The design process for compressor blades is a highly iterative and often slow process. This research applied and measured the impact of using surrogates to quickly model the stresses on a compressor blade. By modeling distinct points on a finite element (FE) model with unique surrogates, the stress field of the entire FE model was quickly predicted. This required that the distinct points remain in the same relative location on each blade used in training the surrogate. This research studied the ability of mesh morphing, and using the surface nodes as those distinct points, to satisfy this requirement. The results show that mesh morphing performed well on the tested compressor blades. The research also found that the surrogate accuracy depended not only on the number of training samples, but also the number and types of parameters being emulated. The surrogate models achieved less than 5% error on all the tested blades. Finally, the method provided a 96% decrease in time required for a structural iteration of a compressor blade. Such speeds eliminate bottlenecks that may occur in the structural design process. The combination of mesh morphing and surrogate modeling in compressor blade analysis enables exploration of various geometric parameters and their effect on structural responses. Application of this process would produce a more thoroughly refined and understood compressor blade design.
AbstractDesign space exploration (DSE) is the process whereby a designer seeks to understand some results across a set of design variations. Structural DSE of turbomachinery compressor blades is often challenging because the large number of design variables make it difficult to learn the effect that each variable has upon the stress contours. Principal component analysis (PCA) of the stress contours is used as a way to understand how the stress contours change over the design space. Two methods are introduced to address the challenge of understanding how the stress changes over a large number of variables. First, a two-point correlation is applied to relate the design variables to the scores of each principal component. Second, a coupling of the stress and coordinate location of each node in PCA is developed which also indicates how the stress variations relate to geometric variations. These provide insight to how design variables influence the stress. It is shown how these methods use PCA as DSE tools to better explore the structural design space of compressor blades. Better DSE can improve compressor blades and the computational cost needed for their design.
Design space exploration is an important part of design in engineering fields. Recent research employs surrogate models to emulate finite element analyses across a design space, allowing rapid design space exploration. With interactive speeds, there exists a need for tools that help designers compare designs to one another. This difference model is comprised of a three-dimensional rendering of the geometry with the location and magnitude of differences between the two designs displayed as colors on the model surface. This visualization is combined with juxtaposed views of the objects being compared. A user experiment was conducted with 28 volunteers to demonstrate whether or not including the difference model in the visualization can improve speed and accuracy for various comparative design tasks. It was found that, for certain comparison tasks, there is indeed statistical evidence that using the difference model alongside the two juxtaposed views improved speed and accuracy when making design judgments between two different designs.
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