Responsibilities include:

• Conduct high-fidelity CFD simulations of turbulent flows with evolving complex interfaces using phase-field methods, characterizing boundary layer physics and heat transfer performance.
• Develop end-to-end Python simulation setup, automation, and post-processing pipelines to parse and extract key metrics from large-scale 3D datasets.
• Build memory-efficient visualization and data analysis tools for multi-terabyte HPC datasets, enabling rapid validation and design iteration of complex flow structures.
• Coordinate code deployment pipelines across high-performance environments to preserve software continuity.

Responsibilities include:

• Instructed undergraduate students in thermal-fluid fundamentals, thermodynamics, and fluid dynamics principles to support experimental validation.
• Facilitated hands-on laboratory work for 100+ students on thermal-fluid cycles and aerodynamic characterization of systems.

Responsibilities include:

• Evaluated and optimized numerical stability of higher-order finite-differences for cut-cell methods using dispersive wave theory.
• Formulated novel high-order stable cut-cell stencils to solve hyperbolic conservation laws with complex geometric boundaries using automated scripts in Python and Mathematica.
• Implemented and scaled up high-order (up to 8th order) stencil algorithms in C++ within massive Linux HPC environments, reducing computational footprint while preserving accuracy.

Responsibilities include:

• Developed a high-fidelity gas-solid multiphase solver in FORTRAN tailored for static and moving complex geometries using custom volume-filtering techniques.
• Benchmarked the custom code against canonical aeromechanics datasets, achieving 99.3% validation accuracy compared to experimental measurements.
• Extended the Volume-Filtered Immersed Boundary (VFIB) method to handle complex geometric boundaries that do not align with uniform structured meshes for acoustic and thermal analysis.
• Discovered a 20% skin-friction drag reduction mechanism in wall-bounded turbulence using advanced Eulerian-Lagrangian methods.
• Extracted structural flow dynamics from large datasets using spatial/temporal averaging to isolate performance-limiting vortical features.
• Managed code repositories using Git and utilized large-scale parallel scaling (OpenMPI) on Linux cluster resources to optimize numerical code throughput.
• Mentored undergraduate students on high-performance computing (HPC) environments and data analysis scripting.

Responsibilities include:

• Applied the specialized Volume-Filtered Immersed Boundary (VF-IB) framework to hyperbolic and parabolic PDEs to model thermal and acoustic environments near complex topological surfaces.
• Compared performance metrics against existing cut-cell solvers, proving identical resolution of flow physics with significantly lower grid-generation and computational costs.
• Quantified sub-grid scale (SGS) structural terms within filtering windows to assess grid-independence and model accuracy.

Responsibilities include:

• Performed high-fidelity Large-Eddy Simulations (LES) of a liquid-liquid coaxial swirl injector under the high-order unstructured CharLES solver platform.
• Manufactured physical prototype hardware and validated aerodynamic/spray morphology metrics against LES data using experimental Particle Image Velocimetry (PIV) techniques.
• Managed thermal-structural optimization passes across the entire engine assembly to identify and eliminate high-flux thermal runoff scenarios.
• Co-founded a university rocketry organization, leading a 10-student propulsion engineering team through design, simulation, and hardware validation phases.

Responsibilities include:

• Conducted high-speed aerodynamic compressible flow analysis using CharLES and completed conjugate heat transfer runs in ANSYS Fluent to mitigate thermal failures.
• Executed multi-parameter vehicle stability and structural vibration studies using MATLAB to predict dynamic system limits.
• Managed precise CNC and lathe-based components fabrication processes to align exact parameters with computer modeling limits.