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13 results on '"strain reconstruction"'

1. Novel measurement method for full-field bridge strain and displacement with limited long-gauge strain sensors.

Author

Zhang, Qingqing, Li, Ruixiao, and Yuan, Huijun

Subjects
STRAIN sensors, DISPLACEMENT (Mechanics), PROPER orthogonal decomposition, SENSOR placement, BRAGG gratings, IMPACT loads
Abstract

In efforts to resolve the limitations of incomplete bridge measurements in accurately identifying structural parameters, this study introduces a novel approach for precise measurement of full-field bridge strain and displacement using a limited number of long-gauge fibre Bragg grating strain sensors. This method consists of two algorithms: a double reconstruction algorithm for strain reconstruction and an improved conjugate beam algorithm (ICBA) for displacement identification. The dual reconstruction algorithm exploits proper orthogonal decomposition to establish a comprehensive mapping relationship between units, thereby achieving precise strain estimation. This intricate mapping process enables the algorithm to accurately compute strain responses. By leveraging the derived full-field strain data, the ICBA effectively captures complete displacement responses. The conventional sensor placement configuration monitors only a limited number of units. To enhance full-field measurement accuracy, this method categorizes unmonitored units into two levels based on sensor placement. The double reconstruction algorithm then estimates the strain response sequentially, contributing to enhanced precision. Numerical simulations validate the proposed method (PM), which is demonstrated to be efficient and robust under various vehicle loads, impact loads, and noised levels. A physical experiment further demonstrates the efficacy of the PM in practice. The results underscore the potency of the PM as powerful theoretical and practical approach for full-field strain and displacement measurement. [ABSTRACT FROM AUTHOR]

Published
2024
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2. 基于BP神经网络的TBM刀盘关键位置应变重构.

Author

霍军周, 葛利涵, 张占葛, and 李高瑞

Subjects
*EXPERIMENTAL design
Abstract

To solve the problem of difficult and inaccurate strain monitoring of the key main load-bearing structures of the full face hard rock tunnel boring machine(TBM)under harsh working conditions, a strain reconstruction method for key positions of the TBM cutterhead based on BP neural network and finite element analysis was proposed. Firstly, the key vulnerable positions of the TBM cutterhead were determined through static and dynamic finite element analysis, and the typical vulnerable feature substructures of the cutterhead were extracted. Secondly, the static finite element analysis of standard parts and feature substructures under multiple loads was carried out based on the design of experiments(DOE), and the load-strain database was constructed.Finally, a strain reconstruction model for the standard sample and cutterhead feature substructure was established using BP neural network, and experimental verification of the standard samples were conducted.The results showed that the average error of the reconstructed strains is 10%, which verifies the feasibility of the method and provides a feasible method for strain reconstruction of complex TBM cutterhead. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors

Author

Paolo Bettini, Erika Guerreschi, and Giuseppe Sala

Subjects
structural health monitoring, Chirped Fibre Bragg Gratings, Draw Tower Grating Array, strain reconstruction, Chemical technology, TP1-1185
Abstract

The interest of the aerospace industries in structural health and usage monitoring systems is continuously increasing. Among the techniques available in literature those based on Fibre Bragg Grating sensors are much promising thanks to their peculiarities. Different Chirped Bragg Grating sensor configurations have been investigated in this paper. Starting from a numerical model capable of simulating the spectral response of a grating subjected to a generic strain profile (direct problem), a new code has been developed, allowing strain reconstruction from the experimental validation of the program, carried out through different loading cases applied on a chirped grating. The wavelength of the reflection spectrum for a chirped FBG has a one-to-one correspondence to the position along the gauge section, thus allowing strain reconstruction over the entire sensor length. Tests conducted on chirped FBGs also evidenced their potential for SHM applications, if coupled with appropriate numerical strain reconstructions tools. Finally, a new class of sensors—Draw Tower Grating arrays—has been studied. These sensors are applicable to distributed sensing and load reconstruction over large structures, thanks to their greater length. Three configurations have been evaluated, having different spatial and spectral characteristics, in order to explore possible applications of such sensors to SHM systems.

Published
2015
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4. A new optimal sensor placement method for virtual sensing of composite laminate.

Author

Zhang, Zifan, Peng, Chang, Wang, Guangjun, Ju, Zengye, and Ma, Long

Subjects
*SENSOR placement, *LAMINATED materials, *OPTIMIZATION algorithms, *ERRORS-in-variables models, *INVERSE problems, *COMPOSITE materials
Abstract

• A novel OSP method for virtual strain sensing of composite materials is proposed. • This new method can obtain the global unbiased estimation of modal coordinates. • A K-L divergence measuring the information reduction is derived. • A variance selection method is proposed to improve the robustness of the solution. • NSGA-II algorithm is used to obtain the final optimal sensor placement. Identifying modal coordinates from output-only data is a key link of virtual sensing technology based on the modal extension method. It is also one of the goals of optimal sensor placement (OSP). Traditional OSP methods are based on the underlying assumption that the estimated values of modal coordinates are unbiased estimates of real values. However, due to uncertainty and the characteristics of an inverse problem, the unbiased estimation obtained from the output-only data may seriously deviate from the true value. This study proposes a new OSP method for composite virtual strain sensing, which can obtain the global unbiased estimation of modal coordinates. First, a Bayesian probabilistic model for virtual sensing considering model uncertainty and measurement error is formulated. Then, the K-L divergence, which measures the reduction in utility by removing sensors from the full configuration, is used to obtain the unbiased estimation of modal parameters. Finally, using the regularization mechanism in the Bayesian method, a new variance determination method is proposed to improve the stability of the solution. Considering both unbiasedness and stability, NSGA-II multi-objective optimization algorithm and two OSP evaluation criteria are used to obtain the final optimal sensor placement. To illustrate the effectiveness of the proposed method, an example involving a laminate plate is considered, accompanied by comprehensive discussions. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Calibration of advanced Virgo and reconstruction of the detector strain h(t) during the observing run O3

Author

Virgo Collaboration and Virgo Collaboration

Abstract

The three advanced Virgo and LIGO gravitational wave detectors participated to the third observing run (O3) between 1 April 2019 15:00 UTC and 27 March 2020 17:00 UTC, leading to several gravitational wave detections per month. This paper describes the advanced Virgo detector calibration and the reconstruction of the detector strain h(t) during O3, as well as the estimation of the associated uncertainties. For the first time, the photon calibration technique as been used as reference for Virgo calibration, which allowed to cross-calibrate the strain amplitude of the Virgo and LIGO detectors. The previous reference, so-called free swinging Michelson technique, has still been used but as an independent cross-check. h(t) reconstruction and noise subtraction were processed online, with good enough quality to prevent the need for offline reprocessing, except for the two last weeks of September 2019. The uncertainties for the reconstructed h(t) strain, estimated in this paper in a 20-2000 Hz frequency band, are frequency independent: 5% in amplitude, 35 mrad in phase and 10 mu s in timing, with the exception of larger uncertainties around 50 Hz.

