Design, Build & Test
Modern engineering systems are becoming increasingly complex and interconnected, which requires a holistic approach, starting from conceptual design through mission design, operations analysis, and sustainment. Multiphysics multiscale modeling has become an important design tool for the development of new materials and components in severe environments — and there is no substitute for actually building and testing the hardware.
LIDAR-Drone Mapping for Mars Entry, Descent and Landing
As part of my research at the Mars Desert Research Station (MDRS) with Crew 306, I led the development and test of an innovative drone-based LIDAR system aimed at enhancing Entry, Descent, and Landing (EDL) procedures in planetary exploration. Traditional EDL protocols often rely on aerial or orbital observations, which provide limited resolution for landing site analysis. My project sought to improve upon this by using a drone equipped with a LIDAR scanner, GPS, and an Inertial Measurement Unit (IMU) to generate high-resolution terrain maps, enabling more precise landing site selection. This fieldwork grew out of my role as co-founder and president of Purdue's Space & Earth Analogs Research Chapter (SEARCH), whose NASA SUITS team earned the "Pay It Forward Award".
Hardware Integration
The first step in this project was designing a custom 3D-printed mount for the LIDAR system. Using SolidWorks, my team and I created a lightweight yet durable mount tailored specifically for the DJI Mavic 2 Pro. The mount had to balance structural integrity with minimal weight to avoid exceeding the drone's payload capacity.
The LIDAR module was configured from scratch to ensure optimal data capture. This involved calibrating sensor parameters, adjusting scan frequencies, and implementing custom-made codes to obtain accurate point cloud mappings. Proper alignment of the LIDAR with the drone's center of gravity was also a critical consideration.
To power the LIDAR and auxiliary systems, my team and I carefully integrated LiPo batteries and voltage converters, ensuring a stable power supply without exceeding the drone's 700g maximum payload limit. This was determined through iterative testing to find the optimal weight-power tradeoff.
Raspberry Pi & ROS2 Configuration
A Raspberry Pi was configured from scratch to serve as the central processing unit for data acquisition. I set up ROS2 (Robot Operating System 2) to handle real-time data streaming from the LIDAR, IMU, and GPS. The system was programmed to synchronize all data sources, enabling accurate mapping.
At MDRS, my team and I developed a custom SSH protocol to allow remote access to the Raspberry Pi during extravehicular activities (EVAs). This enabled real-time monitoring and debugging without direct physical interaction. Additionally, Python scripts were written to automate data collection, perform preliminary filtering, and facilitate post-processing of the LIDAR data to generate 3D terrain maps.
Tests
Throughout the mission, the drone system underwent iterative refinements based on field testing conducted during multiple EVAs. Early flights revealed mechanical instabilities in the LIDAR mount and electromagnetic interference affecting sensor readings. To address these issues, I reinforced the backup 3D-printed mount for safety and implemented additional radiation shielding for the cables. The Raspberry Pi onboard computer played a key role in managing power distribution and data acquisition, running custom scripts for automated logging and remote SSH control.
Further tests conducted at various MDRS locations demonstrated the enhanced capabilities of the improved system. The combination of LIDAR data, IMU gyroscopic readings, and GPS coordinates allowed the generation of detailed 3D point clouds of the terrain. These high-resolution maps provide valuable insights into potential landing hazards and safe zones, paving the way for future autonomous drone scouts on Mars.
ACECam — Autonomous Computer Vision Embedded Camera
As the Chief Commercial Officer of Stellerian, I am actively involved in the development of ACECam, a cutting-edge optical navigation system designed for spacecraft engaged in relative navigation and proximity operations. ACECam is a compact, embedded vision system leveraging advanced image processing algorithms and AI-driven analytics to determine the precise position and orientation of spacecraft in orbit.
My contributions to this project focus on hardware integration, including assembling the system's Raspberry Pi-based processing unit and calibrating the onboard camera for real-time image acquisition. The goal of ACECam is to enhance autonomy in space missions by enabling precise pose estimation and automated spacecraft docking. This technology is crucial for upcoming satellite servicing missions, constellation coordination, and in-space inspections.
