iSYS R&D(Intelligent SYStem Reliability & Design Laboratory)
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Research Risk Analysis & Design Prognosis & Health Management(PHM) Energy Harvester Design Verification & Validation	It consists of three research topics: 1) Data Mining and Knowledge Extraction, 2) Predictive Modeling and 3) Risk-based Design Optimization. 1) Data Mining and Knowledge Extraction Motivation: A big challenge in engineering decision-making is that it demands on adequate data to support the decisions, but in a real world, considerable kinds of data, especially subjective data (e.g., expert knowledge, customer survey) and certain testing data (i.e., failure data for nuclear plant or aircraft), are very difficult to obtain and in a limited amount. Additionally, due to the insufficiency of the data, treated results are always in a large uncertainty. This makes engineering decision-making more difficult. Therefore, the objective of this research is to find an effective way to mine for subjective and insufficient data, extract statistical knowledge of those data, and provide valuable information for engineering decision-making. Project Highlight: · A model has been developed to elicit subjective data, and then to find out the relevant objective data to help decision-making. This model contains four sub models: Decision Decomposition Model, Subjective Data Model, Measured Data Model and Decision Model. · This model has been applied to a cell-phone case study. Relevant Publications: 1. 2) Predictive Modeling Motivation: Nowadays, people are more and more relying on predictive models for components and system design considering cost, time limitation, planning, and other technical and economic factors. Accurate prediction of system performance is critical to produce reliable products. The existence of uncertainties makes it difficult to predict the system performance accurately and efficiently. Furthermore, epistemic uncertainty due to lack of data is inevitable challenges in this area. Project Highlight: • Eigenvector Dimension Reduction (EDR) method is proposed for accurate and efficient uncertainty propagation analysis. EDR method is able to handle correlated/uncorrelated variables, arbitrary symmetric/asymmetric input distributions. A probability density function (PDF) of system response is constructed in the method. • An adaptive dimension-reduction-based polynomial chaos expansion (DR_PCE) method is proposed for accurate and efficient uncertainty propagation analysis while remove some limitations in the EDR method. DR_PCE method is able to handle nonlinear correlated variables, arbitrary input distributions, and predict multi-mode response PDF. • Bayesian updating technique (conjugate/non-conjugate) is employed to deal with epistemic uncertainty. Reliability function is proposed using Bayesian theory with both aleatory and epistemic uncertainties. EDR method is incorporated for efficiency improvement for Bayesian reliability analysis. • A generic framework to characterize a random field (spatial variability) structure is proposed for reliability analysis and design. Proper consideration of the random field is quite significant to variability in system performances in many engineering applications, especially, geometry-sensitive failures (e.g., buckling, fillet failure), small-scale applications in which tolerance control is more challenging. Relevant Publications: 1. Youn B.D., Xi Z., and Wang P.F., “Eigenvector Dimension-Reduction (EDR) Method for Sensitivity-Free Uncertainty Quantification,” Structural Multidisciplinary Optimization, v37, n1, 2008 2. Wang, P., Youn, B.D., Xi Z., and Artemis, Kloess, "Bayesian Reliability Analysis with Subjective, Insufficient, and Evolving Data Sets,” Accepted, Journal of Mechanical Design, ASME, 2008;  (ASME - DAC Best Paper 2008). 3. Hu C. and Youn B.D., “Advances in Polynomial Chaos Expansion for Structural Reliability Analysis and Design,” Probabilistic Engineering Mechanics, Submitted, 2008 4. Xi Z. and Youn B.D., “An Effective Random Field Characterization for Probability Analysis and Design,” Structural Multidisciplinary Optimization, Submitted, 2009 3) Risk-based Design Optimization Motivation: Traditional risk-based design optimization (RBDO) is haunted with lack of accuracy, efficiency, and stability. Sampling method is too expensive to be applicable in reality. Expansion method can only provide moderate accuracy while requiring sensitivity. MPP-based method generates better accuracy than expansion method with the aid of sensitivity. However, relatively large error is expected with high nonlinear response or multiple MPPs. Response surface method is suffered from the curse of dimensionality. Recently proposed dimension reduction (DR) method is not able to maintain high accuracy without sacrificing efficiency. All these obstacles prevent a promising RBDO method in real engineering application. Project Highlight: • Eigenvector Dimension Reduction (EDR) method is proposed for sensitivity-free reliability analysis with high accuracy and efficiency. • An approximate response surface facilitates design sensitivity calculation where the response surface is constructed using the eigenvector samples from the EDR method. Thus, sensitivity analysis for design optimization becomes very efficient and simple. • The proposed RBDO methodology has a single-loop structure. One EDR execution evaluates a set of quality (objective) and reliability (constraint) functions. Each evaluation in one EDR execution can be computed independently by a parallel computing power. RBDO can be far more efficient. Relevant Publications: 1. Youn B.D., Xi Z., and Wang P.F., “Eigenvector Dimension-Reduction (EDR) Method for Sensitivity-Free Uncertainty Quantification,” Structural Multidisciplinary Optimization, v37, n1, 2008 2. Youn B.D. and Xi Z., “Reliability-based Robust Design Optimization Using the Eigenvector Dimension Reduction (EDR) Method”, Structural Multidisciplinary Optimization, v37, n5, 2009