Published
2022

6. Calibration of advanced Virgo and reconstruction of the detector strain h( t) during the observing run O3

Author

Virgo Collaboration, Sub String Theory Cosmology and ElemPart, Sub GRASP, Sub Subatomic Physics (SAP), Gravitational and Subatomic Physics (GRASP), and Instituut Theoretische Fysica

Subjects
O3 observing run, Physics and Astronomy (miscellaneous), Virgo, strain uncertainties, calibration, gravitational wave, strain reconstruction
Abstract

The three advanced Virgo and LIGO gravitational wave detectors participated to the third observing run (O3) between 1 April 2019 15:00 UTC and 27 March 2020 17:00 UTC, leading to several gravitational wave detections per month. This paper describes the advanced Virgo detector calibration and the reconstruction of the detector strain h(t) during O3, as well as the estimation of the associated uncertainties. For the first time, the photon calibration technique as been used as reference for Virgo calibration, which allowed to cross-calibrate the strain amplitude of the Virgo and LIGO detectors. The previous reference, so-called free swinging Michelson technique, has still been used but as an independent cross-check. h(t) reconstruction and noise subtraction were processed online, with good enough quality to prevent the need for offline reprocessing, except for the two last weeks of September 2019. The uncertainties for the reconstructed h(t) strain, estimated in this paper in a 20-2000 Hz frequency band, are frequency independent: 5% in amplitude, 35 mrad in phase and 10 μs in timing, with the exception of larger uncertainties around 50 Hz.

