Hi, I am Adarsh.

I’m an Aerospace Engineering student at Georgia Tech. My work spans structural design, spacecraft modeling, propulsion research, and intelligent engineering systems.

I enjoy combining hands-on engineering with computational tools to solve complex problems and turn ideas into working systems.

View Projects

Professional portrait of Adarsh Narayanan

Projects and Experience

AMIGO/CADRE: Multidisciplinary Spacecraft Modeling

Through Georgia Tech’s Structures and Multidisciplinary Design Optimization Group, I am using AMIGO, an engineering modeling and optimization framework, to reproduce and study disciplines from CADRE, an open-source multidisciplinary spacecraft design benchmark. The research focuses on building, validating, and connecting physics-based computational components for system-level analysis and design optimization.

AMIGO Repository CADRE Repository

Three CADRE rover prototypes traversing rocky terrain

View Project Details Hide Project Details

One multidisciplinary model, built problem by problem.

CADRE

AMIGO

Orbit

Six-state spacecraft propagation, AMIGO implementation, and numerical validation against an independent RK4 reference.

The What

Build a reliable orbit component for a multidisciplinary spacecraft model. The component must propagate position and velocity accurately before it can interact with other disciplines or support optimization.

The How

I created an independent Python reference, translated the same physics into AMIGO, and compared the trajectories, state histories, equation outputs, and grid behavior.

The Results

The model completed a 94.6-minute orbit, matched the original state-derivative and RK4 equations to numerical precision, and showed decreasing AMIGO-to-reference error as the time grid was refined.

Propagated trajectory and state history

Three-dimensional reference orbit propagated around Earth — Reference trajectoryThe independently propagated 94.6-minute orbit used as the validation baseline.

Altitude and speed histories over one propagated orbit — State-history behaviorAltitude remained between 493.7 and 500.0 km while velocity magnitude was tracked across the orbit.

Independent reference vs. AMIGO

Maximum state derivative and RK4 differences over one orbit — Equation-level validationReference and original CADRE formulations agreed to numerical precision, with one-step RK4 differences below 1 × 10⁻¹².

AMIGO and RK4 three-dimensional orbit trajectories overlaid — Trajectory comparisonThe AMIGO solution overlaid with the independent RK4 reference trajectory.

Difference across the propagated orbit

AMIGO versus RK4 position difference over time — Position differencePosition error across the 568-interval orbit solution.

AMIGO versus RK4 velocity difference over time — Velocity differenceVelocity error across the same propagation.

AMIGO orbit position difference across three time-step sizes — Grid convergenceMaximum position difference decreases as the time step is refined from 284 to 1,136 intervals.

Skills

Python
Computational Modeling
Scientific Computing
Model Validation
Optimization
Aerospace Systems

More problems to come

Yellow Jacket Space Program: Structural Engineer

For the Vespula rocket, the internal structure needed to withstand compressive loads without buckling while fitting a tightly constrained assembly. Working with other YJSP members, I helped redesign and model an aluminum stringer in SolidWorks, refining its geometry to use material more efficiently. The final design increased the allowable buckling load, reduced material waste, and was incorporated into the flight vehicle that reached a confirmed apogee of 56,590 feet.

Vespula rocket record-setter graphic showing a confirmed apogee of 56,590 feet

View Project Details Hide Project Details

Initial SolidWorks iteration of the Vespula stringer — Initial conceptEstablished the structural form and attachment geometry.

Final refined SolidWorks design of the Vespula stringer — Refined designUsed aluminum more efficiently and increased resistance to buckling.

Completed stringer installed inside the Vespula rocket assembly — Implemented hardwareIntegrated into the Vespula rocket assembly.

The What

As a structural engineer with the Yellow Jacket Space Program, I helped develop an aluminum stringer for the Vespula rocket. The component needed to resist buckling while meeting the vehicle’s geometric and integration constraints.

The How

I modeled the initial concept in SolidWorks and worked with other YJSP members to refine its geometry. The final design used aluminum more efficiently and improved resistance to buckling.

The Results

The redesigned stringer increased the allowable buckling load while reducing material waste. The final design was incorporated into the Vespula rocket assembly.

