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DOE OSTI2024

Distributed strain sensing using Bi-metallic coated fiber optic sensors embedded in stainless steel

Silica fiber optic sensors are resistant to corrosive environments and high temperatures, making them attractive candidates for harsh conditions found in nuclear and aerospace industries. Moreover, fibers can be deployed remotely for continuous measuring of spatially distributed temperatures and strains. Here, this study investigated embedding a Ni/Cu bi-metallic coated fiber in a stainless-steel 316 (SS316) matrix using laser powder bed fusion towards functionalizing metal components for site-specific health monitoring. The embedded fiber was continuously interrogated during controlled heating to 1000°C. The measured fiber strains were similar to the expected differential thermal strains between the fiber and the SS316 matrix, until divergent behavior was observed at temperatures >500°C. No debonding at the matrix–coating–fiber interfaces was observed during microscopy, but significant interactions between the coatings and matrix resulted in diffusion-driven chemistry variations and Kirkendall void formation. Applying the strain-lag theory revealed plastic behavior in the Ni coating at temperatures >500°C, limiting the strain transfer to the fiber at higher temperatures. It was estimated that the elastic modulus in the Ni coating had decreased from ~200 GPa at room temperature to below 40 GPa, starting at 600°C. The low elastic modulus above 600°C is within the margin of what the tangent modulus would be in the case of bilinear isotropic hardening. Regardless of the divergent strain transfer at higher temperatures, the fiber was exposed to the equivalent of 1.9 % engineering strain at 1000°C, but measured only a 0.7 % engineering strain due to the poor strain transfer. Although compensating for the plastic behavior of Ni proved challenging, the bonding of a brittle silica fiber to a metal matrix surviving to 1000°C invites potential iterations on coating material for future application. For example, the embedded fiber is sufficient for acoustic energy transfer, realizing high temperature distributed acoustic sensing.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

High resolution diagnostic tools for superconducting radio frequency cavities

Superconducting radio-frequency (SRF) cavities are one of the fundamental building blocks of modern particle accelerators. To achieve the highest quality factors (10 10 –10 11 ), SRF cavities are operated at liquid helium temperatures. Magnetic flux trapped on the surface of SRF cavities during cool-down below the critical temperature is one of the leading sources of residual RF losses. Instruments capable of detecting the distribution of trapped flux on the cavity surface are in high demand in order to better understand its relation to the cavity material, surface treatments and environmental conditions. We have designed, developed, and commissioned two high-resolution diagnostic tools to measure the distribution of trapped flux at the surface of SRF cavities. One is a magnetic field scanning system, which uses cryogenic Hall probes and anisotropic magnetoresistance sensors that fit the contour of a 1.3 GHz cavity. This setup has a spatial resolution of ~ 13 μm in the azimuthal direction and ~ 1 cm along the cavity contour. The second setup is a stationary, combined magnetic and temperature mapping system, which uses anisotropic magnetoresistance sensors and carbon resistor temperature sensors, covering the surface of a 3 GHz SRF cavity. This system has a spatial resolution of 5 mm close to the iris and 11 mm at the equator. Initial results show a non-uniform distribution of trapped flux on the cavities’ surfaces, dependent on the magnitude of the applied magnetic field during field-cooling below the critical temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Embedded Fiber-Optic Sensors for In-Pile Applications

Qualification and commercialization of new nuclear fuels and materials requires a comprehensive set of data regarding behavior under irradiation. There are currently very limited options for in-situ monitoring of material evolution during irradiation due to the extremely harsh environment (i.e., high temperatures and intense radiation) of materials test reactors. This paper describes work being performed at Oak Ridge National Laboratory to embed metal-coated fiber-optic sensors into in-core irradiation experiments to enable measurement of radial dimensional changes and spatially distributed temperature and strain. Some critical issues that must be addressed before embedded fiber optics can be deployed in-core include (1) embedding of metal-coated fibers without failure or prohibitively large signal attenuation, (2) embedding in curved channels to allow for radial dimensional measurements, and (3) demonstrating that embedded fibers can survive the large stresses that result from differential thermal expansion between the glass fiber and the surrounding metal matrix. This work shows how optical fibers have been successfully embedded in aluminum and copper alloys in both straight and curved channels with various bend radii. The embedded fibers have also survived heating to temperatures of 500°C and cooling to room temperature. This paper presents some of the experimental results including measured light attenuation resulting from embedding with and without bends and high-temperature testing.

