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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗
DOE OSTI2024

Towards Diverse and Representative Global Pretraining Datasets for Remote Sensing Foundation Models

The design of a pretraining dataset is emerging as a critical component for the generality of foundation models. In the remote sensing realm, large volumes of imagery and benchmark datasets exist that can be leveraged to pretrain foundation models, however using this imagery in absence of a well-crafted sampling strategy is inefficient and has the potential to create biased and less generalizable models. Here, we provide a discussion and vision for the curation and assessment of pretraining datasets for remote sensing geospatial foundation models. We highlight the importance of geographic, temporal, and image acquisition diversity and review possible strategies to enable such diversity at global scale. In addition to these characteristics, support for various spatial-temporal pretext tasks within the dataset is also critical. Ultimately, our primary objective is to place emphasis on and draw attention to the data curation stage of the foundation model development pipeline. By doing so, we think it is possible to reduce biases of geospatial foundation models, as well as enable broader generalization to downstream remote sensing tasks and applications.

Arndt, Jacob↗
DOE OSTI2026

VoroClust: Scalable Clustering for Remote Sensing

Although supervised machine learning provides a powerful framework for image classification and segmentation, it requires comprehensive consistent datasets, which are not available for many remote-sensing applications. Remote-sensing datasets are expensive to collect, and each is acquired under different environmental conditions or with significant variations in system operating parameters. Unsupervised clustering algorithms analyze the structure of each dataset independently, rather than drawing on similarities with existing “training” examples, and are thus well suited for practical remote-sensing applications. We introduce VoroClust, a fast density-based unsupervised clustering algorithm applicable to high-resolution and high-dimensional data. VoroClust runs as fast as distance-based clustering methods, while capturing complex regional geometries at least as well as current-density-based methods. It uses a data-centered sphere cover to reduce computational demands, while still capturing data topology. It then propagates clusters outward from local peaks in density. We show that VoroClust provides fast state-of-the-art clustering for both high-resolution polarimetric synthetic aperture radar and high-dimensional hyperspectral imaging datasets.

42 ENGINEERING↗
DOE OSTI2023

Topographic Correction of Optical Remote Sensing Images in Mountainous Areas: A systematic review

Rugged terrain distorts optical remote sensing observations and subsequently impacts land cover classification and biophysical and biochemical parameter retrieval over mountainous areas. Therefore, topographic correction (TC) is a prerequisite for many remote sensing applications. Although various TC methods have been explored over the past four decades to mitigate topographic effects, a systematic and global review of these studies is still lacking. Using a multicomponent bibliometric approach, we extracted bibliometric metadata from 426 publications identified by searching titles, keywords, and abstracts for research on “topographic correction” and “topographic effects” in Scopus and Web of Science (WoS) from 1980 to 2022. Here this systematic review revealed a rapid growth in the number of TC studies since the 1980s, primarily driven by the availability of decametric-resolution remote sensing observations and digital elevation models (DEMs). Most of the research has focused on relatively low-elevation regions, with increasing attention beyond American and European regions, particularly in China. The seasonal distribution of satellite acquisition for TC showed considerable imbalance, mainly concentrated in months with favorable solar illumination conditions (e.g., May to October). Important themes emerged from the keyword analysis, including satellite sensors, DEMs, TC methods, evaluation criteria, and applications.

58 GEOSCIENCES↗
DOE OSTI2023

Self-Powered Autonomous Sensing System for Arctic Ocean using a Frequency-multiplied Cylindrical Triboelectric Nanogenerator

