Collaborative Research: Model Reduction for Probabilistic Analysis and Design Under Uncertainty

2012 ◽  
Author(s):  
Matthias Heinkenschloss ◽  
Danny C. Sorensen ◽  
Karen Willcox
Keyword(s):  
Model Reduction ◽  
Probabilistic Analysis ◽  
Collaborative Research ◽  
Design Under Uncertainty ◽  
Research Model ◽  
Analysis And Design

A dynamic probabilistic design method for blade-tip radial running clearance of aeroengine high-pressure turbine

2014 ◽  
Vol 229 (10) ◽  
pp. 1861-1872 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Wen-Zhong Tang ◽  
Guang-chen Bai ◽  
Zhi-Ying Chen
Keyword(s):  
High Pressure ◽  
Response Surface ◽  
Response Surface Method ◽  
Probabilistic Analysis ◽  
High Reliability ◽  
Probabilistic Design ◽  
High Pressure Turbine ◽  
Analysis And Design ◽  
Surface Method ◽  
Blade Tip

Around the engineering background of the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) which conduces to the high-performance and high-reliability of aeroengine, a distributed collaborative extremum response surface method (DCERSM) was proposed for the dynamic probabilistic analysis of turbomachinery. On the basis of investigating extremum response surface method (ERSM), the mathematical model of DCERSM was established. The DCERSM was applied to the dynamic probabilistic analysis of BTRRC. The results show that the blade-tip radial static clearance δ = 1.82 mm is advisable synthetically considering the reliability and efficiency of gas turbine. As revealed by the comparison of three methods (DCERSM, ERSM, and Monte Carlo method), the DCERSM reshapes the possibility of the probabilistic analysis for turbomachinery and improves the computational efficiency while preserving computational accuracy. The DCERSM offers a useful insight for BTRRC dynamic probabilistic analysis and optimization. The present study enrichs mechanical reliability analysis and design theory.

A Component-Based Parametric Reduced-Order Modeling Technique and Its Application to Probabilistic Vibration Analysis and Design Optimization

2006 ◽  
Author(s):  
Keychun Park ◽  
Geng Zhang ◽  
Matthew P. Castanier ◽  
Christophe Pierre
Keyword(s):  
Design Optimization ◽  
Probabilistic Analysis ◽  
Structural Design ◽  
Vibration Analysis ◽  
Reduced Order Modeling ◽  
Design Parameters ◽  
Model Parameters ◽  
Reduced Order ◽  
Analysis And Design ◽  
Structural Design Optimization

In this paper, a component-based parametric reduced-order modeling (PROM) technique for vibration analysis of complex structures is presented, and applications to both structural design optimization and uncertainty analysis are shown. In structural design optimization, design parameters are allowed to vary in the feasible design space. In probabilistic analysis, selected model parameters are assumed to have predefined probability distributions. For both cases, each realization corresponding to a specific set of parameter values could be evaluated accurately based on the exact modes for the system with those parametric values. However, as the number of realizations increases, this approach becomes prohibitively expensive, especially for largescale finite element models. Recently, a PROM method that employs a fixed projection basis was introduced to avoid the eigenanalysis for each variation while retaining good accuracy. The fixed basis is comprised of a combination of selected mode sets of the full model calculated at only a few sampling points in the parameter space. However, the preparation for the basis may still be cumbersome, and the simulation cost and the model size increase rapidly as the number of parameters increases. In this work, a component-based approach is taken to improve the efficiency and effectiveness of the PROM technique. In particular, a component mode synthesis method is employed so that the parameter changes are captured at the substructure level and the analysis procedure is accelerated. Numerical results are presented for two example problems, a design optimization of a pickup truck and a probabilistic analysis of a simple L-shaped plate. It is shown that the new component-based approach significantly improves the efficiency of the PROM technique.

ENGINEERING PROBABILISTIC ANALYSIS USING MODEL REDUCTION AND AUGMENTED RADIAL BASIS FUNCTIONS

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Qian Wang
Keyword(s):  
Model Reduction ◽  
Reliability Analysis ◽  
Radial Basis Functions ◽  
Probabilistic Analysis ◽  
Basis Functions ◽  
Engineering System ◽  
Performance Function ◽  
First Order ◽  
Engineering Problems ◽  
Radial Basis

Probabilistic analysis of practical engineering problems has long been based on traditional sampling-based approaches, such as Monte Carlo Simulations (MCS) and gradient-based first-order and second-order methods. Since the finite element (FE) or other numerical methods are required to evaluate engineering system responses, such as forces or displacements, it is not efficient to directly integrate FE and sampling-based analysis approaches. Over the years, various approximate methods have been developed and applied to the reliability analysis of engineering problems. In this study, an efficient model reduction technique based on high-dimensional model reduction (HDMR) method has been developed using augmented radial basis functions (RBFs). The basic idea is to use augmented RBFs to construct HDMR component functions. The first-order HDMR model only requires sample points along each variable axis. The HDMR model, once created and used to explicitly express a performance function, is further combined with MCS to perform probabilistic calculations. As test problems, a mathematical problem and a 10-bar truss example are studied using the proposed reliability analysis approach. The proposed method works well, and accurate reliability analysis results are found with a small number of original performance function evaluations, i.e., FE simulations.

scholarly journals A Community Based Collaborative Research Model to Fight Coronavirus Disease (COVID-19). A Report on Nairobi, Kenya COVD-19 Efforts

2021 ◽  
Vol 7 (5) ◽  
pp. 01-03
Author(s):  
Samson Chama
Keyword(s):  
Collaborative Research ◽  
Intervention Research ◽  
Research Model ◽  
Community Based ◽  
Research Project ◽  
Preventative Measures ◽  
Corona Virus ◽  
The World ◽  
The Impact

Today nearly almost every part of the world has been impacted by the corona virus COVID-19 pandemic. COVID-19 has been unrelenting in its impact as thousands and thousands of people have died from its infections and resulting complications. Some countries have experienced severe effects of the virus than others. However, in both situations the virus has been vicious and many of those who have recovered and survived its infections have testified of how deadly this virus is. In the wake of this pandemic nations around the world have taken measures to curb its impact and spread in communities and societies. Preventative measures taken have disrupted in most cases schools and the workforce with many people being forced to work remotely from their homes and with schools going virtual. However, progress in fighting this virus is in the offing as promising vaccines are on the horizon. This paper discusses a preliminary intervention research project whose goal is to mitigate the impact of COVID-19 in Nairobi, Kenya. Results of this study may have promise of being replicated in other parts of Africa affected by this pandemic.

Efficient Probabilistic Analysis and Design Optimization Using Data Classification Decision Boundaries

2010 ◽  
Author(s):  
Liping Wang ◽  
Arun K. Subramaniyan ◽  
Don Beeson
Keyword(s):  
Probabilistic Analysis ◽  
Adaptive Sampling ◽  
Optimization Design ◽  
Data Classification ◽  
Sampling Technique ◽  
Support Vector ◽  
Classification Methods ◽  
Analysis And Design ◽  
Using Data ◽  
Decision Boundaries

A new technique for performing probabilistic analysis and optimization design using data classification methods is investigated. The approach is based on nonlinear decision boundaries constructed from data classification methods. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. An adaptive sampling technique is used to generate samples and update the approximated decision function. The proposed approach is demonstrated with several benchmark and engineering problems.

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