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NASA NTRSDate not supplied

A Heritage BioSensor for Lunar Biology Experiments

Introduction: Automated biological experiments on small spacecraft missions have gained prominence over the past decade due to their simplicity, accessibility, and small mass, volume, and power needs. Most recently, the BioSensor microfluidic CubeSat payload aboard BioSentinel used an automated microfluidic cell culture system to study the effects of environmental stressors like deep space radiation and microgravity on yeast growth and metabolism. BioSentinel’s successor, the Lunar Explorer Instrument for space biology Applications (LEIA), will study the effects of lunar gravity and radiation using an improved version of the BioSensor microfluidic platform. The BioSensor payload has great adaptability to host a diverse range of biological experiments with single- and multi-celled organisms in both crewed and uncrewed missions, making it a compelling candidate for future space biology studies in a lunar surface environment. BioSensor Instrumentation on BioSentinel: The first spaceflight mission with the BioSensor, BioSentinel’s biology experiments occurred at three locations -- deep space, ISS and ground. The payload contained 18 microfluidic cards, each featuring 16 growth wells (a total of 288 growth wells). Each well was loaded before launch with desiccated yeast. In space, liquid culture medium (nutrients) was automatically introduced to batches of wells at a time to initiate a series of biology experiments. Temperature was maintained by thin film heaters on both sides of each card. Each well was equipped with three LEDs emitting at 570 nm, 630 nm, and 850 nm, paired with photodetectors to measure cell concentration and the alamarBlue (metabolic indicator dye) color transition from blue to pink. Phenotypic parameters like cell viability, metabolic rate, and generation time can be derived from these measurements. The sequence and timing of fluid fills, optical measurements, and thermal control were stored onboard, but could be updated asynchronously via ground communication. LEIA: LEIA is slated for launch no earlier than 2026 on a CLPS lander. BioSentinel’s BioSensor has been modified for use in LEIA. These improvements include: (a) storage for multiple culture medium types, (b) additional LED color (465 nm) for a new biological assay for antioxidant (carotenoid) production, (c) housing modifications for later biology load before launch, (d) improved isolation between electronic and fluidic components, and (e) improved humidity control for prolonged organism viability in case of post-load launch delay. Future Prospects: The consistent and successful demonstration of complex fluidics platforms alongside reliable instrument operations in a space environment is poised to create strong momentum for BioSensor-based biological experiment payloads. Planned future developments with the BioSensor include extending compatibility to a broader range of organisms and assays. Preliminary work has already demonstrated successful growth of Arabidopsis seedlings in fluidic cards. With a few modifications to the optical assembly, the setup could easily measure photosynthetic traits in plants and cyanobacteria. The addition of fluorescence measurements and generation of novel luminescent assays will elevate BioSensor’s functionality further. Beyond the BioSensor’s potential uses on free-flyer missions, ISS and Gateway, and CLPS landers, deploying the BioSensor to the lunar surface or in an artificial habitat on crewed missions could enable pioneering research on both how life responds to lunar conditions and future bioproduction capabilities making the BioSensor an indispensable tool for future space biology research.

Chinmayee Govinda Raj↗
NASA NTRSDate not supplied

Lunar Explorer Instrument for space biology Applications (LEIA): An overview of planned science concept of operations

Radiation and reduced gravity pose biological risks to crewed deep space exploration. To better understand deep space radiation biology, the LEIA mission will measure charged particles and fast neutrons as well as yeast growth, metabolic rate, and bioengineered production of carotenoids at the lunar surface. LEIA will be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. The LEIA science concept of operations was developed to isolate radiation and partial gravity from other environmental conditions experienced by the payload. Due to CLPS integration requirements, there will be at least eight months from loading yeast into the payload until activation on the lunar surface. Replicate yeast strains will be loaded in a randomized complete block design to minimize batch differences in desiccation tolerance and positional effects in the microfluidics culture system. Temperature and relative humidity will be controlled throughout integration and flight to maintain yeast viability and to enable measurement of yeast growth parameters within the lunar surface operations timeframe, with the ground control matching environmental conditions where possible. LEIA is co-manifested with the European Space Agency’s PROSPECT mission, which will be operating a drill during yeast growth. Vibration translated through the lunar lander will be mitigated and quantified to account for potential impacts on LEIA optical measurements and yeast growth rate. Total space radiation dose, including the transit exposure from Earth to the Moon, will be measured to obtain more accurate charged particle and fast neutron dose rates on the lunar surface. These radiation data will also yield ground truth radiation dose experienced by the yeast during the mission. Quantifying these environmental factors impacting the LEIA payload will allow more accurate ground control experiments to better isolate the biological responses to radiation and reduced gravity at the lunar surface.