Published
2022

7. Calibration of advanced Virgo and reconstruction of the detector strain h(t) during the observing run O3

Author

Acernese, F, Agathos, M, Ain, A, Albanesi, S, Allocca, A, Amato, A, Andrade, T, Andres, N, Andrić, T, Ansoldi, S, Antier, S, Arène, M, Arnaud, N, Assiduo, M, Astone, P, Aubin, F, Babak, S, Badaracco, F, Bader, M K M, Bagnasco, S, Baird, J, Ballardin, G, Baltus, G, Barbieri, C, Barneo, P, Barone, F, Barsuglia, M, Barta, D, Basti, A, Bawaj, M, Bazzan, M, Bejger, M, Belahcene, I, Benedetto, V, Bernuzzi, S, Bersanetti, D, Bertolini, A, Bhardwaj, U, Bini, S, Bischi, M, Bitossi, M, Bizouard, M-A, Bobba, F, Boer, M, Bogaert, G, Boldrini, M, Bonavena, L D, Bondu, F, Bonnand, R, Boom, B A, Boschi, V, Boudart, V, Bouffanais, Y, Bozzi, A, Bradaschia, C, Branchesi, M, Breschi, M, Briant, T, Brillet, A, Brooks, J, Bruno, G, Bulik, T, Bulten, H J, Buskulic, D, Buy, C, Cagnoli, G, Calloni, E, Canepa, M, Canevarolo, S, Cannavacciuolo, M, Carapella, G, Carbognani, F, Carpinelli, M, Carullo, G, Diaz, J Casanueva, Casentini, C, Caudill, S, Cavalier, F, Cavalieri, R, Cella, G, Cerdá-Durán, P, Cesarini, E, Chaibi, W, Chanial, P, Chassande-Mottin, E, Chaty, S, Chiadini, F, Chiarini, G, Chierici, R, Chincarini, A, Chiofalo, M L, Chiummo, A, Christensen, N, Ciani, G, Cieślar, M, Ciecielag, P, Cifaldi, M, Ciolfi, R, Cipriano, F, Cirone, A, Clesse, S, Cleva, F, Coccia, E, Codazzo, E, Cohadon, P-F, Cohen, D E, Colombo, A, Colpi, M, Conti, L, Cordero-Carrión, I, Corezzi, S, Corre, D, Cortese, S, Coulon, J-P, Croquette, M, Cudell, J R, Cuoco, E, Curyło, M, Dabadie, P, Canton, T Dal, Dall’Osso, S, D’Angelo, B, Danilishin, S, D’Antonio, S, Dattilo, V, Davier, M, De Laurentis, M, De Lillo, F, De Matteis, F, De Pietri, R, De Rosa, R, De Rossi, C, De Simone, R, Degallaix, J, Deléglise, S, Del Pozzo, W, Depasse, A, Di Fiore, L, Di Giorgio, C, Di Giovanni, F, Di Giovanni, M, Di Girolamo, T, Di Lieto, A, Di Pace, S, Di Palma, I, Di Renzo, F, Dietrich, T, D’Onofrio, L, Drago, M, Ducoin, J-G, Durante, O, D’Urso, D, Duverne, P-A, Eisenmann, M, Errico, L, Estevez, D, Fafone, V, Farinon, S, Favaro, G, Fays, M, Fenyvesi, E, Ferrante, I, Fidecaro, F, Figura, P, Fiori, I, Fittipaldi, R, Fiumara, V, Flaminio, R, Font, J A, Frasca, S, Frasconi, F, Fronzé, G G, Gamba, R, Garaventa, B, Garufi, F, Gemme, G, Gennai, A, Ghosh, Archisman, Giacomazzo, B, Giacoppo, L, Giri, P, Gissi, F, Goncharov, B, Gosselin, M, Gouaty, R, Grado, A, Granata, M, Granata, V, Greco, G, Grignani, G, Grimaldi, A, Grimm, S J, Gruning, P, Guerra, D, Guidi, G M, Guixé, G, Guo, Y, Gupta, P, Haegel, L, Halim, O, Hannuksela, O, Harder, T, Haris, K, Harms, J, Haskell, B, Heidmann, A, Heitmann, H, Hello, P, Hemming, G, Hennes, E, Hild, S, Hofman, D, Hui, V, Idzkowski, B, Iess, A, Jacqmin, T, Janquart, J, Janssens, K, Jaranowski, P, Jonker, R J G, Juste, V, Kéfélian, F, Kalaghatgi, C, Karathanasis, C, Katsanevas, S, Khetan, N, Koekoek, G, Koley, S, Kolstein, M, Królak, A, Kuijer, P, La Rosa, I, Lagabbe, P, Laghi, D, Lamberts, A, Lartaux-Vollard, A, Lazzaro, C, Leaci, P, Lemaître, A, Leroy, N, Letendre, N, Leyde, K, Linde, F, Llorens-Monteagudo, M, Longo, A, Portilla, M Lopez, Lorenzini, M, Loriette, V, Losurdo, G, Lumaca, D, Macquet, A, Magazzù, C, Magnozzi, M, Majorana, E, Maksimovic, I, Man, N, Mangano, V, Mantovani, M, Mapelli, M, Marchesoni, F, Marion, F, Marquina, A, Marsat, S, Martelli, F, Martinez, M, Martinez, V, Masserot, A, Mastrogiovanni, S, Meijer, Q, Menendez-Vazquez, A, Mereni, L, Merzougui, M, Miani, A, Michel, C, Milano, L, Miller, A, Miller, B, Milotti, E, Minazzoli, O, Minenkov, Y, Mir, Ll M, Miravet-Tenés, M, Montani, M, Morawski, F, Mours, B, Muciaccia, F, Mukherjee, Suvodip, Musenich, R, Nagar, A, Napolano, V, Nardecchia, I, Naticchioni, L, Neilson, J, Nelemans, G, Nguyen, C, Nissanke, S, Nitoglia, E, Nocera, F, Oganesyan, G, Olivetto, C, Périgois, C, Pagano, G, Pagliaroli, G, Palomba, C, Pang, P T H, Pannarale, F, Paoletti, F, Paoli, A, Paolone, A, Pascucci, D, Pasqualetti, A, Passaquieti, R, Passuello, D, Patricelli, B, Pegoraro, M, Perego, A, Pereira, A, Perreca, A, Perriès, S, Phukon, K S, Piccinni, O J, Pichot, M, Piendibene, M, Piergiovanni, F, Pierini, L, Pierro, V, Pillant, G, Pillas, M, Pilo, F, Pinard, L, Pinto, I M, Pinto, M, Piotrzkowski, K, Placidi, E, Plastino, W, Poggiani, R, Polini, E, Popolizio, P, Porter, E K, Poulton, R, Pracchia, M, Pradier, T, Principe, M, Prodi, G A, Prosposito, P, Puecher, A, Punturo, M, Puosi, F, Puppo, P, Raaijmakers, G, Radulesco, N, Rapagnani, P, Razzano, M, Regimbau, T, Rei, L, Rettegno, P, Ricci, F, Riemenschneider, G, Rinaldi, S, Robinet, F, Rocchi, A, Rolland, L, Romanelli, M, Romano, R, Romero-Rodríguez, A, Ronchini, S, Rosa, L, Rosińska, D, Roy, S, Rozza, D, Ruggi, P, Salafia, O S, Salconi, L, Salemi, F, Samajdar, A, Sanchis-Gual, N, Sanuy, A, Sassolas, B, Sayah, S, Schmidt, S, Seglar-Arroyo, M, Sentenac, D, Sequino, V, Setyawati, Y, Sharma, A, Shcheblanov, N S, Sieniawska, M, Singh, N, Singha, A, Sipala, V, Soldateschi, J, Sordini, V, Sorrentino, F, Sorrentino, N, Soulard, R, Spagnuolo, V, Spera, M, Srinivasan, R, Stachie, C, Steer, D A, Steinlechner, J, Steinlechner, S, Stratta, G, Sur, A, Swinkels, B L, Szewczyk, P, Tacca, M, Tanasijczuk, A J, San Martín, E N Tapia, Taranto, C, Tonelli, M, Torres-Forné, A, Tosta e Melo, I, Trapananti, A, Travasso, F, Tringali, M C, Troiano, L, Trovato, A, Trozzo, L, Tsang, K W, Turbang, K, Turconi, M, Utina, A, Valentini, M, van Bakel, N, van Beuzekom, M, van den Brand, J F J, van den Broeck, C, van der Schaaf, L, van Heijningen, J V, van Remortel, N, Vardaro, M, Vasúth, M, Vedovato, G, Verkindt, D, Verma, P, Vetrano, F, Viceré, A, Vinet, J-Y, Virtuoso, A, Vocca, H, Walet, R C, Was, M, Zadrożny, A, Zelenova, T, Zendri, J-P (The Virgo Collaboration), Null, Acernese, F, Agathos, M, Ain, A, Albanesi, S, Allocca, A, Amato, A, Andrade, T, Andres, N, Andrić, T, Ansoldi, S, Antier, S, Arène, M, Arnaud, N, Assiduo, M, Astone, P, Aubin, F, Babak, S, Badaracco, F, Bader, M K M, Bagnasco, S, Baird, J, Ballardin, G, Baltus, G, Barbieri, C, Barneo, P, Barone, F, Barsuglia, M, Barta, D, Basti, A, Bawaj, M, Bazzan, M, Bejger, M, Belahcene, I, Benedetto, V, Bernuzzi, S, Bersanetti, D, Bertolini, A, Bhardwaj, U, Bini, S, Bischi, M, Bitossi, M, Bizouard, M-A, Bobba, F, Boer, M, Bogaert, G, Boldrini, M, Bonavena, L D, Bondu, F, Bonnand, R, Boom, B A, Boschi, V, Boudart, V, Bouffanais, Y, Bozzi, A, Bradaschia, C, Branchesi, M, Breschi, M, Briant, T, Brillet, A, Brooks, J, Bruno, G, Bulik, T, Bulten, H J, Buskulic, D, Buy, C, Cagnoli, G, Calloni, E, Canepa, M, Canevarolo, S, Cannavacciuolo, M, Carapella, G, Carbognani, F, Carpinelli, M, Carullo, G, Diaz, J