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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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NASA NTRS2026

Ultrasonic Washer–Dryer System for Space Habitats: Design Upgrades and Parabolic Flight Readiness

Clothing makes up nearly 25% of all non-food supplies sent to the International Space Station (ISS). To support sustainable human missions in deep space, NASA’s Life Support and Habitation Systems Focus Area looks to advance technologies to support and improve logistics. Our team is creating a compact ultrasonic clothing washer/dryer system that bypasses traditional limitations and is suitable for space. Thermal drying uses a lot of energy to evaporate water, while our ultrasonic drying method offers a quicker, more efficient alternative. It uses piezoelectric transducers to vibrate textiles at the micron scale, mechanically removing water as cold mist rather than evaporating it. This speeds up drying and reduces energy use, no matter the fabric makeup. This paper details recent upgrades to a full-scale ultrasonic washer–dryer system, readying it for parabolic flight testing. Improvements include an enhanced human–machine interface, better packaging, and optimized performance and control. We also present extensive pre-flight ground tests conducted to ensure reliability and identify potential risks. Collaborating with P&G, we report preliminary cleaning tests using various detergents. These results lay a crucial foundation for the laundry system designed specifically for space. By cutting clothing-related payload and waste by over 97%, this technology supports long-term human exploration missions on the ISS, the Moon, Mars, and beyond.

Ultrasonic↗
DOE OSTI2025

Revealing the evolution of order in materials microstructures using multi-modal computer vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Integration of ultra-low coverage whole-genome sequences for reconstructing the evolutionary history of Galapagos giant tortoises

Genomic data from contemporary and historical samples often need to be coupled for evolutionary reconstructions of multitaxon complexes. However, the genetic data recovered from historical samples may result only in ultra-low coverage whole-genome sequences (ulcWGS; <0.15× depth), leading to inaccurate evolutionary inferences given a preponderance of missing data. Using the Galapagos giant tortoise radiation as a study system (Chelonoidis spp., composed of 13 extant and four extinct lineages), we assembled a novel methodological pipeline that removes potential noise introduced by the missing data and enhances the evolutionary signal from ulcWGS samples. We leveraged existing tools for phylogenomic placement (EPA-ng), population genomic structure (smartsnp) and admixture (Admixfrog, NGSadmix) to demonstrate that the evolutionary history of samples can be uncovered with sequencing depths as low as 0.008–0.139×. Importantly, these approaches do not use genotype imputation of the ulcWGS samples, which would require extensive reference datasets. Our application to two cases of extinct lineages of Galapagos giant tortoises, with and without references from the same lineage, demonstrates the general value of the approach. We confirm where the extinct lineages from San Cristóbal and Santa Fe islands fit into the Galapagos giant tortoise radiation, and that these lineages were evolutionarily distinct entities.

ancient DNA↗
DOE OSTI2026

Upper ocean mixing, surface heat fluxes, and heat content variability in the upper 150 m during Hurricane Laura (2020)

Observations (from thermistor sensors and a hurricane glider) and model-based temperature were used to answer questions including the following: 1) What physical and environmental conditions explain the rapid intensification of Hurricane Laura? 2) How did the pre-existing warm mixed layer at Stone mooring (StM) influence the degree of cooling in the mixed layer? 3) How did vertical mixing, surface heat fluxes, and advective processes collectively shape the mixed layer heat evolution at StM? Observations measured by thermistor sensors showed that Hurricane Laura induced a cooling of -1.2oC in the mixed layer when Hurricane Laura’s wind speed increased to 47 m/s on 26 August. This contrasts with the -1.04oC mixed layer temperature change estimated from model-based temperature. At StM, the presence of a 31oC warm mixed layer and elevated heat content (60-80kJ/cm2) effectively preconditioned the upper ocean ahead of Hurricane Laura’s passage. After Hurricane Laura passed, turbulent mixing associated with the storm transported the heat anomalies downward into subsurface layers. In addition, this study shows that a surface heat flux of 5.04 kJ/cm2 supported the intensity of Hurricane Laura as it traversed over the StM. Using a mixed layer heat balance model, this study shows that entrainment flux and surface flux mostly contributed to the observed change in the mixed layer temperature.

