Computational Design Optimization for S-Ducts

Abstract

In this work, we investigate the computational design of a typical S-Duct that is found in the literature. We model the design problem as a shape optimization study. The design parameters describe the 3D geometrical changes to the shape of the S-Duct and we assess the improvements to the aerodynamic behavior by considering two objective functions: the pressure losses and the swirl. The geometry management is controlled with the Free-Form Deformation (FFD) technique, the analysis of the flow is performed using steady-state computational fluid dynamics (CFD), and the exploration of the design space is achieved using the heuristic optimization algorithm Tabu Search (MOTS). The results reveal potential improvements by 14% with respect to the pressure losses and by 71% with respect to the swirl of the flow. These findings exceed by a large margin the optimality level that was achieved by other approaches in the literature. Further investigation of a range of optimum geometries is performed and reported with a detailed discussion.

IPCP: Immersive Parallel Coordinates Plots for Engineering Design Processes

“…Moreover, the two participants working with a substantially larger dataset in terms of its complexity and size, i.e. the number of dimensions per data item and the total number of data items, found similar patterns as those given in D'Ambros et al 37 and identified additional candidates for potential patterns. This demonstrates the potential for immersive VR in engineering design processes.…”

“…Interestingly, some overlapping results were reported by both participants, as can be seen in Table 3, namely pattern candidate 6 for P10 and pattern candidate 2 for P11. Moreover, both participants identified additional groups of points that in their opinion constituted patterns not originally identified by the experts in D'Ambros et al 37 As mentioned before, in the case of visual analytics, we aim to provide additional insight about the dataset, hence we were interested in the new candidates for potential patterns found by the two participants (P10 and P11). To do this, we requested a domain-expert to re-analyze and comment on the new pattern candidates identified.…”

“…The four bottom rows contain the four patterns that were identified by domain experts. 37 In the case of P11 we excluded repeated pattern selections. After extending the system with additional capabilities, such as the support of basic undo & redo operations we reran our validation study with another two domain-experts (hereafter referred to as P10-P11).…”

“…This dataset describes the results of the optimization study of S-Duct design (see Fig. 17), as presented in D'Ambros et al 37 In addition, the near-misses in terms of selection were the main observable issues with our selection technique. To support the user's selection task, one can use a form of target acquisition aid, that in our case, has been loosely inspired by the Bubble Cursor proposed by Grossman et al 38 We leveraged the features natively supported by the Unity game engine by on-the-fly manipulation of the size of the sphere collider 39 component attached to the interactive objects (cubes) in the IPCP.…”

Supporting Decision Making in Engineering Design Using Parallel Coordinates in Virtual Reality

“…In addition, we discuss the positive and negative aspects of the application of VR in the decision making process. The overall task we study is the identification of clusters and sets of solutions in two optimization studies for the aerodynamic design of compressor blades [2,3], and the design of S-ducts [4]. However, we do not consider the physical interpretation and understanding of the designed compressor blades.…”