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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Featureless Infrared Transmission Spectrum for the Super-puff Planet Kepler-79d

Extremely low-density planets (“super-puffs”) are a small but intriguing subset of the transiting planet population. With masses in the super-Earth range (1 − 10 M{sub ⊕}) and radii akin to those of giant planets (> 4 R{sub ⊕}), their large envelopes may have been accreted beyond the water snow line and many appear to be susceptible to catastrophic mass loss. Both the presence of water and the importance of mass loss can be explored using transmission spectroscopy. Here, we present new Hubble space telescope WFC3 spectroscopy and updated Kepler transit depth measurements for the super-puff Kepler-79d. We do not detect any molecular absorption features in the 1.1 − 1.7 μm WFC3 bandpass, and the combined Kepler and WFC3 data are consistent with a flat-line model, indicating the presence of aerosols in the atmosphere. We compare the shape of Kepler-79d’s transmission spectrum to predictions from a microphysical haze model that incorporates an outward particle flux due to ongoing mass loss. We find that photochemical hazes offer an attractive explanation for the observed properties of super-puffs like Kepler-79d, as they simultaneously render the near-infrared spectrum featureless and reduce the inferred envelope mass-loss rate by moving the measured radius (optical depth unity surface during transit) to lower pressures. We revisit the broader question of mass-loss rates for super-puffs and find that the age estimates and mass-loss rates for the majority of super-puffs can be reconciled if hazes move the photosphere from the typically assumed pressure of ∼10 mbar to ∼10 μbar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2023

EVs@Scale Next-Gen Profiles - EV Profile Capture 2023

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. Under nominal conditions, most EVs can achieve the original equipment manufacturer (OEM) rated peak-power and charge times. Peak power across EVs has variance due to the vehicle design, battery topology, charging strategy, etc. These 10-100% nominal preconditioned charge profiles across 13 EVs (11 light-duty (LD), 2 heavy-duty (HD)) were used for analysis in power curve analysis, power distribution, SOC and range comparison, thermal impacts of current draw, battery pack size and energy charged, and ramp rates. Power curve analysis show the uniqueness in power vs time curves across all EVs, breaking down the features of a typical profile and where variation is typically seen. Power distribution results showed that over 50% of charge time is spent below 50kW and only 12.1% is spent above 200kW. SOC and range performance yielded different top performing EVs when exploring goals of performance from SOC and range gained after 10-min (EV2) and 20-min (EV8), and time-to-achieve 80% SOC (EV8) and 200 miles of range (EV1). Current draw from 400-volt EVs had a higher thermal impact to cable/connector temperatures when compared to 800-volt EVs, due to a higher current requirement to achieve similar power levels. There was high variance in C rating, a useful metric when comparing the relationship between peak/average charge session power and relative battery pack size, across EVs under test. Ramp rates during initial power transfer were examined, fastest and slowest speeds ranging from 192.5kW/second to 2.6kW/second respectively. A similar analysis of ramp rates was also conducted for OCPP curtailment testing, where EVs underwent a 2-minute 65A curtailment request before returning to full-power charge. Under off-nominal conditions, most EVs experienced variation from the nominal condition profiles. EVs that underwent the full set of NGP defined testing conditions were compared, analysis of which was categorized by 800-volt and 400-volt EVs. It should be noted that the 800-volt EVs had significantly higher peak power ratings than the 400-volt EVs, and thus were more prone to variance. Initial state of charge, battery temperature, and vehicle condition had a considerable impact on peak power levels and charge time for the 800-volt vehicles under test. EVSE limited tests lowered achievable power down to 200kW, greatly impacted 800-volt charging power, and had little to no effect on 400-volt charging power. Adapter testing was performed on a single 400-volt EV, where power and current limitations were found but overall charge time was not significantly impacted. Adapter and boost converter testing was performed on a single 800-volt EV, where both charge power and charge time were greatly impacted. Charge profiles are unique, and comparing such requires analysis that examines starting conditions, vehicle and battery topologies,

Charging↗
DOE OSTI2020

Strategies for the Photoreduction of Tc-99 Pertechnetate to Low-Valent Tc by Keggin Polyoxometalates

