Research Assistant | Experience

Executive Summary

Developed and executed a comprehensive computational framework to model the transport and dispersal of bio-inspired robotic jellyfish within dynamic, wave-driven ocean flows. By integrating the Maxey-Riley equation into a high-iteration simulation environment, I analyzed how morphological parameters (diameter, geometry) and environmental variables (wave steepness, specific gravity) influence vehicle trajectory. The project successfully identified optimal design configurations to maximize dispersal while minimizing energy expenditure, establishing a critical foundation for autonomous underwater vehicle (AUV) deployment in oceanographic research.

Key Responsibilities & Core Systems

Fluid Dynamics Modeling: Modeled the motion of non-spherical particles (hemispheres) in linear wave fields by integrating the Maxey-Riley equation to account for advection, added mass, Stokes drag, and buoyancy forces.

A 3D visualization of a hemispherical particle model used to calculate hydrodynamic resistance tensors within the Maxey-Riley framework.

Systematic Methodology Validation: Conducted a comparative analysis between the ‘Regular Particle’ method and established literature to quantify variance and error margins in resistance tensor calculations.
Multi-Variable Analysis: Evaluated the impact of initial orientation on particle dispersion by simulating 728 unique configurations to generate high-fidelity probability density distributions.
Technical Documentation: Authored comprehensive technical reports and presented findings to academic stakeholders, translating complex fluid dynamics into actionable design insights for autonomous systems.

Engineering Initiatives & System Optimization

To ensure the integrity of the navigation model, I performed a Comparative Analysis of Resistance Tensor Methods. By benchmarking the ‘Regular Particle’ method against known geometries (disks, oblate spheroids, prolate spheroids, and cylinders), I identified critical variance points:

This diagram illustrates a cylinder with defined dimensions A (height) and B (diameter) used as a baseline geometry to validate resistance tensor calculations in fluid dynamics simulations.

This plot quantifies the error margins between the Regular Particle method and established literature across various aspect ratios for different geometric profiles.

This plot quantifies the error margins of the Regular Particle method compared to literature values across various geometric profiles and aspect ratios.

These plots visualize the comparative trajectory analysis between the proposed 'Regular Particle' method and established literature across four distinct geometric profiles to validate resistance tensor accuracy.

These plots visualize the comparative analysis between the Regular Particle method and established literature across various geometric shapes to quantify error margins in resistance tensor calculations.

Accuracy Auditing: Identified that while the Regular Particle method demonstrated high accuracy for disks (under 8% error), it exhibited significant deviation when applied to oblate spheroids (~32% error).

This diagram illustrates the geometric cross-section of an ellipse, providing a visual reference for the dimensions used to calculate resistance coefficients for non-spherical particles like oblate spheroids.

Geometry Optimization: Analyzed how shape-specific resistance coefficients influence trajectory, establishing a roadmap for refining the ‘Regular Particle’ algorithm to minimize modeling uncertainty.

This diagram illustrates the geometric cross-section and primary axes (A and B) of the oblate spheroids used to calculate shape-specific resistance coefficients in the simulation model.

This plot compares the trajectories of hemispherical cups versus solid hemispheres, visually demonstrating how specific geometric modifications impact spatial dispersion in fluid environments.

Automation & Data Infrastructure

I engineered a robust simulation pipeline using MATLAB 2023A to automate the processing of complex fluid dynamics:

Automated Rotation Matrices: Developed scripts to rotate added mass and resistance tensors to align with real-time particle orientation.
High-Volume Simulation Loops: Implemented automated ‘Parameter Sweeps’ to evaluate 100 constant intervals across both diameter (1mm to 0.1mm) and specific gravity (1.0 to 1.5).
Data Processing: Utilized ode15s solvers with high-precision tolerances ( $2.22 \times 10^{-14}$ ) to ensure numerical stability during large-scale iterative simulations.

Quantified Impact & Operational Savings

Optimization of Design Parameters: Identified that a reduction in particle diameter to 0.1mm significantly increases dispersion ( $k_x$ values), providing specific, actionable targets for manufacturing specifications.

This plot illustrates the inverse relationship between particle diameter and maximum $k_x$ values, demonstrating how smaller diameters (approaching 0.1mm) optimize trajectory dispersion in wavy ocean flows.

Risk Mitigation: Quantified the impact of specific gravity on buoyancy stability, determining that a range of 1.0 to 1.5 is required to maintain buoyancy while maximizing movement in turbulent flows.

This plot illustrates the inverse relationship between vehicle specific gravity and $k_x$ values, demonstrating how lower specific gravity significantly enhances trajectory dispersion within the simulated environment.

Model Validation: Successfully validated the ‘Regular Particle’ method’s utility for standard geometries, establishing a high-confidence baseline for future iterations involving complex, non-standard shapes.

Frameworks & Tooling

MATLAB Computation