An autonomous sensing system for collecting environmental observations in the Arctic region is critical for the estimation and prediction of climate change. Ocean waves are a great source of energy for these sensing systems but there has been limited research done on small-scale energy harvesting applications in the Arctic Ocean (subsea or surface). The available wave energy in the Arctic Ocean is lower than the typical ocean wave energy due to the low wave frequency, height, and operating months. Although the available wave energy depends on the specific location in the Arctic Ocean, the wave height decreases everywhere during the winter (Jan-March) [1]. Our target location in this work is the Beaufort and Chukchi Seas, which are ice covered for about 195 days per year leaving only a few months (June-November) for wave energy harvesting [2]. During these few months, the average wave frequency is 0.2 Hz and the most common wave frequency is around 0.15 Hz. Many energy harvesting methods are unsuitable for use in the Arctic Ocean because of to the cold temperature, low wave frequency, and low wave height. Triboelectric nanogenerators (TENG) are one of the few energy harvesting methods that excel in these conditions. Zhong et al. [3] designed a stacked pendulum-structured TENG for low-frequency ocean wave energy and reported the device generated a peak power density of 11.2 W/m3 under the low wave frequency of 0.2 Hz. The autonomous sensing system we developed for the Arctic Ocean, the Arctic-TENG, is based on a frequency-multiplied cylindrical TENG (FMC-TENG) which is an optimized TENG configuration for Arctic Ocean conditions due to the high power density under low-frequency wave conditions [4]. Figure 1 shows an FMC-TENG with multiple pairs of free-standing triboelectric-layer mode materials (Aluminum and FEP). The mass and magnet attached to the rotor store gravitational potential energy which is released as kinetic energy when the potential energy overcomes the repulsive magnetic force generated by the opposing magnet attached to the stator. This force unbalance triggers a sudden rotation and swinging motion of the mass which increases the angular velocity of the system and therefore enhances the output power of the TENG system. The main components of the Arctic-TENG are a 3D-printed rotor and stator, electric and dielectric material adhesive tape, and bearings. All these materials were cold-soaked and tested at -40 °C in a chest freezer. Both the 3D-printed parts and adhesives were confirmed to have a minimal effect from the cold temperature. Multiple bearings were tested and the starting torque of each one was compared both at room temperature and -40 °C. The bearing with the lowest starting torque at -40 °C was selected for use in the Arctic-TENG. The Arctic-TENG was tested using an out-of-water motor-driven wave simulator (Figure 2). The wave simulator allows for controlled testing at a wave height of 0.2 m and frequencies between 0.1 Hz to 0.5 Hz. This wave simulator was used for both room temperature and -40 °C testing. The Arctic-TENG generated significantly more power at -40 °C compared to room temperature for each frequency tested. The system stored energy in a supercapacitor via a power management circuit, and the amount of energy stored per day was calculated at different wave frequencies. Based on the conditions at the proposed deployment location (days of non-ice-covered ocean and wave frequency), the amount of stored energy per year from Arctic-TENG was calculated to be enough energy for two transmissions every day. Durability testing was completed on the Arctic-TENG at room temperature to determine the lifetime. The rotor of the Arctic-TENG was attached to a DC motor and spun continuously for several millions of cycles without degradation of the electrical output, demonstrating the feasibility for long-term operation.

Jung, Hyunjun↗
DOE OSTI2023

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? In conclusion, the analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Additive Manufacturing of Controlled Porous Elastomeric Nanocomposites for Enhanced Sensing Function

Sensors manufactured using piezoresistive elastomeric nanocomposites have a wide range of applications in many fields such as structural health monitoring, robotics, and biomedical industries. Additive manufacturing of these sensors, achieved using Direct Ink Writing (DIW), has provided numerous advantages including increased sensitivity and the fabrication of complex geometry. However, solid sensors manufactured either additively or using conventional methods, display a greater level of hysteresis, especially at strains higher than 10%, which limits their sensitivity. The solid sensors also display a greater level of piezoresistive relaxation where the resistance change with time either in a static or dynamic loading environment. Porosity can be introduced in the form of varying infill patterns and densities to reduce bulk material effects. In this paper, a lattice structure with four infill densities are investigated, and the samples are printed using a grid infill pattern. The fabricated samples are characterized using a scanning electron microscope (SEM) to validate the microstructural features and layer bonding. The pressure-sensing capability of each sensor is investigated using cyclic compression loading at various strain levels. Sensing results show that an increase in sensitivity, as well as a decrease in hysteresis with an increase in porosity, compared to solid samples (100% in-fill). The highest porosity was measured to be 62.3% with a corresponding 112% increase in the gauge factor compared to the solid bulk. These results indicate that introducing porosity using DIW is a sensible strategy to improve the piezoresistive performance of nanocomposites and to allow for the tunability of sensing capacity in pressure sensors

Sukati, Musa↗
DOE OSTI2024

Subtleties of nanophotonic lithium niobate waveguides for on-chip evanescent wave sensing