Lunar Surface Mission↗
NASA NTRS2017

HST WFC3 Observations of Uranus' 2014 Storm Clouds and Comparison with VLT/SINFONI and IRTF/Spex Observations

In November 2014 Uranus was observed with the Wide Field Camera 3 (WFC3) instrument of the Hubble Space Telescope as part of the Hubble 2020: Outer Planet Atmospheres Legacy program, OPAL. OPAL annually maps Jupiter, Uranus and Neptune (and will also map Saturn from 2018) in several visible near- infrared wavelength filters. The Uranus 2014 OPAL observations were made on the 89th November at a time when a huge cloud complex, first observed by de Pater et al. (2015) and subsequently tracked by professional and amateur astronomers (Sayanagi et al., 2016), was present at 30-40deg N. We imaged the entire visible atmosphere, including the storm system, in seven filters spanning 467924 nm, capturing variations in the coloration of Uranus clouds and also vertical distribution due to wavelength dependent changes in Rayleigh scattering and methane absorption optical depth. Here we analyse these new HST observations with the NEMESIS radiative-transfer and retrieval code in multiple-scattering mode to determine the vertical cloud structure in and around the storm cloud system. The same storm system was also observed in the H-band (1.4-1.8 micrometers) with the SINFONI Integral Field Unit Spectrometer on the Very Large Telescope (VLT) on 31st October and 11th November, reported by Irwin et al. (2016, 10.1016j.icarus.2015.09.010). To constrain better the cloud particle sizes and scattering properties over a wide wavelength range we also conducted a limb-darkening analysis of the background cloud structure in the 30-40deg N latitude band by simultaneously fitting: a) these HSTOPAL observations at a range of zenith angles; b) the VLTSINFONI observations at a range of zenith angles; and c) IRTFSpeX observations of this latitude band made in 2009 at a single zenith angle of 23deg, spanning the wavelength range 0.8-1.8 micrometers (Irwin et al., 2015, 10.1016j.icarus.2014.12.020). We find that the HST observations, and the combined HSTVLTIRTF observations at all locations are well modelled with a three-component cloud comprised of: 1) a vertically thin, but optically thick deep tropospheric cloud at a pressure of approximately 2 bars; 2) a methane-ice cloud based at the methane-condensation level of approximately 1.23 bar, with variable vertical extent; and 3) a vertically extended tropospheric haze, also based at the methane-condensation level of 1.23 bar. We find that modelling both haze and tropospheric cloud with particles having an effective radius of approximately 0.1 micron provides a good fit the observations, although for the tropospheric cloud, particles with an effective radius as large as 1.0 micron provide a similarly good fit. We find that the particles in both the tropospheric cloud and haze are more scattering at short wave- lengths, giving them a blue color, but are more absorbing at longer wavelengths, especially for the tropospheric haze. We find that the spectra of the storm clouds are well modelled by localized thickening and vertical extension of the methane-ice cloud. For the particles in the storm clouds, which we assume to be composed of methane ice particles, we find that their mean radii must lie somewhere in the range 0. 1 1. 0 m. We find that the high clouds have low integrated opacity, and that streamers reminiscent of convective thunderstorm anvils are confined to levels deeper than 1 bar. These results argue against vigorous moist convective origins for the cloud features.

atmospheres↗
DOE OSTI2025

Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography

Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.

deep learning↗
NASA NTRS2003

On-board autonomous attitude maneuver planning for planetary spacecraft using genetic algorithms

