Corrosion prediction for preventive protection of aircraft heritage
Abstract The paper presents a study on corrosion prediction for preventive aeronautical heritage protection, considering the aeronautical heritage stored or exhibited in an aviation museum. For the purpose of the study, the hangar with exhibited historical aircraft of significant cultural and societ...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | Heritage Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40494-024-01212-6 |
| _version_ | 1797233376972767232 |
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| author | Michal Kuchař Cyril Oswald Jaromír Fišer Miroslav Khol Goran Simeunović Tomáš Vyhlídal Elodie Guilminot Jane Echinard |
| author_facet | Michal Kuchař Cyril Oswald Jaromír Fišer Miroslav Khol Goran Simeunović Tomáš Vyhlídal Elodie Guilminot Jane Echinard |
| author_sort | Michal Kuchař |
| collection | DOAJ |
| description | Abstract The paper presents a study on corrosion prediction for preventive aeronautical heritage protection, considering the aeronautical heritage stored or exhibited in an aviation museum. For the purpose of the study, the hangar with exhibited historical aircraft of significant cultural and societal value is located in the Aviation Museum Kbely, Prague, Czech Republic. Until now, such a preventive approach to protecting the aircraft heritage constituted from ancient aluminum alloys, in particular, has not been presented rigorously. Monitoring the hangar meteorological, pollution, and environmental data are acquired and interrelated with measured corrosion data to find a statistical model describing atmospheric corrosion in the hangar environment. The statistical model searched represents a Gaussian process based on a likelihood approach. As a result, the Gaussian process model is regressed to predict the corrosion of aluminum alloy-based artifacts in the monitored hangar with the marginal likelihood that is compared to machine learning-based prediction. Finally, it is shown that atmospheric corrosion is accurately predicted only when, among others, a synergistic effect of airborne pollutants and wind speed is considered. |
| first_indexed | 2024-04-24T16:15:12Z |
| format | Article |
| id | doaj.art-29738154f0bb4e0bb0e02022dd10259c |
| institution | Directory Open Access Journal |
| issn | 2050-7445 |
| language | English |
| last_indexed | 2024-04-24T16:15:12Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj.art-29738154f0bb4e0bb0e02022dd10259c2024-03-31T11:28:32ZengSpringerOpenHeritage Science2050-74452024-03-0112112210.1186/s40494-024-01212-6Corrosion prediction for preventive protection of aircraft heritageMichal Kuchař0Cyril Oswald1Jaromír Fišer2Miroslav Khol3Goran Simeunović4Tomáš Vyhlídal5Elodie Guilminot6Jane Echinard7Department of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in PragueDepartment of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in PragueDepartment of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in PragueAviation Museum Kbely, Military History Institute PragueDepartment of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in PragueDepartment of Instrumentation and Control Engineering, Faculty of Mechanical Engineering, Czech Technical University in PragueDépartement de Loire Atlantique, GPLA-Arc’AntiqueDépartement de Loire Atlantique, GPLA-Arc’AntiqueAbstract The paper presents a study on corrosion prediction for preventive aeronautical heritage protection, considering the aeronautical heritage stored or exhibited in an aviation museum. For the purpose of the study, the hangar with exhibited historical aircraft of significant cultural and societal value is located in the Aviation Museum Kbely, Prague, Czech Republic. Until now, such a preventive approach to protecting the aircraft heritage constituted from ancient aluminum alloys, in particular, has not been presented rigorously. Monitoring the hangar meteorological, pollution, and environmental data are acquired and interrelated with measured corrosion data to find a statistical model describing atmospheric corrosion in the hangar environment. The statistical model searched represents a Gaussian process based on a likelihood approach. As a result, the Gaussian process model is regressed to predict the corrosion of aluminum alloy-based artifacts in the monitored hangar with the marginal likelihood that is compared to machine learning-based prediction. Finally, it is shown that atmospheric corrosion is accurately predicted only when, among others, a synergistic effect of airborne pollutants and wind speed is considered.https://doi.org/10.1186/s40494-024-01212-6Preventive approachAeronautical heritageAluminum alloy artifactPollution depositionGaussian process modelLikelihood approach |
| spellingShingle | Michal Kuchař Cyril Oswald Jaromír Fišer Miroslav Khol Goran Simeunović Tomáš Vyhlídal Elodie Guilminot Jane Echinard Corrosion prediction for preventive protection of aircraft heritage Heritage Science Preventive approach Aeronautical heritage Aluminum alloy artifact Pollution deposition Gaussian process model Likelihood approach |
| title | Corrosion prediction for preventive protection of aircraft heritage |
| title_full | Corrosion prediction for preventive protection of aircraft heritage |
| title_fullStr | Corrosion prediction for preventive protection of aircraft heritage |
| title_full_unstemmed | Corrosion prediction for preventive protection of aircraft heritage |
| title_short | Corrosion prediction for preventive protection of aircraft heritage |
| title_sort | corrosion prediction for preventive protection of aircraft heritage |
| topic | Preventive approach Aeronautical heritage Aluminum alloy artifact Pollution deposition Gaussian process model Likelihood approach |
| url | https://doi.org/10.1186/s40494-024-01212-6 |
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