Casanueva, Casentini, C, Caudill, S, Cavalier, F, Cavalieri, R, Cella, G, Cerdá-Durán, P, Cesarini, E, Chaibi, W, Chanial, P, Chassande-Mottin, E, Chaty, S, Chiadini, F, Chiarini, G, Chierici, R, Chincarini, A, Chiofalo, M L, Chiummo, A, Christensen, N, Ciani, G, Cieślar, M, Ciecielag, P, Cifaldi, M, Ciolfi, R, Cipriano, F, Cirone, A, Clesse, S, Cleva, F, Coccia, E, Codazzo, E, Cohadon, P-F, Cohen, D E, Colombo, A, Colpi, M, Conti, L, Cordero-Carrión, I, Corezzi, S, Corre, D, Cortese, S, Coulon, J-P, Croquette, M, Cudell, J R, Cuoco, E, Curyło, M, Dabadie, P, Canton, T Dal, Dall’Osso, S, D’Angelo, B, Danilishin, S, D’Antonio, S, Dattilo, V, Davier, M, De Laurentis, M, De Lillo, F, De Matteis, F, De Pietri, R, De Rosa, R, De Rossi, C, De Simone, R, Degallaix, J, Deléglise, S, Del Pozzo, W, Depasse, A, Di Fiore, L, Di Giorgio, C, Di Giovanni, F, Di Giovanni, M, Di Girolamo, T, Di Lieto, A, Di Pace, S, Di Palma, I, Di Renzo, F, Dietrich, T, D’Onofrio, L, Drago, M, Ducoin, J-G, Durante, O, D’Urso, D, Duverne, P-A, Eisenmann, M, Errico, L, Estevez, D, Fafone, V, Farinon, S, Favaro, G, Fays, M, Fenyvesi, E, Ferrante, I, Fidecaro, F, Figura, P, Fiori, I, Fittipaldi, R, Fiumara, V, Flaminio, R, Font, J A, Frasca, S, Frasconi, F, Fronzé, G G, Gamba, R, Garaventa, B, Garufi, F, Gemme, G, Gennai, A, Ghosh, Archisman, Giacomazzo, B, Giacoppo, L, Giri, P, Gissi, F, Goncharov, B, Gosselin, M, Gouaty, R, Grado, A, Granata, M, Granata, V, Greco, G, Grignani, G, Grimaldi, A, Grimm, S J, Gruning, P, Guerra, D, Guidi, G M, Guixé, G, Guo, Y, Gupta, P, Haegel, L, Halim, O, Hannuksela, O, Harder, T, Haris, K, Harms, J, Haskell, B, Heidmann, A, Heitmann, H, Hello, P, Hemming, G, Hennes, E, Hild, S, Hofman, D, Hui, V, Idzkowski, B, Iess, A, Jacqmin, T, Janquart, J, Janssens, K, Jaranowski, P, Jonker, R J G, Juste, V, Kéfélian, F, Kalaghatgi, C, Karathanasis, C, Katsanevas, S, Khetan, N, Koekoek, G, Koley, S, Kolstein, M, Królak, A, Kuijer, P, La Rosa, I, Lagabbe, P, Laghi, D, Lamberts, A, Lartaux-Vollard, A, Lazzaro, C, Leaci, P, Lemaître, A, Leroy, N, Letendre, N, Leyde, K, Linde, F, Llorens-Monteagudo, M, Longo, A, Portilla, M Lopez, Lorenzini, M, Loriette, V, Losurdo, G, Lumaca, D, Macquet, A, Magazzù, C, Magnozzi, M, Majorana, E, Maksimovic, I, Man, N, Mangano, V, Mantovani, M, Mapelli, M, Marchesoni, F, Marion, F, Marquina, A, Marsat, S, Martelli, F, Martinez, M, Martinez, V, Masserot, A, Mastrogiovanni, S, Meijer, Q, Menendez-Vazquez, A, Mereni, L, Merzougui, M, Miani, A, Michel, C, Milano, L, Miller, A, Miller, B, Milotti, E, Minazzoli, O, Minenkov, Y, Mir, Ll M, Miravet-Tenés, M, Montani, M, Morawski, F, Mours, B, Muciaccia, F, Mukherjee, Suvodip, Musenich, R, Nagar, A, Napolano, V, Nardecchia, I, Naticchioni, L, Neilson, J, Nelemans, G, Nguyen, C, Nissanke, S, Nitoglia, E, Nocera, F, Oganesyan, G, Olivetto, C, Périgois, C, Pagano, G, Pagliaroli, G, Palomba, C, Pang, P T H, Pannarale, F, Paoletti, F, Paoli, A, Paolone, A, Pascucci, D, Pasqualetti, A, Passaquieti, R, Passuello, D, Patricelli, B, Pegoraro, M, Perego, A, Pereira, A, Perreca, A, Perriès, S, Phukon, K S, Piccinni, O J, Pichot, M, Piendibene, M, Piergiovanni, F, Pierini, L, Pierro, V, Pillant, G, Pillas, M, Pilo, F, Pinard, L, Pinto, I M, Pinto, M, Piotrzkowski, K, Placidi, E, Plastino, W, Poggiani, R, Polini, E, Popolizio, P, Porter, E K, Poulton, R, Pracchia, M, Pradier, T, Principe, M, Prodi, G A, Prosposito, P, Puecher, A, Punturo, M, Puosi, F, Puppo, P, Raaijmakers, G, Radulesco, N, Rapagnani, P, Razzano, M, Regimbau, T, Rei, L, Rettegno, P, Ricci, F, Riemenschneider, G, Rinaldi, S, Robinet, F, Rocchi, A, Rolland, L, Romanelli, M, Romano, R, Romero-Rodríguez, A, Ronchini, S, Rosa, L, Rosińska, D, Roy, S, Rozza, D, Ruggi, P, Salafia, O S, Salconi, L, Salemi, F, Samajdar, A, Sanchis-Gual, N, Sanuy, A, Sassolas, B, Sayah, S, Schmidt, S, Seglar-Arroyo, M, Sentenac, D, Sequino, V, Setyawati, Y, Sharma, A, Shcheblanov, N S, Sieniawska, M, Singh, N, Singha, A, Sipala, V, Soldateschi, J, Sordini, V, Sorrentino, F, Sorrentino, N, Soulard, R, Spagnuolo, V, Spera, M, Srinivasan, R, Stachie, C, Steer, D A, Steinlechner, J, Steinlechner, S, Stratta, G, Sur, A, Swinkels, B L, Szewczyk, P, Tacca, M, Tanasijczuk, A J, San Martín, E N Tapia, Taranto, C, Tonelli, M, Torres-Forné, A, Tosta e Melo, I, Trapananti, A, Travasso, F, Tringali, M C, Troiano, L, Trovato, A, Trozzo, L, Tsang, K W, Turbang, K, Turconi, M, Utina, A, Valentini, M, van Bakel, N, van Beuzekom, M, van den Brand, J F J, van den Broeck, C, van der Schaaf, L, van Heijningen, J V, van Remortel, N, Vardaro, M, Vasúth, M, Vedovato, G, Verkindt, D, Verma, P, Vetrano, F, Viceré, A, Vinet, J-Y, Virtuoso, A, Vocca, H, Walet, R C, Was, M, Zadrożny, A, Zelenova, T, Zendri, J-P (The Virgo, Collaboration), Null, Other Research IHEF (IoP, FNWI), High Energy Astrophys. & Astropart. Phys (API, FNWI), Astroparticle Physics (IHEF, IoP, FNWI), Gravitation and Astroparticle Physics Amsterdam, IoP (FNWI), (Astro)-Particles Physics, Spectroscopies optiques des matériaux verres, amorphes et à nanoparticules (SOPRANO), Institut Lumière Matière [Villeurbanne] (ILM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut des Fonctions Optiques pour les Technologies de l'informatiON (Institut FOTON), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL), Laboratoire des deux Infinis de Toulouse (L2IT), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Joseph Louis LAGRANGE (LAGRANGE), Université Paris-Est Marne-la-Vallée (UPEM), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Observatoire de la Côte d'Azur (OCA), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut des Hautes Etudes Scientifiques (IHES), IHES, VIRGO, Virgo Collaboration, Andric, T, Arene, M, Bader, M, Bizouard, M, Bonavena, L, Boom, B, Bulten, H, Diaz, J, Cerda-Duran, P, Chiofalo, M, Cieslar, M, Cohadon, P, Cohen, D, Cordero-Carrion, I, Coulon, J, Cudell, J, Curylo, M, Canton, T, Dall'Osso, S, D'Angelo, B, D'Antonio, S, Deleglise, S, D'Onofrio, L, Ducoin, J, D'Urso, D, Duverne, P, Font, J, Fronze, G, Ghosh, A, Grimm, S, Guidi, G, Guixe, G, Jonker, R, Kefelian, F, Krolak, A, Lemaitre, A, Portilla, M, Magazzu, C, Mir, L, Miravet-Tenes, M, Mukherjee, S, Perigois, C, Pang, P, Perries, S, Phukon, K, Piccinni, O, Pinto, I, Porter, E, Prodi, G, Romero-Rodriguez, A, Rosinska, D, Salafia, O, Shcheblanov, N, Steer, D, Swinkels, B, Tanasijczuk, A, San Martin, E, Torres-Forne, A, Tosta E Melo, I, Tringali, M, Tsang, K, Van Bakel, N, Van Beuzekom, M, Van Den Brand, J, Van Den Broeck, C, Van Der Schaaf, L, Van