo Analysis With Monovariate Perturbation Results

2006 ◽  
Author(s):  
Kerri L. Spencer ◽  
Jeffrey R. Friedman ◽  
Terry B. Sullivan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Test ◽  
Performance Testing ◽  
Monte Carlo Analysis ◽  
Combined Cycle ◽  
Plant Performance ◽  
The Monte Carlo Method ◽  
The Impact ◽  
Test Uncertainty

This paper focuses on the calculation of the test uncertainty of an ASME PTC 46 [1], overall plant performance test of a combined cycle by two separate methods. It compares the combined cycle corrected plant output and heat rate systematic uncertainty results that are generated using monovariate perturbation analysis with the Monte Carlo method. The Monte Carlo method has not been used widely in power plant performance testing applications. It offers insights into the results of the Monte Carlo analysis method, which is less intuitive than the conventional method. This study shows that utilizing two distinctly different methods of calculation of test uncertainty serves to corroborate assumptions, or to isolate flaws in one or both methods. In developing the method for calculation of test uncertainty, the authors conclude that it is prudent to validate the calculation method of choice of test uncertainty, and to consider the correlations in measurement uncertainties. Also discussed in detail are the impact of correlated uncertainty assumptions, and recommendations on their application. Correlated uncertainty has not been extensively discussed in the literature concerning specific applications in performance testing, although it should be a critical consideration in any uncertainty analysis. Details of determination of instrumentation uncertainty, measurement uncertainty of a parameter, and calculation of sensitivity factors are included in this paper.

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