Technetium-99 (half-life of 2.1 × 10 5 yrs, β max = 0.29 MeV) is a hazardous radiological contaminant, which, in its predominant form of pertechnetate (TcO 4 – ), is highly mobile in the environment. Most strategies for the removal of pertechnetate from the environment involve uptake and/or absorption of pertechnetate using resins, clays, cationic metal-organic frameworks and even thorium borate ceramic like materials. Alternative approaches have involved the reduction and subsequent sequestration of lower valent technetium species using iron, sulfides, or iron sulfides. Here, our lab has explored this strategy using the lacunary alpha-2 Wells–Dawson polyoxometalate (α 2 -[P 2 W 17 O 61 ] 10– ) to both reduce and sequester lower valent technetium, and we have reported on the ligand features that stabilize the reduced species. In this work we investigate the potential of “plenary” Keggin POMs (XW 12 O 40 n – ) (X = P, Si, Al, n = 3, 4, 5, respectively) to both reduce TcO 4 – and stabilize the reduced Tc species. Specifically, we report on the mechanism by which the reduction of technetium occurs and find that PW 12 , SiW 12 , and AlW 12 promote the reduction of TcO 4 – to lower valent states. X-ray absorption spectroscopy was used to confirm a combination of Tc IV {in the form of TcO 2 · 2H 2 O and Tc 2 (µ-O) 2 4+ } and Tc V , which is subsequently complexed into a POM defect as a Tc V =O species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

On rational points in CFT moduli spaces

Motivated by the search for rational points in moduli spaces of two-dimensional conformal field theories, we investigate how points with enhanced symmetry algebras are distributed there. We first study the bosonic sigma-model with S 1 target space in detail and uncover hitherto unknown features. We find for instance that the vanishing of the twist gap, though true for the S 1 example, does not automatically follow from enhanced symmetry points being dense in the moduli space. We then explore the supersymmetric sigma-model on K3 by perturbing away from the torus orbifold locus. Though we do not reach a definite conclusion on the distribution of enhanced symmetry points in the K3 moduli space, we make several observations on how chiral currents can emerge and disappear under conformal perturbation theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Current challenges and potential directions towards precision microscale additive manufacturing – Part III: Energy induced deposition and hybrid electrochemical processes

The Part III of the four-part series of articles discusses the challenges and opportunities in microscale additive manufacturing processes, specifically focusing on energy-induced deposition and electrochemical processes. Compared to the direct ink write (DIW) and laser-based processes, the energy-induced deposition methods can fabricate high-resolution, high aspect ratio and complex parts, while the hybrid electrochemical process can be used to fabricate complex parts using a wide range of conductive and photoactive materials. However, the volumetric throughput of these processes is lower than their DIW and laser-based counterparts. The processes that have been explored in this process are Focused-ion Beam Induced Deposition (FIBID), Laser Chemical Vapor Deposition (LCVD), Menicus-confined Electrodeposition (MCED) and Laser-Enabled Electrochemical Printing (LECP). The range of processable materials, feature-size resolution, geometry and volumetric throughput are used as factors to evaluate the current state-of-the-art for these processes. Finally, novel approaches have been proposed in the article to address these challenges associated with microscale AM processes.

42 ENGINEERING↗
DOE OSTI2022

Analysis of diverse double-strand break synapsis with Polλ reveals basis for unique substrate specificity in nonhomologous end-joining

DNA double-strand breaks (DSBs) threaten genomic stability, since their persistence can lead to loss of critical genetic information, chromosomal translocations or rearrangements, and cell death. DSBs can be repaired through the nonhomologous end-joining pathway (NHEJ), which processes and ligates DNA ends efficiently to prevent or minimize sequence loss. Polymerase λ (Polλ), one of the Family X polymerases, fills sequence gaps of DSB substrates with a strict specificity for a base-paired primer terminus. There is little information regarding Polλ’s approach to engaging such substrates. We used in vitro polymerization and cell-based NHEJ assays to explore the contributions of conserved loop regions toward DSB substrate specificity and utilization. In addition, we present multiple crystal structures of Polλ in synapsis with varying biologically relevant DSB end configurations, revealing how key structural features and hydrogen bonding networks work in concert to stabilize these tenuous, potentially cytotoxic DNA lesions during NHEJ.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fine-scale vegetation composition and structure shape spatiotemporal variation in surface albedo across a low Arctic tundra landscape