Petrie, Christian↗

DOE OSTI2022

A distributed temperature profiling system for vertically and laterally dense acquisition of soil and snow temperature

Abstract. Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Manufacturing control systems and logic for prognosis of defects in composite materials

Presented are manufacturing control systems for composite-material structures, methods for assembling/operating such systems, and transfer molding techniques for predicting and ameliorating void conditions in fiber-reinforced polymer panels. A method for forming a composite-material construction includes receiving a start signal indicating a fiber-based preform is inside a mold cavity, and transmitting a command signal to inject pressurized resin into the mold to induce resin flow within the mold cavity and impregnate the fiber-based preform. An electronic controller receives, from a distributed array of sensors attached to the mold, signals indicative of pressure and/or temperature at discrete locations on an interior face of the mold cavity. The controller determines a measurement deviation between a calibrated baseline value and the pressure and/or temperature values for each of the discrete locations. If any one of the respective measurement deviations exceeds a calibrated threshold, a void signal is generated to flag a detected void condition.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Tunable diode laser absorption spectroscopy-based tomography system for on-line monitoring of two-dimensional distributions of temperature and H{sub 2}O mole fraction

To monitor two-dimensional (2D) distributions of temperature and H{sub 2}O mole fraction, an on-line tomography system based on tunable diode laser absorption spectroscopy (TDLAS) was developed. To the best of the authors’ knowledge, this is the first report on a multi-view TDLAS-based system for simultaneous tomographic visualization of temperature and H{sub 2}O mole fraction in real time. The system consists of two distributed feedback (DFB) laser diodes, a tomographic sensor, electronic circuits, and a computer. The central frequencies of the two DFB laser diodes are at 7444.36 cm{sup −1} (1343.3 nm) and 7185.6 cm{sup −1} (1391.67 nm), respectively. The tomographic sensor is used to generate fan-beam illumination from five views and to produce 60 ray measurements. The electronic circuits not only provide stable temperature and precise current controlling signals for the laser diodes but also can accurately sample the transmitted laser intensities and extract integrated absorbances in real time. Finally, the integrated absorbances are transferred to the computer, in which the 2D distributions of temperature and H{sub 2}O mole fraction are reconstructed by using a modified Landweber algorithm. In the experiments, the TDLAS-based tomography system was validated by using asymmetric premixed flames with fixed and time-varying equivalent ratios, respectively. The results demonstrate that the system is able to reconstruct the profiles of the 2D distributions of temperature and H{sub 2}O mole fraction of the flame and effectively capture the dynamics of the combustion process, which exhibits good potential for flame monitoring and on-line combustion diagnosis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2011

Thermoelectric powered wireless sensors for spent fuel monitoring

This paper describes using thermoelectric generators to power wireless sensors to monitor spent nuclear fuel during dry-cask storage. OrigenArp was used to determine the decay heat of the spent fuel at different times during the service life of the dry-cask. The Engineering Equation Solver computer program modeled the temperatures inside the spent fuel storage facility during its service life. The temperature distribution in a thermoelectric generator and heat sink was calculated using the computer program Finite Element Heat Transfer. From these temperature distributions the power produced by the thermoelectric generator was determined as a function of the service life of the dry-cask. In addition, an estimation of the path loss experienced by the wireless signal can be made based on materials and thickness of the structure. Once the path loss is known, the transmission power and thermoelectric generator power requirements can be determined. This analysis estimates that a thermoelectric generator can produce enough power for a sensor to function and transmit data from inside the dry-cask throughout its service life. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques

Brillouin Optical Time Domain Analysis (BOTDA) sensor systems play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond tens of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Optimizing Raman pump and probe wave profiles ensures the robustness of Brillouin scattering signals, effectively countering attenuation-induced losses. Additionally, enhancing weak Brillouin signals by strategically placing EDFAs at optimal fiber locations keeps sensor sensitivity high. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. In summary, this research has shown a holistic exploration of extending BOTDA’s distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.

Bhatta, Hari↗

DOE OSTI2015

Modeling the dark current histogram induced by gold contamination in complementary-metal-oxide-semiconductor image sensors

We propose a model for dark current induced by metallic contamination in a CMOS image sensor. Based on Shockley-Read-Hall kinetics, the expression of dark current proposed accounts for the electric field enhanced emission factor due to the Poole-Frenkel barrier lowering and phonon-assisted tunneling mechanisms. To that aim, we considered the distribution of the electric field magnitude and metal atoms in the depth of the pixel. Poisson statistics were used to estimate the random distribution of metal atoms in each pixel for a given contamination dose. Then, we performed a Monte-Carlo-based simulation for each pixel to set the number of metal atoms the pixel contained and the enhancement factor each atom underwent, and obtained a histogram of the number of pixels versus dark current for the full sensor. Excellent agreement with the dark current histogram measured on an ion-implanted gold-contaminated imager has been achieved, in particular, for the description of the distribution tails due to the pixel regions in which the contaminant atoms undergo a large electric field. The agreement remains very good when increasing the temperature by 15 °C. We demonstrated that the amplification of the dark current generated for the typical electric fields encountered in the CMOS image sensors, which depends on the nature of the metal contaminant, may become very large at high electric field. The electron and hole emissions and the resulting enhancement factor are described as a function of the trap characteristics, electric field, and temperature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Code Description for "Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy ground surface temperature sensors"

Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in ground surface temperature. To our knowledge, this is the first time that small ground surface temperature sensors have been used to estimate snow depth. The model performs well at sites where the model was trained and at pan-arctic evaluation sites (RMSE <= 0.15 m). Small temperature sensors are cheap and easy-to-deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring to an extent previously infeasible. The model is flexible and can be applied to datasets retroactively to retrieve snow depth estimates at additional sites. This code package includes a *.joblib file of the trained random forest model and a *.ipynb file showing how to clean input data, train the random forest model, and apply the model.

