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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Uniform Recalibration of Common Spectrophotometry Standard Stars onto the CALSPEC System Using the SuperNova Integral Field Spectrograph

Abstract We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Pawar, Nikhil M. (ORCID:0000000211613289)↗
DOE OSTI2021

J-GEM optical and near-infrared follow-up of gravitational wave events during LIGO’s and Virgo’s third observing run

The Laser Interferometer Gravitational-wave Observatory Scientific Collaboration and Virgo Collaboration (LVC) sent out 56 gravitational-wave (GW) notices during the third observing run (O3). The Japanese Collaboration for Gravitational wave ElectroMagnetic follow-up (J-GEM) performed optical and near-infrared observations to identify and observe an electromagnetic (EM) counterpart. We constructed a web-based system that enabled us to obtain and share information on candidate host galaxies for the counterpart, and the status of our observations. Candidate host galaxies were selected from the GLADE catalog with a weight based on the 3D GW localization map provided by LVC. We conducted galaxy-targeted and wide-field blind surveys, real-time data analysis, and visual inspection of observed galaxies. We performed galaxy-targeted follow-ups to 23 GW events during O3, and the maximum probability covered by our observations reached 9.8%. Among these, we successfully started observations for 10 GW events within 0.5 days after the detection. This result demonstrates that our follow-up observation has the potential to constrain EM radiation models for a merger of binary neutron stars at a distance of up to ~100 Mpc with a probability area of ≤ 500 deg 2 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machined phononic crystals to block high-order Lamb waves and crosstalk in through-metal ultrasonic communication systems

For systems that require complete metallic enclosures (e.g., containment buildings for nuclear reactors), it is impossible to access interior sensors and equipment using standard electromagnetic techniques. A viable way to communicate and supply power through metallic barriers is the use of elastic waves and ultrasonic transducers, introducing several design challenges that must be addressed. Specifically, the use of multiple communication channels on the same enclosure introduces an additional mechanism for signal crosstalk between channels: guided waves propagating in the barrier between channels. This work numerically and experimentally investigates a machined phononic crystal to block MHz Lamb wave propagation between ultrasonic communication channels, greatly reducing wave propagation and the resulting crosstalk voltage. Blind grooves are machined into one or both sides of a metallic barrier to introduce a periodic unit cell, greatly altering the guided wave dispersion in the barrier. Numerical simulations are used to determine a set of groove geometries for testing, and experiments were performed to characterize the wave-blocking performance of each design. The best-performing design was tested using piezoelectric transducers bonded to the barrier, showing a 14.4 dB reduction in crosstalk voltage. Overall, the proposed periodic grooving method is a promising technique for completely isolating ultrasonic power/data transfer systems operating in a narrow frequency range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Application of artificial neural network to prompt gamma neutron activation analysis for chemical warfare agents identification

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with a probabilistic decision tree (PDT). The effectiveness of the chemical identification algorithm is determined by the availability of a wide range of data to train the algorithm to identify chemical-fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to leverage such a database with the Artificial Neural Network (ANN) technique. Therefore, an ANN-based chemical identification algorithm was developed as an independent verification of the current algorithm. The ANN-based algorithm’s performance was evaluated against the U.S. Army blind test data, and results were presented and discussed in this study.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Towards a predictive, physics-based friction model for the dynamics of jointed structures

Bolted connections are ubiquitous in mechanical designs and pose a significant challenge to understanding and predicting the vibration response of assembled structures. The present paper develops a physics-based rough contact model of the frictional interactions within a joint. This model sums over the probable interactions of asperities – defined as locally maximum surface features – to determine the contact forces. Here, the tangential contact forces vary smoothly between sticking and slipping and allow the model to better capture the qualitative trends of experimental amplitude dependent frequency and damping than previous studies. Furthermore, the novel model is generalized to allow for arbitrarily varying normal pressure including potential separation to better represent the interfacial dynamics. This includes developing a new, computationally tractable approximation to the analytical Mindlin partial slip solution for tangential loading of contacting spheres. Further, the results highlight the importance of accurately characterizing the as-built topology of the interface, the plastic behavior of the contacting asperities, the relevant length scale of asperities, and the eccentricity of asperities. A predictive friction coefficient based on plasticity provides a poor match to experiments, so fitting the friction coefficient is also considered. Numerical results are compared to experiments on the Brake-Reuß Beam to assess the predictive potential of the models. While blind predictions over-predict the slip limit, the current model presents a significant improvement in physics-based modeling and highlights areas for ongoing research.

