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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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DOE OSTI2022

Active meta-learning for predicting and selecting perovskite crystallization experiments

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. Here in this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography

Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples.

Efimov, Timofey [ORNL] (ORCID:000900090098471X)↗

DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Water Sorption Study of Various Polymers and Polymeric Composites

In the formulations of many composite materials, polymeric materials are often used as binder materials to enhance processability, functionality, and safety during manufacturing. For instance, poly(ester urethane), such as Estane® 5703, is used in the formulation of PBX 9501, and vinyl copolymer elastomer (VCE) is used in the formulation of VCE/filler compos ites. The stability of these binders directly impacts the overall stability and performance of their composites. Estane and VCE are prone to hydrolytic degradation, with water sorption behavior playing a significant role in their long-term stability and aging behavior. Hence, it is critical to study their water sorption behavior under various humidity and tempera ture conditions. To improve the accuracy and reduce labor-intensive work involved in water sorption experiments, an automated Dynamic Vapor Sorption (DVS) system was employed, which can generate large datasets. To efficiently process and analyze these datasets, Python scripts were developed. These scripts not only streamlined data processing, but also accu rately derived the Arrhenius parameters related to water diffusion and sorption properties for the tested materials. This approach offers a robust framework for evaluating water trans port and sorption processes in polymeric materials and adds valuable capabilities to future water sorption testing efforts.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Accuracy and Precision in the Southern Hemisphere Additional Ozonesondes (SHADOZ) Dataset in Light of the JOSIE-2000 Results

Since 1998 the Southern Hemisphere ADditional OZonesondes (SHADOZ) project has provided over 2000 ozone profiles over eleven southern hemisphere tropical and subtropical stations. Balloon-borne electrochemical concentration cell (ECC) ozonesondes are used to measure ozone. The data are archived at: &ttp://croc.gsfc.nasa.gov/shadoz>. In analysis of ozonesonde imprecision within the SHADOZ dataset, Thompson et al. [JGR, 108,8238,20031 we pointed out that variations in ozonesonde technique (sensor solution strength, instrument manufacturer, data processing) could lead to station-to-station biases within the SHADOZ dataset. Imprecisions and accuracy in the SHADOZ dataset are examined in light of new data. First, SHADOZ total ozone column amounts are compared to version 8 TOMS (2004 release). As for TOMS version 7, satellite total ozone is usually higher than the integrated column amount from the sounding. Discrepancies between the sonde and satellite datasets decline two percentage points on average, compared to version 7 TOMS offsets. Second, the SHADOZ station data are compared to results of chamber simulations (JOSE-2000, Juelich Ozonesonde Intercomparison Experiment) in which the various SHADOZ techniques were evaluated. The range of JOSE column deviations from a standard instrument (-10%) in the chamber resembles that of the SHADOZ station data. It appears that some systematic variations in the SHADOZ ozone record are accounted for by differences in solution strength, data processing and instrument type (manufacturer).

Witte, Jacquelyn C.↗

NASA NTRS2016

Passive vs. Parachute System Architecture for Robotic Sample Return Vehicles

The Multi-Mission Earth Entry Vehicle (MMEEV) is a flexible vehicle concept based on the Mars Sample Return (MSR) EEV design which can be used in the preliminary sample return mission study phase to parametrically investigate any trade space of interest to determine the best entry vehicle design approach for that particular mission concept. In addition to the trade space dimensions often considered (e.g. entry conditions, payload size and mass, vehicle size, etc.), the MMEEV trade space considers whether it might be more beneficial for the vehicle to utilize a parachute system during descent/landing or to be fully passive (i.e. not use a parachute). In order to evaluate this trade space dimension, a simplified parachute system model has been developed based on inputs such as vehicle size/mass, payload size/mass and landing requirements. This model works in conjunction with analytical approximations of a mission trade space dataset provided by the MMEEV System Analysis for Planetary EDL (M-SAPE) tool to help quantify the differences between an active (with parachute) and a passive (no parachute) vehicle concept.

Maddock, Robert W.↗

DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗

NASA NTRS2015

Investigation of Spiral Bevel Gear Condition Indicator Validation via AC-29-2C Combining Test Rig Damage Progression Data with Fielded Rotorcraft Data

This is the final of three reports published on the results of this project. In the first report, results were presented on nineteen tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Test Rig on spiral bevel gear sets designed to simulate helicopter fielded failures. In the second report, fielded helicopter HUMS data from forty helicopters were processed with the same techniques that were applied to spiral bevel rig test data. Twenty of the forty helicopters experienced damage to the spiral bevel gears, while the other twenty helicopters had no known anomalies within the time frame of the datasets. In this report, results from the rig and helicopter data analysis will be compared for differences and similarities in condition indicator (CI) response. Observations and findings using sub-scale rig failure progression tests to validate helicopter gear condition indicators will be presented. In the helicopter, gear health monitoring data was measured when damage occurred and after the gear sets were replaced at two helicopter regimes. For the helicopters or tails, data was taken in the flat pitch ground 101 rotor speed (FPG101) regime. For nine tails, data was also taken at 120 knots true airspeed (120KTA) regime. In the test rig, gear sets were tested until damage initiated and progressed while gear health monitoring data and operational parameters were measured and tooth damage progression documented. For the rig tests, the gear speed was maintained at 3500RPM, a one hour run-in was performed at 4000 in-lb gear torque, than the torque was increased to 8000 in-lbs. The HUMS gear condition indicator data evaluated included Figure of Merit 4 (FM4), Root Mean Square (RMS) or Diagnostic Algorithm 1(DA1), + 3 Sideband Index (SI3) and + 1 Sideband Index (SI1). These were selected based on their sensitivity in detecting contact fatigue damage modes from analytical, experimental and historical helicopter data. For this report, the helicopter dataset was reduced to fourteen tails and the test rig data set was reduced to eight tested gear sets. The damage modes compared were separated into three cases. For case one, both the gear and pinion showed signs of contact fatigue or scuffing damage. For case two, only the pinion showed signs of contact fatigue damage or scuffing. Case three was limited to the gear tests when scuffing occurred immediately after the gear run-in. Results of this investigation highlighted the importance of understanding the complete monitored systems, for both the helicopter and test rig, before interpreting health monitoring data. Further work is required to better define these two systems that include better state awareness of the fielded systems, new sensing technologies, new experimental methods or models that quantify the effect of system design on CI response and new methods for setting thresholds that take into consideration the variance of each system.