Tsai, Senam↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Machine Learning↗
DOE OSTI2026

Ionic Precursors Transformed Into Vinyl Acetate Synthesis Catalyst via Reaction-Driven Restructuring

Conventional preparation of supported bimetallic catalysts relies on solution-mediated metal salt immobilization and pre-formation of alloy nanoparticles before reaction. Here, we report a fundamentally different synthesis strategy of using a physical mixture of salt precursors to generate an active catalyst during reaction. The catalytic structure is generated in situ from Pd3(OAc)6, Au(OH)3, and KOAc through H2 treatment and reaction-driven restructuring under vinyl acetate monomer (VAM) synthesis conditions. Ascertained from in situ X-ray diffraction and operando infrared spectroscopy analyses, reduction treatment produces segregated Pd and Au domains, and subsequent exposure to a VAM reaction mixture triggers dynamic extraction of Pd from the metal surface. This latter process, mediated by acetate-assisted redox cycles, facilitates Pd migration toward Au domains to form a near-surface localized Pd50Au50 alloy phase. Monometallic Pd domains serve as a reservoir of Pd to the alloy phase, leading to and sustaining a more Pd-enriched active surface and a higher population of accessible Pd sites, compared to a conventionally prepared K-PdAu/SiO2 catalyst. Consequently, this leads to a twofold increase in the VAM formation rate, demonstrating highly active bimetallic catalysts can be generated through the gas-phase treatment of physically mixed ionic precursors.

Cha, Byeong Jun [Rice University]↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2026

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗
DOE OSTI2025

Taking three-dimensional x-ray diffraction (3DXRD) from the synchrotron to the laboratory scale

Three-dimensional x-ray diffraction (3DXRD), a rotating x-ray diffraction technique, is a powerful tool for studying the micromechanical behavior of polycrystalline materials, capable of measuring the volume, position, orientation, and strain of thousands of grains simultaneously. However, its application has been historically limited to synchrotron facilities. Here, we present the first demonstration of laboratory-scale 3DXRD (Lab-3DXRD) using a liquid-metal-jet source. Lab-3DXRD achieves accuracy comparable to synchrotron-based 3DXRD, as validated against laboratory diffraction contrast tomography (LabDCT) and synchrotron-3DXRD. Over 96% of the grains detected with Lab-3DXRD are cross-validated, particularly for coarse grains (> ~60 μm), while the results suggest that finer grains should be accessible by taking advantage of high-efficiency detectors. We further demonstrate that its sensitivity to finer grains is enhanced by incorporating pre-characterization into the analysis. This study establishes Lab-3DXRD as a practical alternative to synchrotron techniques, making 3DXRD accessible to a wider range of academic and industrial researchers.

characterization and analytical techniques↗
DOE OSTI2026

Best Practices for Resilience Hub Development and Management

The Carbon League and its community partners in East St. Louis, Illinois, have identified five facilities to serve as resilience hubs. These hubs are intended to support the local community through a range of services and resources during blue-sky (everyday), gray-sky (pre-event), and black-sky (emergency) conditions. Transforming these facilities into fully functional resilience hubs requires a broad operational improvement and programmatic planning roadmap. This memo outlines best practices to guide the development of these resilience hubs in East St. Louis, including recommendations for infrastructure services; safety and physical protection; community services; operational protocols; and a phased implementation strategy aligned with realistic funding and capacity constraints. Infrastructure recommendations include strengthening electric power, communications, water, sanitation, and transportation/logistics capabilities, all of which are essential for hubs that may serve as cooling and warming centers, distribution points, and information hubs during emergencies. Safety recommendations focus on accessibility, emergency action planning, indoor air quality, and secure storage of critical equipment. A phased roadmap provides guidance from immediate, low-cost readiness actions to long-term optimization and community integration. Performance metrics and maintenance protocols ensure continuous improvement and operational readiness. This guidance draws on best practices that can be used to support the development of resilient, community-centered hubs capable of enhancing public safety, health, and well-being during everyday operations and emergencies alike.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2010