The unprecedented rate of warming in the Arctic is driving changes in the structure and composition of tundra vegetation. Increases in deciduous tall shrub cover, height, and density are of particular concern, as these changes alter local surface albedo in ways that could amplify effects on the regional surface energy budget (SEB). Despite this importance, significant uncertainties remain in understanding the interplay between fine-scale vegetation patterns and emergent albedo dynamics across space and time. Here, we address these uncertainties by (1) quantifying spatiotemporal variation in surface shortwave albedo and (2) determining the relative influence of fine-scale vegetation composition, structure, and environmental conditions on albedo across a representative low-Arctic tundra landscape on Alaska’s Seward Peninsula. To do this, we synthesized multi-scale, multi-platform remote sensing observations, including a novel Landsat-derived albedo time series, a fine-scale map of Arctic plant functional type (PFT) fractional cover, and airborne LiDAR estimates of canopy height and topography. We show that there are substantial reductions in winter albedo for pixels dominated by tall, woody PFTs (28.13%) relative to pixels dominated by non-woody vegetation, but almost no change in summer albedo (3% increase). Further, we identified a unimodal trend in the relationship between canopy height and the timing of the springtime transition from high (snowy) to low (leafy) albedo (peak at 5.5 m), possibly because of competing ‘snow-fence’ and ‘protrusion’ snow-shrub interactions. To explore the primary drivers of albedo, we constructed a random forest model and found that canopy height and the fractional cover of woody PFTs were as- or more important predictors of winter albedo than topographic features. These findings provide strong evidence for the impacts of local vegetation characteristics on regional surface albedo, highlighting the need for better quantification of snow-shrub interactions to accurately predict the Arctic’s SEB under future environmental change.

Arctic↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging intermediate resonances to probe CP violation at colliders

We explore the phenomenological impact of interference in tree-level contributions to three-body final states in $2\rightarrow 3$ scattering processes. This work introduces a novel search strategy leveraging asymmetries to enable sensitivity to CP-violating effects in less well-explored regions of phase space. Analytically, we demonstrate the effectiveness of this observable in probing interference between Standard Model charged-current decays and effective left-handed vector interactions, illustrated in a toy model featuring a scalar leptoquark, $S_1 \sim (3, 1, -\,1/3)$. Numerically, we apply this framework to studying the process $pp\rightarrow b \tau \nu $; unlike traditional high-$p_T$ searches or “bump hunts”, this approach utilizes an intermediate energy regime – where new physics is neither light enough to be produced on shell or heavy enough to justify an effective field theory treatment. A proof-of-principle analysis at parton level demonstrates a percent-level asymmetry, with sensitivity also to BSM weak-CP phase. While the specific phase sensitivity is diminished at particle level due to showering and detector effects, a machine learning classifier can recover sensitively to the presence of SM-BSM interference, significantly outperforming standard analysis methods. Notably discrimination between BSM signal and SM background could be achieved at the 2$\sigma $ level for the current LHC dataset and 8$\sigma $ at the High-Luminosity LHC. Moreover, this asymmetry observable as defined can also be more broadly applied to other searches for CP-violation in $2\rightarrow 3$ processes in present and future collider environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Mesh Failure Prediction Using Deep Learning Techniques

The goal of this joint effort between SRI and LLNL has been to investigate the use of state-of-the-art deep learning architectures to effectively explore the correlations between a set of mesh zone attributes and manifestations of failure in a given mesh simulation and draw useful conclusions towards a practical failure detection system. Each zone attribute is a scalar feature that is recorded separately for each zone during simulation, resulting in a multivariate time series signal for each zone. Currently, these features are used to manually decide where to apply manual relaxations so that simulations run without any failures. Our long-term vision is to build a machine learning system that is capable of reliably detecting upcoming failures and is able to apply finer-grained targeted relaxations automatically to relevant zones to avoid an expected failure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

LocOO3D User's Manual

LocOO3D is a software tool that computes geographical locations for seismic events at regional to global scales. This software has a rich set of features, including the ability to use custom 3D velocity models, correlated observations and master event locations. The LocOO3D software is especially useful for research related to seismic monitoring applications, since it allows users to easily explore a variety of location methods and scenarios and is compatible with the CSS3.0 data format used in monitoring applications. The LocOO3D software, User's Manual, and Examples are available on the web at: https://github.com/sandialabs/LocOO3D For additional information on GeoTess, SALSA3D, RSTT, and other related software, please see: https://github.com/sandialabs/GeoTessJava, www.sandia.gov/geotess, www.sandia.gov/salsa3d, and www.sandia.gov/rstt