42 ENGINEERING↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ultrafast x-ray imaging of coherently controlled molecular dynamics in real space and time

Coherent control aims to manipulate chemical processes on the latent length and timescales of atoms and bonds, scales that are intrinsic to molecular dynamics but not directly resolved by most experimental probes. As a result, existing coherent-control experiments, which overwhelmingly rely on spectroscopic observables, leave a crucial blind spot: the direct, real-space recovery of all atomic and molecular rearrangements in response to coherently controlled excitations. Here, we overcome this limitation by integrating a Tannor-Kosloff-Rice pump-control-probe scheme with ultrafast X-ray scattering to capture snapshots of the wavepacket motion in a benchmark molecular system. We demonstrate this by photoexciting diatomic iodine vapor with a visible pump pulse, selectively steering the wavepacket toward ground-state recombination or dissociative pathways with a time-delayed near-IR control pulse, and recording the dynamics with angstrom and femtosecond precision with an ultrashort hard X-ray probe pulse. By comparing these structural observations with numerical solutions of the time-dependent Schrödinger equation, we reveal how coherent control actively reshapes the molecular charge density distribution. Our results pave the way for leveraging structural feedback as a control handle and provide a fundamental microscopic visualization of quantum decoherence and energy redistribution at the atomic level.

Hopper, Thomas R. [SLAC National Accelerator Labor↗
DOE OSTI2023

Younger facial looks are associate with a lower likelihood of several age-related morbidities in the middle-aged to elderly

Abstract Background Looking older for one’s chronological age is associated with a higher mortality rate. Yet it remains unclear how perceived facial age relates to morbidity and the degree to which facial ageing reflects systemic ageing of the human body. Objectives To investigate the association between ΔPA and age-related morbidities of different organ systems, where ΔPA represents the difference between perceived age (PA) and chronological age. Methods We performed a cross-sectional analysis on data from the Rotterdam Study, a population-based cohort study in the Netherlands. High-resolution facial photographs of 2679 men and women aged 51.5–87.8 years of European descent were used to assess PA. PA was estimated and scored in 5-year categories using these photographs by a panel of men and women who were blinded for chronological age and medical history. A linear mixed model was used to generate the mean PAs. The difference between the mean PA and chronological age was calculated (ΔPA), where a higher (positive) ΔPA means that the person looks younger for their age and a lower (negative) ΔPA that the person looks older. ΔPA was tested as a continuous variable for association with ageing-related morbidities including cardiovascular, pulmonary, ophthalmological, neurocognitive, renal, skeletal and auditory morbidities in separate regression analyses, adjusted for age and sex (model 1) and additionally for body mass index, smoking and sun exposure (model 2). Results We observed 5-year higher ΔPA (i.e. looking younger by 5 years for one’s age) to be associated with less osteoporosis [odds ratio (OR) 0.76, 95% confidence interval (CI) 0.62–0.93], less chronic obstructive pulmonary disease (OR 0.85, 95% CI 0.77–0.95), less age-related hearing loss (model 2; B = −0.76, 95% CI −1.35 to −0.17) and fewer cataracts (OR 0.84, 95% CI 0.73–0.97), but with better global cognitive functioning (g-factor; model 2; B = 0.07, 95% CI 0.04–0.10). Conclusions PA is associated with multiple morbidities and better cognitive function, suggesting that systemic ageing and cognitive ageing are, to an extent, externally visible in the human face.

Mekić, Selma↗
DOE OSTI2023

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

PV Performance Modeling and Stakeholder Engagement (Final Technical Report)