spiral bevel gears↗

DOE OSTI2024

TEAMER: Results of Investigating Structural Design Concepts and Alternative Materials for a Wave Power System

Included here are materials from a study on the design of a three-body Wave Energy Converter (WEC) utilizing a heave plate, dual Power Take Offs (PTOs), and single point mooring. A material trade study has been conducted to evaluate the effects of introducing various metallic and fiber-reinforced polymer (FRP) elements to the design. The baseline design is C-Power's patented k2 Wave Power System (WPS). This Testing Expertise and Access for Marine Energy Research (TEAMER) Request for Technical Support (RFTS) 9 effort resulted in alternative designs of the WPS center member, the nacelle. The analysis resulting from this study will inform the design of a lightweight structure with lower manufacturing and operating costs that increases energy conversion and power-to-weight ratio of the device. The dataset contains several components: finite element analysis (FEA) data, hand calculations, and CAD models. FEA files includes those used for and generated by finite element simulations. Calculations include hand calculations performed using Microsoft Excel, which assess core laminate properties, buckling, and other structural aspects in accordance with risk management DNV-RP-C201 standards. CAD materials include Solidworks files for parts, assemblies, and drawings of the evaluated models.

16 TIDAL AND WAVE POWER↗

NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗

DOE OSTI2024

Western Interconnection Baseline Study

The purpose of the baseline study is to evaluate the degree to which current industry planning processes meet the national 2035 decarbonization goals for the Western Interconnection. This analysis serves as a comparative baseline for the scenario analysis conducted in the NTP Study using a Western Interconnection dataset that is readily available to industry. This baseline analysis differs from the production cost modeling analysis and power flow analysis in the main NTP Study report (forthcoming). In particular, the analysis presented in this report reflects a business-as-usual future with an optimistic build out of specific planned transmission projects and foreseeable generation. In contrast, the NTP Study models a future generation and transmission expansion based on optimization from a capacity expansion model. The analysis presented herein also reflects a 2030 timeframe, whereas the main NTP Study production cost modeling analysis and power flow analysis reflect a 2035 time frame. This baseline analysis utilizes industry’s most reliable data to account for future transmission projects across various stages of development, with a particular focus on those in the permitting stage. Additionally, it incorporates projections for changes in generation capacity (both additions and retirements). This baseline analysis outlines a probable trajectory, given current process and practice, for the future of the bulk power system with a horizon extending to 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec.

Baembitov, Rashid↗

DOE OSTI2021

Omics-Lethal Human Viruses, MERS-CoV Experiment MM001

The purpose of this experiment was to evaluate the host response to wild-type Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV) virus infection. Sample data was obtained from primary mouse lung for mRNA, proteomics, lipidomics, and metabolomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Underway Sampling of Marine Inherent Optical Properties on the Tara Oceans Expedition as a Novel Resource for Ocean Color Satellite Data Product Validation

Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate-the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.

remote sensing↗

NASA NTRS2022

Real-World Testing of LiDAR-Inertial Based Navigation and Mapping for Precision Landing

The fusion of LiDAR and inertial measurements duringspacecraft descent and landing can be used to estimate alander’s navigation state and map the terrain below. Together,these data products can be used to enable safe and preciselanding on celestial bodies for which a priori orbital reconnaissanceis insufficient for hazard detection and avoidance.Unlike camera images used in visual terrain relative navigation,LiDAR scans are insensitive to changes in illumination; as aresult, the technique can be used to land in poorly lit areas,or at times of day when the lighting conditions are incongruentwith existing orbital imagery. In this paper, we extend previouswork in which we introduced a factor graph based smoothingapproach for LiDAR-inertial navigation and mapping. Whereasthe algorithms were previously tested on simulated data, thispaper presents testing on real-world data. Data from theAutonomous Landing Hazard Avoidance Technology (ALHAT)airplane flight tests in the Yucca Flats and Death Valley in2009 (FT3), the Morpheus vertical take off and landing flighttests at Kennedy Space Center in 2014 (FT6), and the landingof Perseverance and Ingenuity on Mars in 2021 (M2020) wereused to evaluate algorithm performance. In this paper, weextend our LiDAR-inertial technique to work with a variety ofranging technologies: single point laser altimetry (FT3), denseflash LiDAR (FT6), and six-beam radar (M2020). A thoroughperformance analysis for all three datasets is presented. Datasetpreparation, improvements in algorithm robustness, and outlierrejection, which were necessitated by the transition to realworlddata, are discussed.

Trawny, Nikolas↗

NASA NTRSDate not supplied

High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗

NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Materials discovery↗