{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/135086"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/135086","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"An image processing technique for erosion analysis on compressor blade geometry","abstract":"Particle ingestion into aircraft engines is a major concern due to its physical- and performance-related implications; largely due to compressor blade material erosion. Current methods for monitoring this erosion (such as visual tools) lack adequate reliability and consistency, while only providing qualitative insight into changes in blade geometry. This study presents a novel application for image processing to assess compressor blade erosion using two methods: 1) quantitative derivation of total chord, blade thickness, leading and trailing edge radii, and area at the blade tip using image binarization and a pixel-to-inch scaling factor and 2) qualitative visualization of material loss on the blade surface by aligning pre- and post-ingestion images of a rotor blade using feature-based detection. These methods are tested on a Rolls-Royce M250-C20B turboshaft engine, in which full-scale engine testing is performed to study particle ingestion effects on performance. The results showed a <1% uncertainty for blade geometry measurements, with the total chord also having a 1% difference compared to the value collected from a 10 µm resolution caliper. Furthermore, image registration showed substantial span-wise leading edge erosion at the 1st-stage rotor and the start of trailing edge erosion at the tip - which amounted to a ∼13% decrease of total chord - after a particle exposure of 1.3 hours and a dose of 2.6 kg of 100 µm sieved quartz. These erosion patterns showed results comparable to those of other studies on compressor blade particle erosion in the literature. The findings found in this study lay the foundation for image processing to be used as an accurate alternative to traditional aircraft engine inspection methods.","abstract_html":"Particle ingestion into aircraft engines is a major concern due to its physical- and performance-related implications; largely due to compressor blade material erosion. Current methods for monitoring this erosion (such as visual tools) lack adequate reliability and consistency, while only providing qualitative insight into changes in blade geometry. This study presents a novel application for image processing to assess compressor blade erosion using two methods: 1) quantitative derivation of total chord, blade thickness, leading and trailing edge radii, and area at the blade tip using image binarization and a pixel-to-inch scaling factor and 2) qualitative visualization of material loss on the blade surface by aligning pre- and post-ingestion images of a rotor blade using feature-based detection. These methods are tested on a Rolls-Royce M250-C20B turboshaft engine, in which full-scale engine testing is performed to study particle ingestion effects on performance. The results showed a &lt;1% uncertainty for blade geometry measurements, with the total chord also having a 1% difference compared to the value collected from a 10 µm resolution caliper. Furthermore, image registration showed substantial span-wise leading edge erosion at the 1st-stage rotor and the start of trailing edge erosion at the tip - which amounted to a ∼13% decrease of total chord - after a particle exposure of 1.3 hours and a dose of 2.6 kg of 100 µm sieved quartz. These erosion patterns showed results comparable to those of other studies on compressor blade particle erosion in the literature. The findings found in this study lay the foundation for image processing to be used as an accurate alternative to traditional aircraft engine inspection methods.","abstract_has_math":false,"creators":["Olivera, Leonardo Manuel"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Ng, Wing Fai","Lowe, Kevin T."],"committee_members":["Gonzales, David"],"year":2025,"date_issued":"2025-06-05","date_published":"2025-06-05","updated_at":"2026-07-22T22:18:49Z","subjects":["Particle-laden flow","compressor erosion","1st-stage rotor","image processing","image registration"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44010"],"render_values":[{"text":"vt_gsexam:44010","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/135086","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Ng, Wing Fai","Lowe, Kevin T."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gonzales, David"]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Olivera, Leonardo Manuel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-06T08:01:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-06T08:01:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-05"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Particle-laden flow","compressor erosion","1st-stage rotor","image processing","image registration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44010"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/135086"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Particle ingestion into aircraft engines is a major concern due to its physical- and performance-related implications; largely due to compressor blade material erosion. Current methods for monitoring this erosion (such as visual tools) lack adequate reliability and consistency, while only providing qualitative insight into changes in blade geometry. This study presents a novel application for image processing to assess compressor blade erosion using two methods: 1) quantitative derivation of total chord, blade thickness, leading and trailing edge radii, and area at the blade tip using image binarization and a pixel-to-inch scaling factor and 2) qualitative visualization of material loss on the blade surface by aligning pre- and post-ingestion images of a rotor blade using feature-based detection. These methods are tested on a Rolls-Royce M250-C20B turboshaft engine, in which full-scale engine testing is performed to study particle ingestion effects on performance. The results showed a <1% uncertainty for blade geometry measurements, with the total chord also having a 1% difference compared to the value collected from a 10 µm resolution caliper. Furthermore, image registration showed substantial span-wise leading edge erosion at the 1st-stage rotor and the start of trailing edge erosion at the tip - which amounted to a ∼13% decrease of total chord - after a particle exposure of 1.3 hours and a dose of 2.6 kg of 100 µm sieved quartz. These erosion patterns showed results comparable to those of other studies on compressor blade particle erosion in the literature. The findings found in this study lay the foundation for image processing to be used as an accurate alternative to traditional aircraft engine inspection methods."