Mr. Pengdi Zhang Profile

Pengdi Zhang

at Univ of Pittsburgh

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Author

Publications (4)

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.

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