Thin-film lithium niobate (TFLN) is promising for optical sensing due to its high nonlinearities, but its material properties present unique design challenges. We compare the sensing performance of the fundamental modes on a TFLN waveguide with a fluorescent dye sample. The TM mode has better overlap with the sample, with a 1.4 × greater sample absorption rate versus the TE mode. However, the TM mode also scatters at a 1.4 × greater rate, yielding less fluorescence overall. The TE mode is, therefore, more appropriate for sensing. Our findings have important implications for TFLN-based sensor designs.

optics↗
DOE OSTI2026

Spectrometer-free quantitative vapor sensing and classification via spatiotemporal imaging of porous silicon metasurfaces

Metasurfaces offer a compact platform for optical vapor sensing, but their practical deployment has been limited by weak evanescent light–matter interactions and reliance on spectrally resolved instrumentation. Here, we report porous silicon (pSi) metasurfaces for spectrometer-free quantitative detection of volatile organic compounds (VOCs) with strongly enhanced light–matter interaction. The engineered porosity increases sensitivity by >100× relative to non-porous dielectric metasurfaces, enabling limits of detection of 1.65 ppm for methanol and 9.1 ppm for ethanol across a broad dynamic range (<10 ppm to >103 ppm). Imaging-based readout provides a lightweight, spectrometer-free pathway for real-time quantitative sensing. Beyond quantitative detection, the mesoporous architecture introduces adsorption–desorption kinetics as an additional information channel. Analysis of the resulting spatiotemporal signatures enables kinetic fingerprinting without reliance on infrared spectral features or surface functionalization, and a lightweight machine-learning classifier differentiates acetone, methanol, ethanol, and isopropanol with 91.6% accuracy. These results establish porous metasurfaces as spatiotemporal sensing elements that couple quantitative vapor detection with kinetic fingerprinting through real-time dynamical responses, enabling low-cost, high-performance optical sensors.

Dash, Tomoshree [Clemson University]↗
DOE OSTI2025

Enhanced biochemical sensing with high- Q transmission resonances in free-standing membrane metasurfaces

Optical metasurfaces provide solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light–matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q ~ 734 at λ ~ 8.8 µm) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32 cm -1 ) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

Rosas, Samir [Univ. of Wisconsin, Madison, WI (Uni↗
DOE OSTI2023

Metabolic multireactor: Practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

We present a system for carrying out small batch reactor oxygen consumption experiments on water and sediment samples for environmental questions. In general, it provides several advantages that can help researchers achieve impactful experiments at relatively low costs and high data quality. In particular, it allows for multiple reactors to be operated and their oxygen concentrations to be measured simultaneously, providing high throughput and high time-resolution data, which can be advantageous. Most existing literature on similar small batch-reactor metabolic studies is limited to either only a few samples, or only a few time points per sample, which can restrict the ability for researchers to learn from their experiments. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. As such we do not delve deeply into the specifics of the fluorescent dye sensing mechanism. Instead, we focus on practical considerations. We describe the construction and operation of the calibration and experimental systems, and answer many of the questions likely to come up when other researchers choose to build and operate a similar system themselves (questions we ourselves had when we first built the system). In this way, we hope to provide an approachable and easy to use research article that can help other researchers construct and operate a similar system that can be tailored to ask their own research questions, with a minimum of confusion and missteps along the way.

3D printing↗
DOE OSTI2023

Optode performance data associated with: Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

This data package is associated with the publication “Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments”, submitted to PlosONE (Kaufman et al. 2023; 10.1101/2023.03.28.534656).We carried out many testing and calibration experiments on a system of small oxygen consumption batch reactors designed for use with water and sediment samples for environmental questions. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. Our primary focus was on practical considerations, such as temperature effects, lighting angle effects, sterilization, and other similar situations that a user may find useful. Most of the tests required comparing “base” calibration curves to “treatment” calibration curves to determine the extent to which the treatment impacted the reported measurements. This data package contains the performance and calibration data collected for that purpose.This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) diffusion test result files; (5) limit of detection test result files; (6) temperature impact files; (7) a main data file that contains test results for all other tests; and (8) an R script that uses Kolmogorov-Smirnov tests to determine whether treatment calibrations are significantly different from their respective base calibrations. All files are .csv, .txt, .Rmd, or .pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2023

Interpretation of Fracture Initiation Points by In-Well Low-Frequency Distributed Acoustic Sensing in Horizontal Wells