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This paper presents an approach for attitude path planning that makes full use of a priori constraint knowledge and is computationally tractable enough to be executed on-board a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used 'as is' or as an initial solution to initialize additional deterministic optimization algorithms. A number of example simulations are presented including the case examples of a generic Europa Orbiter spacecraft in cruise as well as in orbit around Europa. The search times are typically on the order of minutes, thus demonstrating the viability of the presented approach. The results are applicable to all future deep space missions where greater spacecraft autonomy is required. In addition, onboard autonomous attitude planning greatly facilitates navigation and science observation planning, benefiting thus all missions to planet Earth as well.

genetic algorithm↗
DOE OSTI2021

Reversal of carbonate-silicate cation exchange in cold slabs in Earth’s lower mantle

The stable forms of carbon in Earth’s deep interior control storage and fluxes of carbon through the planet over geologic time, impacting the surface climate as well as carrying records of geologic processes in the form of diamond inclusions. However, current estimates of the distribution of carbon in Earth’s mantle are uncertain, due in part to limited understanding of the fate of carbonates through subduction, the main mechanism that transports carbon from Earth’s surface to its interior. Oxidized carbon carried by subduction has been found to reside in MgCO 3 throughout much of the mantle. Experiments in this study demonstrate that at deep mantle conditions MgCO 3 reacts with silicates to form CaCO 3 . In combination with previous work indicating that CaCO 3 is more stable than MgCO 3 under reducing conditions of Earth’s lowermost mantle, these observations allow us to predict that the signature of surface carbon reaching Earth’s lowermost mantle may include CaCO 3 .

58 GEOSCIENCES↗
NASA NTRS2006

Deep Reactive Ion Etching (DRIE) of High Aspect Ratio SiC Microstructures using a Time-Multiplexed Etch-Passivate Process

High aspect ratio silicon carbide (SiC) microstructures are needed for microengines and other harsh environment micro-electro-mechanical systems (MEMS). Previously, deep reactive ion etching (DRIE) of low aspect ratio (AR less than or = 1) deep (greater than 100 micron) trenches in SiC has been reported. However, existing DRIE processes for SiC are not well-suited for definition of high aspect ratio features because such simple etch-only processes provide insufficient control over sidewall roughness and slope. Therefore, we have investigated the use of a time-multiplexed etch-passivate (TMEP) process, which alternates etching with polymer passivation of the etch sidewalls. An optimized TMEP process was used to etch high aspect ratio (AR greater than 5) deep (less than 100 micron) trenches in 6H-SiC. Power MEMS structures (micro turbine blades) in 6H-SiC were also fabricated.

Evans, Laura J.↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗
DOE OSTI2022

Study Non-Conventional Deep Eutectic Electrolytes for Lithium Ion Batteries [Poster]

In the field of energy storage, electrolytes are the materials or liquids used to transport ions or redox active chemicals to facilitate an electrochemical process. While conventional electrolytes are generally inexpensive and work well with lithium-ion batteries, they have many shortfalls however, such as corrosion of the cell, flammability, toxicity, price, and incompatibility with other metals. Our work highlights the formation of a novel deep eutectic electrolyte (DEE) based on methyl carbamate and lithium salts. This DEE shows good cycling performance with the potential for low temperature operation. This study provides insights into a relatively unexplored field of non-conventional electrolyte systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Discovering Elusive Dynamics Across Frontiers (Final Technical Report)

