Thesis

Orifice Cavitation Analysis

Propulsion • Empirical analysis • Data processing • 2025

At a glance

Context
Cavitation onset prediction and effects
My role
Research, testing, data analysis
Deliverables
Interactive test database, data processing pipeline, research outcome

Disclosure

This page only uses non-proprietary information. Specific operating conditions, hardware details and performance data are intentionally excluded.

Bachelor thesis

Research

This project formed my Bachelor's thesis and focused on cavitation onset in orifice restriction plates of the HELIX engine, with a primary focus on fuel and ignition feed lines.

  • Empirical comparison of different cavitation onset effects
  • Structured data processing for repeatable test comparison
Orifice inside pipeline.

Why Cavitation Matters

Flow can behave very differently once cavitation starts. For propulsion systems, predicting the onset is important because it affects stability, component sizing, and test interpretation.

The work focused on building an understanding of the cavitation behavior of engine-specific components, supported by research on applicable flow mechanics properties and performing the magnitude of test campaigns.

Public P and ID diagram of the cavitation test loop

Testing

Simplified P&ID

The diagram shows the principles of the investigation only at a high level. The system could control upstream flow conditions while collecting data from independent measurement points along the path, providing a precise flow characterization.

Test Article
A set of restriction plates with varying geometries, engine components.
Conceptual plot of cavitation onset and choked flow behavior

Flow characteristics

Research on cavitation focused on detailed system-specific behavior. The goal was to find a way to determine the threshold of cavitation onset to minimize test discrepancies and enable repeatable comparison of different test runs and their flow characteristics.

Flowchart of the cavitation data-processing workflow

Analysis Workflow

The dataset from the test rig was processed to compare onset trends across different test runs and hardware configurations.

Final deliverables included an interactive TOML database of test results and a structured data processing pipeline using Python to enable repeatable analysis of future tests.

The results matched the theoretical behavior of flow and created a comprehensive understanding of the cavitation dynamics of engine components.