Personal Projects

Computational framework for linear static finite element analysis, supporting plane, solid, shell, and curved-shell elements. It includes linear and quadratic element types, multiple numerical-integration schemes, various boundary conditions, and stress post-processing.

Computational framework for dynamic finite element analysis, incorporating various integration methods, additional mass formulations, and dynamic load functions.It supports dynamic loads based on fixed patterns with varying amplitude in time, completely dynamic load patterns, and ground-motion acceleration.

Computational framework for response-spectrum analysis, incorporating both eigen and Ritz vectors methods, and the possibility of additional masses. It includes multiple response estimation techniques (modal combination formulas) to evaluate the structural response.

Computational framework for topology optimization, supporting objectives such as compliance minimization and control-point displacement constraints. It includes different material-interpolation schemes, two- and three-field filtering techniques, various update schemes, and the option to use continuation for multiple parameters.

Computational framework for shape optimization, built on a constrained topology-optimization framework. This approach enables shape optimization directly on the structural domain, without the need to predefine a parametric boundary.

Computational framework for multi-load topology optimization. The implementation supports different methods for handling multiple loads and also provides the possibility of defining multiple objectives associated to each individual load case.

Computational framework for topology optimization with subdomain selection. The framework enables independent control of the global volume fraction, the number of active subdomains, and supports various material interpolation schemes for material densities as well as subdomain penalization.

Computational framework for multi-material topology optimization. The implementation allows assigning different material interpolation schemes, defining passive regions locked to a chosen material, and selecting among various update schemes.

Computational framework for dynamic topology optimization. The implementation enables topology optimization under dynamic load cases derived from static patterns, dynamic patterns, and even ground motions. The framework supports optimization in both the time and frequency domains, with multiple methods available for each approach.

Computational framework for stress-constrained topology optimization. The implementation supports different material interpolation and projection schemes, offers multiple techniques for handling vanishing constraints, and allows selection of the number of Gauss points at which stress constraints are enforced.

Computational framework for topology optimization of building facades subjected to static equivalent wind loads. It includes the use of pattern repetition and multi-objective optimization techniques.