Profound puzzles, such as the nature of dark matter, the origins of the electroweak scale, the mechanism behind the small neutrino mass, and the strong CP problem, suggested new physics beyond the Standard Model and drove the particle physics program in search of the associated new particles. Despite extensive searches, conventional realizations of new physics have not yet provided conclusive evidence. This raised the serious possibility that new dynamics might be more elusive, perhaps due to a richer gauge and matter structure than previously considered. Notably, the existence of dark matter and advancements in understanding the naturalness problem urged exploration into sectors with complex gauge and matter structures. Through experiments like the LHC, DUNE, and small-scale experiments, the robust US HEP program played a critical role in pursuing these well-motivated but under-explored scenarios for new physics. I explored these physics opportunities in depth, focusing on novel searches and significant improvements in parameter space coverage. The elusive dynamics revealed rich information about the underlying theory and were essential in identifying observable opportunities. Understanding the observable consequences required a deep comprehension of the theory, which I also developed. The proposal included essential components aimed at coherently increasing our knowledge in well-motivated elusive dynamics models. Through research on high-quality axions, composite neutrinos, the Higgs boson as a portal to hidden strong dynamics, and new scalar potentials to generate alternative electroweak phase transitions, I focused on identifying new signatures and parameter regions in plausible elusive dynamics models. The exploration emphasized generic possibilities motivated by broad classes of elusive dynamics models. These signatures were not effectively probed previously due to various challenges such as triggering, background suppression, or experimental design. My research involved close interaction with experimental colleagues to overcome these difficulties, leveraging new theoretical and experimental possibilities. These efforts included identifying new observables such as timing information and substructure in calorimetries, new multiple-hit techniques, new scattering events, and new resonance searches in liquid argon detectors. This work created a positive feedback loop: theory and experimental work inspired each other, revealing new exciting opportunities that supported both programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep mitigation of CO 2 and non-CO 2 greenhouse gases toward 1.5 °C and 2 °C futures

Stabilizing climate change well below 2 °C and towards 1.5 °C requires comprehensive mitigation of all greenhouse gases (GHG), including both CO 2 and non-CO 2 GHG emissions. Here we incorporate the latest global non-CO 2 emissions and mitigation data into a state-of-the-art integrated assessment model GCAM and examine 90 mitigation scenarios pairing different levels of CO 2 and non-CO 2 GHG abatement pathways. We estimate that when non-CO 2 mitigation contributions are not fully implemented, the timing of net-zero CO 2 must occur about two decades earlier. Conversely, comprehensive GHG abatement that fully integrates non-CO 2 mitigation measures in addition to a net-zero CO 2 commitment can help achieve 1.5 °C stabilization. While decarbonization-driven fuel switching mainly reduces non-CO 2 emissions from fuel extraction and end use, targeted non-CO 2 mitigation measures can significantly reduce fluorinated gas emissions from industrial processes and cooling sectors. Our integrated modeling provides direct insights in how system-wide all GHG mitigation can affect the timing of net-zero CO 2 for 1.5 °C and 2 °C climate change scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurement of a radial flow profile with eddy current flow meters and deep neural networks

Eddy current flow meters (ECFMs) measure flows of conductive fluids. Recent interest in ECFMs has increased due to applications in advanced nuclear reactors. ECFMs are well suited for such applications, as they can provide non-invasive measurements of flow in fluids that are often difficult to measure. Traditionally, ECFMs are operated using an alternating current at a single frequency, limiting ECFMs to measure average fluid velocities, blockages, or voids. Here, we expand the capabilities of ECFMs by measuring the fluid radial velocity profile of liquid mercury. To accomplish this, we made several ECFM sensitivity measurements at a range of frequencies. Different frequencies vary the electromagnetic skin depth of the device. By adjusting frequencies, we probed the fluid velocity at various radial locations and constructed a flow-velocity profile. The relationship between the ECFM measurements and velocity profile is nonlinear and requires solving an inverse problem. Using electromagnetic finite-element simulations to train a deep neural network (DNN), we created a model that provides a stable general relationship between the sensitivity measurements of an ECFM and the fluid velocity profile. Using ECFM measurements of liquid mercury, our DNN model calculates a flow profile that agrees well with computational fluid dynamics (CFD) simulations. This technique has potential to improve flow monitoring for optimization, safe operation of conductive fluid loops, and/or validating complex CFD models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A strainmeter array as the fulcrum of novel observatory sites along the Alto Tiberina Near Fault Observatory