High-Precision Shape Control of In-Space Deployable Large Membrane/Thin-Shell Reflectors

This innovation has been developed to improve the resolutions of future spacebased active and passive microwave antennas for earth-science remote sensing missions by maintaining surface figure precisions of large membrane/thin-shell reflectors during orbiting. The intention is for these sensing instruments to be deployable at orbit altitudes one or two orders of magnitude higher than Low Earth Orbit (LEO), but still being able to acquire measurements at spatial resolution and sensitivity similar to those of LEO. Because active and passive microwave remote sensors are able to penetrate through clouds to acquire vertical profile measurements of geophysical parameters, it is desirable to elevate them to the higher orbits to obtain orbital geometries that offer large spatial coverage and more frequent observations. This capability is essential for monitoring and for detailed understanding of the life cycles of natural hazards, such as hurricanes, tropical storms, flash floods, and tsunamis. Major components of this high-precision antenna-surface-control system include a membrane/thin shell reflector, a metrology sensor, a controller, actuators, and corresponding power amplifier and signal conditioning electronics (see figure). Actuators are attached to the back of the reflector to produce contraction/ expansion forces to adjust the shape of the thin-material reflector. The wavefront-sensing metrology system continuously measures the surface figure of the reflector, converts the surface figure to digital data and feeds the data to the controller. The controller determines the control parameters and generates commands to the actuator system. The flexible, piezoelectric polymer actuators are thus activated, providing the control forces needed to correct any distortions that exist in the reflector surface. Piezoelectric polymer actuators are very thin and flexible. They can be implemented on the back of the membrane/thin-shell reflector without introducing significant amounts of mass or stiffness to the reflector. They can be rolled up or folded to accommodate the packaging needed for launch. An analytical model of the system, which includes the membrane reflector, actuator, and controller has been developed to investigate the functionality of this control system on a 35-meter-diameter membrane reflector. The performance of this system under external disturbances such as in space thermal loads and W-error due to inflation has been investigated. A subscale breadboard has been developed, and the functionality of this control concept has been demonstrated by this breadboard.

Houfei Fang↗
DOE OSTI2026

Full ribosomal operon sequencing of anaerobic gut fungi (phylum Neocallimastigomycota ): insights on its markers and phylogenetic resolution

The phylogenetic affiliations of anaerobic gut fungi (Neocallimastigomycota) are typically evaluated using single-gene markers. However, this approach often fails to resolve relationships between closely related lineages. To address this issue and identify alternative markers, we created a curated database comprising the complete ribosomal operon sequences of 156 isolates, representing 20 of the 22 recognized genera and two new genus-level clades. Using long-read sequencing, we obtained ~9 kbp operon sequences and developed a robust analysis pipeline. Incorporating both coding genes and non-coding regions (excluding IGS1) improved phylogenetic resolution. This phylogenetic approach successfully resolved the Cyllamyces and Caecomyces clades (hard-to-distinguish genetically), as well as seven analysed Piromyces species. We also scanned the operon for markers that are suitable for short-read sequencing platforms, with the aim of enhancing biodiversity and phylogenetic studies. Notably, the ETS1 genetic region also enabled the distinction between these lineages, indicating its phylogenetic value within the ribosomal operon. The resulting database is a valuable resource for expanding and strengthening phylogenetic frameworks.

