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Your search keyword '"Dow, Eric"' showing total 2 results
Start Over Author "Dow, Eric" Topic monte carlo method
2 results on '"Dow, Eric"'

1. ACTIVE SUBSPACE METHODS IN THEORY AND PRACTICE: APPLICATIONS TO KRIGING SURFACES.

Author

CONSTANTINE, PAUL G., DOW, ERIC, and QIQI WANG

Subjects
*GAUSSIAN processes, *SUBSPACES (Mathematics), *KRIGING, *RANDOM variables, *MONTE Carlo method, *PERTURBATION theory
Abstract

Many multivariate functions in engineering models vary primarily along a few directions in the space of input parameters. When these directions correspond to coordinate directions, one may apply global sensitivity measures to determine the most influential parameters. However, these methods perform poorly when the directions of variability are not aligned with the natural coordinates of the input space. We present a method to first detect the directions of the strongest variability using evaluations of the gradient and subsequently exploit these directions to construct a response surface on a low-dimensional subspace--i.e., the active subspace--of the inputs. We develop a theoretical framework with error bounds, and we link the theoretical quantities to the parameters of a kriging response surface on the active subspace. We apply the method to an elliptic PDE model with coefficients parameterized by 100 Gaussian random variables and compare it with a local sensitivity analysis method for dimension reduction. [ABSTRACT FROM AUTHOR]

Published
2014
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2. Simultaneous Robust Design and Tolerancing of Compressor Blades

Author

Eric Dow, Qiqi Wang, Massachusetts Institute of Technology. Aerospace Computational Design Laboratory, Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, Wang, Qiqi, and Dow, Eric A.

Subjects
Engineering, Blade (geometry), business.industry, Monte Carlo method, Mechanical engineering, Robust optimization, Blade geometry, Variance (accounting), Structural engineering, business, Gas compressor, Manufacturing cost, Gaussian random field
Abstract

The manufacturing processes used to create compressor blades inevitably introduce geometric variability to the blade surface. In addition to increasing the performance variability, it has been observed that introducing geometric variability tends to reduce the mean performance of compressor blades. For example, the mean adiabatic efficiency observed in compressor blades with geometric variability is typically lower than the efficiency in the absence of variability. This “mean-shift” in performance leads to increased operating costs over the life of the compressor blade. These detrimental effects can be reduced by using robust optimization techniques to optimize the blade geometry. The impact of geometric variability can also be reduced by imposing stricter tolerances, thereby directly reducing the allowable level of variability. However, imposing stricter manufacturing tolerances increases the cost of manufacturing. Thus, the blade design and tolerances must be chosen with both performance and manufacturing cost in mind. This paper presents a computational framework for performing simultaneous robust design and tolerancing of compressor blades subject to manufacturing variability. The manufacturing variability is modelled as a Gaussian random field with non-stationary variance to simulate the effects of spatially varying manufacturing tolerances. The statistical performance of the compressor blade system is evaluated using the Monte Carlo method. A gradient based optimization scheme is used to determine the optimal blade geometry and distribution of manufacturing tolerances.

Published
2014
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