Heijningen, J, Van Remortel, N, Vasuth, M, Vicere, A, Vinet, J, Walet, R, Zadroa1/4ny, A, Zendri, J, Grav. waves and fundamental physics, RS: FSE Grav. waves and fundamental physics, RS: FSE MSP, Physics, Theoretical Physics, Elementary Particle Physics, Faculty of Sciences and Bioengineering Sciences, ARTeMIS, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Kastler Brossel (LKB (Lhomond)), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des matériaux avancés (LMA), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Navier (NAVIER UMR 8205), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Laboratoire de Mathématiques Appliquées Agrocampus (LMA2), AGROCAMPUS OUEST, Van Swinderen Institute for Particle Physics and G, Acernese, F., Agathos, M., Ain, A., Albanesi, S., Allocca, A., Amato, A., Andrade, T., Andres, N., Andric, T., Ansoldi, S., Antier, S., Arene, M., Arnaud, N., Assiduo, M., Astone, P., Aubin, F., Babak, S., Badaracco, F., Bader, M. K. M., Bagnasco, S., Baird, J., Ballardin, G., Baltus, G., Barbieri, C., Barneo, P., Barone, F., Barsuglia, M., Barta, D., Basti, A., Bawaj, M., Bazzan, M., Bejger, M., Belahcene, I., Benedetto, V., Bernuzzi, S., Bersanetti, D., Bertolini, A., Bhardwaj, U., Bini, S., Bischi, M., Bitossi, M., Bizouard, M. -A., Bobba, F., Boer, M., Bogaert, G., Boldrini, M., Bonavena, L. D., Bondu, F., Bonnand, R., Boom, B. A., Boschi, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brooks, J., Bruno, G., Bulik, T., Bulten, H. J., Buskulic, D., Buy, C., Cagnoli, G., Calloni, E., Canepa, M., Canevarolo, S., Cannavacciuolo, M., Carapella, G., Carbognani, F., Carpinelli, M., Carullo, G., Diaz, J. C., Casentini, C., Caudill, S., Cavalier, F., Cavalieri, R., Cella, G., Cerda-Duran, P., Cesarini, E., Chaibi, W., Chanial, P., Chassande-Mottin, E., Chaty, S., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Christensen, N., Ciani, G., Cieslar, M., Ciecielag, P., Cifaldi, M., Ciolfi, R., Cipriano, F., Cirone, A., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colombo, A., Colpi, M., Conti, L., Cordero-Carrion, I., Corezzi, S., Corre, D., Cortese, S., Coulon, J. -P., Croquette, M., Cudell, J. R., Cuoco, E., Curylo, M., Dabadie, P., Canton, T. D., Dall'Osso, S., D'Angelo, B., Danilishin, S., D'Antonio, S., Dattilo, V., Davier, M., De Laurentis, M., De Lillo, F., De Matteis, F., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Degallaix, J., Deleglise, S., Del Pozzo, W., Depasse, A., Di Fiore, L., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Pace, S., Di Palma, I., Di Renzo, F., Dietrich, T., D'Onofrio, L., Drago, M., Ducoin, J. -G., Durante, O., D'Urso, D., Duverne, P. -A., Eisenmann, M., Errico, L., Estevez, D., Fafone, V., Farinon, S., Favaro, G., Fays, M., Fenyvesi, E., Ferrante, I., Fidecaro, F., Figura, P., Fiori, I., Fittipaldi, R., Fiumara, V., Flaminio, R., Font, J. A., Frasca, S., Frasconi, F., Fronze, G. G., Gamba, R., Garaventa, B., Garufi, F., Gemme, G., Gennai, A., Ghosh, A., Giacomazzo, B., Giacoppo, L., Giri, P., Gissi, F., Goncharov, B., Gosselin, M., Gouaty, R., Grado, A., Granata, M., Granata, V., Greco, G., Grignani, G., Grimaldi, A., Grimm, S. J., Gruning, P., Guerra, D., Guidi, G. M., Guixe, G., Guo, Y., Gupta, P., Haegel, L., Halim, O., Hannuksela, O., Harder, T., Haris, K., Harms, J., Haskell, B., Heidmann, A., Heitmann, H., Hello, P., Hemming, G., Hennes, E., Hild, S., Hofman, D., Hui, V., Idzkowski, B., Iess, A., Jacqmin, T., Janquart, J., Janssens, K., Jaranowski, P., Jonker, R. J. G., Juste, V., Kefelian, F., Kalaghatgi, C., Karathanasis, C., Katsanevas, S., Khetan, N., Koekoek, G., Koley, S., Kolstein, M., Krolak, A., Kuijer, P., La Rosa, I., Lagabbe, P., Laghi, D., Lamberts, A., Lartaux-Vollard, A., Lazzaro, C., Leaci, P., Lemaitre, A., Leroy, N., Letendre, N., Leyde, K., Linde, F., Llorens-Monteagudo, M., Longo, A., Portilla, M. L., Lorenzini, M., Loriette, V., Losurdo, G., Lumaca, D., Macquet, A., Magazzu, C., Magnozzi, M., Majorana, E., Maksimovic, I., Man, N., Mangano, V., Mantovani, M., Mapelli, M., Marchesoni, F., Marion, F., Marquina, A., Marsat, S., Martelli, F., Martinez, M., Martinez, V., Masserot, A., Mastrogiovanni, S., Meijer, Q., Menendez-Vazquez, A., Mereni, L., Merzougui, M., Miani, A., Michel, C., Milano, L., Miller, A., Miller, B., Milotti, E., Minazzoli, O., Minenkov, Y., Mir, L. M., Miravet-Tenes, M., Montani, M., Morawski, F., Mours, B., Muciaccia, F., Mukherjee, S., Musenich, R., Nagar, A., Napolano, V., Nardecchia, I., Naticchioni, L., Neilson, J., Nelemans, G., Nguyen, C., Nissanke, S., Nitoglia, E., Nocera, F., Oganesyan, G., Olivetto, C., Perigois, C., Pagano, G., Pagliaroli, G., Palomba, C., Pang, P. T. H., Pannarale, F., Paoletti, F., Paoli, A., Paolone, A., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patricelli, B., Pegoraro, M., Perego, A., Pereira, A., Perreca, A., Perries, S., Phukon, K. S., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierro, V., Pillant, G., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, K., Placidi, E., Plastino, W., Poggiani, R., Polini, E., Popolizio, P., Porter, E. K., Poulton, R., Pracchia, M., Pradier, T., Principe, M., Prodi, G. A., Prosposito, P., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Raaijmakers, G., Radulesco, N., Rapagnani, P., Razzano, M., Regimbau, T., Rei, L., Rettegno, P., Ricci, F., Riemenschneider, G., Rinaldi, S., Robinet, F., Rocchi, A., Rolland, L., Romanelli, M., Romano, R., Romero-Rodriguez, A., Ronchini, S., Rosa, L., Rosinska, D., Roy, S., Rozza, D., Ruggi, P., Salafia, O. S., Salconi, L., Salemi, F., Samajdar, A., Sanchis-Gual, N., Sanuy, A., Sassolas, B., Sayah, S., Schmidt, S., Seglar-Arroyo, M., Sentenac, D., Sequino, V., Setyawati, Y., Sharma, A., Shcheblanov, N. S., Sieniawska, M., Singh, N., Singha, A., Sipala, V., Soldateschi, J., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Spagnuolo, V., Spera, M., Srinivasan, R., Stachie, C., Steer, D. A., Steinlechner, J., Steinlechner, S., Stratta, G., Sur, A., Swinkels, B. L., Szewczyk, P., Tacca, M., Tanasijczuk, A. J., San Martin, E. N. T., Taranto, C., Tonelli, M., Torres-Forne, A., Tosta E Melo, I., Trapananti, A., Travasso, F., Tringali, M. C., Troiano, L., Trovato, A., Trozzo, L., Tsang, K. W., Turbang, K., Turconi, M., Utina, A., Valentini, M., Van Bakel, N., Van Beuzekom, M., Van Den Brand, J. F. J., Van Den Broeck, C., Van Der Schaaf, L., Van Heijningen, J. V., Van Remortel, N., Vardaro, M., Vasuth, M., Vedovato, G., Verkindt, D., Verma, P., Vetrano, F., Vicere, A., Vinet, J. -Y., Virtuoso, A., Vocca, H., Walet, R. C., Was, M., Zadroa1/4ny, A., Zelenova, T., and Zendri, J. -P.

