Research on Uncertainty Analysis Method of Aircraft’s HWIL Simulation

2016 ◽  
pp. 524-532
Author(s):  
Huapin Geng ◽  
Wenhua Kong ◽  
Yingkang Wang
Keyword(s):  
Uncertainty Analysis ◽  
Analysis Method

An Uncertainty Analysis Method for Coordinate Referencing in Manufacturing Systems

1995 ◽  
Vol 117 (1) ◽  
pp. 42-48 ◽  
Author(s):  
Y. Shen ◽  
N. A. Duffie
Keyword(s):  
Uncertainty Analysis ◽  
Coordinate Transformation ◽  
Manufacturing Systems ◽  
Coordinate Transformations ◽  
Analysis Method ◽  
Uncertainty Interval ◽  
Coordinate Measurement ◽  
Uncertainty Sources ◽  
Design And Manufacturing ◽  
Coordinate Measurements

Accurate and consistent transformations between design and manufacturing coordinate systems are essential for high quality part production. Fixturing and coordinate measurement are common coordinate referencing techniques which are used to locate points or measurement points on workpiece reference surfaces to establish these coordinate transformations. However, uncertainty sources such as geometric form deviations in workpiece surfaces, tolerances on fixture locators, and errors in coordinate measurements exist. A result is that coordinate transformations established using the locating and measurement points are in herently uncertain. An uncertainty analysis method for coordinate referencing is presented in this paper. The uncertainty interval concept is used to describe essential characteristics of uncertainty sources in coordinate referencing and coordinate transformation relationships. The method is applied to estimating uncertainties in simple and compound coordinate transformation obtained using coordinate referencing in an experimental mold manufacturing system. Results of Monte Carlo simulations are used to show that the uncertainty analysis method gives a consistent and high percentage of coverage in evaluating coordinate referencing in the examples studied.

Application of Data Reconciliation Method to Increase CCPP Performance Test Result Accuracy

2015 ◽  
Author(s):  
Zengqian Wang ◽  
Jingjin Ji ◽  
Xinghao Wang ◽  
Bo Sun ◽  
Lei He ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Power Output ◽  
Performance Test ◽  
Heat Rate ◽  
Performance Data ◽  
Data Reconciliation ◽  
Acceptance Test ◽  
Analysis Method ◽  
Reconciliation Method ◽  
Test Result

Performance acceptance test for gas-steam Combined Cycle Power Plant (CCPP) is of great significance for both equipment manufacturer and customer. The influence of measurement error on the calculation of guaranteed performance data as power output and heat rate can lead to unnecessary loss for either party. Commonly used uncertainty analysis method based on ASME PTC 19.1 would require all measuring instrumentation working at designed accuracy range. Meanwhile, due to the complexity of CCPP system and large number of measuring items, and as well the propagation of measurement and data reduction error, the uncertainty of corrected performance data could be significant. In this paper, process data reconciliation method based on VDI 2048 is introduced. With access to complete performance test data from a CCPP project, data reconciliation calculation is performed with an appropriate thermodynamic model. Several measurement values with gross error are identified and verified in heat balance calculation. Moreover, after recalculating with the reconciled data instead of raw data for the corrected power output and heat rate, comparison with the common uncertainty analysis method is also carried out. It is shown that with this reconciliation method, it is not only possible to find out gross errors such as instrumentation drift, but also able to dramatically increase the test result accuracy, which is of great value for both manufacturer and customer.

An efficient epistemic uncertainty analysis method using evidence theory

2018 ◽  
Vol 339 ◽  
pp. 443-466 ◽  
Author(s):  
Z. Zhang ◽  
X.X. Ruan ◽  
M.F. Duan ◽  
C. Jiang
Keyword(s):  
Uncertainty Analysis ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Analysis Method

Predicting the Performance of a Bistable Micro Mechanism Using Design-Stage Uncertainty Analysis

2002 ◽  
Author(s):  
Jonathan W. Wittwer ◽  
Larry L. Howell ◽  
Sydney M. Wait ◽  
Michael S. Cherry
Keyword(s):  
Uncertainty Analysis ◽  
Linear Displacement ◽  
Design Stage ◽  
Analysis Method ◽  
Deflection Curve ◽  
Time To Market ◽  
Joint Clearances ◽  
Bistable Mechanism ◽  
First Time ◽  
Estimate Uncertainty

Significant reduction in cost and time to market can be realized by implementing design-stage uncertainty analysis to predict whether a device will meet specified requirements. This paper demonstrates a generalized uncertainty analysis method appropriate for surface micromachined devices and uses a micro linear-displacement bistable mechanism as an example. Dimensional variations, joint clearances, material property uncertainty and friction are included as sources of error. Using matrix notation, the model consists of a system of implicit, nonlinear equations. The analysis is performed at multiple deflections to estimate uncertainty bands around the force-deflection curve of the mechanism. These results can then be used to predict the performance of the mechanism. Applying these techniques resulted in a functional first-time prototype of a bistable mechanism that can be actuated using a non-amplified thermal actuator.

An adaptive collocation method for structural fuzzy uncertainty analysis

2020 ◽  
Vol 37 (9) ◽  
pp. 2983-2998
Author(s):  
Lei Wang ◽  
Chuang Xiong ◽  
Qinghe Shi
Keyword(s):  
Uncertainty Analysis ◽  
Collocation Method ◽  
Membership Function ◽  
Interval Uncertainty ◽  
Engineering Practice ◽  
Analysis Method ◽  
Content Type ◽  
Fuzzy Uncertainty ◽  
Uncertain Variables ◽  
The Membership Function

Purpose Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis. Design/methodology/approach ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis. Findings The proposed ACM has a high accuracy without too much additional computational efforts. Originality/value A novel ACM is developed for the structural fuzzy uncertainty analysis.

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