Bachand, Claire↗

DOE OSTI2024

Advanced Distributed Optical Fiber Sensor Systems for Pipeline Integrity Monitoring

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.

Lalam, Nageswara↗

DOE OSTI2021

Geophysical and Environmental Monitoring Data, Lower Watershed, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2017-2019

This data set contains geophysical and environmental monitoring data acquired between September 2017 and 2019 at the lower Teller watershed, Seward Peninsula, Alaska. Geophysical data comprises processed resistivity data, acquired daily between Spring and Fall of 2018 and 2019, and a baseline measurement of Fall 2017. The environmental monitoring data comprises depth resolved, distributed soil moisture and soil temperature data. These measurements were obtained used Decagon 5TE sensors placed at 0.1 m, 0.2 m , 0.3 m, and 0.4 m depth. In addition, temperature data at 0.5 m, 1.0 m, and 1.5 m were acquired using Hobo temperature sensors. Data were collected to improve our understanding of the hydrological response of discontinuous permafrost systems, in particular focusing on multi-annual dynamics and short term disturbances, such as snowmelt or precipitation events. The electrical resistivity tomography (ERT) monitoring data are included as processed resistivity models, with model cells x dimension equal to the distance along the profile, and model z dimension being elevation. The start and end point of the transect are (UTM Zone 3N): E 454881.66 m, N 7178949.45 m, and E 454772.60, N 7178885.81. Locations of the soil moisture and temperature sensors are provided in the data package. This dataset is discussed in detail in the Uhlemann, S. et al 2021 paper listed in the references. This dataset includes one *.pdf user guide and 315 *.csv data files included within four zipped files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Monolithically integrated mid-infrared sensor using narrow mode operation and temperature feedback

A method to improve the sensitivity and selectivity of a monolithically integrated mid-infrared sensor using a distributed feedback laser (DFB) is presented in this paper. The sensor is based on a quantum cascade laser/detector system built from the same epitaxial structure and with the same fabrication approach. The devices are connected via a dielectric-loaded surface plasmon polariton waveguide with a twofold function: it provides high light coupling efficiency and a strong interaction of the light with the environment (e.g., a surrounding fluid). The weakly coupled DFB quantum cascade laser emits narrow mode light with a FWHM of 2 cm{sup −1} at 1586 cm{sup −1}. The room temperature laser threshold current density is 3 kA∕cm{sup 2} and a pulsed output power of around 200 mW was measured. With the superior laser noise performance, due to narrow mode emission and the compensation of thermal fluctuations, the lower limit of detection was expanded by one order of magnitude to the 10 ppm range.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

RIC Advanced Sensors & Controls FWP: Novel Sensors for Boilers

Overview and brief project update on 3 tasks within the RIC Advanced Sensors & Controls FWP with direct applications to coal fired boiler temperature sensing. Task 32, Field demonstration of distributed temperature measurement with optical fiber for fossil energy power generation applications. Task 47, In-situ temperature measurements via reactive particles. Task 48, Ultrafast laser measurements for power generation environments.

coal boilers↗

DOE OSTI2020

Multi-Functional Distributed Fiber Sensors for Pipeline Monitoring and Methane Detections. Final Report

As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.

03 NATURAL GAS↗

DOE OSTI2021

Systematic evaluation of magnetic sensitivities of anisotropic magnetoresistive sensors at liquid helium temperature for superconducting cavities

Trapped magnetic flux in bulk superconductors reduces the quality factor Q in superconducting radio-frequency (SRF) cavities. However, the mechanisms underlying flux trapping and radio-frequency loss are not well understood. Detailed observation of the magnetic distributions is important for understanding such phenomena. Magnetic field mapping is useful for observing the magnetic field distribution around SRF cavities. Measuring the change in the magnetic field around the cavity elucidates the flux trapping behavior. Anisotropic magnetoresistive (AMR) sensors are inexpensive and small devices that can detect magnetic flux density. The magnetic sensitivities of AMR sensors need to be evaluated at liquid helium temperature for the magnetic field mapping of SRF cavities. In this study, a test stand was constructed to calibrate the magnetic sensitivities of AMR sensors in liquid helium, and 110 AMR sensors were tested using this stand. The magnetic sensitivities were evaluated systematically. A solenoid coil was used to control the uniform external magnetic field and to measure the magnetic sensitivity at low temperatures. All AMR sensors exhibited suitable sensitivities to the magnetic field around the SRF cavity. The variation in these sensitivities in all AMR sensors was ~1%. Further, the AMR sensors were found to have sufficient sensitivity for mapping the magnetic field around the exterior surface of the SRF cavity.

43 PARTICLE ACCELERATORS↗