Karthik Kumaran

Model Order Reduction for Full-Vehicle Dynamic Simulations • DevelopingaCraig-BamptonbasedComponentModeSynthesis(CMS)reductionframeworkfordynamicfull vehicle FE simulations using the PERMAS solver • Validating reduced order models against full order FE results to ensure accuracy of dynamic response prediction across relevant frequency ranges • Implementing automated Python workflows for model setup, reduction, result comparison and overall deliverables

• Developed simulation process for transient full-vehicle analyses (modal transient), integrating it into existing data pipelines, model hierarchies and tool structures at Daimler Buses. • Developed ANSA Python scripts, with GUI interactions, eliminating manual post-processing across multiple simulation variants • Developed an automated reporting pipeline based on Python and LaTeX, expanding the scope of reportable metrics while reducing manual reporting time from days to minutes

• Conducted research on auxetic structures for aerospace powertrain applications • Performed rotodynamics simulations and extracted Campbell diagrams using complex eigenfrequency analysis of aviation drivetrains with auxetics using Abaqus FEA • Developed Python scripts to automate simulation workflows, improving efficiency and repeatability, significantly reducing simulation setup time.

• Conducted structural stress analyses, including thermomechanical, sealing, fatigue, dynamic and modal analyses for PEMFC and DNOx systems, using Abaqus, HyperMesh and FEMFAT, validating component reliability with complex FEA results and analytical and spreadsheet evaluations. • Modelled non-linear material behaviours for fibre reinforced thermoplastics, elastomers and metals, material models with unreliable results and improving cor relation with physical test data.

Grade: 1,9