This core capability project’s objective is to increase the value of photovoltaic (PV) performance models by improving their functionality, demonstrating, and quantifying their validity, and offering a wide range of stakeholder engagement opportunities. In FY22-24, we developed new and improved modeling algorithms and functions to represent PV performance more accurately in a variety of environments and conditions. The “Model parameter toolkit” was developed and includes functions to translate between different module temperature models, incidence angle modifier models, and single-diode models. A new modeling capability named “PV Atlas” was also developed leveraging Sandia’s High Performance Computing resources. This capability allows us to investigate several questions and provide climate-specific best practices and geographic data files; all these are hosted on an interactive website on Sandia’s GitHub and can be used for training, system optimization, or to provide best practices for uncertainty reduction. For model validation, we published high-quality PV performance, and weather data; these data are well documented, filtered, and processed for quality and include examples on how to run PV simulations. We also developed well documented, standardized methods for validating PV models and ran independent model validation and 2 blind modeling intercomparisons engaging with 49 organizations from 17 countries. We co-led and contributed to a growing, well documented and maintained suite of open-source functions for PV modeling (i.e., the pvlib-python) and we outreached to the PV modeling stakeholders via the PVPMC workshops and web resources. In addition, this project supported US representation and leadership for the International Energy Agency (IEA) PVPS Task 13; specifically, members of our team led and supported 3 subtasks on: 1) Best practices for the optimization of bifacial photovoltaic tracking, 2) Extreme weather events and their multiple impact on PV power plants: Risks, failure mechanisms and mitigation strategies, and 3) Best practice guidelines for the use of economic and technical Key Performance Indicators (KPIs). This project resulted in the publications of 14 peer reviewed journal papers, 37 conference presentations, 6 SAND reports, 5 public datasets and 6 new webpages on the PVPMC website. It supported the release of 13 pvlib-python versions where 28 enhancements were from this PV Performance Modeling project. We co-organized 5 PVPMC workshops in FY22-24 with the participation of 214 unique institutions and around 700 participants. The PVPMC website was redesigned, and its reliability was improved; it receives over 50,000 visitors/year from 202 unique countries.

14 SOLAR ENERGY↗
DOE OSTI2022

AIS-based characterization of navigation conflicts along the US Atlantic Coast prior to development of wind energy

This study characterizes navigation conflicts in a region with a large traffic volume along the US Atlantic Coast, utilizing Automated Identification System (AIS) data for 2010. The region includes areas proposed for wind energy development. The characterization could be useful in evaluating the effect of offshore wind areas on navigation conflicts. The study processes the AIS data to provide pairwise comparisons of vessel interactions (encounters and near-misses) as they occurred. Using the vessel encounter data, analyses are made using a ‘blind’ vessel assumption to evaluate the potential for both near-misses and collisions. Then statistical analyses are made to estimate the point values and uncertainty for each type of encounter (crossing, head-on, overtaking). Examination of the frequency/number of collisions from actual observations is made. The examination of actual near-misses, potential near-misses, and potential collisions provides comparable results in the number of near-misses and collisions. The potential near-miss analyses include an examination of the timing of responses made by vessels to prevent near-misses. This informed the statistical analysis but may also have utility in the simulation of navigation conflicts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Allometric Trophic Networks From Individuals to Socio-Ecosystems: Consumer–Resource Theory of the Ecological Elephant in the Room

A well-known parable is that of the blind men studying an elephant each of which assert the elephant is the part they first hold in their hands, e.g., “rope!” says the tail holder while the leg holder asserts “tree!” The various subdisciplines of ecology appear similar in that we each engage in our enthusiastic but at least somewhat myopic study with remarkably limited agreement or even discussion about the overall system which we all study. Allometric trophic network (ATN) theory offers a path out of this dilemma by integrating across scales, taxa, habitats and organizational levels from physiology to ecosystems based on consumer-resource interactions among co-existing organisms. The network architecture and the metabolic and behavioral processes that determine the structure and dynamics of these interactions form the first principles of ATN theory, which in turn provides a synthetic overview and powerfully predictive framework for ecology from organisms to ecosystems. Beyond ecology, ATN theory also synthesizes eco-evolutionary and socio-ecological research still largely based on consumer-resource mechanisms but respectively integrated with different processes including natural selection and market mechanisms. This paper briefly describes foundations, advances, and future directions of ATN theory including predicting an ecosystem’s phenotype from its community’s genotype in order to accelerate more predictive and unified understanding of the complex systems studied by ecologists and other environmental scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hawaii Play Fairway Analysis: Noble Gas Raw Data for Hawaii, Maui, Oahu, Kauai, and Lanai islands

