..., corresponding to vertical force and torque, thus enabling power generation at each branch. The analysis of the system is conducted by utilizing three established methods suitable for systems subject to stochastic excitation: Monte Carlo Simulation (MCS), moment equations, and ∥H∥ 2 method. Additionally...

... accurately overhead — and direct costs, energy and waste generation. The inherent uncertainty is modeled in terms of fuzzy numbers, but solved numerically using the Monte Carlo simulation technique, which allows us to perform a sensitivity analysis. This enables us to not only estimate the costs, energy...

... to effectively identify robust solutions? Three approaches for modeling noise when approximate performance models are sought are tested and compared in this paper: statistical expected value and Taylor’s expansion, design of experiments (DOE)-based Monte Carlo simulation, and product arrays. The focus...

... Abstract It is important in reliability evaluation to take an approach in which the required calculations can be performed efficiently in terms of time and cost. In this study, an approach is proposed whereby reliability analysis is carried out by means of Monte Carlo simulation in which...

... size and grain distribution is crucial to a welding engineer. The nature of grain growth in the weldment is known to be stochastic. Hence, using a deterministic method for predicting the final grain size of weldment should be reconsidered. In this paper Monte Carlo simulation of grain growth at Heat...

... Abstract The paper discusses automotive crash simulation in a stochastic context, whereby the uncertainties in vehicle properties, boundary and initial conditions are taken in to account by means of Monte Carlo simulation techniques. It is argued that, since crash is a non-repeatable phenomenon...

...) or path analysis. We finally demonstrate a power analysis of SEM for determining the appropriate sample size for studying the team effectiveness model. team formation team effectiveness model structural equation modeling sample size power analysis Monte Carlo simulation A POWER ANALYSIS...

... is defined by utilizing the Dirac delta function. Then, Monte Carlo simulation is applied to calculate constraints and probabilistic constraints with the worst combination of interval variables, and their sensitivities. The important merit of the proposed method is that it does not require gradients...

... Monte Carlo simulation 1 Copyright © 2012 by ASME ADAPTIVE ORTHONORMAL BASIS FUNCTIONS FOR HIGH DIMENSIONAL METAMODELING WITH EXISTING SAMPLE POINTS KAMBIZ HAJI HAJIKOLAEI G. GARY WANG PhD student Professor khajihaj@sfu.ca gary_wang@sfu.ca Product Design and Optimization Lab (PDOL) Mechatronic...

... method can estimate the probability of failure accurately as a function of the input standard deviation compared to the Monte Carlo simulation results. As anticipated, the sampling-based RBDO using the surrogate models and the equivalent standard deviation helps find the optimum design very efficiently...

... the component reliability, system reliability, or statistical moments and their sensitivities by applying Monte Carlo simulation (MCS) to the accurate surrogate model. Since the surrogate model is used, the computational cost for the stochastic sensitivity analysis is negligible. The copula is used to model...

... Design under uncertainty probabilistic sufficiency factor Monte Carlo simulation response surface approximation Monte Carlo simulation is commonly employed to evaluate system probability of failure for problems with multiple failure modes in design under uncertainty. The probability...

... of a particular system. Next, a Monte Carlo simulation is applied to the Cost-Based FMEA to account for the uncertainties in: detection time, fixing time, occurrence, delay time, down time, and model complex scenarios. This paper compares and contrasts these three different FMEAs: RPN, Life Cost-based point...

... monitoring systems. Variation analysis and modeling Monte Carlo simulation Quality control Design for vehicle health monitoring Probabilistic vibration models yProceedings of DETC 02 200 ptem IN C AN g aerospace devices, it is common to use well-established models of vibration features...

... constraint programming Monte Carlo simulation interval analysis uncertainty design space exploration Design space exploration during conceptual design is an active research field. Most approaches generate a number of feasible design points (complying with the constraints) and apply...

... through the use of probability density functions and Monte Carlo simulation is performed. Simulation results provide insight into the uncertainty of model outputs and the risks associated with ensuing decision making. This model may be used by product designers to support decision making efforts...