PSI - Issue 14

ScienceDirect Available online at www.sciencedirect.com Available online at ww.sciencedire t.com Sci ceDirect Structural Integrity Procedia 00 (2016) 000 – 000 Procedia Structu al Integrity 14 (2019) 282–289 Available online at www.sciencedirect.com ScienceDirect Structural I t gri y Procedia 00 (2018) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2018) 000–000

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XV Portuguese Conference on Fracture, PCF 2016, 10-12 February 2016, Paço de Arcos, Portugal Thermo-mechanical modeling of a high pressure turbine blade of an airplane gas turbine engine P. Brandão a , V. Infante b , A.M. Deus c * a Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal b IDMEC, Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal c CeFEMA, Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal Abstract During their operation, modern aircraft engine components are subjected to increasingly demanding operating conditions, especially the high pressure turbine (HPT) blades. Such conditions cause these parts to undergo different types of time-dependent degradation, one of which is creep. A model using the finite element method (FEM) was developed, in order to be able to predict the creep behaviour of HPT blades. Flight data records (FDR) for a specific aircraft, provided by a commercial aviation company, were used to obtain thermal and mechanical data for three different flight cycles. In order to create the 3D model needed for the FEM analysis, a HPT blade scrap was scanned, and its chemical composition and material properties were obtained. The data that was gathered was fed into the FEM model and different simulations were run, first with a simplified 3D rectangular block shape, in order to better establish the model, and then with the real 3D mesh obtained from the blade scrap. The overall expected behaviour in terms of displacement was observed, in particular at the trailing edge of the blade. Therefore such a model can be useful in the goal of predicting turbine blade life, given a set of FDR data. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND licens (https://creativecommons.org/licens /by-nc- d/4.0/) Selection and peer-review unde responsibility of Peer-review under responsibility of the SICE 2018 organizers. 2nd International Conference on Structural Integrity and Exhibition 2018 Detection of Subtle Damage in Structures through Smart Signal R construction K Lakshmi a, *, A Rama Mohan Rao a a CSIR-Structural Engineering Research Centre, Taramani, Chennai-600113, India. Abstract The primary function of structural health monitoring (SHM) is the process of extracting the damage features from the measured raw data, recorded using sensors on the structure of interest. The efficiency of SHM techniques lies in their capability to detect early damage, which alters the dynamic characteristics of only a few modal responses but in a feeble manner, in its incipient stage. Isolating these modal responses, hidden in the overall raw esponse, for damage diagnosis, is a real ch llenge to the SHM community. In order to handle this issue, an improved version of Empirical Mode Decomposition (EMD) is employed in this paper. EMD decomposes the measured response signals into mono-component signals, called intrinsic mode functions (IMFs). The mixed modes in EMD are handled using Intermittency criteria in the proposed EMD. Once the IMFs are extracted from the raw signal, the IMFs (signal components) which possess the valuable information of incipient damage called ‘critical IMFs’, are isolated. To determine the spatial location of damage, these critical IMFs are combined to reconstruct a new signal with enriched information on minor/incipient damage. ARMAX model is employed on the new signal with enriched damage information. A normalized distance measure of ARMAX models, in terms of subspace angles, is used as a damage indicator. The numerical and experimental investigations presented in this paper clearly reflect that the proposed output-only damage diagnostic technique using the smart reconstruction of the measured raw signal is capable of detecting the incipient subtle damages in the structures. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. Keywords: Structural health monitoring, Damage diagnosis, subtle damage, signal decomposition, time series analysis, Empirical mode decomposition, ARMAX model. 2nd International Conference on Structural Integrity and Exhibition 2018 Detection of Subtle Damage in Structures through Smart Signal Reconstruction K Lakshmi a, *, A Rama Mohan Rao a a CSIR-Structural Engineering Research Centre, Taramani, Chennai-600113, India. Abstract The primary function of structural health monitoring (SHM) is the process of extracting the damage features from the measured raw data, recorded using sensors on the structure of interest. The efficiency of SHM techniques lies in their capability to detect early damage, which alters the dynamic characteristics of only a few modal responses but in a feeble manner, in its incipient stage. Isolating these modal responses, hidden in the overall raw response, for damage diagnosis, is a real challenge to the SHM community. In order to handle this issue, an improved version of Empirical Mode Decomposition (EMD) is employed in this paper. EMD decomposes the measured response signals into mono-component signals, called intrinsic mode functions (IMFs). The mixed mode in EMD are handled using Intermittency criteria in the proposed EMD. Once the IMFs are extracted from the raw signal, the IMFs (signal components) which possess the valuable information of incipient damage called ‘critical IMFs’, are isolated. To determine t e spatial location of damage, these critical IMFs are combined to rec nstruct a ew signal with enriched information on minor/incipient damage. ARMAX model is employed on the new signal with enriched damage information. A normalized distance measure of ARMAX models, in terms of subspace angles, is used as a damage indicator. The numerical and experimental investigations presented in this paper clearly reflect that the proposed output-only damage diagnostic technique using the smart reconstruction of the measured raw signal is capable of detecting the incipient subtle damages in the structures. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND lic nse (https://creativecommons.org/licenses/by- c-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. Keyword : Structural health monitoring, Damage diagnosis, subtle damage, signal decomposition, time series analysis, Empirical mode decomposition, ARMAX model. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of PCF 2016.

Keywords: High Pressure Turbine Blade; Creep; Finite Element Method; 3D Model; Simulation.

* Corresponding author. Tel.: +91-44-22545721; fax: +91-44-22541508. E-mail address: lakshmik@serc.res.in * Corresponding author. Tel.: +91-44-22545721; fax: +91-44-22541508. E-mail address: lakshmik@serc.res.in

2452-3216 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. 2452-3216 © 2018 The Authors. Published by Elsevier B.V. This is a open access article under the CC BY-NC-ND lic nse (https://creativecommons.org/licenses/by- c-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers.

* Corresponding author. Tel.: +351 218419991. E-mail address: amd@tecnico.ulisboa.pt

2452-3216 © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of PCF 2016. 2452-3216  2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. 10.1016/j.prostr.2019.05.036