Mr. Pengdi Zhang Profile

Pengdi Zhang

at Univ of Pittsburgh

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Author

Publications (7)

Proceedings Article | 10 June 2024 Presentation + Paper

Numerical modeling of cylindrical non-axisymmetric elastic waves for damage detection using fiber optic sensors

E. Sarcinelli, P. Zhang, K. Naeem, R. Wright, N. Lalam, P. Ohodnicki

Proceedings Volume 13044, 130440M (2024) https://doi.org/10.1117/12.3013782

KEYWORDS: Waveguides, Sensors, Wave propagation, Data modeling, Fiber optics sensors, Pipes, Nondestructive evaluation, Elasticity, Actuators, Education and training

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Current ultrasonic acoustic NDE methods for long distance inspection in cylindrical structures are primarily focused on axisymmetric guided waves excitation. However, there are many occasions where the physical limitations imposed by the system to be inspected restrain the ability to utilize equipment capable of exciting those waves. This study explores the excitation of the flexural guided wave modes by a limited number of piezoelectric transducers for damage detection in hollow cylinders with limited surface access and large diameter. In addition, the use of distributed optical fiber system as the guided wave receptor is investigated as an alternative to piezoelectric transducers (PZT), as their capability to acquire spatial-temporal data synergizes with the complexity in a signal containing several flexural GW modes. More specifically, the study is conducted based on a numerical analysis of the guided waves excited by a 2 PZT configuration in a pipe available for experimental testing. The resulting flexural modes and its interaction with welds and local loss of material are analyzed in terms of the time series data of a local sensor in the surface, and the angular profile differences from a healthy case. A method based on the analytical solution of an infinite cylinder is introduced in preliminary stage to extract the behavior of the dominant modes from simulation and experimental results and used as a simulation-experiment similarity comparison. Finally, a simplified convolutional neural network (CNN) is trained to demonstrate feasibility of using flexural modes excited by limited actuators for damage detection. Overall, this study contributes to the development of a damage detection method applicable to cylindrical structures with dimensional and access limitations, by enhancing the understanding of how simultaneous several flexural modes interact with mechanical features, presenting an early-stage interpretable method to compare simulation and experimental fiber optic sensor data, and demonstrating the feasibility of using DAS like data for analyzing the structure.

Proceedings Article | 10 June 2024 Presentation + Paper

Investigation of data augmentation techniques for ultrasonic acoustic fiber sensing signals in guided wave-based pipeline damage detection

Pengdi Zhang, Khurram Naeem, Enrico Sarcinelli, Abhishek Venketeswaran, Sandeep Bukka, Nageswara Lalam, Ruishu Wright, Paul Ohodnicki

Proceedings Volume 13044, 130440J (2024) https://doi.org/10.1117/12.3013996

KEYWORDS: Data modeling, Waveguides, Fiber optics sensors, Sensors, Ultrasonics, Histograms, Acoustics, Pipes, Machine learning, Interference (communication)

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Proceedings Article | 7 June 2024 Presentation + Paper

Pipeline damage detection using multimode fiber optic acoustic sensor and ultrasonic guided waves

Khurram Naeem, Pengdi Zhang, Enrico Sarcinelli, Dolendra Karki, Tulika Khanikar, Yang-Duan Su, Nageswara Lalam, Ruishu Wright, Paul Ohodnicki

Proceedings Volume 13044, 130440O (2024) https://doi.org/10.1117/12.3013838

KEYWORDS: Pipes, Acoustics, Sensors, Ultrasonics, Fiber optics sensors, Accelerometers, Vibration, Nondestructive evaluation, Multiplexing, Fiber optics

