Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This paper presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDA) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/με and 0.9 MHz/°C for peak 1; and 47.2 kHz/με, and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber on a 1-inch steel pipe under pressure up to 1,000 psi at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-inch outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28με/psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

In this paper, we introduce a reduced order model (ROM) for the propagation of nonlinear multi-directional ocean wave-fields. The ROM relies on Galerkin projection of Zakharov equations embedded in the high-order spectral (HOS) method, which describes the evolution of nonlinear waves. The dominant flow features of wave evolution are computed from proper orthogonal decomposition (POD) and these modes are used for the projection. The HOS scheme to compute the vertical velocity is treated in a novel way for an efficient implementation of POD-based ROM. We refer to this alternative formalism of HOS as HOS-simple. The final reduced order model (ROM) is derived from the Galerkin projection of HOS-simple. For the case of irregular waves, where the number of modes required are in the range of 200, the ROM has no significant advantage since both HOS and HOS-simple are much faster than real-time. The real advantage is demonstrated in multi-directional (or short-crested) irregular waves, where the ROM is the only model capable of achieving real-time computation, a major improvement to the standard HOS method. The potential use of the ROM in propagating short-crested waves from far-field to near-field for real-world applications involving wave probes in a wave tank/controlled environment as well as X-band radar in open ocean is also demonstrated.

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

This research focuses on enabling real-time monitoring of critical infrastructure including pipelines in oil and gas transportation and the various threats they face, including human error, environmental factors, and material corrosion. The study proposes a novel approach to Structural Health Monitoring (SHM) using ultrasonic in-line pipe inspection with affixed transducers. More specifically, it explores the use of optical fiber sensors in various configurations for continuous monitoring. A significant innovation is the combination of guided wave technology with fiber optic sensors for pipeline and other structural health monitoring applications. The research employs machine learning and artificial intelligence (AI) for corrosion classification through guided waves, using Finite Element Analysis (FEA) simulations to generate labeled datasets for AI training. However, the scarcity of labeled training data in industrial contexts is a challenge. The study also investigates domain adaptation as a tool for knowledge generalization, applying insights from a well-labeled source domain to a less-explored target domain. A novel framework is demonstrated that leverages domain adaptation AI models by integrating simulation data with real-world measurements, thereby enhancing system state estimation and laying the groundwork for future digital twin systems. The thesis presents an innovative approach to infrastructure monitoring by integrating distributed fiber optic sensing, acoustic non-destructive evaluation, and artificial intelligence. This includes corrosion monitoring, reduced-order modeling, integration of AI frameworks with physical-based simulation models, and strategic deployment of optimized acoustic sensing based upon optical fiber sensors.

Reliable defect measurement in industrial pipelines remains difficult under small-sample conditions, where experimental guided-wave data are limited and affected by environmental and instrumentation noise. Although physics-based simulation can provide scalable training data, discrepancies between simulated and measured signals introduce measurement uncertainty and limit the practical reliability of data-driven models. To address this problem, this study presents a small-sample fiber guided-wave defect measurement framework that combines reduced-order modeling, signal adaptation, and domain-aligned classification.In the proposed framework, a reduced-order model derived from high-fidelity wave-propagation simulations is used to efficiently generate physically meaningful guided-wave responses over a broad defect parameter space. To reduce the discrepancy between simulated and experimental data, a neural-style-transfer-based signal adaptation step is introduced to reproduce experimental background characteristics in the time-frequency representation while maintaining the main defect-relevant wave structures. The adapted synthetic signals are then used together with limited experimental measurements to support defect identification in the target domain.The framework is evaluated using experimental guided-wave data collected from two pipeline configurations with repeated measurements at 11 sensor positions. To reduce overlap between training and testing conditions, the target-domain data are partitioned using a position-wise holdout strategy, in which measurements from held-out sensor locations are excluded from training. Results show that the proposed framework achieves the best performance among the compared methods in this held-out setting and improves the practical consistency between simulation-based training data and experimental measurements. These findings suggest that the proposed approach provides a useful route for reducing measurement uncertainty in small-sample fiber guided-wave defect measurement.

High-fidelity finite element numerical simulations of guided wave propagation in structures like pipelines are computationally demanding, posing significant challenges for real-time structural health monitoring (SHM) and advanced signal processing frameworks. The prohibitive computational cost of full-order models makes them impractical for rapid assessment, extensive parametric studies, or integration into inverse analysis algorithms, where swift and continuous data interpretation is essential. The advent of high-dimensional spatiotemporal data, from systems like Distributed Acoustic Sensing (DAS), further necessitates efficient analysis methods. This paper presents a computational framework for constructing reduced-order models (ROMs) as a surrogate for complex wave propagation and scattering phenomena. The proposed framework can efficiently construct ROM surrogates by combining Tucker tensor low-rank approximation and spectral polynomials. The surrogate model consists of the tensor product of one-dimensional basis functions, where the coefficient tensor is approximated in the low-rank Tucker form. To this end, we sample nodal displacements or accelerations using finite element analysis with representative parameter sets at a set of collocation points. The data are decomposed into a set of mode matrices and one small core tensor using Tucker decomposition. Lagrange interpolation is then applied to the mode matrices to predict values within the data range. Finally, the full spatiotemporal time history responses with new parameter sets are generated by multiplying the expanded mode matrices and small core tensors. The performance of the proposed method is studied by constructing ROMs for guided wave pipeline simulations based on limited learning data. The proposed ROMs can accurately predict behavior even for new parameter sets using the Lagrange interpolation scheme. An extremely high data compression ratio (more than 200) and efficient predictions of the response surfaces (2000 times faster than that of full finite element analyses using all parameter sets) can be realized. Furthermore, the inherent dimensionality reduction offered by Tucker decomposition provides compact, physically meaningful features. The parametric factor matrix, extracted from this decomposition, directly encodes how wave propagation patterns vary with defect parameters. These tensor-derived features are then effectively utilized as inputs for automated defect characterization, successfully estimating critical defect parameters—including distance, length, and width—with high coefficients of determination.