Subjects
experimental methods, O3 observing run, Physics and Astronomy (miscellaneous), Astronomy, gr-qc, interferometer, calibration, gravitational wave, strain reconstruction, strain uncertainties, Virgo, O3, FOS: Physical sciences, detector: noise, General Relativity and Quantum Cosmology (gr-qc), VIRGO: calibration, 01 natural sciences, General Relativity and Quantum Cosmology, Gravitational waves, Interferometer, 0103 physical sciences, detector: calibration, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], LIGO, mirror, 010306 general physics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Settore FIS/01, 010308 nuclear & particles physics, Physics, photon, gravitational radiation, gravitational radiation detector, O3, Virgo, Calibration, experimental equipment, Calibration, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], strain uncertaintie, Astrophysics - Instrumentation and Methods for Astrophysics, Gravitational wave, astro-ph.IM
Abstract

The three Advanced Virgo and LIGO gravitational wave detectors participated to the third observing run (O3) between 1 April 2019 15:00 UTC and 27 March 2020 17:00 UTC,leading to several gravitational wave detections per month. This paper describes the Advanced Virgo detector calibration and the reconstruction of the detector strain $h(t)$ during O3, as well as the estimation of the associated uncertainties. For the first time, the photon calibration technique as been used as reference for Virgo calibration, which allowed to cross-calibrate the strain amplitude of the Virgo and LIGO detectors. The previous reference, so-called free swinging Michelson technique, has still been used but as an independent cross-check. $h(t)$ reconstruction and noise subtraction were processed online, with good enough quality to prevent the need for offline reprocessing, except for the two last weeks of September 2019. The uncertainties for the reconstructed $h(t)$ strain, estimated in this paper in a 20-2000~Hz frequency band, are frequency independent: 5% in amplitude, 35 mrad in phase and 10 $\mu$s in timing, with the exception of larger uncertainties around 50 Hz., Comment: 50 pages, 30 figures. Submitted to Class. and Quantum Grav.. Includes revision after referee's comments (resubmitted Nov. 2021)