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Structural Health Monitoring (SHM) of pipelines using nondestructive testing/evaluation (NDT/E) techniques is important particularly for the energy industries and for the oil/gas distribution which helps reduction in maintenance costs as well as increased service lifespan. Among various NDE techniques, ultrasonic guidedwaves (GWs) technique is popular for inspection and monitoring of pipes due to its advantages e.g., long-distance monitoring using a fixed sensor probe, full volumetric coverage, and inspection for invisible or inaccessible structure. Recently, performance and scope of the GWs method is explored using optical fiber sensing technology such as fiber Bragg gratings are demonstrated for many ultrasonic sensing applications. The optical fiber sensors bring the advantage of remote sensing, large acoustic bandwidth, and multiplexing capability of the sensors to extend the range of GWs based NDE method. This work describes the health monitoring of damaged pipeline structure in a nondestructive manner using alternative No-core fiber (NCF) based quasi-distributed fiber-optic acoustic sensor combined with ultrasonic GWs excitation. We set up two similar 6-inch carbon-steel pipes (16-ft long), one consists of various defects and the other is healthy without any defect for reference. The pipes are actively excited by employing different ultrasonic sources; (1) magnetostrictive collar (MR) to generate the axisymmetric (torsion) GWs and (2) conventional piezoelectric patches to generate the antisymmetric flexural waves on the exterior surface, and the characteristics of acoustic-ultrasonic signals are studied using NCF based multiplexed fiber-optic sensor. Fiber optic sensor is an inline multimode interferometer made by sandwiching a piece of NCF (~5cm) between the single mode fibers. The NCF sensor is remotely bonded at 45° w.r.t pipe axis on one end and has an ultrasonic sensing range of >600kHz. Finally, the measured acousto-ultrasonic signals for different ultrasonic sources are compared to those obtained by the numerical simulation or electrical-based sensor for the healthy and damaged test pipes. The proposed work presents useful insight for damage detection in pipes using an NCF-based quasi-distributed fiber-optic acoustic sensor combined with ultrasonic GWs excitation.

Proceedings Article | 13 June 2023 Presentation + Paper

Nuclear canister integrity monitoring using quasi-distributed fiber acoustic sensors and physics-based modeling

P. Ohodnicki, E. Sarcinelli, P. Zhang, K. Naeem, D. Karki, R. Meyer, N. Lalam, R. Wright

Proceedings Volume 12532, 125320I (2023) https://doi.org/10.1117/12.2665084

KEYWORDS: Acoustics, Fiber optics sensors, Corrosion, Vibration, Modeling, Sensors, Finite element methods, Structural health monitoring, Optical sensing, Waveguides

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Proceedings Article | 13 June 2023 Presentation + Paper

Feature analysis acoustic signals for fiber-optic-sensing-based NDE for welded structures

Enrico Sarcinelli, Pengdi Zhang, Abhishek Venketeswaran, Ryan Meyer, Ruishu Wright, Hessam Babaee, Paul Ohodnicki

Proceedings Volume 12532, 125320D (2023) https://doi.org/10.1117/12.2664135

KEYWORDS: Sensors, Nondestructive evaluation, Acoustics, Reflection, Modeling, Wave propagation, Data acquisition, Acoustic waves

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Proceedings Article | 13 June 2023 Presentation + Paper

Machine learning data analytics based on distributed fiber sensors for pipeline feature detection

Pengdi Zhang, Abhishek Venketeswaran, Sandeep Bukka, Enrico Sarcinelli, Nageswara Lalam, Ruishu Wright, Paul Ohodnicki

Proceedings Volume 12532, 125320C (2023) https://doi.org/10.1117/12.2663225

KEYWORDS: Corrosion, Matrices, Simulations, Machine learning, Singular value decomposition, Waveguides, Data modeling, Fiber optics sensors, Wave propagation, Acoustics

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Proceedings Article | 27 May 2022 Presentation + Paper

Feature extraction for pipeline defects inspection based upon distributed acoustic fiber optic sensing data

Pengdi Zhang, Abhishek Venketeswaran, Ruishu Wright, Kayte Denslow, Hessam Babaee, Paul Ohodnicki

Proceedings Volume 12105, 1210503 (2022) https://doi.org/10.1117/12.2618263

KEYWORDS: Corrosion, Waveguides, Acoustics, Fiber optics sensors, Wave propagation, Finite element methods, Ultrasonics, Nondestructive evaluation, Signal detection, Feature extraction

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Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Showing 5 of 7 publications

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