High-throughput sequencing↗
NASA NTRSDate not supplied

On-Orbit Calibration Assessment of NOAA-21 VIIRS Thermal Emissive Bands and Implications for JPSS-4 VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the NOAA-21 (N21) satellite has been successfully operating for over three years collecting valuable scientific measurements. A large suite of weather forecasting models and scientific research applications are supported with the VIIRS measurements from N21 combined with those acquired from the VIIRS instrument onboard the S-NPP and NOAA-20 spacecraft. Among the 22 VIIRS spectral bands, there are seven thermal emissive bands (TEB) covering the 3.7 to 12.2 µm spectral range at two different spatial resolutions. The VIIRS TEB detectors are calibrated onboard using a blackbody at a controlled temperature and a space view for background signal measurement. In this paper, we discuss the on-orbit performance of the N21 VIIRS TEB using various parameters such as the detector gain, noise, and offset. This on-orbit assessment provides critical insights into instrument behavior and calibration performance that will inform pre-launch testing and post-launch validation strategies for the upcoming JPSS-4 VIIRS mission scheduled for launch in 2027. The methodologies and lessons learned from characterizing the N21 TEB performance will enable more efficient and comprehensive radiometric assessment of the next VIIRS instrument. Specific focus is placed on identifying performance trends and anomaly signatures observed during the N21 commissioning phase that can enhance the JPSS4 instrument checkout procedures and accelerate the transition to routine on-orbit operations. The established performance baselines and uncertainty estimates will serve as an acceptance criterion for the JPSS-4 commissioning activities, ensuring mission readiness and data continuity for operational users.

Amit Angal↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗
NASA NTRS2014

High Efficiency Centrifugal Compressor for Rotorcraft Applications

The report "High Efficiency Centrifugal Compressor for Rotorcraft Applications" documents the work conducted at UTRC under the NRA Contract NNC08CB03C, with cost share 2/3 NASA, and 1/3 UTRC, that has been extended to 4.5 years. The purpose of this effort was to identify key technical barriers to advancing the state-of-the-art of small centrifugal compressor stages; to delineate the measurements required to provide insight into the flow physics of the technical barriers; to design, fabricate, install, and test a state-of-the-art research compressor that is representative of the rear stage of an axial-centrifugal aero-engine; and to acquire detailed aerodynamic performance and research quality data to clarify flow physics and to establish detailed data sets for future application. The design activity centered on meeting the goal set outlined in the NASA solicitation-the design target was to increase efficiency at higher work factor, while also reducing the maximum diameter of the stage. To fit within the existing Small Engine Components Test Facility at NASA Glenn Research Center (GRC) and to facilitate component re-use, certain key design parameters were fixed by UTRC, including impeller tip diameter, impeller rotational speed, and impeller inlet hub and shroud radii. This report describes the design effort of the High Efficiency Centrifugal Compressor stage (HECC) and delineation of measurements, fabrication of the compressor, and the initial tests that were performed. A new High-Efficiency Centrifugal Compressor stage with a very challenging reduction in radius ratio was successfully designed, fabricated and installed at GRC. The testing was successful, with no mechanical problems and the running clearances were achieved without impeller rubs. Overall, measured pressure ratio of 4.68, work factor of 0.81, and at design exit corrected flow rate of 3 lbm/s met the target requirements. Polytropic efficiency of 85.5 percent and stall margin of 7.5 percent were measured at design flow rate and speed. The measured efficiency and stall margin were lower than pre-test CFD predictions by 2.4 percentage points (pt) and 4.5 pt, respectively. Initial impressions from the experimental data indicated that the loss in the efficiency and stall margin can be attributed to a design shortfall in the impeller. However, detailed investigation of experimental data and post-test CFD simulations of higher fidelity than pre-test CFD, and in particular the unsteady CFD simulations and the assessment with a wider range of turbulence models, have indicated that the loss in efficiency is most likely due to the impact of unfavorable unsteady impeller/diffuser interactions induced by diffuser vanes, an impeller/diffuser corrected flow-rate mismatch (and associated incidence levels), and, potentially, flow separation in the radial-to-axial bend. An experimental program with a vaneless diffuser is recommended to evaluate this observation. A subsequent redesign of the diffuser (and the radial-to-axial bend) is also recommended. The diffuser needs to be redesigned to eliminate the mismatching of the impeller and the diffuser, targeting a slightly higher flow capacity. Furthermore, diffuser vanes need to be adjusted to align the incidence angles, to optimize the splitter vane location (both radially and circumferentially), and to minimize the unsteady interactions with the impeller. The radial-to-axial bend needs to be redesigned to eliminate, or at least minimize, the flow separation at the inner wall, and its impact on the flow in the diffuser upstream. Lessons were also learned in terms of CFD methodology and the importance of unsteady CFD simulations for centrifugal compressors was highlighted. Inconsistencies in the implementation of a widely used two-equation turbulence model were identified and corrections are recommended. It was also observed that unsteady simulations for centrifugal compressors require significantly longer integration times than what is current practice in industry.