Summary Low-frequency distributed acoustic sensing (LF-DAS) exploits the optical phase shift of Rayleigh backscatter in fiber-optic cables to obtain distributed measurements of changes in strain and temperature. Fiber-optic cables are often installed for multistage hydraulic fracture diagnostics in horizontal wells. LF-DAS in an untreated well provides far-field strain measurements, while offset wells are hydraulically fractured. Such a configuration is called crosswell LF-DAS sensing. Crosswell LF-DAS measurements have proved useful in diagnosing fracture hits, fracture azimuth, planarity, cluster efficiency, fracture propagation rates, and the dynamic distance to the fracture front. In contrast, in-well LF-DAS is conducted on the actively fractured well. Due to cool fracture fluid being injected at high injection rates, the strain component of the LF-DAS response is largely obscured by temperature changes. In permanent fiber-optic cable installations, distributed temperature sensing (DTS) is often conducted simultaneously with LF-DAS. An opportunity exists to decouple the temperature and strain components of LF-DAS sensors to observe strain changes on in-well LF-DAS. The LF-DAS response is modeled as linearly dependent on strain and temperature changes. Theoretical LF-DAS temperature and strain sensitivity coefficients are derived based on the changes to the index of refraction and length of the fiber. Using the DTS measurements, temperature changes are computed, smoothed, filtered, and compared to the LF-DAS response. Crossplots of the in-well LF-DAS measurements and temperature changes from DTS measurements far from the actively fractured region are used to validate the theoretical sensitivity coefficients. Uncertainty in the temperature component of the LF-DAS response is quantified. The difficulty in corresponding the different spatial and temporal resolutions of the DTS and LF-DAS measurements is overcome by comparing the responses over a moving temporal and spatial window. If the LF-DAS response at the center of the window agrees with the DTS response within uncertainty, the measurement is filtered out. After filtering, the remaining nonzero in-well LF-DAS measurements are due to changes in strain. The data are then visualized in waterfall plots. The results indicate that the theoretical and observed strain and temperature coefficients agree within 10%. After the temperature component of the in-well LF-DAS response is extracted, the remaining nonzero measurements are located primarily within the actively treated region. Locations with peaks in the strain response are interpreted to indicate fracture initiation points. These fracture initiation points are compared with in-well DTS and high-frequency DAS noise measurements across multiple stages to better understand fracture initiation along the horizontal well.

Engineering↗
DOE OSTI2021

First Annual Report on Development of Microwave Resonant Cavity Transducer for Fluid Flow Sensing: Development of Sensor Performance Model of Microwave Cavity Flow Meter for Advanced Reactor High Temperature Fluids

We are investigating a microwave cavity-based transducer for in-core high-temperature fluid flow sensing in molten salt cooled reactors (MSCR) and sodium fast reactors (SFR). This sensor is a hollow metallic cylindrical cavity, which can be fabricated from stainless steel, and as such is expected to be resilient to radiation, high temperature and corrosive environment of MSCR and SFR. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. Feasibility of the sensor was initially investigated with analytical derivations and with COMSOL RF Module computer simulations of resonant frequency spectral shift due to uniform load. We also investigated the mechanical integrity of the flowmeter’s membrane through analytical modelling and COMSOL Structural Mechanics Module computer simulations. Both the analytic model and COMSOL model showed that maximum stresses on the plate, which are at the radial boundary of the plate, are three orders of magnitude smaller than the material’s yield strength and ultimate tensile strength. This indicates that the sensor is at a low risk of mechanical failure. Using results from models, we have developed an initial design for a microwave K-band sensor. A cylindrical resonator prototype was fabricated from brass for the initial tests. The external dimensions of the cavity are matched to the flange of a standard WR-42 waveguide. Microwave field is coupled into the resonant cavity through a subwavelength-size aperture. A test article was developed consisting of a piping Tee with a bulkhead WR-42 microwave waveguide installed in a leak-proof assembly. A microwave waveguide circulator was installed in the setup to suppress the effect of reflections at the cavity entrance by increasing the isolation between the input and the output port. Preliminary spectral characterization of cavity spectral response was performed with a portable PXIe chassis microwave VNA with a custom GUI. Preliminary dry tests of the transducer response were conducted with a set of calibrated weights. Transducer frequency shift was shown to be monotonically increasing with increasing pressure. The next steps will involve investigation of the transducer performance for water flow sensing.

