Engineering topics
Publications and source records attributed to Buric, Michael.
Distributed fiber optic sensors allow the measurement of structural parameters such as static/dynamic strain, temperature, pressure, and vibrations at thousands of locations along a single fiber cable. Deep neural network (DNN) algorithms were developed for rapid data processing speed and vibration event classification.
Fibers composed of single crystal (SC) materials are mechanically, thermally, and chemically robust to harsh conditions, and thus attractive for sensing applications in harsh environments. However, SC fibers grown via the laser-heated pedestal growth (LHPG) technique do not intrinsically have a functional cladding layer. We investigate self-segregation of dopants in a crystal matrix within a SC fiber. Ti and Cr dopants within a sapphire matrix were grown via the LHPG method. Cross-sectional dopant concentrations were measured via electron-probe micro-analysis (EPMA). The cross-sectional constituent profiles and resulting transmissivities were characterized as a function of growth parameters.
Quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. A growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. NETL is leveraging experimental and computational quantum tools to enhance sensitivity of hybrid quantum-classical ultrasensitive sensors for the detection of hydrocarbons and rare earth elements (REEs).
The National Energy Technology Laboratory (NETL) and Westinghouse will collaborate in testing a new NETL invention known as the single-crystal optical fiber Raman Distributed Temperature Sensor (DTS). Westinghouse will use the NETL Raman DTS to test performance of their new e-Vinci heat-pipe reactor systems.
The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, in this paper, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.