research workflow and technology stack

This infographic outlines the integrated methodology and technical infrastructure that underpin my research and development processes. It highlights a systematic approach bridging analytical modeling, high-fidelity simulations, and scalable data management to accelerate scientific discovery and engineering innovation. The diagram covers the modeling, simulation, and data side of that workflow; the measurement side — building a setup suited to the experiment and orchestrating the instruments into one automated system — is documented on the acoustical field and mechanical vibration characterization pages.

Models

• Analytic model - Enabled low-cost design space exploration by establishing relationships between design parameters and key performance metrics.
• FEM model - Applied for high-fidelity simulations and design for manufacturing, validating performance with practical prototypes.
• Component-based model - Implemented using Simulink, Simscape, and LTspice to capture multiphysics behavior, an approach particularly suited when modularity, hierarchy, and extensibility are required.

Languages/Software

• MATLAB COMSOL Multiphysics SolidWorks
  Integrated workflow: developed analytical models and applied optimization algorithms in MATLAB, automated FEM simulations through COMSOL LiveLink, and generated parameterized CAD geometries in SolidWorks for iterative design validation.
• Simulink/Simscape (Modelica-based) - Component-based system modeling for transient, multi-domain simulations.
• LTspice - Applied for electrical and electronic circuit simulations as well as multiphysics system studies, using distributed- and lumped-element models.

Infrastructure & Version Control

• Git & DVC - Managed source codes, simulation scripts, collaborative development, and tracked large datasets.
• GitHub / Nextcloud / TrueNAS - Deployed these scalable data infrastructures from the ground up to accelerate research and development workflows.
• Slurm & HPC Clusters - Managed resource allocation and batch job scheduling on High-Performance Computing clusters to accelerate high-fidelity FEM simulations.