Gorazd Medic↗
DOE OSTI2026

Multi-sensor Observations of a High-velocity Fireball over the South Atlantic on 2026 April 1

A high-velocity fireball was detected over the South Atlantic (41.9°S, 54.7°W) on 2026 April 1 at 02:13:14 UTC by U.S. Government (USG) sensors, with peak brightness at 90.5 km altitude. The event was well-observed from geostationary orbit by two civilian lightning imagers with near-orthogonal geometry, the Geostationary Operational Environmental Satellite-East Geostationary Lightning Mapper (GLM) and the Exploitation of Meteorological Satellites Meteosat Third Generation Imager 1 Lightning Imager, and it produced low-frequency acoustic signatures. The GLM measured a total radiated energy of 2.4 × 10 10 J, corresponding to a calculated impact energy of 0.086 kt TNT equivalent. Stereoscopic triangulation of the imager tracks yields an independent pre-atmospheric velocity of ~57 km s −1 , some 18% below the USG-reported value. Because orbital provenance is acutely sensitive to the entry velocity, whose reported uncertainty could be substantial, this discrepancy is notable. We document the multi-sensor record and identify the analysis required to assess provenance, which a subsequent study will present.

Silber, Elizabeth Allaryce [Sandia National Lab. (↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
NASA NTRS2026

Surprisingly Enriched CO and CH4 in Halley-type Comet 13P/Olbers: Clues to Its Interstellar Heritage

The native ice composition of Halley-type Comet 13P/Olbers (hereafter 13P) was measured post-perihelion, on UT 2024 August 16 using iSHELL, the 1–5 μm cross-dispersed, high-resolution facility spectrograph at the NASA InfraRed Telescope Facility. At that time, the heliocentric distance (Rh) of 13P was 1.37 au and the geocentric distance (Δ) was 1.99 au. Using a slit that delivered spectral resolving power RP λ/Δλ 4.5 × 104, two instrument settings targeted rovibrational emissions of seven parent molecules released into the coma through sublimation of ices in the nucleus. These settings provided broad and contiguous or nearly contiguous spectral coverage, one simultaneously measuring H2O, CO, and OCS, and the other simultaneously measuring CH4, C2H6, CH3OH, and H2CO. Meaningful abundances were obtained for all seven species. Our study revealed substantial enrichments of CO and CH4, the two most volatile species systematically targeted at IR wavelengths in comets. Relative to their respective mean abundance ratios among measured comets from the Oort cloud reservoir, CO in 13P was enriched by a factor of about six and CH4 was enriched by a factor of about three. Together with near-average abundances measured for CH3OH and C2H6, this suggests large endowments of CO and CH4 on interstellar grains, yet it could indicate inhibited surface chemistry on the pre-cometary grains subsequently contained in the nucleus or 13P. Assuming that this represents the primordial composition of 13P, it could reflect very low temperatures in its natal environment (∼10 K or lower).

Michael A Disanti↗