42 ENGINEERING↗
DOE OSTI2022

Demonstration of Microwave Resonant Cavity Transducer Performance in Fluid Flow Sensing: Development of Sensor Performance Model of Microwave Cavity Flow Meter for Advanced Reactor High Temperature Fluids

We are investigating a microwave cavity-based transducer for in-core high-temperature fluid flow sensing in molten salt cooled reactors (MSCR) and sodium fast reactors (SFR). This sensor is a hollow metallic cylindrical cavity. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. To validate sensor physics, we have performed proof-of-principle test of flow sensing in water. For this test, we have developed a cylindrical resonator for K-band, which was machined from brass. The cavity was excited through WR-42 waveguide through a subwavelength hole on the side of the wall of the cylinder. To increase the spectral signal visibility, we developed a signal processing method for baseline subtraction. A flow loop for proof-of-principle test of transducer performance in water was assembled. A commercial flow meter was installed in the loop for reference measurements. Cylindrical cavity was excited in the TEM 011 mode with resonant frequency f ≈ 17.8GHz. Frequency shift of cavity spectral response was obtained by gradually increasing water flow rate from 0 to 60gpm. Corresponding monotonic increase of resonant frequency shift by several MHz was observed.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Development of self-sensing materials for extreme environments based on metamaterial concept and additive manufacturing

Structural health monitoring of an engineered component in a harsh environment is critical for multiple DOE missions including nuclear fuel cycle, subsurface energy production/storage, and energy conversion. Supported by a seeding Laboratory Directed Research & Development (LDRD) project, we have explored a new concept for structural health monitoring by introducing a self-sensing capability into structural components. The concept is based on two recent technological advances: metamaterials and additive manufacturing. A self-sensing capability can be engineered by embedding a metastructure, for example, a sheet of electromagnetic resonators, either metallic or dielectric, into a material component. This embedment can now be realized using 3-D printing. The precise geometry of the embedded metastructure determines how the material interacts with an incident electromagnetic wave. Any change in the structure of the material (e.g., straining, degradation, etc.) would inevitably perturbate the embedded metastructures or metasurface array and therefore alter the electromagnetic response of the material, thus resulting in a frequency shift of a reflection spectrum that can be detected passively and remotely. This new sensing approach eliminates complicated environmental shielding, in-situ power supply, and wire routing that are generally required by the existing active-circuit-based sensors. The work documented in this report has preliminarily demonstrated the feasibility of the proposed concept. The work has established the needed simulation tools and experimental capabilities for future studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of Microwave Cavity Transducer Performance in High Temperature Fluid Flow Sensing: Development of Microwave Cavity Flow Meter for Advanced Reactor High Temperature Fluids

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with one of the flat walls of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the microwave resonant frequency, which can be detected with a spectrum analyzer. We have performed a preliminary proof-of-principle test of flow sensing in high temperature liquid sodium in environment, which is similar to that of an advanced reactor. The liquid sodium setup consists of a cylindrical vessel with a center feed line, where a transducer inserted through the top cap of the vessel measures velocity of the impinging liquid jet. For this test, we have developed a cylindrical resonator, which was machined from stainless steel 316 and electroplated with silver on the interior surfaces. Outer diameter of the cylindrical cavity is approximately 3cm, and the thin wall is approximately 200µm thick. We have also developed and insertion probe consisting of a 50cm WR-42 brass waveguide enclosed in a protective SS316 tube. The cavity was excited through a WR-42 waveguide through a subwavelength hole on the side of the wall of the cylinder in the TE 011 mode with resonant frequency f ≈ 17.8GHz. As a calibration experiment, we have assembled a water vessel with a center feed, and with dimensions similar to those of the liquid sodium setup. Frequency shift of the cavity spectral response was obtained at room temperature and ambient pressure by gradually increasing water flow rate from 0 to 25GPM (gallons per minute). Corresponding monotonic increase of resonant frequency by several hundred KHz was observed. Next, a sodium flow rate sensing study was performed in a liquid sodium vessel at 340° C temperature and ambient pressure. The flow rate was changed by varying sodium pump power, and a frequency shift of several hundred KHz was observed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