Byeng Youn | Seoul National University (original) (raw)

Papers by Byeng Youn

Volume 3A: 39th Design Automation Conference, 2013

ABSTRACT This paper presents a new system design platform and approaches leading to the developme... more ABSTRACT This paper presents a new system design platform and approaches leading to the development of resilient engineered systems through integrating design of system functions and prognosis of function failures in a unified design framework. Failure prognosis plays an increasingly important role in complex engineered systems since it detects, diagnoses, and predicts the system-wide effects of adverse events, therefore enables a proactive approach to deal with system failures at the life cycle use phase. However, prognosis of system functional failures has been largely neglected in the past at early system design stage, mainly because quantitative analysis of failure prognosis in the early system design stage is far more challenging than these activities themselves that have been mainly carried out at the use phase of a system life cycle. In this paper, a generic mathematical formula of resilience and predictive resilience analysis will be introduced, which offers a unique way to consider lifecycle use phase failure prognosis in the early system design stage and to systematically analyze their costs and benefits, so that it can be integrated with system function designs concurrently to generate better overall system designs. Engineering design case studies will be used to demonstrate the proposed design for resilience methodology.

1. Abstract The objective of this paper is to develop and apply the reliability-based design opti... more 1. Abstract The objective of this paper is to develop and apply the reliability-based design optimization (RBDO) process for structural durability to obtain a reliable and durable design under manufacturing constraints. Since uncertainty propagation to structural fatigue under transient dynamic loading is numerically complicated and computationally expensive, it is challenging to integrate the reliability analysis with durability optimization. For efficient

Mechanical fatigue subject to external and inertia transient loads in the service life of mechani... more Mechanical fatigue subject to external and inertia transient loads in the service life of mechanical systems often leads a structural failure due to accumulated damage. Structural durability analysis that predicts the fatigue life of mechanical components subject to dynamic stresses and strains is a compute intensive multidisciplinary simulation process, since it requires an integration of several computer- aided engineering tools

In the Army mechanical fatigue subject to external and inertia transient loads in the service lif... more In the Army mechanical fatigue subject to external and inertia transient loads in the service life of mechanical systems often leads to a structural failure due to accumulated damage. Structural durability analysis that predicts the fatigue life of mechanical components subject to dynamic stresses and strains is a compute intensive multidisciplinary simulation process, since it requires the integration of several

Dimensional variation is inherent to any manufacturing process. In order to minimize its impact o... more Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products it is important to understand how the variation propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many ...

Since deterministic optimum designs obtained without considering uncertainty lead to unreliable d... more Since deterministic optimum designs obtained without considering uncertainty lead to unreliable designs, it is vital to develop design methods that take account of the input uncertainty. When the input data contain sufficient information to characterize statistical distribution, the design optimization that incorporates the probability method is called a reliability-based design optimization (RBDO). It involves evaluation of probabilistic output performance measures.

Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C, 2007

ABSTRACT The continual advances in wireless technology and low power electronics have allowed the... more ABSTRACT The continual advances in wireless technology and low power electronics have allowed the deployment of small remote sensor networks. However, current portable and wireless devices must be designed to include electrochemical batteries as the power source. The use of batteries can be troublesome due to their limited lifespan, thus necessitating their periodic replacement. Furthermore, the growth of battery technology has remained relatively stagnant over the past decade while the performance of computing systems has grown steadily, which leads to increased power usage from the electronics. In the case of wireless sensors that are to be placed in remote locations, the sensor must be easily accessible or of disposable nature to allow the device to function over extended periods of time. For this reason the primary question becomes how to provide power to each node. This issue has spawned the rapid growth of the energy harvesting field. Energy scavenging devices are designed to capture the ambient energy surrounding the electronics and convert it into usable electrical energy. The concept of power harvesting works towards developing self-powered devices that do not require replaceable power supplies. However, when designing a vibration based energy harvesting system the maximum energy generation occurs when the resonant frequency of the system is tuned to the input. This poses certain issues for their practical application because structural systems rarely vibrate at a signal frequency. Therefore, this effort will investigate the optimal geometric design of two dimensional energy harvesting systems for maximized bandwidth. Topology and shape optimization will be used to identify the optimal geometry and experiments will be performed to characterize the energy harvesting improvement when subjected to random vibrations.

11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006

10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2004

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2009

ABSTRACT This paper presents a Generalized Complementary Intersection Method (GCIM) that can pred... more ABSTRACT This paper presents a Generalized Complementary Intersection Method (GCIM) that can predict system reliability for series, parallel, and mixed systems. The GCIM is an extension of the original study, referred to as the Complementary Intersection Method (CIM). The CIM was developed to assess system reliability for series systems. The contribution of this paper is to generalize the original CIM so that it can be used for system reliability analysis regardless of system structures (series, parallel, and mixed system). First, we derive a closed-form system reliability formula for a parallel system through its transformation into a series system using De Morgan's law. Second, a unified system reliability analysis framework is proposed for mixed systems by defining a new System Structure matrix (SS-matrix) and employing the Binary Decision Diagram (BDD) technique. The SS-matrix is used to present any system structure in a comprehensive matrix form. Then the BDD technique together with the SS-matrix automates the process to identify system's mutually exclusive path sets, of which each path set is a series system. As a result, system reliability with any system structure can be decomposed into the probabilities of the mutually exclusive path sets. Five engineering examples are used to demonstrate that the proposed GCIM can assess system reliability regardless of the system structures. [DOI:10.1115/1.4004198]

III European Conference on Computational Mechanics, 2006

Page 1. Stochastic Response Surface Using the Enhanced Dimension-Reduction (eDR) Method for Relia... more Page 1. Stochastic Response Surface Using the Enhanced Dimension-Reduction (eDR) Method for Reliability-Based Robust Design Optimization Byeng D. Youn1, Zhimin Xi1, Lee J. Wells1, and David A. Lamb2 1 Department ...

For the Aerospace, Automotive and Ship Industries, 2007

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002

SAE Technical Paper Series, 2005