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De Laval Nozzle Optimization & Thrust Analysis

Investigating how exit cone geometry (\(10^{\circ}\) to \(30^{\circ}\)) influences supersonic exhaust efficiency.

Tools: C++, SolidWorks, Arduino Category: Independent Research Year: 2022

Overview

As a 10th-grade researcher, I designed and executed a study to analyze how the exit angle of a de Laval nozzle affects the conversion of thermal energy into kinetic thrust. This project required building a custom thrust-testing stand from scratch and developing a C++ numerical model to bridge the gap between theoretical fluid dynamics and empirical data.

Download Full Report (PDF) Static Fire Data (CSV)

Methodology & Testing Hardware

  • Numerical Baseline: Used the Rocket Thrust Equation to fix nozzle top area (25x throat width) and entrance angles at \(30^{\circ}\) to isolate the exit angle as the independent variable.
  • Static Fire Test Stand: Constructed a cube-shaped aluminum frame housing a 20kg Load Cell sensor and HX711 amplifier.
  • Propellant Synthesis: Formulated KNSB fuel (65% Potassium Nitrate, 35% Sucrose) and utilized specialized cellophane molds to ensure consistent grain geometry.
  • Data Capture: Built an Arduino-based data logger that sampled thrust values every 0.1 seconds, recording over 200 data points per static fire.

Technical Struggles & Engineering Mitigations

1. High-Temperature Material Failure

Initial tests used PLA 3D-printed nozzles which would deform under the extreme heat of KNSB combustion.

Mitigation: Used 3D-printed designs only as molds for cement-cast nozzles. This allowed for the complex internal geometry of a de Laval nozzle while providing the necessary thermal resistance.

2. Numerical Precision & Latency

The initial C++ code and Load Cell setup suffered from signal noise and measurement latency, making it difficult to capture the exact peak of the thrust curve.

$$ I = \int F \, dt \approx \sum (F_{avg} \times \Delta t) $$ Approximating total impulse through discrete 0.1s time-steps.

Mitigation: Implemented Standard Deviation analysis to quantify "radicalness" (combustion intensity) and used a line of best fit to determine a correlation coefficient of $-0.98$ for thrust efficiency.

Reflections & Modern Re-Analysis

  • Historical Context: This project taught me that engineering is a compromise between parameters like weight, mechanical strength, and performance.
  • The Lesson of 15°: My results corroborated professional findings that exit angles near \(15^{\circ}\) optimize the conversion of subsonic to supersonic flow while minimizing turbulence.
  • Current Goal: I am currently re-processing this 2022 dataset using Savitzky-Golay Filter to replace the original linear approximations.
  • Growth: Moving from 10th-grade "basic" C++ to my current work at USC RPL reflects a shift from empirical "guessing" to high-fidelity numerical simulation.
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