Particle Sensing in Gas Turbine Inlets Using Optical Measurements and Machine Learning

Propulsion systems are exposed to a variety of foreign objects that can significantly damage or impact their performance. These threats can range from severe dangers such as sandstorms and volcanic eruptions, which can induce engine failure in minutes, to condensation and moisture during ground tests that can negatively impact the engine's fuel efficiency. While numerous computational and experimental studies have investigated the effects of particle ingestion on the component level, an accurate in-situ measurement technique is needed for a systematic understanding of the effects and real-time engine health monitoring. Optical measurement techniques are attractive for this application due to their non-intrusive nature. However, conventional optical particle measurement methods assume the particle to be spherical, which introduces large errors for measuring particles with complex and irregular shapes, such as sand, volcanic ash, and ice crystals. The light-particle interaction contains information on the desired parameters, such as particle shape and size. The research presented in this dissertation uses this idea for a novel particle sensor concept. Scattering and extinction of light by particles are chosen as crucial features that can identify the particle as its unique signature. Numerical tools are used to simulate the scattering and extinction for particles the sensor is expected to encounter. Machine learning models are trained using the data to use scattering and extinction as inputs and estimate the particle parameters. Different types and applications of supervised machine learning models were investigated, including a layered approach with numerous models and a generalized approach with a single neural network. The particle sensor is first demonstrated using data found in the literature. This study confirmed the importance of non-spherical particles in the library to guide the machine learning models. Further demonstrations are made at a full engine and wind tunnel scale to measure injected condensation and sand sprays, respectively. The mass flow rates of the ingested material were calculated using the model outputs and validated.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Moon, Chi Young

Chair dc:contributor.committeechair

• Lowe, K. Todd

Committee members dc:contributor.committeemember

• Paterson, Eric G.
• Ma, Lin
• Alexander, William Nathan

• foreign object damage
• engine health monitoring
• laser diagnostics

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:28991

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/101969