Fault slip is a complex natural phenomenon involving multiple spatiotemporal scales from seconds to days to weeks. To understand the physical and chemical processes responsible for the full fault slip spectrum, a multidisciplinary approach is highly recommended. The Near Fault Observatories (NFOs) aim at providing high-precision and spatiotemporally dense multidisciplinary near-fault data, enabling the generation of new original observations and innovative scientific products. The Alto Tiberina Near Fault Observatory is a permanent monitoring infrastructure established around the Alto Tiberina fault (ATF), a 60 km long low-angle normal fault (mean dip 20°), located along a sector of the Northern Apennines (central Italy) undergoing an extension at a rate of about 3 mm yr –1 . The presence of repeating earthquakes on the ATF and a steep gradient in crustal velocities measured across the ATF by GNSS stations suggest large and deep (5–12 km) portions of the ATF undergoing aseismic creep. Both laboratory and theoretical studies indicate that any given patch of a fault can creep, nucleate slow earthquakes, and host large earthquakes, as also documented in nature for certain ruptures (e.g., Iquique in 2014, Tōhoku in 2011, and Parkfield in 2004). Nonetheless, how a fault patch switches from one mode of slip to another, as well as the interaction between creep, slow slip, and regular earthquakes, is still poorly documented by near-field observation. With the strainmeter array along the Alto Tiberina fault system (STAR) project, we build a series of six geophysical observatory sites consisting of 80–160 m deep vertical boreholes instrumented with strainmeters and seismometers as well as meteorological and GNSS antennas and additional seismometers at the surface. By covering the portions of the ATF that exhibits repeated earthquakes at shallow depth (above 4 km) with these new observatory sites, we aim to collect unique open-access data to answer fundamental questions about the relationship between creep, slow slip, dynamic earthquake rupture, and tectonic faulting.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Assessing the Variability of Radiation, Water and Energy in the Deep Tropics Over the Last 3 Decades

Global surface energy closure and its variability depends heavily upon the surface radiative energy budget in the deep tropics. A key process observed is the change in convection regimes from disorganized convection to organized deep convection. During these periods an imbalance is observed between energy and water fluxes in terms of energy closure in some atmospheric reanalysis systems, implying that processes are not simulated well. A recent paper by Hsaio et al (2023, pre-published version) found that the transition process has wind shear and longwave cloud radiative feedback signatures. In this presentation, we assess the surface radiative budget in the deep tropics by comparing multiple data products (GEWEX SRB Rel4IP, CERES Ed4.1, ISSCP FH, etc.) and describing the variability across the deep tropics for the period from 1988 to near present. We assess this variability of the radiative flux anomalies (including the net TOA, surface and the atmospheric divergence fluxes) against water vapor divergence, cloud properties (include ISCCP “Weather States, Tselioudis et al. 2021) and larger scale wind shear. We include further analysis contrasting 6 key tropical oceanic regions (Indian Ocean east and west, tropical western, central and eastern Pacific Ocean, and Tropical Atlantic Ocean) and the sensitivity of the fluxes as a function of fluctuations in cloud types in response to various larger scale atmospheric processes (e.g. El Niño, Indian Ocean Dipole). These are contrasted against the modeled radiative fluxes from ERA-5 and MERRA-2. Ocean buoy measurements of radiative fluxes are utilized to help assess data radiative flux uncertainty over the nearly 40 year period. In general, the variability for overlap periods of these various data products agrees well, but there are significant differences in the net fluxes that vary according to the rendering of surface, atmospheric, aerosol and cloud properties. We conclude with recommendations for continuing work.

Radiation budget↗
DOE OSTI2024

Techno-economic feasibility of geothermal energy production using inactive oil and gas wells for district heating and cooling systems in Tuttle, Oklahoma