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Particle ingestion is a large concern for aircraft engines due to the physical- and performance-based effects, as particles collide with the compressor blades and erode material. This gradual erosion requires frequent monitoring using visual inspection tools. However, these tools are unreliable and oftentimes convey misconstrued results. This study presents two image processing methods that monitor blade geometry: 1) quantitative derivation of important blade tip features and 2) qualitative visualization of blade material loss. These methods were tested on an engine that had ingested 2.6 kg of particles across 1.3 hours. The results showed a <1% uncertainty for blade geometry measurements derived pre-ingestion. Furthermore, the visual method showed severe material loss at the front of the blade, with erosion increasing from the base to the tip of the blade. This erosion pattern was similar to particle ingestion studies from other research teams. The findings found in this study give confidence in using image processing as an accurate and reliable alternative to analyze compressor blade geometry both pre- and post-particle ingestion."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["An image processing technique for erosion analysis on compressor blade geometry"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Ng, Wing Fai","Lowe, Kevin T."],"dc:contributor.committeemember":["Gonzales, David"],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Olivera, Leonardo Manuel"],"dc:date.accessioned":["2025-06-06T08:01:34Z"],"dc:date.available":["2025-06-06T08:01:34Z"],"dc:date.issued":["2025-06-05"],"dc:description.abstract":["Particle ingestion into aircraft engines is a major concern due to its physical- and performance-related implications; largely due to compressor blade material erosion. Current methods for monitoring this erosion (such as visual tools) lack adequate reliability and consistency, while only providing qualitative insight into changes in blade geometry. This study presents a novel application for image processing to assess compressor blade erosion using two methods: 1) quantitative derivation of total chord, blade thickness, leading and trailing edge radii, and area at the blade tip using image binarization and a pixel-to-inch scaling factor and 2) qualitative visualization of material loss on the blade surface by aligning pre- and post-ingestion images of a rotor blade using feature-based detection. These methods are tested on a Rolls-Royce M250-C20B turboshaft engine, in which full-scale engine testing is performed to study particle ingestion effects on performance. The results showed a <1% uncertainty for blade geometry measurements, with the total chord also having a 1% difference compared to the value collected from a 10 µm resolution caliper. Furthermore, image registration showed substantial span-wise leading edge erosion at the 1st-stage rotor and the start of trailing edge erosion at the tip - which amounted to a ∼13% decrease of total chord - after a particle exposure of 1.3 hours and a dose of 2.6 kg of 100 µm sieved quartz. These erosion patterns showed results comparable to those of other studies on compressor blade particle erosion in the literature. The findings found in this study lay the foundation for image processing to be used as an accurate alternative to traditional aircraft engine inspection methods."],"dc:description.abstractgeneral":["Particle ingestion is a large concern for aircraft engines due to the physical- and performance-based effects, as particles collide with the compressor blades and erode material. This gradual erosion requires frequent monitoring using visual inspection tools. However, these tools are unreliable and oftentimes convey misconstrued results. This study presents two image processing methods that monitor blade geometry: 1) quantitative derivation of important blade tip features and 2) qualitative visualization of blade material loss. These methods were tested on an engine that had ingested 2.6 kg of particles across 1.3 hours. The results showed a <1% uncertainty for blade geometry measurements derived pre-ingestion. Furthermore, the visual method showed severe material loss at the front of the blade, with erosion increasing from the base to the tip of the blade. This erosion pattern was similar to particle ingestion studies from other research teams. The findings found in this study give confidence in using image processing as an accurate and reliable alternative to analyze compressor blade geometry both pre- and post-particle ingestion."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44010"],"dc:identifier.uri":["https://hdl.handle.net/10919/135086"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Particle-laden flow","compressor erosion","1st-stage rotor","image processing","image registration"],"dc:title":["An image processing technique for erosion analysis on compressor blade geometry"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:49Z"}