Dr. Nageswara Lalam Profile

Dr. Nageswara Lalam

Research Scientist at National Energy Technology Lab

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Author

Publications (30)

Proceedings Article | 3 October 2024 Presentation + Paper

Nondestructive pipeline monitoring using intelligent fiber-optic acoustic sensor system

Khurram Naeem, Pengdi Zhang, Khawar Naheem, Enrico Sarcinelli, Dolendra Karki, Nageswara Lalam, Ruishu Wright, Paul Ohodnicki

Proceedings Volume 13138, 131380M (2024) https://doi.org/10.1117/12.3028530

KEYWORDS: Pipes, Acoustics, Fiber optics sensors, Ultrasonics, Sensors, Nondestructive evaluation, Acoustic emission, Signal detection, Optical sensing, Fiber optics

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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

Petroleum product pipeline monitoring by optical frequency domain reflectometry

Matthew Brister, Nageswara Lalam, Ruishu Wright

Proceedings Volume 13044, 130440K (2024) https://doi.org/10.1117/12.3012899

KEYWORDS: Mach Zehnder interferometers, Single mode fibers, Optical sensing, Optical fibers, Reflectometry, Time metrology, Reflection, Temperature metrology, Temperature distribution, Spatial resolution

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Proceedings Article | 7 June 2024 Presentation + Paper

Distributed optical fiber sensor systems: application to natural gas pipeline monitoring

Nageswara Lalam, Hari Bhatta, Michael Buric, Paul Ohodnicki, Ruishu Wright

Proceedings Volume 13044, 130440N (2024) https://doi.org/10.1117/12.3012866

KEYWORDS: Optical sensing, Fiber optics sensors, Vibration, Pipes, Acoustics, Single mode fibers, Safety, Rayleigh scattering, Photons, Optical fibers

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Proceedings Article | 7 June 2024 Presentation + Paper

Ultra-long-distance BOTDA sensor system employing hybrid amplification and advanced noise reduction technique

Hari Bhatta, Nageswara Lalam, Michael Buric, Ruishu Wright

Proceedings Volume 13044, 1304402 (2024) https://doi.org/10.1117/12.3013592

KEYWORDS: Signal attenuation, Pulse signals, Tunable filters, Sensing systems, Sensors, Optical filters, Signal to noise ratio, Relative intensity noise, Optical fibers

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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 | 7 June 2024 Presentation + Paper

Lab scale demonstration of pipeline third-party damage classification using convolutional neural networks

Sandeep Bukka, Nageswara Lalam, Hari Bhatta, Ruishu Wright

Proceedings Volume 13057, 130570U (2024) https://doi.org/10.1117/12.3014005

KEYWORDS: Acoustics, Sensors, Fiber optics sensors, Deep learning, Data modeling, Machine learning, Convolutional neural networks, Convolution, Safety

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Gas pipelines are critical for transporting vast quantities of natural gas across regions. Third-party damage, such as unauthorized excavation activities is a primary cause of accidents and damage to these pipelines, leading to significant economic losses, environmental harm, and potential threats to human safety. The importance of detecting third-party damages as early as possible cannot be overstated, as it allows pipeline operators to take timely actions to prevent damage. Recent technological advancements, particularly the development of fiber optic sensors, offer promising solutions for real-time monitoring and early warning systems against such damages. Analyzing incidents related to third-party damages present significant challenges due to their non-adherence to physical laws, in contrast to phenomena like corrosion, temperature, and pressure changes. Traditional analytical or empirical models fall short of detecting such damages effectively. However, deep learning techniques have demonstrated notable success in identifying distinctive features from non-physical data sources, including images, speech, and third-party damage acoustic signals pertinent to this study. The efficacy of deep learning methods is contingent upon the availability of a robust dataset for training. The scarcity of fiber optic sensor data pertaining to third-party damages is a critical limitation in this field. This research aims to mitigate this challenge by generating a dataset of third-party damage events on a laboratory scale utilizing a single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional neural networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.

Proceedings Article | 13 March 2024 Paper

Brillouin scattering-based simultaneous and discriminative strain and temperature sensing with double Brillouin peak fibers

Hari Bhatta, Nageswara Lalam, Michael Buric, Ruishu Wright

Proceedings Volume 12835, 128350M (2024) https://doi.org/10.1117/12.3002849

KEYWORDS: Signal to noise ratio, Measurement uncertainty, Error analysis, Scattering, Sensing systems

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Proceedings Article | 11 March 2024 Presentation + Paper

Multi-parameter distributed fiber sensing system based on hybrid Rayleigh and Brillouin scattering

Nageswara Lalam, Hari Bhatta, Michael Buric, Ruishu Wright

Proceedings Volume 12893, 128930J (2024) https://doi.org/10.1117/12.3001195

KEYWORDS: Sensing systems, Rayleigh scattering, Vibration, Sensors, Fiber optics sensors, Signal detection, Optical fibers, Temperature metrology, Optical sensing, Industrial applications

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Proceedings Article | 4 October 2023 Presentation + Paper

Plasmonic and interferometric optical fiber sensors for electrical system monitoring applications

P. Ohodnicki, Y. Su, D. Karki, T. Khanikar, K. Naeem, N. Lalam, J. Wuenschell, M. Buric, R. Wright

Proceedings Volume 12647, 126470H (2023) https://doi.org/10.1117/12.2677290

KEYWORDS: Sensors, Fiber optics sensors, Transformers, Acoustics, Magnetism, Chemical analysis, Acetylene, Magnetic sensors, Plasmonics, Vibration

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