Published
2022
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8. Optimal sensor placement for strain sensing of a beam of high-speed EMU.

Author

Zhang, Zifan, Peng, Chang, Wang, Guangjun, Ju, Zengye, and Ma, Long

Subjects
*STRAIN sensors, *FIBER Bragg gratings, *SENSOR placement, *STRAIN energy, *FORWARD error correction, *COVARIANCE matrices, *MEASUREMENT errors
Abstract

• A strain virtual sensing algorithm based on optimal sensor placement is proposed. • The uncertainties of parameters are included in the Bayesian framework. • A measurement error weighted by modal strain energy is proposed to improve SNR. • Based on the stiffness error, the covariance matrix of modal error is established. • A method for determining mode number based on modal strain energy is proposed. • A validation experiment is presented based on FBG and full-scale beam test-rig. Aiming at the strain sensing of a beam of high-speed EMU, optimal sensor placement (OSP) is studied based on the information gain and modal expansion method within the Bayesian framework. In addition, the role of the prediction error in OSP and strain reconstruction is investigated in detail. Two new prediction errors are proposed: the measurement error weighted by modal strain energy and the modeling error based on the change of element stiffness. The covariance matrix of prediction error is obtained by probability method, which improves the calculation efficiency. Also, a method to determine the number of modes based on the change rate of modal strain energy is proposed. With reference to a case study of a beam, the effect of the prediction error on OSP and strain sensing is described and the reliability and the robustness of the proposed method are tested. Finally, the proposed method is further validated through a case study of full-scale beam monitoring systems based on Fiber Bragg grating (FBG) sensors. The errors between the real and reconstructed strains are compared through the measured data. The results show that the errors between the reconstructed strain and the real strain are very small, which illustrates that this method can be used for strain sensing. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors.

Author

Bettini, Paolo, Guerreschi, Erika, and Sala, Giuseppe

Subjects
*STRUCTURAL health monitoring, *AEROSPACE industry research, *BRAGG gratings, *DETECTORS, *SPECTRAL sensitivity
Abstract

The interest of the aerospace industries in structural health and usage monitoring systems is continuously increasing. Among the techniques available in literature those based on Fibre Bragg Grating sensors are much promising thanks to their peculiarities. Different Chirped Bragg Grating sensor configurations have been investigated in this paper. Starting from a numerical model capable of simulating the spectral response of a grating subjected to a generic strain profile (direct problem), a new code has been developed, allowing strain reconstruction from the experimental validation of the program, carried out through different loading cases applied on a chirped grating. The wavelength of the reflection spectrum for a chirped FBG has a one-to-one correspondence to the position along the gauge section, thus allowing strain reconstruction over the entire sensor length. Tests conducted on chirped FBGs also evidenced their potential for SHM applications, if coupled with appropriate numerical strain reconstructions tools. Finally, a new class of sensors--Draw Tower Grating arrays--has been studied. These sensors are applicable to distributed sensing and load reconstruction over large structures, thanks to their greater length. Three configurations have been evaluated, having different spatial and spectral characteristics, in order to explore possible applications of such sensors to SHM systems. [ABSTRACT FROM AUTHOR]

Published
2015
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10. Blade dynamic strain non-intrusive measurement using L1/2-norm regularization and transmissibility.

Author

Ao, Chunyan, Qiao, Baijie, Chen, Lei, Xu, Jinghui, Liu, Meiru, and Chen, Xuefeng

Subjects
*STRAIN gages, *MODE shapes, *FREQUENCIES of oscillating systems, *POLYNOMIAL chaos, *MATHEMATICAL regularization, *PARAMETER identification
Abstract

• Dynamic strain measurement of rotating blades is conducted via Blade-Tip Timing. • Blade vibration parameters are identified based on L 1/2 -norm regularization. • Blade dynamic strain is reconstructed based on displacement and transmissibility. • The reconstructed results are compared with the strain gauges' measurements. • Numerical cases and a rotor test validate the strain reconstruction method. Dynamic strain measurement is critical in blade vibration monitoring for evaluating the residual life and health condition of rotating blades. Blade tip time (BTT) is recognized as a promising technique to replace traditional strain gauge measurement. This paper presents a method for dynamic strain reconstruction as well as vibration parameter identification based on BTT via the L 1/2 -norm sparse regularization and response transmissibility. First, a sparse representation model based on the L 1/2 -norm penalty is developed to identify blade vibration frequency and blade-tip displacement. Second, a displacement-to-strain transmissibility (DST) is proposed for strain reconstruction from identified displacement. The mode shapes of the blade are needed to calculate the DST. The proposed method is named L 1/2 -DST, which has a great advantage of achieving non-intrusive strain measurement for rotating blades using BTT. Numerical simulation and spin experiment show that the L 1/2 -DST method enables accurate dynamic strain reconstruction and robustness to noise. [ABSTRACT FROM AUTHOR]

