Reduced Order Modeling Methods for Turbomachinery Design

Bibliographic Details
Main Author:	Brown, Jeffrey M.
Language:	English
Published:	Wright State University / OhioLINK 2008
Subjects:	Mechanical Engineering Turbine Engine Reduced Order Modeling Kriging Mistuning Approximation Eigensensitivity Component Mode Synthesis IBR Rotor Blisk Airfoil Blade HCF forced response modal analysis Uncertainty Quantification Statistical Confidence
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=wright1229962254

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-wright12299622542021-08-03T06:16:58Z Reduced Order Modeling Methods for Turbomachinery Design Brown, Jeffrey M. Mechanical Engineering Turbine Engine Reduced Order Modeling Kriging Mistuning Approximation Eigensensitivity Component Mode Synthesis IBR Rotor Blisk Airfoil Blade HCF forced response modal analysis Uncertainty Quantification Statistical Confidence Design of structural components is constrained by both iteration time and prediction uncertainty. Iteration time refers to the computation time each simulation requires and controls how much design space can be explored given a fixed period. A comprehensive search of the space leads to more optimum designs. Prediction uncertainty refers to both irreducible uncertainties, such as those caused by material scatter, and reducible uncertainty, such as physics-based model error. In the presence of uncertainty, conservative safety factors and design margins are used to ensure reliability, but these negatively impact component weight and design life. This research investigates three areas to improve both iteration time and prediction uncertainty for turbomachinery design. The first develops an error-quantified reduced-order model that predicts the effect of geometric deviations on airfoil forced response. This error-quantified approximation shows significant improvements in accuracy compared to existing methods because of its bias correction and description of random error. The second research area develops a Probabilistic Gradient Kriging approach to efficiently model the uncertainty in predicted failure probability caused by small sample statistics. It is shown that the Probabilistic Gradient Kriging approach is significantly more accurate, given a fixed number of training points, compared to conventional Kriging and polynomial regression approaches. It is found that statistical uncertainty from small sample sizes leads to orders of magnitude variation in predicted failure probabilities. The third research area develops non-nominal and nominal mode Component Mode Synthesis methods for reduced-order modeling of the geometric effects on rotor mistuning. Existing reduced-order methods approximate mistuning with a nominal-mode, or design intent, basis and airfoil modal stiffness perturbation. This assumption introduces error that can be quantified when compared to a finite elment model prediction of a geometrically perturbed rotor. It is shown that the nominal-mode approach can produce significant errors, whereas the non-nominal approach accurately predicts blade-to-blade mistuned response. 2008 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1229962254 http://rave.ohiolink.edu/etdc/view?acc_num=wright1229962254 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Mechanical Engineering Turbine Engine Reduced Order Modeling Kriging Mistuning Approximation Eigensensitivity Component Mode Synthesis IBR Rotor Blisk Airfoil Blade HCF forced response modal analysis Uncertainty Quantification Statistical Confidence
spellingShingle	Mechanical Engineering Turbine Engine Reduced Order Modeling Kriging Mistuning Approximation Eigensensitivity Component Mode Synthesis IBR Rotor Blisk Airfoil Blade HCF forced response modal analysis Uncertainty Quantification Statistical Confidence Brown, Jeffrey M. Reduced Order Modeling Methods for Turbomachinery Design
author	Brown, Jeffrey M.
author_facet	Brown, Jeffrey M.
author_sort	Brown, Jeffrey M.
title	Reduced Order Modeling Methods for Turbomachinery Design
title_short	Reduced Order Modeling Methods for Turbomachinery Design
title_full	Reduced Order Modeling Methods for Turbomachinery Design
title_fullStr	Reduced Order Modeling Methods for Turbomachinery Design
title_full_unstemmed	Reduced Order Modeling Methods for Turbomachinery Design
title_sort	reduced order modeling methods for turbomachinery design
publisher	Wright State University / OhioLINK
publishDate	2008
url	http://rave.ohiolink.edu/etdc/view?acc_num=wright1229962254
work_keys_str_mv	AT brownjeffreym reducedordermodelingmethodsforturbomachinerydesign
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Reduced Order Modeling Methods for Turbomachinery Design

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