Structural Health Management of Damaged Aircraft Structures Using the Digital Twin Concept

The development of multidisciplinary integrated Structural Health Management (SHM) tools will enable accurate detection, and prognosis of damaged aircraft under normal and adverse conditions during flight. As part of the digital twin concept, methodologies are developed by using integrated multiphysics models, sensor information and input data from an in-service vehicle to mirror and predict the life of its corresponding physical twin. SHM tools are necessary for both damage diagnostics and prognostics for continued safe operation of damaged aircraft structures. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and structural damage conditions. A major concern in these structures is the growth of undetected damage/cracks due to fatigue and low velocity foreign object impact that can reach a critical size during flight, resulting in loss of control of the aircraft. To avoid unstable, catastrophic propagation of damage during a flight, load levels must be maintained that are below a reduced load-carrying capacity for continued safe operation of an aircraft. Hence, a capability is needed for accurate real-time predictions of damage size and safe load carrying capacity for structures with complex damage configurations. In the present work, a procedure is developed that uses guided wave responses to interrogate damage. As the guided wave interacts with damage, the signal attenuates in some directions and reflects in others. This results in a difference in signal magnitude as well as phase shifts between signal responses for damaged and undamaged structures. Accurate estimation of damage size, location, and orientation is made by evaluating the cumulative signal responses at various pre-selected sensor locations using a genetic algorithm (GA) based optimization procedure. The damage size, location, and orientation is obtained by minimizing the difference between the reference responses and the responses obtained by wave propagation finite element analysis of different representative cracks, geometries, and sizes.

[1]  Kuo-Ting Wu,et al.  Applications of Flexible Ultrasonic Transducer Array for Defect Detection at 150 °C , 2013, Sensors.

[2]  X. Jia.  Modal analysis of Lamb wave generation in elastic plates by liquid wedge transducers , 1997 .

[3]  Jeong‐Tae Kim,et al.  Improved damage identification method based on modal information , 2002 .

[4]  Thiagarajan Krishnamurthy,et al.  Damage Characterization Using the Extended Finite Element Method for Structural Health Management , 2011 .

[5]  Toshihiko Sugiura,et al.  Detection of a Closed Crack by Nonlinear Acoustics Using Ultrasonic Transducers , 2006 .

[6]  Victor Giurgiutiu,et al.  Predictive simulation of nonlinear ultrasonics , 2012, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[7]  L. Wang,et al.  Scattering of elastic waves by a crack in a isotropic plate , 1997 .

[8]  Hoon Sohn,et al.  A Nonlinear Acoustic Technique for Crack Detection in Metallic Structures , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[9]  L. Ye,et al.  Quantitative assessment of through-thickness crack size based on Lamb wave scattering in aluminium plates , 2008 .

[10]  Michel Castaings,et al.  Modal decomposition method for modeling the interaction of Lamb waves with cracks. , 2002, The Journal of the Acoustical Society of America.

[11]  Qing Zhang,et al.  Numerical Simulation of Nonlinear Lamb Waves Used in a Thin Plate for Detecting Buried Micro-Cracks , 2014, Sensors.

[12]  Victor Giurgiutiu,et al.  WFR-2D: an analytical model for PWAS-generated 2D ultrasonic guided wave propagation , 2014, Smart Structures.

[13]  Fabrizio Ricci,et al.  A Conceptual Structural Health Monitoring System based on Vibration and Wave Propagation , 2005 .

[14]  Fu-Kuo Chang,et al.  Structural Health Monitoring , 2016 .

[15]  Steven E. Olson,et al.  Analytical Modeling of Lamb Waves for Structural Health Monitoring (Preprint) , 2006 .

[16]  Shijiu Jin,et al.  Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network , 2013, Sensors.

[17]  Charles R. Farrar,et al.  Integrated Structural Health Assessment Using Piezoelectric Active Sensors , 2005 .

[18]  Richard W. Ross,et al.  Characterization of Aircraft Structural Damage Using Guided Wave Based Finite Element Analysis for In-Flight Structural Health Management , 2016 .

[19]  Laurence J. Jacobs,et al.  Modeling elastic wave propagation in waveguides with the finite element method , 1999 .

[20]  Lei Wang,et al.  Active damage localization technique based on energy propagation of Lamb waves , 2007 .

[21]  Michel Castaings,et al.  The interaction of the S0 Lamb mode with vertical cracks in an aluminium plate. , 2002, Ultrasonics.

[22]  Thiagarajan Krishnamurthy,et al.  Damage Characterization Method for Structural Health Management Using Reduced Number of Sensor Inputs , 2012 .