Skills

SolidWorks
Structural Analysis
Design Optimization
Manufacturing
Problem Solving
Team Collaboration

Jet Engine Remaining Useful Life Prediction

I built a predictive-maintenance pipeline using NASA’s C-MAPSS FD001 sensor histories to estimate how many operating cycles remained before simulated turbofan failure. I compared four tree-based regressors, analyzed where their predictions broke down, and then expanded the workflow with time-dependent features, multiple RUL caps, a 30-cycle LSTM, and an ensemble model. The project connects machine-learning performance to an engineering decision: identifying which engines should receive maintenance attention first.

Cutaway view of a turbofan jet engine

View Project Details Hide Project Details

The What

The goal was to predict Remaining Useful Life (RUL) from simulated turbofan engine sensor data. Rather than treating each engine cycle independently, I wanted the models to capture how sensor behavior changed as an engine moved closer to failure.

The How

I built the full workflow in Python, from cleaning and preparing the FD001 dataset to feature engineering and model evaluation. I compared multiple regression models, tested different RUL caps, trained an LSTM on 30-cycle sensor sequences, and combined the strongest models in an ensemble.

The Results

The models were able to capture the overall degradation trend, with the best predictions occurring as engines approached failure. Comparing the models also showed that how the sensor history was represented mattered just as much as the choice of algorithm, which became the main focus for improving the model.

Actual remaining useful life compared with four machine-learning model predictions — Model comparisonActual RUL compared with four tree-based regression models across the test engines.

Random Forest and Gradient Boosting predictions compared with actual remaining useful life for 100 test engines — Prediction comparisonActual NASA RUL compared with the two baseline models across the test fleet.

Absolute prediction error by engine for Random Forest and Gradient Boosting models — Error analysisEngine-level errors revealed where different models struggled on the same cases.

Skills

Python
Pandas
Scikit-learn
TensorFlow/Keras
Feature Engineering
Model Evaluation

View project on GitHub →

Ammonia Combustion Research

As an undergraduate researcher in Georgia Tech’s combustion laboratory, I simulated ammonia flame conditions in ANSYS Chemkin and processed the outputs in Excel and MATLAB. I compared temperature and species profiles across operating conditions to study flame behavior, emissions, and energy-efficiency trends. The work combined computational modeling with experimental analysis, and iterative improvements to the testing workflow increased experimental throughput by 10%.

Lit Bunsen burner on a combustion laboratory workbench

View Project Details Hide Project Details

The What

The research examined how ammonia combustion behavior changed across operating conditions. The goal was to use simulation and experimental data to better understand flame structure, reactive species, emissions, and temperature trends.

The How

I ran simulations in ANSYS Chemkin, extracted the outputs, and used MATLAB and Excel to process and visualize the data. I developed comparisons between corrected and uncorrected profiles for NO, NH, NH₂, temperature, and major species.

The Results

The final visualizations made changes in peak species concentration and flame behavior easier to compare across operating conditions. Iterative testing and data-driven workflow adjustments improved experimental throughput by 10%.

$Four flame-profile plots comparing major species mole fractions and temperature across equivalence ratios$

Flame Species & TemperatureMajor species and temperature profiles across four equivalence ratios, showing how flame structure changes with mixture condition.

Corrected and uncorrected nitric oxide concentration profiles across flame distance — NO comparisonCorrected and uncorrected NO concentration profiles across four equivalence ratios, showing how predicted NO formation changes with mixture condition.

Corrected and uncorrected NH radical concentration profiles across flame distance — NH comparisonCorrected and uncorrected NH radical profiles across four equivalence ratios, highlighting the sharp concentration peak near the flame front.

Corrected and uncorrected NH2 radical concentration profiles across flame distance — NH₂ comparisonCorrected and uncorrected NH₂ radical profiles across four equivalence ratios, showing the narrow concentration peak associated with the reaction zone.

Skills

ANSYS Chemkin
MATLAB
Combustion Modeling
Data Analysis
Experimental Research
Technical Visualization

About

I’m currently pursuing a B.S. in Aerospace Engineering at Georgia Tech, and I enjoy working across both the physical and computational sides of engineering. My experience includes structural design with the Yellow Jacket Space Program, spacecraft modeling through Georgia Tech’s Structures & MDO Group, controls and combustion research, and data-driven projects like predictive maintenance for jet engines. I enjoy projects where I can move between design, simulation, testing, and analysis, and I’m especially interested in aerospace systems, structures, numerical modeling, and multidisciplinary engineering.

Technical Skills

DesignSolidWorks
Structures
Manufacturability

ComputationPython
MATLAB
Numerical Modeling

ResearchValidation
Experimentation
Technical Communication