Probabilistic condition assessment of structures by multiple FE model identification considering measured data uncertainty

2015 ◽  
Vol 15 (3) ◽  
pp. 751-767
Author(s):  
Hyun-Joong Kim ◽  
Hyun-Moo Koh
Keyword(s):  
Model Identification ◽  
Condition Assessment ◽  
Measured Data ◽  
Data Uncertainty ◽  
Fe Model

Urban Railway Bridge Fast Evaluation Based on Cloud Computing

2011 ◽  
Vol 71-78 ◽  
pp. 4501-4505
Author(s):  
Ming Chen ◽  
Wan Zhou
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Railway Bridge ◽  
Fast Evaluation ◽  
Railway Bridges ◽  
Computing Model ◽  
Bridge Condition Assessment

Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge

Managing Flow Induced Vibration in Subsea Jumpers

2021 ◽  
Author(s):  
Rakshith Naik ◽  
Yetzirah Urthaler ◽  
Scot McNeill ◽  
Rafik Boubenider
Keyword(s):  
Dynamic Properties ◽  
Fatigue Analysis ◽  
Management Program ◽  
Measured Data ◽  
Operating Conditions ◽  
Valuable Insight ◽  
Fe Model ◽  
Flow Induced Vibration ◽  
Vibration Measurements

Abstract Certain subsea jumper design features coupled with operating conditions can lead to Flow Induced Vibration (FIV) of subsea jumpers. Excessive FIV can result in accumulation of allowable fatigue damage prior to the end of jumper service life. For this reason, an extensive FIV management program was instated for a large development in the Gulf of Mexico (GOM) where FIV had been observed. The program consisted of in-situ measurement, modeling and analysis. Selected well and flowline jumpers were outfitted with subsea instrumentation for dedicated vibration testing. Finite Element (FE) models were developed for each jumper and refined to match the dynamic properties extracted from the measured data. Fatigue analysis was then carried out using the refined FE model and measured response data. If warranted by the analysis results, action was taken to mitigate the deleterious effects of FIV. Details on modeling and data analysis were published in [5]. Herein, we focus on the overall findings and lessons learned over the duration of the program. The following topics from the program are discussed in detail: 1. In-situ vibration measurement 2. Overall vibration trends with flow rate and lack of correlation of FIV to flow intensity (rho-v-squared); 3. Vibration and fatigue performance of flowline jumpers vs. well jumpers 4. Fatigue analysis conservatism Reliance on screening calculations or predictive FE analysis could lead to overly conservative operational limits or a high degree of fatigue life uncertainty in conditions vulnerable to FIV. It is proposed that in-situ vibration measurements followed by analysis of the measured data in alignment with operating conditions is the best practice to obtain a realistic understanding of subsea jumper integrity to ensure safe and reliable operation of the subsea system. The findings from the FIV management program provide valuable insight for the subsea industry, particularly in the areas of integrity management of in-service subsea jumpers; in-situ instrumentation and vibration measurements and limitations associated with predictive analysis of jumper FIV. If learnings, such as those discussed here, are fed back into design, analysis and monitoring guidelines for subsea equipment, the understanding and management of FIV could be dramatically enhanced compared to the current industry practice.

Mathematical model identification of gas turbine engine with account for initial data uncertainty

2021 ◽  
Vol 28 (3) ◽  
pp. 171-185
Author(s):  
Oleg Baturin ◽  
Paul Nikolalde ◽  
Grigorii Popov ◽  
Anastssia Korneeva ◽  
Ivan Kudryashov
Keyword(s):  
Mathematical Model ◽  
Initial Data ◽  
Gas Turbine ◽  
Model Identification ◽  
Gas Turbine Engine ◽  
Data Uncertainty ◽  
Turbine Engine

scholarly journals Dynamic Characteristics Analysis of the Six-Axis Force/Torque Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Liyue Fu ◽  
Aiguo Song
Keyword(s):  
Natural Frequency ◽  
Dynamic Characteristics ◽  
Dynamic Models ◽  
Dynamic Range ◽  
Model Identification ◽  
Step Response ◽  
Fe Model ◽  
De Algorithm ◽  
Characteristics Analysis ◽  
Experimental Values