With support from an Air Force STTR Phase I award, ACECam is currently undergoing rigorous testing to validate its capabilities. Future iterations will incorporate more robust AI models for improved object recognition and tracking, ensuring its effectiveness for commercial and military space applications.
Rocketry
I worked for 1 year as an Aerodynamics & Structures member in University of São Paulo's rocket team, Project Jupiter — which took 2nd place at the 2019 Latin America Space Challenge. After that, I spent a semester as a Structures member in University of Notre Dame's Rocket Team, and came back as the Coordinator of Structures for another year in Project Jupiter, having the opportunity of working in four different rockets in total.
Preliminary Design
The first step for designing anything is the definition of the requirements. This involves iterations over the design, of course, but most importantly, it requires a structured approach to connect all the systems involved.
I implemented a simple method of optimizing the dimensions of our rockets, such as tail radius, nosecone length and fin parameters, through a merit function that is maximized when a) the static margin (distance from Center of Mass to Center of Pressure) is close to the optimal value of 2.0 and b) the area of the rocket fins is minimal, in order to diminish drag.
This analysis was implemented in our in-house 6 degree-of-freedom rocket trajectory simulator, RocketPy, which also makes use of the aerodynamic equations to foresee many characteristics of the rocket path.
Computer Aided Design
Parallel to preliminary design, it is also necessary to iterate over the project's CAD. After working extensively with extruding, inspecting, defining joints, etc in Fusion 360, I've grown very fond of creating computer-aided designs.
Simulation
Solid mechanics and fluid mechanics are an essential part of simulating an engineered design before actually manufacturing it. Here, I worked mainly with structural analysis of joints using ANSYS Mechanical, even though I have also worked in CFD simulations in ANSYS Fluent.
Manufacturing
The final step in the design process is Manufacturing. I've had the opportunity of developing a vacuum infusion, carbon fiber laminated structure for three of Project Jupiter's rockets, through a technique that uses vacuum pressure to drive resin into a laminate. Materials are laid dry into the mold and the vacuum is applied before resin is introduced. Vacuum bagging greatly improves the fiber-to-resin ratio, and results in a stronger and lighter product.
This process was developed by us and used to build our rockets' tubes and fins, using a carbon/kevlar reinforced hybrid composite for the fins. We also laminated the structure externally for our rocket Callisto, whose launch can be seen here. Last but not least, for a few of our rockets we also 3D printed nosecones, using glass fiber reinforcement in the inside to make it more rigid.
Academic Background
Intro to Digital Manufacturing with Autodesk Fusion 360
The manufacturing industry is making a digital transformation, allowing companies to customize production through advances in machine learning, sustainable design, generative design, and collaboration, with integrated design and manufacturing processes. This course introduces innovations in CAD and digital manufacturing and explores foundational concepts behind Autodesk Fusion 360 CAD/CAM — a cloud-based tool for collaborative product development that combines industrial design, mechanical engineering, and machine tool programming into one software solution.
Introduction to Mechanical Engineering Design and Manufacturing with Fusion 360
Explores the design-for-manufacture workflow and shows how to validate models and create the G code needed to instruct CNC machines. Practices the basics of part and assembly design, and tools such as animation, rendering, and simulations using Autodesk Fusion 360 — taking digital parts to physical prototypes.
AAE 539 — Advanced Rocket Propulsion
Graduate-level treatment of chemical rocket propulsion. Thermochemistry and chemical equilibrium related to standard industry codes like NASA CEA; fundamentals of incompressible and compressible flows applied to propellant feed systems and nozzles; heat transfer processes in chemical rockets; solid and hybrid rocket ballistic models, burning rate theory, and erosive burning; liquid rocket engine cycle analysis and turbopump design; generalized internal compressible and incompressible flows; advanced topics in solid rocket motor performance and internal ballistics, hybrid rockets, and thermal-nuclear engines.
AAE 548 — Mechanical Behavior of Aerospace Materials
Structure of materials, the microstructure connection to mechanical properties, and ultimately failure mechanisms — in the context of components and extreme environments. Material anisotropy, micromechanisms, and elasto-plastic properties at the atomic, single-crystal, and polycrystal levels explaining deformation and failure in metals, polymers, and ceramics; failure mechanisms and toughening in composites; aerospace materials: metal alloys, ceramic-matrix composites, and fiber-reinforced polymer composites. Topics include indicial notation, crystallography, elasticity, stress-strain relationships and yielding, dislocation mechanics, crystallographic slip, twinning and shape memory effects, strengthening mechanisms, creep, residual stress, probability of failure, polymer viscoelasticity, and composites.
