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Technical Paper

System Level RBDO for Military Ground Vehicles using High Performance Computing

The Army continues to improve its Reliability-based Design Optimization (RBDO) process, expanding from component optimization to system optimization. We are using the massively parallel computing power of the Department of Defense (DoD) High Performance Computing (HPC) systems to simultaneously optimize multiple components which interact with each other in a mechanical system. Specifically, we have a subsystem of a military ground vehicle, consisting of more than four components and are simultaneously optimizing five components of that subsystem using RBDO methods. We do not simply optimize one component at a time, sequentially, and iterate until convergence. We actually simultaneously optimize all components together. This can be done efficiently using the parallel computing environment. We will discuss the results of this optimization, and the advantages and disadvantages of using HPC systems for this work.
Technical Paper

Reliability-Based Robust Design Optimization Using the EDR Method

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.
Technical Paper

Application of Reliability-Based Design Optimization to Durability of Military Vehicles

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 computer-aided engineering tools and considerable data communication and computation. Uncertainties in geometric dimensions due to manufacturing tolerances cause the indeterministic nature of the fatigue life of a mechanical component.
Technical Paper

Reliability-Based Robust Design Optimization Using the Performance Moment Integration Method and Case Study of Engine Gasket-Sealing Problem

Reliability-based robust design optimization deals with two objectives of structural design methodologies subject to various uncertainties: reliability-based design and robust design. A reliability-based design optimization deals with the probability of failure, while a robust design optimization minimizes the product quality loss. In general, the product quality loss is described by using the first two statistical moments: mean and standard deviation. In this paper, a performance moment integration (PMI) method is proposed by using numerical integration scheme for output response to estimate the product quality loss. For the reliability part of the reliability-based robust design optimization, the performance measure approach (PMA) and its numerical method, hybrid-mean value (HMV) method, are used.
Technical Paper

Integration of Reliability- and Possibility-Based Design Optimizations Using Performance Measure Approach

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. The enriched performance measure approach (PMA+) has been developed for efficient and robust design optimization process. This is integrated with the enhanced hybrid mean value (HMV+) method for effective evaluation of non-monotone and/or highly nonlinear probabilistic constraints. When sufficient information of input data cannot be obtained due to restrictions of budgets, facilities, human, time, etc., the input statistical distribution is not believable.