In this study, dynamic characteristics of a robot six-axis wrist force/torque (F/T) sensor with crossbeam elastomer are analyzed by two methods of model identification, a method for simultaneous identification of order and parameters of the model (SIM) and a method based on the differential evolution (DE) algorithm. Firstly, by establishing the simplified mechanical model and finite element (FE) model, respectively, natural frequency of the six-axis wrist F/T sensor is calculated. Secondly, dynamic calibration experiment is conducted. Lastly, two dynamic models of the sensor are identified by SIM and DE methods and the dynamic characteristics of the sensor, such as natural frequency and working band, are further analyzed. Comparing experimental values with the theoretical values, the results show that this sensor has a wide dynamic range with the first natural frequency at more than 1600 Hz, working bands (±5%) are more than 400 Hz, and the step response oscillation is intense. This study can provide a reference for the application of the six-axis F/T sensor in the field of dynamic measurement.

2509 System identification and control based on FE model and measured data

2015 ◽  
Vol 2015.25 (0) ◽  
pp. _2509-1_-_2509-10_
Author(s):  
Sosuke SATO ◽  
Itsuro KAJIWARA ◽  
Toshihiro ARISAKA
Keyword(s):  
System Identification ◽  
Measured Data ◽  
Fe Model ◽  
And Control

A Modified Surface Spline for Expansion and Smoothing of Frequency Response Functions

1996 ◽  
Vol 210 (1) ◽  
pp. 77-92
Author(s):  
T P Waters ◽  
N A J Lieven
Keyword(s):  
Frequency Response ◽  
Model Updating ◽  
Measured Data ◽  
Fe Model ◽  
Response Functions ◽  
Frequency Response Functions ◽  
Surface Spline ◽  
Measured Frequency Response ◽  
Modified Surface ◽  
Noise Contamination

Measured frequency response functions (FRFs) are becoming more widely used to correlate finite element (FE) models with test structures. However, most model updating techniques require measured data at every degree of freedom (DOF) in the FE model, a necessity that is rarely met by experiment. Furthermore, the sensitivity of updating techniques to noise measurement demands experimental data of the highest attainable quality, and often beyond. A modified surface spline is presented which can expand measured FRFs to unmeasured DOFs and also apply smoothing to reduce noise contamination.

scholarly journals Thermovision in the Validation Process of Numerical Simulation of Braking

2014 ◽  
Vol 21 (2) ◽  
pp. 329-340 ◽  
Author(s):  
Pawel Baranowski ◽  
Krzysztof Damaziak ◽  
Jerzy Malachowski ◽  
Lukasz Mazurkiewicz ◽  
Henryk Polakowski ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Numerical Modeling ◽  
Measured Data ◽  
Measurement Technology ◽  
Fe Model ◽  
Validation Process ◽  
Validation Parameters ◽  
Infrared Technology ◽  
Special Stand

Abstract This article presents the validation process of a brake FE model by means of temperature measured on a special stand using infrared technology. Unlike many other publications, the authors try to show the interaction between measurement technology and numerical modeling rather than only nice, perfectly correlated graphs. Some difficulties in choosing and using validation parameters are also pointed out and discussed. Finally, results of FE analyses are compared with measured data, followed by explanation of applied numerical technology and estimation of validation process effectiveness.

Impacts of measured data uncertainty on urban stormwater models

2014 ◽  
Vol 508 ◽  
pp. 28-42 ◽  
Author(s):  
C.B.S. Dotto ◽  
M. Kleidorfer ◽  
A. Deletic ◽  
W. Rauch ◽  
D.T. McCarthy
Keyword(s):  
Measured Data ◽  
Data Uncertainty ◽  
Urban Stormwater
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