Ofertes treballs fi d'estudishttps://eseiaat.upc.edu/ca/curs-actual/treballs-fi-estudis/ofertes-treballs-fi-destudishttps://eseiaat.upc.edu/++resource++plone-logo.svg
2010 - GRAU EN ENGINYERIA EN TECNOLOGIES AEROESPACIALS
2010 - GRAU EN ENGINYERIA EN VEHICLES AEROESPACIALS
Identificador de l'oferta:
205-06430
Modalitat:
Universitat
Possibilitat de beca/finançament:
No
Idioma d'elaboració del treball:
Anglès
Descripció:
This thesis investigates numerical strategies to accelerate the solution of large-scale structural mechanics problems, with a focus on enabling advanced structural optimization workflows. The work centers on implementing and analyzing domain decomposition preconditioners, specifically FETI-DP and BDDC, within the structural analysis code SWAN. Their performance will be systematically compared against the EIFEM method to evaluate improvements in convergence, computational efficiency, and scalability for large 3D structural analyses commonly used in topology and shape optimization.
Objectius:
1. Study and implement the FETI-DP and BDDC preconditioning techniques in the SWAN finite element environment; 2. Analyze their effectiveness in accelerating iterative solvers for large-scale structural problems; 3. Compare the performance of FETI-DP and BDDC against the EIFEM method in terms of convergence rate, scalability, and computational cost; 4. Demonstrate how improved solver performance supports advanced structural optimization workflows; 5. Provide guidelines on the suitability of each method for different classes of structural problems.
Tasques a realitzar / Característiques:
1. Literature Review: Review domain decomposition methods (FETI-DP, BDDC), EIFEM, and iterative solvers for structural mechanics; 2. Implementation: Integrate FETI-DP and BDDC preconditioners into the SWAN code framework; 3. Benchmark Setup: Construct representative large-scale structural test cases relevant for optimization; 4. Numerical Experiments: Evaluate solver performance, scalability, and robustness against the EIFEM approach; 5. Optimization Context: Demonstrate improvements in overall optimization runtimes using the enhanced solvers; 6. Analysis: Compare methods quantitatively and identify strengths and limitations; 7. Thesis Writing: Document theoretical background, implementation details, numerical studies, results, and conclusions.
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