Volume 2B: Turbomachinery 2014
DOI: 10.1115/gt2014-25795
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Simultaneous Robust Design and Tolerancing of Compressor Blades

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 are typically lower than the efficiency in the absence of variability [?]. This "mean-shift" in performance leads t… Show more

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Cited by 14 publications
(13 citation statements)
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“…Such design variables include those that alter the camber and stagger of the blade. Similar behavior was observed when optimizing a subsonic rotor blade [18]. When modifying the geometry at smaller length scales, the design procedures recommended by Goodhand et al in [4] should be adopted.…”
Section: Discussionmentioning
confidence: 56%
“…Such design variables include those that alter the camber and stagger of the blade. Similar behavior was observed when optimizing a subsonic rotor blade [18]. When modifying the geometry at smaller length scales, the design procedures recommended by Goodhand et al in [4] should be adopted.…”
Section: Discussionmentioning
confidence: 56%
“…The squared exponential covariance has been extensively used in robust optimisation studies involving surface perturbations (see ref. 7,35,25 ). This covariance function is infinitely differentiable, which means that the GP with this covariance function has derivatives of all orders (in the mean squared sense), and is thus very smooth 36 .…”
Section: Stochastic Model For Manufacturing Uncertaintiesmentioning
confidence: 99%
“…A zero‐mean Gaussian process δ(x) is imposed along the normal direction to this nominal surface to generate the perturbed surface xδ as, boldxδ=boldx+δfalse(boldxfalse)truen^. In addition, the random variables δi=δ(xi) and δj=δ(xj) at any two arbitrary points i and j on the surface (see Figure 5) are assumed to have a squared exponential spatial covariance as shown in Equation (42). The squared exponential covariance has been extensively used in robust optimisation studies involving surface perturbations (see References 6,29,39). This covariance function is infinitely differentiable, which means that the GP with this covariance function has derivatives of all orders (in the mean squared sense), and is thus very smooth 40 .…”
Section: Stochastic Model For Manufacturing Uncertaintiesmentioning
confidence: 99%
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