Oil and gas wells have been drilled in the United States and often encounter hot co-produced water possibly suitable for geothermal direct-use applications. Here this study evaluated geothermal resources, heating and cooling demand, and techno-economic potential of four oil and gas wells repurposed for geothermal energy production to serve two public schools and 250 nearby houses in Tuttle, Oklahoma, USA. Subsurface geology in the study area primarily consists of Permian to Mississippian sandstone and limestone formations approximately from 2 km to 3.5 km depth. With a geothermal gradient of 25.3 °C/km, the temperature of geothermal energy production was estimated as 68.2 °C at 2.1 km. The potential of calcite scaling within boreholes and pipes was minimal. Using the characterized reservoir properties, techno-economic analyses were conducted for evaluating levelized costs of geothermal energy production for direct-use heating and cooling and district heating systems with five different production scenarios. Heating and cooling demand in two schools and 250 houses and additional 0.3 MW and 0.6 MW base heating loads for potential geothermal direct-use applications in the study area were also incorporated in the modeling. Results indicated that the levelized cost of heating of geothermal district heating systems utilizing existing wells were significantly lower than those of conventional geothermal energy systems that include well drillings. The geothermal energy production with doublet and quartet configurations was generally sufficient to supply total annual heating demand in the district, while a peaking boiler was used to supply peak loads. Doublet configurations showed higher production temperature with a lower thermal drawdown than the quartet configurations. The doublet system at 2.1 km production depth with 9.3 kg/s flow rate represented the optimal levelized cost of $71/MWh and 91.2% utilization factor. These results imply that the Tuttle geothermal district heating system is techno-economically feasible and comparable to geothermal district heating systems in the United States and the regional natural gas rate for residentials by recycling inactive oil and gas wells with no drillings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Deep-learning-based domain adaptation for cavity fault prediction at Jefferson Laboratory

Superconducting radio-frequency (SRF) cavities are the core components of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab, providing high-power electron beams for nuclear physics experiments. The facility comprises 418 SRF cavities, and any fault in these cavities can lead to interruptions in the electron beam supply. Cavity faults are the leading cause of beam trips in CEBAF. Predicting and mitigating those faults before onset can help maintain normal operation. Existing models face challenges in distinguishing between normal and fault signals when changes occur in the underlying time-series data, from changes in control software, operational parameters, or the environment. This work proposes a deep learning domain adaptation model that leverages transfer learning to address fault prediction challenges by improving accuracy. The model is trained and fine-tuned using a dataset collected for faulty and normal operation using a data acquisition system in CEBAF. Our deep learning-based domain adaptation model achieves a prediction accuracy of 89.61% of the fault and normal signals. The developed model effectively predicts normal running signals compared to the baseline approach without domain adaptation. This capacity is essential for the fault prediction task in the CEBAF because of heavily imbalanced data containing vast amounts of normal signals. The model performs well for predicting faults several hundred milliseconds before the fault onset compared to other models where no adaptation is applied. Incorporating deep learning-based domain adaptation techniques will significantly improve the fault prediction performance.

Rahman, Md Monibor [Old Dominion Univ., Norfolk, V↗
NASA NTRS2016

The Spectacular Radio-Near-IR-X-Ray Jet of 3C 111: the X-Ray Emission Mechanism and Jet Kinematics

Relativistic jets are the most energetic manifestation of the active galactic nucleus (AGN) phenomenon. AGN jets are observed from the radio through gamma-rays and carry copious amounts of matter and energy from the subparsec central regions out to the kiloparsec and often megaparsec scale galaxy and cluster environs. While most spatially resolved jets are seen in the radio, an increasing number have been discovered to emit in the optical/near- IR and/or X-ray bands. Here we discuss a spectacular example of this class, the 3C 111 jet, housed in one of the nearest, double-lobed FR II radio galaxies known. We discuss new, deep Chandra and Hubble Space Telescope (HST) observations that reveal both near-IR and X-ray emission from several components of the 3C 111 jet, as well as both the northern and southern hotspots. Important differences are seen between the morphologies in the radio, X-ray, and near-IR bands. The long (over 100 kpc on each side), straight nature of this jet makes it an excellent prototype for future, deep observations, as it is one of the longest such features seen in the radio, near-IR/optical, and X-ray bands. Several independent lines of evidence, including the X-ray and broadband spectral shape as well as the implied velocity of the approaching hotspot, lead us to strongly disfavor the EC/CMB model and instead favor a two-component synchrotron model to explain the observed X-ray emission for several jet components. Future observations with NuSTAR, HST, and Chandra will allow us to further constrain the emission mechanisms.

galaxies: active – galaxies: jets – radio cont↗