(Georgia Institute of Technology, 2016-12-08)
Gharbi, Aroua
Aircraft design is a multi-disciplinary and complicated process that takes a long time and requires a large number of trade-offs between customer requirements, various types
of constraints and market competition. Particularly detailed design is the phase that takes most of the time due to the high number of iterations between the component design and
the structural analysis that need to be run before reaching an optimal design.
In this thesis, an innovative approach for detailed design is suggested. It promotes a collaborative framework in which knowledge from the small scale level of components is shared and transferred to the subsystems and systems level leading to more robust and real time decisions that speed up the design time. This approach is called the Single Digital Thread Approach to Detailed Design or shortly STAnDD. The implementation of this approach is laid over a bottom-up plan, starting from the component level up to the aircraft level. In the component level and from a detailed design perspective, three major operations
need to be executed in order to deploy the Single Digital Thread approach. The first one is the automatic geometric extraction of component features from a solid with no design
history, the second phase is building an optimizer around the design and analysis iterations and the third one is the automatic update of the solid. This thesis suggests a methodology
to implement the first phase.
Extracting geometric features automatically from a solid with no history(also called dumb solid) is not an easy process especially in aircraft industry where most of the components have very complex shapes. Innovative techniques from Machine Learning were used allowing a consistent and robust extraction of the data.
(Georgia Institute of Technology, 2016-04-28)
Henneking, Stefan
In a parallel vertex-centered finite element multigrid solver, segmental refinement can be used to avoid all inter-process communication on the fine grids. While domain decomposition methods generally require coupled subdomain processing for the numerical solution to a nonlinear elliptic boundary value problem, segmental refinement exploits that subdomains are almost decoupled with respect to high-frequency error components. This allows to perform multigrid with fully decoupled subdomains on the fine grids, which was proposed as a sequential low-storage algorithm by Brandt in the 1970s, and as a parallel algorithm by Brandt and Diskin in 1994. Adams published the first numerical results from a multilevel segmental refinement solver in 2014, confirming the asymptotic exactness of the scheme for a cell-centered finite volume implementation. We continue Brandt’s and Adams’ research by experimentally investigating the scheme’s accuracy with a vertex-centered finite element segmental refinement solver. We confirm that full multigrid accuracy can be preserved for a few segmental refinement levels, although we observe a different dependency on the segmental refinement parameter space. We show that various strategies for the grid transfers between the finest conventional multigrid level and the segmental refinement subdomains affect the solver accuracy. Scaling results are reported for a Cray XC30 with up to 4096 cores.