PSI - Issue 14

ScienceDirect Available online at www.sciencedirect.com Av ilable o line at ww.sciencedire t.com ScienceDirect Structural Integrity Procedia 00 (2016) 000 – 000 Procedia Structu al Integrity 14 (2019) 712–719 Available online at www.sciencedirect.com ScienceDirect Structural Integrity 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 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. 2nd International Conference on Structural Integrity and Exhibition 2018 Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring Saurabh Zajam, Tushar Joshi, Bishakh Bhattacharya* Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, U.P., India Abstract Defects due to corrosion and fatigue in pipelines may create significant hazards in the transportation of natural gas. To detect such damages, traditionally, pigging equipment like pipeline inspection gauge (PIG) are inserted into the pipeline. However, the perform nce of such devices may be enhanced by embedding vibration sensors externally. Such sensors equipped with machine learning capability could be used for damage detection and verification of the PIG data. This study investigates the applicability of Support Vecto Machine (a supervised machine learning classifier) and wavelet analysis on vibration response in the detection of various kinds of defects present in gas pipelines. Both ends fixed a d simply supported bo ndary conditions are incorporated on a pipe with outer diameter 200 mm pipe with 25 mm pipe thickness, made of structural steel to simulate the real transportation pipeline laying conditions above the ground. In this study, the pigging process is simulated in ANSYS by considering inspection gauge as moving load inside the pipeline with a constant velocity. The velocity and acceleration time history data at a fixed point on the pipe for gauge moving from one end to the other is obtained from ANSYS corresponding to different loading conditions and load moving velocities of the inspection gauge. These data are then post-processed in MATLAB environment. Wavelet analysis has been carried out on this data, to obtain spectral components of frequency contained in the data. Further, Support Vector Machine classifier is used to separate the segment of data corresponding to defect region, which can be mapped back to identify the physical location of the defect on the pipeline. The obtained results show good accuracy of defect identification and its location prediction, which can be integrated with intelligent PIG devices and pipe crawling robots. Keywords:Pipeline ; crack ; structural health monitoring; pipe inspection gauge (PIG); wavelet analysis; machine learning; support vector machine 2nd International Conference on Structural Integrity and Exhibition 2018 Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring Saurabh Zajam, Tushar Joshi, Bishakh Bhattacharya* Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, U.P., India Abstract Defects due to corrosion and fatigue in pipelines may create significant hazards in the transportation of natural gas. To detect such damages, traditionally, pigging equipment like pipeline inspection gauge (PIG) are inserted into the pipeline. However, the performance of such devices may be enhanced by embedding vibration sensors externally. Such sensors equipped with machine learning capability could be used for damage d tection and verificatio of the PIG data. This study investigates the applicability of Support Vector Machine (a supervised machine learning cla sifier) and wavelet analysis on vibration respons i the det ction of va i us kinds of defects pr ent in gas pipelines. Both en s fixed and simply support d boundary co ditions are incorporated o a pipe with outer diameter 200 mm pipe with 25 mm pipe thickn ss, made of s ructural steel to sim late th real ransportation p peline laying conditions bove the ground. In this study, the pi ging process is simula ed in ANSYS y c sideri inspection gauge as moving loa insi e the pip line with a constant vel city. The velocity and acceleration time history data at a fix d point n he pipe for gauge moving from one end to the other is obtained from ANSYS corresponding to differ n loading condi ions and load moving velocities of the inspection gauge. These data are then post-processed in M TLAB enviro m nt. Wavelet analysis h been carrie out on t is data, to obtain spectral components of frequency contained in the data. Further, Support Vector Machine classifier is used to separate the segment f data corresponding to defect region, which can be mapped back to identify the physical location of the defect on the ipeli e. The obtain d results s ow g od ac uracy of defect identificati and its location prediction, which can be in egrated wi h intellige t PIG devices a d pipe crawling rob ts. Keywords:Pipeline ; crack ; structural health monitori g; pipe inspection gauge (PIG); wavelet analysis; machine learning; support vector machine © 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- 512-259-7824; fax: + 91-512-259-7408. E-mail address: bishakh@iitk.ac.in

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.2019.05.089 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 responsi ility of Peer-review under responsibility of the SICE 2018 organizers. 2452-3216© 2018 The Authors. Published by Elsevier B.V. This is n 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. * Corresponding author. Tel.: +351 218419991. E-mail address: amd@tecnico.ulisboa.pt * Corresponding author. Tel.: +91- 512-259-7824; fax: + 91-512-259-7408. E-mail address: bishakh@iitk.ac.in