Understand the system
Connect ignition requirements with propellant delivery and operating constraints.
MECHANICAL ENGINEERING · RICE UNIVERSITY
Curiosity.
Analysis.
Engineering.
I’m Sean Weiss. I connect propulsion, fluid systems, and heat transfer through computational analysis and engineering design.
Explore my work ↓
FIG. 01 / HYPERGOLIC ENGINE CONCEPTSOLIDWORKS · SPRING 2026
HONOLULU ROOTS. HOUSTON CLASSROOM.B.S. MECHANICAL ENGINEERING / SPRING 2027
SELECTED WORK / 01—03From physical models to design decisions.
01Methalox torch igniter
SENIOR DESIGN · IN PROGRESS↗02Hypergolic propulsion system
MATLAB · COMPUTATIONAL DESIGN↗03Solar thermal & waste heat
RESEARCH · PYTHON↗01 / SENIOR DESIGN · 2026–2027
A small igniter.
A systems-level challenge.
Propellant injector lead for a methalox torch igniter developed in partnership with Intuitive Machines.
● In development
Our team is developing an oxygen–methane torch igniter intended to serve as an ignition source for a main engine and support hot-gas reaction control applications.
My focus is the propellant injector: translating flow requirements into a design approach, investigating injector configurations, and considering how injection interacts with the feed system.
- My role
- Injector Lead
- Collaboration
- Intuitive Machines
- Design target
- 6–10 lbf thrust
Develop the injector
Use MATLAB sizing and injector literature to investigate geometry and flow behavior.
Design with the team
Work within a mentored capstone effort spanning injector, thermal, and fluid-system considerations.
02 / AEROSPACE PROPULSION · SPRING 2026
From MATLAB
to a lunar lander concept.
An individual academic design study of a pressure-fed MMH/NTO propulsion system for a large lunar lander.
I connected mission sizing, NASA CEA combustion analysis, injector design, nozzle geometry, and heat-transfer modeling in one iterative workflow.
Read the full project report ↗Download MATLAB source ↓43 kNDesign thrust per engine
339 sEstimated vacuum specific impulse
50:1Nozzle expansion ratio
MATLABAnalysis → geometry → CAD
Preliminary values reported in the academic study; not measured or hot-fire-validated performance.
The computational workflow
Explore how the analysis informed the design.
PARAMETRIC ANALYSIS
Make the trade space visible.
Using a NASA CEA MATLAB wrapper, I swept chamber pressure and oxidizer-to-fuel ratio, tracking specific impulse, characteristic velocity, mass flow, throat area, thrust coefficient, and burn time.
Design decision
The reported sweep showed greater performance sensitivity to mixture ratio than chamber pressure. The study selected an O/F ratio of 2.27 and a chamber pressure of 100 psia.
CEA inputs → parameter sweeps → performance plots → design selection
Inside the MATLAB implementation ↕
Select the best-performing mixture ratioDownload .m ↓
[~,of_index]=max(ISP_vals(2,:)); % s, vacuum ISP and its maximizing index
of=of_vals(of_index); % O/F ratio at ISP maximizing indexThe script locates the maximum vacuum specific impulse from the mixture-ratio sweep and retrieves its corresponding O/F ratio.
Original excerpts from mech590_engine.m. The full script requires the CEA MATLAB interface and thermo_lib.mat; these dependencies are not included. Figures above are from the original report.
Connecting the models matters.
The thermal analysis reduced estimated specific impulse from 342 s to 339 s after accounting for film cooling. That updated estimate fed back into mission and propulsion sizing—linking component analysis to the overall design.
03 / ENERGY SYSTEMS LAB · RICE UNIVERSITY
Finding opportunity
in low-grade heat.
Solar thermal modeling in the context of data-center waste-heat recovery and organic Rankine cycle research.
I developed a Python notebook to investigate evacuated-tube collector performance, combining weather inputs, solar position, heat-transfer correlations, and collector configuration models.
Download the research notebook ↓Weather in.
Thermal behavior out.
- Resolve solar input
Use pvlib to calculate solar position and plane-of-array irradiance from location and weather data.
- Model the collector
Estimate heat losses, collector efficiency, useful heat gain, and outlet temperature.
- Explore configurations
Propagate temperature through series-connected tubes and compare flow rate and inlet-temperature cases.
Inside the Python implementation +
The configuration function passes each tube’s outlet temperature into the next tube and aggregates useful heat gain across parallel rows.
T_out_i = T_in_i + Qu_i / (m_dot_per_row * Cp)
T_out_collector[i] = T_out_i
T_in_i = T_out_i
Qu_collector[i] = Qu_i
Qu_total = sum(Qu_collector.values()) * N_parallel_rowsExcerpt from the supplied notebook. Reproducing its saved plots requires the external weather CSV referenced in the notebook.
BEYOND THE PROJECTS
Built on curiosity.
Grounded in collaboration.
I’m a mechanical engineering student at Rice University, specializing in aerospace and graduating in Spring 2027.
Originally from Hawaiʻi, I’m interested in propulsion and thermal-fluid systems—especially work that connects analytical models with physical design. Alongside project work and research, I’ve gained engineering internship experience at R.M. Towill Corporation and Carollo Engineers, and supported students as a thermodynamics teaching assistant.
Outside engineering, I enjoy surfing, spearfishing, and spending time in the ocean. Growing up in Hawaiʻi made those interests a big part of my life, and I also enjoy hands-on projects that give me a chance to build something of my own.
Analysis & programming
MATLAB · Python · NASA CEA · Excel
Design & engineering tools
SolidWorks · Civil 3D · Pipe-Flo · Bluebeam
Hands-on experience
Machining · Welding · 3D printing · Rice Eclipse rocketry
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