58 GEOSCIENCES↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2024

Modeling Radiation-Induced Epithelial Cell Injury in Murine Three-Dimensional Esophageal Organoids

Esophageal squamous cell carcinoma (ESCC) is a deadly consequence of radiation exposure to the esophagus. ESCC arises from esophageal epithelial cells that undergo malignant transformation and features a perturbed squamous cell differentiation program. Understanding the dose- and radiation quality-dependence of the esophageal epithelium response to radiation may provide insights into the ability of radiation to promote ESCC. We have explored factors that may play a role in esophageal epithelial radiosensitivity and their potential relationship to ESCC risk. We have utilized a murine three-dimensional (3D) organoid model that recapitulates the morphology and functions of the stratified squamous epithelium of the esophagus to study persistent dose- and radiation quality-dependent changes. Interestingly, although high-linear energy transfer (LET) Fe ion exposure induced a more intense and persistent alteration of squamous differentiation and 53BP1 DNA damage foci levels as compared to Cs, the MAPK/SAPK stress pathway signaling showed similar altered levels for most phospho-proteins with both radiation qualities. In addition, the lower dose of high-LET exposure also revealed nearly the same degree of morphological changes, even though only ~36% of the cells were predicted to be hit at the lower 0.1 Gy dose, suggesting that a bystander effect may be induced. Although p38 and ERK/MAPK revealed the highest levels following high-LET exposure, the findings reveal that even a low dose (0.1 Gy) of both radiation qualities can elicit a persistent stress signaling response that may critically impact the differentiation gradient of the esophageal epithelium, providing novel insights into the pathogenesis of radiation-induced esophageal injury and early stage esophageal carcinogenesis.

3D organoid culture↗
DOE OSTI2022

X-Ray Performance of Critical-angle Transmission Grating Prototypes for the Arcus Mission

Arcus is a proposed Explorer Class soft X-ray grating spectrometer. It aims to explore cosmic feedback by mapping hot gases within and between galaxies and galaxy clusters and characterizing jets and winds from supermassive black holes and to investigate the dynamics of protoplanetary disks and stellar accretion. Arcus features 12 m focal-length grazing-incidence silicon pore optics (SPO) developed for the Athena mission. Critical-angle transmission (CAT) gratings efficiently disperse high diffraction orders onto CCDs. We report new and improved X-ray performance results for Arcus-like CAT gratings, including a record resolving power for two coaligned CAT gratings. Multiple Arcus prototype grating facets were illuminated by an SPO at the PANTER facility. The facets consist of 32 × 32.5 mm 2 patterned silicon membranes, bonded to metal frames. The bonding angle is adjusted according to the measured average tilt angle of the grating bars in the membrane. Two simultaneously illuminated facets show a minor broadening of the Al-K α doublet in the 18th and 21st orders with the best-fit record effective resolving power of ${R}_{G}\approx {1.3}_{-0.5}^{+\infty }\times {10}^{4}$ (3σ), about three to four times the Arcus requirement. We measured the diffraction efficiency of quasi-fully illuminated gratings at O-K wavelengths in orders 4–7 in an Arcus-like configuration and compare results with synchrotron spot measurements. After corrections for geometrical effects and bremsstrahlung continuum, we find agreement between full and spot illumination at the two different facilities, as well as with the models used for Arcus effective area predictions. We find that these flight-like gratings meet the diffraction efficiency and greatly exceed the resolving power Arcus requires.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Architectural Survey of the Pluto Test Bunker Facility, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), in conjunction with the National Nuclear Security Administration Nevada Field Office (NNSA/NFO), proposes to reactivate Building 26-2205 as part of a project to detect foundry-type operations with remote sensing. The project is known as the Signals Exploration Testbed, or SET for short. Building 26-2205 (State Historic Preservation Office [SHPO] Resource No. B11558) is the former Compressor Building for the Pluto Program in Area 26 of the Nevada National Security Site (NNSS). Proposed actions at Building 26-2205 include removal of exterior building and site features, such as fencing, concrete pads, a large steel tank, and visible utilities, as well as interior changes to the building’s floor plan. These activities constitute an undertaking subject to review under Section 106 of the National Historic Preservation Act (NHPA) (54 United States Code [USC] § 306101) and its implementing regulations, 36 Code of Federal Regulations (CFR) Part 800.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