Identification in Rotordynamics: Uncertainty Analysis and Quality Estimation

2009 ◽  
Author(s):  
Qingyu Wang ◽  
Brian Pettinato ◽  
Eric Maslen
Keyword(s):  
Experimental Data ◽  
Uncertainty Analysis ◽  
Analytical Model ◽  
Uncertainty Propagation ◽  
Measurement Methods ◽  
Quality Estimation ◽  
Identification Process ◽  
Model Based ◽  
Parameters Uncertainty ◽  
Different Types

Critical to the result value of an identification process is establishment of the reliability or accuracy of the identified parameters. Uncertainty in the identification process can stem both from uncertainty in the analytical model and from uncertainty in the test data. The uncertainty propagation turns out to be difficult to estimate due to rather complicated identification process and the dimension of the analytical model. Currently, there is no uncertainty analysis and quality estimation available in the literature to the author’s knowledge for model-based identification in rotordynamics. This paper borrows linear fractional transformation (LFT) and μ-analysis from the controls community to perform this job. The basic idea is that the uncertainty of the identified result can be expressed as a system with uncertainties, and therefore quality estimation is equal to bounding the gain of this system. This system is built in two steps: first, different types of source uncertainties are expressed as LFT format, and second, the whole identification process with uncertainties is transformed into a single LFT format. μ-analysis is then used to bound the gain of this LFT. The uncertainty analysis and bounding algorithm are illustrated with the same experimental data used in the last paper, for both model-based and direct measurement methods.

Identification in Rotordynamics: Model-Based vs. Direct Measurements

2009 ◽  
Author(s):  
Qingyu Wang ◽  
Brian Pettinato ◽  
Eric Maslen
Keyword(s):  
Analytical Model ◽  
Direct Measurement ◽  
Dynamic Characteristics ◽  
Measurement Method ◽  
Transfer Functions ◽  
Subsequent Paper ◽  
Quality Estimation ◽  
Model Based ◽  
Equation Solving ◽  
Direct Measurement Method

In a rotor-bearing system, there are usually some under- or unmodeled components, such as foundations and seals. Identifying the dynamic characteristics of these components often requires both an analytical model and test data due to the working conditions, such as running speed above the first bending mode and non-collocation measurements. The existing methods always identify the dynamic characteristics by solving the equations of motion at discrete frequencies of the measured frequency response functions (FRFs). They have two problems: first, the physical background of the identification is buried in the equation solving process, and second, there is no quality estimation of the identified result. This paper discusses the first problem which is the equation solving process. The second problem, quality estimation, is discussed in a subsequent paper [1]. This paper reveals that model-based identification is the interconnection of certain transfer functions. These transfer functions are either generated from an analytical model (the common model-based method), or directly measured (direct measurement method). The process of both these methods is then illustrated by use of experimental data. A novel seal test design is proposed based on the idea of the direct measurement method. Identification under complex situations is also considered as complementary to the main content, such as different input/output locations. The conditions for identifiability are given.

Analytical model based on experimental data of centrifuge ice adhesion tests with different substrates

2016 ◽  
Vol 121 ◽  
pp. 93-99 ◽  
Author(s):  
Frederic Guerin ◽  
Caroline Laforte ◽  
Marie-Isabelle Farinas ◽  
Jean Perron
Keyword(s):  
Experimental Data ◽  
Analytical Model ◽  
Model Based ◽  
Ice Adhesion

Studies on Sensitivity and Uncertainty Analyses for SCOPE and WAFT With Uncertainty Propagation Methods

2017 ◽  
Author(s):  
Wang Zhangli ◽  
Hu Benxue ◽  
Wang Guodong ◽  
Wang Zhe ◽  
Ni Chenxiao ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Analysis ◽  
Flow Rate ◽  
Uncertainty Propagation ◽  
Water Film ◽  
Tolerance Interval ◽  
Target Parameter ◽  
Parameters Uncertainty ◽  
Input Parameters ◽  
Propagation Methods

The purpose of Steam condensation on cold plate experiment facility (SCOPE) and Water film test (WAFT) is to verify the steam condensation and water film evaporation correlation within the parameter variation range of CAP1400 passive containment cooling system. These correlations were used for containment response analysis. Uncertainty and sensitivity analyses were performed for SCOPE and WAFT tests in this paper. Sampling-based sensitivity analysis with uncertainty propagation is a new parameters sensitivity analysis method, and the importance of input parameters could be evaluated by calculating the correlation coefficients between input parameters and the output target parameter. This method was used to acquire the influence of the measured input parameters uncertainty on the output target parameter. The results show that air and steam flow rate, coolant flow rate, inlet and outlet water temperature are the main source of the uncertainty for SCOPE. Inlet film flow rate, inlet air flow velocity and plate surface temperatures are the main source of the uncertainty for WAFT. Sensitivity analysis results may provide support for experiment measurement system optimization to reduce the target parameter error range. Uncertainty analysis is one important aspect of test data analyses, which is meaningful to the assessment of test results. Conventionally the partial derivative with respect to the input parameters is used to transfer uncertainty from the input parameters to the output parameter. However, in this method the partial derivatives of the output parameter sub the input parameters must be calculated. For complex engineering problems, it is usually difficult to acquire theoretical correlations for the partial derivatives. WILKs formula is used to determine the parameter tolerance interval with certain probability content and confidence level. The tolerance interval is a good way to well describe the uncertainty of parameters. The nonparametric statistics with WILKS correlation were widely used in the best-estimate plus uncertainty (BEPU) accident analyses. However, little work has been conducted on the experiment results uncertainty analysis with that method. In this paper nonparametric statistics with WILKS correlation was used to acquire key parameters uncertainty. And the results show that key output parameters uncertainty for SCOPE and WAFT are within the reasonable range. Uncertainty Propagation Methods were implied for test results Sensitivity and Uncertainty Analysis in the paper, which may be conveniently applied to the other experiment data analyses and also valuable to the engineering project.

Uncertainty Analysis in Fatigue Life Prediction of Concrete Using Evidence Theory

2016 ◽  
Vol 866 ◽  
pp. 25-30
Author(s):  
He Sheng Tang ◽  
Jia He Mei ◽  
Wei Chen ◽  
Da Wei Li ◽  
Song Tao Xue
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Life Prediction ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Evidence Theory ◽  
Fatigue Life Prediction ◽  
Theory Method

Various sources of uncertainty exist in concrete fatigue life prediction, such as variability in loading conditions, material parameters, experimental data and model uncertainty. In this article, the uncertainty model of concrete fatigue life prediction based on the S-N curve is built, and the evidence theory method is presented for uncertainty analysis in fatigue life prediction of concrete while considering the epistemic uncertainty of the parameter of the model. Based on the experimental of concrete four-point bending beams, the evidence theory method is applied to quantify the epistemic uncertainty stem from experimental data and model uncertainty. To improve the efficiency of computation, a method of differential evolution is adopted to speedup the works of uncertainty propagation. The efficiency and feasibility of the proposed approach are verified through a comparative analysis of probability theory.

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

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