AME 20241 — Solid Mechanics
Stresses, strains, loads, deformations and displacements in tension, compression, shear, torsion and bending; elasticity and inelasticity; strain energy; transformations of stress and strain; buckling; combined loadings; thermal effects.
4300324 — Fluid Mechanics
1. Notion of fluid. 2. Eulerian and Lagrangian descriptions. 3. Conservation laws, Euler equation (perfect fluid). 4. Statics. 5. Stationary flows. 6. Vorticity and rotational flow. 7. Potential movement. 8. Viscosity, Navier-Stokes equation. 9. Similarity, Reynolds number, Stokes equation, boundary layer. 10. Waves: surface, sound, etc. 11. Instability, intermittency, turbulence.
4302305 — Mechanics I
1. Newton's Laws: Movement of a particle in one dimension, conservative forces. 2. Movement of a particle in two or three dimensions, angular momentum, central forces. 3. Theory of small oscillations, normal modes of vibration and normal coordinates. Coupled Oscillators. 4. Coordinate systems in motion: Non-inertial systems. 5. Variational calculation. Lagrange equation and Hamilton principle.
4300463 — Applied Physics
History of science with emphasis on the technical and science relationship. Materials Science: metals, insulators, semiconductors, superconductors, magnets. Energy: classic and alternative sources. Operation and concepts related to everyday devices: engines, sound, images, etc. Human sensitivity: eye, ear, nerves. Laser and holography.
4302401 — Statistical Mechanics
1. The laws of thermodynamics. 2. Notions of probability. 3. Microcanonical representation. Boltzmann's entropy. 4. Canonical representation. Maxwell distribution of speeds. Partition function and connection with thermodynamics. 5. Einstein model for the specific heat of solids. Ideal monoatomic gas. Gibbs' paradox. Ideal diatomic gas. 6. Photon gas, thermal radiation. Phonon gas, linear chain, Debye theory. 7. Grand canonical representation. Distribution of Bose-Einstein and Fermi-Dirac. 8. Free electron gas, electronic thermal capacity. 9. Gases and liquids, integral configuration, second virial coefficient, van der Waals theory.
4300402 — Introduction to Solid State Physics
Crystalline structure. X-ray diffraction and reciprocal network. Crystalline bonds. Network vibrations, phonons and thermal properties. Free electron Fermi gas. Energy bands. Semiconductors. Metals and Fermi surfaces. Optical processes. Magnetism. Superconductivity.
4302113 / 4302114 — Experimental Physics I & II
Experimental aspects of mechanics and thermodynamics: measurements of dimensions, time, mass and derived quantities; motion kinematics and dynamics; conservation laws; rigid bodies; oscillations; heat physics and phase transitions. Foundations of the scientific method, ethics in science, and laboratory safety. Data processing: direct and indirect measures, instrumental uncertainties, propagation of uncertainties, Gaussian distributions, least squares fits, significance tests, and systematic presentation of results through tables, graphs and histograms.
4302213 / 4302214 — Experimental Physics III & IV
Experimental electromagnetism: DC and AC electric circuits, electric and magnetic field measurements, particle motion in fields, the laws of Gauss, Ampère and Faraday, electromagnetic fields, light wave properties (reflection, refraction, interference and diffraction), and spectrophotometry. Data processing: probability distributions (Gaussian, binomial, Poisson), non-linear function fitting, Student's t-test, Monte Carlo simulation, maximum likelihood, covariance analysis, curve extrapolation, and handling large data volumes. Experiment automation and oral presentation of results.
4302313 — Experimental Physics V
Experiences that supported the formulation of Quantum Mechanics, through complex experiments requiring systematic measurement and correlation: automated experiments, correlation of independent data sets, Monte Carlo simulations, maximum likelihood fits, covariance analysis, propagation of uncertainties with covariance between parameters, curve extrapolation, and synthesis and oral presentation of experimental results.