
Simulation, control software, and aerodynamic data science.
18%
L/D Improvement
3
Technical Reports
200+
Symposium Audience
1:10
Scale Model
Overview
As a software engineering intern in a NASA-affiliated aeronautics research program, I engineered the simulation and control software for a 1:10 scale wind-tunnel system used to study biomimetic airfoils — wing geometries inspired by the flight surfaces of birds and insects. The work sat at the intersection of software, embedded systems, and experimental aerodynamics.
I integrated PCB design, CAD modeling, and computational fluid dynamics (CFD) pipelines into a single reproducible workflow, iterating on airfoil geometry and control strategy until the biomimetic design achieved an 18% improvement in lift-to-drag ratio over the baseline. On the data side, I built a Python-based aerodynamic data-science and processing pipeline using NumPy, OpenCV, Pandas, and Matplotlib to analyze Particle Image Velocimetry (PIV) turbulence data, producing AI-ready datasets and pipelines for model training and validation.
The research culminated in three technical reports — featured in the AREN 2023 Annual Report — and I presented the findings to an audience of 200+ researchers at the NASA GLOBE Symposium. This work also seeded two of my later peer-reviewed publications on biomimetic airfoil optimization and background-oriented Schlieren imaging.
What I did
- —Engineered simulation + control software for a 1:10 scale wind-tunnel system; integrated PCB design, CAD, and CFD pipelines to improve biomimetic airfoil lift-to-drag ratio by 18%
- —Built a Python-based aerodynamic data-science and processing pipeline (NumPy, OpenCV, Pandas, Matplotlib) for PIV turbulence analysis; generated AI-ready data pipelines and datasets for model training and validation
- —Authored 3 technical reports (featured in the AREN 2023 Annual Report) and presented findings to 200+ researchers at the NASA GLOBE Symposium
- —Bridged embedded hardware and scientific software — from Arduino/PCB instrumentation to reproducible, containerized analysis workflows
Tech stack
Languages
Data Science
Engineering
Connect with Dhruv Hegde
See more of Dhruv Hegde's work and background on LinkedIn, GitHub, and ResearchGate.