Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Structural Diagnosis of Solid Rocket Motors Using Neural Networks and Embedded Optical Strain Sensors
Δημιουργός/Συγγραφέας:	Korompili, Georgia Cholevas, Nicholas Anyfantis, Konstantinos N. Mußbach, Günter [EL] Ριζιώτης, Χρήστος Δ.[EN] Riziotis, Christos
Ημερομηνία:	2024
Γλώσσα:	Αγγλικά
ISSN:	2304-6732
DOI:	10.3390/photonics11090799
Περίληψη:	The main failures that could deteriorate the reliable operation of solid rocket motors (SRMs) and lead to catastrophic events are related to bore cracks and delamination. Current SRMs’ predictive assessment and damage identification practices include time-consuming and cost-demanding destructive inspection techniques. By considering state-of-the-art optical strain sensors based on fiber Bragg gratings, a theoretical study on the use of such sensors embedded in the circumference of the composite propellant grain for damage detection is presented. Deep neural networks were considered for the accurate prediction of the presence and extent of the defects, trained using synthetic datasets derived through finite element analysis method. The evaluation of this combined approach proved highly efficient in discriminating between the healthy and the damaged condition, with an accuracy higher than 98%, and in predicting the extent of the defect with an error of 2.3 mm for the bore crack depth and 1.6° for the delamination angle (for a typical ~406 mm diameter grain) in the worst case of coexistent defects. This work suggests the basis for complete diagnosis of solid rocket motors by overcoming certain integration and performance limitations of currently employed dual bond stress and temperature sensors via the more scalable, safe, sensitive, and robust solution of fiber optic strain sensors.
Τίτλος πηγής δημοσίευσης:	Photonics
Τόμος/Κεφάλαιο:	11
Τεύχος:	9
Θεματική Κατηγορία:	[EL] Ηλεκτρική μηχανική. Ηλεκτρονική. Πυρινική μηχανικη[EN] Electrical engineering. Electronics. Nuclear engineering [EL] Φυσική και θεωρητική χημεία[EN] Physical and theoretical chemistry
Λέξεις-Κλειδιά:	solid rocket motors fiber Bragg gratings optical strain sensors finite element analysis structural health monitoring strain sensing neural networks crack delamination
EU Grant:	RocketSens First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant
EU Grant identifier:	2018-0720-1 HFRI-FM17-640, InPhoQuC
Κάτοχος πνευματικών δικαιωμάτων:	© 2024 by the authors. Licensee MDPI, Basel, Switzerland.
Όροι και προϋποθέσεις δικαιωμάτων:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.3390/photonics11090799
Εμφανίζεται στις συλλογές:	Ινστιτούτο Θεωρητικής και Φυσικής Χημείας (ΙΘΦΧ) - Επιστημονικό έργο