Noble gas raw data for the Hawaiian islands of Big Island, Maui, Oahu, Lanai, and Kauai. Based on results from prior phases of the Hawaii Play Fairway Analysis, this project targeted 66 wells on the islands of Hawaii, Maui, Lanai, Oahu, and Kauai for sampling of dissolved noble gases, trace metals, common ions, and the stable isotopes 2H and 18O. Ultimately, 23 of the 66 well targets were sampled. Noble gas data from this study is supplemented with data shared by the United States Geologic Survey for the summit of Kilauea, and by the geothermal energy company Ormat Technologies Inc. for their geothermal power plant Puna Geothermal Venture on the Lower East Rift of Kilauea, and for their exploration of Kona and Hualalai on Hawaii, as well as the Southwest Rift of Haleakala on Maui. The noble gas helium is used as an indicator of geothermal heat when excess 3He and/or 4He is present when compared to the atmospheric ratio of those isotopes (R/Ra). R/Ra is minimally affected by dilution and transport, allowing even those wells not perfectly situated over a geothermal system to indicate a geothermal anomaly. R/Ra anomalies are present on every island in this study. There is a strong correlation between R/Ra anomalies and proximity to rift zones and calderas. The Hawaii Play Fairway project was funded by the U.S. Department of Energy Geothermal Technologies Office (award DE-EE0006729). For more information, see Colin Ferguson's Master of Science thesis "Exploration for Blind Geothermal Resources in the State of Hawaii Utilizing Dissolved Noble Gasses in Well Waters."

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Searches for Extremely Metal-poor Galaxies Using Arecibo Legacy Fast ALFA–Selected Dwarf Galaxies

We present a study of nearby dwarf galaxies selected from the Arecibo Legacy Fast ALFA (ALFALFA) blind H I survey. A primary goal of the project was to utilize a nonstandard selection method with the hope of detecting previously unrecognized extremely metal-poor (XMP) galaxies. The study was motivated by the recent discovery of two XMP galaxies, Leo P and Leoncino, which were both originally found via the ALFALFA survey. We have obtained narrowband H α images for 42 dwarf systems, many of which are located in the Local Void in front of the Pisces–Perseus Supercluster. Spectra for 11 of the best candidates resulted in the determination of metal abundances for 10 of the systems. None were found to be extremely metal-poor, although one system (AGC 123350) was found to have an oxygen abundance of log(O/H)+12 = 7.46, or ~6% solar. One of the galaxies in our sample exhibits a high oxygen abundance for its luminosity, suggesting the possibility that it may have a tidal origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Uncertainty estimation of bifurcated solutions in the Rayleigh–Bénard problem for advanced nuclear reactors applications

Multiphysics models of nuclear reactors frequently comprise nonlinear systems of equations. The nonlinear nature of these models could lead to solution bifurcations, where a small change in a certain parameter, e.g., the thermophysical properties of the coolant, can lead to a sudden change in the system’s behavior. At the point in parameter space where this happens, called a critical point, the Jacobian matrix of the model’s nonlinear operator becomes singular potentially permitting multiple solutions to coexist. In this paper, we perform uncertainty estimation (UE) in a parameter range that includes bifurcated solutions within the context of Rayleigh–Bénard problem. We perform this analysis assuming uncertain temperature difference, and tilt angle for the iterative solution algorithm with a unit Prandtl number (Pr = 1). Also, we perform this analysis under uncertain thermophysical properties for both FLiBe molten salt and liquid sodium as working fluid. We deploy two approaches to compute statistical moments for the resulting distributions of selected flow-field variables. The first approach is the blind computation of the mean and the standard deviation without any consideration of solution bifurcation, while the second approach utilizes k-means clustering to cluster each branch’s solutions together and compute separate statistical moments for each branch. The statistical distributions are obtained by perturbing the selected parameters about nominal values that correspond to a solution on one of the valid branches, and that solution is used as initial guess for the iterative solution algorithm. We found that perturbation of any parameter when its nominal value is close to its critical point always leads to branch jumping, i.e., the iterations converge to a solution on a branch different from the branch of the initial guess. This produces a statistical ensemble comprised of fundamentally different solutions leading to wrong mean values and uncertainty estimates, whereas clustering provides an efficient way to deal with this type of computation. This work is important for developing Gen IV nuclear systems because many of these systems rely on natural convection for cooling especially in accident conditions.

97 - MATHEMATICS AND COMPUTING↗