Published
2022
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11. Development and Experimental Validation of a Numerical Tool for Structural Health and Usage Monitoring Systems Based on Chirped Grating Sensors

Author

Giuseppe Sala, Paolo Bettini, and Erika Guerreschi

Subjects
Solid propellant, Optical fiber, Chirped Fibre Bragg Gratings, Computer science, Physics::Optics, Draw Tower Grating Array, Grating, Aluminum particles, lcsh:Chemical technology, Metal fuel, Biochemistry, Article, Analytical Chemistry, law.invention, Fiber Bragg grating, law, Materials Testing, Electronic engineering, Fiber Optic Technology, lcsh:TP1-1185, Fiber, Electrical and Electronic Engineering, Aerospace, Instrumentation, Optical Fibers, structural health monitoring, business.industry, Chemical activation, Reactivity, Construction Industry, Reproducibility of Results, Gauge (firearms), Atomic and Molecular Physics, and Optics, Wavelength, Structural health monitoring, business, strain reconstruction, Health and usage monitoring systems, Algorithms
Abstract

The interest of the aerospace industries in structural health and usage monitoring systems is continuously increasing. Among the techniques available in literature those based on Fibre Bragg Grating sensors are much promising thanks to their peculiarities. Different Chirped Bragg Grating sensor configurations have been investigated in this paper. Starting from a numerical model capable of simulating the spectral response of a grating subjected to a generic strain profile (direct problem), a new code has been developed, allowing strain reconstruction from the experimental validation of the program, carried out through different loading cases applied on a chirped grating. The wavelength of the reflection spectrum for a chirped FBG has a one-to-one correspondence to the position along the gauge section, thus allowing strain reconstruction over the entire sensor length. Tests conducted on chirped FBGs also evidenced their potential for SHM applications, if coupled with appropriate numerical strain reconstructions tools. Finally, a new class of sensors—Draw Tower Grating arrays—has been studied. These sensors are applicable to distributed sensing and load reconstruction over large structures, thanks to their greater length. Three configurations have been evaluated, having different spatial and spectral characteristics, in order to explore possible applications of such sensors to SHM systems.

Published
2015

12. Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Author

Gao, Zhifan, Wu, Sitong, Liu, Zhi, Luo, Jianwen, Zhang, Heye, Gong, Mingming, and Li, Shuo

Subjects
*IMPLICIT learning, *TISSUE mechanics, *ARTIFICIAL neural networks, *RADIO frequency, *HIGH-intensity focused ultrasound, *COMPUTER simulation
Abstract

• An implicit framework for the strain reconstruction improves the user friendliness, the model adaptation and the effectiveness-to-efficiency ratio. • A causal learning-using-privileged-information paradigm is proposed to directly guide the intermediate state in the learning process. • A physically-based data generation strategy is proposed to ensure the causality of the privileged information with the training examples/labels, as well as address the medical data insufficiency issue. Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from the inexperienced user unfriendliness, high model bias, and low effectiveness-to-efficiency ratio. The three challenges result from the explicitness characteristic (i.e. explicit formulation of the reconstruction model) in these techniques. For these challenges, we are the first to develop an implicit strain reconstruction framework by a deep neural network architecture. However, the classic neural network methods are unsuitable to the strain reconstruction task because they are difficult to impose any direct influence on the intermediate state of the learning process. This may lead the map learned by the neural network to be biased with the desired map. In order to correct the intermediate state of the learning process, our framework proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network. It provides the causal privileged information besides the training examples to help the network learning, while makes these privileged information unavailable at the test stage. This improvement can narrow the search region of the map learned by the network, and thus prompts the network to evolve towards the actual ultrasound elastography process. Moreover, in order to ensure the causality in LUPI, our framework proposes a physically-based data generation strategy to produce the triplets of privileged information, training examples and labels. This data generation process can approximately describes the actual ultrasound elastography process by the numerical simulation based on the tissue biomechanics and ultrasound physics. It thus can build the causal relationship between the privileged information and training examples/labels. It can also address the medical data insufficiency problem. The performance of our framework has been validated on 100 simulation data, 42 phantom data and 4 real clinical data by comparing with the ground truth performed by an ultrasound simulation system and four state-of-the-art methods. The experimental results show that our framework is well agreed (average bias is 0.065 for strain reconstruction) with the ground truth, as well as superior to these state-of-the-art methods. These results can demonstrate the effectiveness of our framework in the strain reconstruction. [ABSTRACT FROM AUTHOR]

Published
2019
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13. The application of geometry corrections for Diffraction Strain Tomography (DST) analysis of a Ni-base superalloy blade.

Author

Baimpas, Nikolaos, Xie, Mengyin, Reinhard, Christina, and Korsunsky, Alexander M.

Subjects
X-ray diffraction, HEAT resistant alloys, TOMOGRAPHY, THREE-dimensional imaging, POWDER metallurgy, HEAT resistant materials
Abstract

X-ray diffraction is commonly used for non-destructive and precise quantitative determination of internal strain distributions. In recent years tomographic imaging has also been established as a powerful tool for precise non-destructive evaluation of internal structure in materials offering submicron resolution 3D imaging of density distributions. “Diffraction Strain tomography” (DST) concept (Korsunsky, Vorster et al. 2006) has been introduced as a means of tomographic reconstruction of two-dimensional internal strain distributions. The application of this approach during in situ loading has been subsequently demonstrated (Korsunsky et al., 2011). In the present study, similar acquisition strategy was used for diffraction data collection from a Ni-base superalloy turbine blade fabricated by DMLS (Direct Metal Laser Sintering, also sometimes referred to as DLD, Direct Laser Deposition). The experiment was conducted on beamline I12 (JEEP) at Diamond Light Source, UK. Each location within the object was multiply “sampled” (i.e. diffraction patterns were collected containing its contribution) by incident X-ray beams travelling through the sample at different angles. The setup of the beamline also allowed to acquire simultaneously a conventional (absorption tomography) reconstruction of the sample shape. The aim of the experiment was to obtain detailed information about the sample shape, structure, and state. The interpretation of diffraction tomography data requires precise calibration of the sample detector distance at different rotations and positions across the sample, and subsequent application of corrections to remove geometry-induced strain aberrations. [ABSTRACT FROM PUBLISHER]

Published
2013
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