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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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959 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Semi-Dynamic Leach Testing of Densified Silicon-based Iodine Waste Forms

Iodine waste forms (IWF) require a conceptual corrosion release model (CCRM) to provide iodine (I) release rates for performance modeling nuclear waste disposal repositories. To develop a CCRM, an understanding of the corrosion mechanisms of the IWF are required along with data from consistent test methods to parameterize the model. The present study has advanced both areas by providing and assessing the corrosion resistance of IWF types based on their processing history in minor variations of semi-dynamic leach tests. The test included a series of semi-dynamic leach tests using monolithic IWFs in deionized water (leachant). Several experiments were conducted under alternate test conditions with changes to temperature, leachant replacement, leachant pH, leachant volume, masking, and surface finish to elucidate if varying these conditions impacted IWF corrosion behavior. Tests were conducted on two classes of IWFs: (1) I-bearing silver-mordenite (AgZ) materials processed by hot isostatic pressing (HIP) at different temperatures, pressures, sizes, and times; and (2) I-bearing silver-functionalized silica aerogels (SFA) processed by either HIP or spark plasma sintering (SPS). The corrosion susceptibility of AgZ samples was influenced by HIP temperature and pressure. The SPS SFAs retained I far better than HIP SFAs. Additional findings in this study include: (1) The iodine dissolution rate decreased with decreasing temperature, (2) a common ion effect may occur and slow dissolution of the host phase if the leachant is not regularly replaced, (3) pH controls the dissolution rate, and (4) the iodine dissolution rate slows with extended test time (up to 224 days). Based on this work, these parameters should thus be represented when developing a CCRM.

corrosion↗
DOE OSTI2026

SAM Finite Volume Method Development Status Update: GCR Application, Restart, and MultiApp

The System Analysis Module (SAM) is being developed as a modern system analysis code for advanced non-light-water-reactor safety analysis under the U.S. DOE NEAMS program. Previous feasibility studies have demonstrated that a staggered-grid finite volume method (SG-FVM), implemented under the MOOSE framework, can deliver more than an order of magnitude speedup over the existing continuous Galerkin finite element method (CG-FEM) solver for liquid-cooled, incompressible but thermally expandable flow systems. This work extends the previous effort to compressible, gas-cooled reactor applications, where pressure couples directly into the mass equation adding additional nonlinearity into the equation system. New code capabilities are implemented for pebble bed high-temperature gas-cooled reactor (PB-HTGR) analysis, including a pebble bed CoreChannel component, built-in pebble bed effective thermal conductivity model and channel-to-channel crossflow model. The capabilities are tested, benchmarked, and demonstrated for problems with increased level of model and physical complexities, including the HTTU effective thermal conductivity test, the SANA passive cooling test, and a demonstration case using the GPBR200 reactor design covering steady-state operation, DLOFC and PLOFC transients. Across all cases, the SG-FVM solver demonstrated strong robustness and efficiency, and the solutions agree well with reference results and data. The finding of this work proves that SG-FVM is a viable and efficient solver pathway for compressible, gas-cooled reactor system analysis in SAM. In addition, work has been done to successfully support SAM-FVM recover/restart code feature that is essential to reactor safety analysis applications, and MultiApp code feature that is essential to multi-scale and multi-physics simulations. In summary, this work continued from previous feasibility studies, and further demonstrated that the SG-FVM will serve as a strong foundation for SAM’s advanced solver algorithm for future deployment.

Zou, Ling↗
NASA NTRS1970

Development of a silver-zinc battery system

Summary report is described of historical documentation and detailed design data for development of silver-zinc battery for use on Surveyor spacecraft. Electrical and physical characteristics of battery models are included, along with data on qualification, acceptance, solar-thermal-vacuum, mission simulation testing, and actual flight performance.

Moses, A. J.↗
DOE OSTI2026

Virtual Growth of SRF Materials

Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.

Tilkin, Anthony [Fermilab]↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2023

Satellite characterization of global stratospheric sulfate aerosols released by Tonga volcano

Large volcanic eruptions create an enhanced layer of sulfate aerosols in the stratosphere. These sulfuric acid droplets persist for many months, altering the climate and stratospheric chemistry. Sulfate aerosols scatter sunlight back to space, cooling the surface of the Earth and absorb outgoing thermal radiation, heating the stratosphere. The calculation of the climate impact of sulfate aerosols depends on their physical properties such as droplet size and chemical composition. These properties are not well known, and this uncertainty contributes to the errors in climate model predictions. Here we derive the first empirical formula that predicts the composition of stratospheric sulfate aerosols from volcanic eruptions from the air temperature and water vapor pressure. Measurements of atmospheric infrared transmittance of the Hunga Tonga-Hunga Ha'apai sulfate aerosol plume by the Atmospheric Chemistry Experiment (ACE) satellite were analyzed to determine composition (weight percent of sulfuric acid) and median particle radius. These data are supplemented by measurements of the Raikoke and Nabro eruptions. Our analysis allows the properties of volcanic aerosols in the stratosphere to be predicted reliably in atmospheric models.

P Bernath↗
NASA NTRS1977

Remote sensing of hurricane waves

A program was undertaken to obtain data on wind generated ocean waves in hurricanes by remote sensing techniques. The sensor, a synthetic aperture radar, has collected the first data ever on the directional wave climate throughout a hurricane. This information was found to have inconsistencies with present hurricane wave generation models.

David B King↗
DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗
NASA NTRS1975

Chapter 3: Thermal Exchanges and Temperature Stress

Thermal comfort during space flight is discussed. Heat production of man during space flight and wear loss as a mean of dissipating heat are described. Water cooled garments are also considered, along with tolerance for extreme heat and body heat storage. Models of human temperature regulation are presented in the form of documented FORTRAN programs.

Paul Webb↗
DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.

Satellites↗
NASA NTRS1991

Preparation of the Ice Certification of the Dornier 328 Regional Airliner By Numerical Simulation and By Ground Test

The Dornier 328, a new regional airliner, is to be qualified to FAR25/JAR25 requirements for operation into known icing conditions. All lifting surfaces are pneumatically deiced. Impingement limits were determined using a validated computational fluid dynamic (CFD) code. Wind tunnel tests with a model of the whole aircraft and the isolated empennage equipped with artificial ice shapes showed a degradation of handling characteristics due to ice accretion. Extensive two and three dimensional CFD calculations evaluated ice accretion at the deflection elevator horn. Icing tunnel tests were performed with a model of the horizontal tail with icing similitude fulfilled. The Dornier Do328 powerplant air induction system has successfully completed a program of icing tests in support of aircraft certification for operation into known icing conditions. Potential flight icing conditions were extensively analyzed using a CFD potential flow code and 3-D particle tracking routine to identify critical flight icing conditions. The tunnel test program verified the analytically predicted critical ice accretion surfaces of the air induction system, the adequacy of the ice protection provisions and demonstrated compliance with the applicable (JAR25) regulatory material.

D Welte↗
DOE OSTI2026

Realization of two-dimensional discrete time crystals with anisotropic Heisenberg coupling

A discrete time crystal (DTC) is an out-of-equilibrium phase of matter that spontaneously breaks discrete time-translation symmetry. Previous studies have been limited to a set of models with Ising-like couplings - and mostly only in one dimension - thus precluding our understanding of the existence (or not) of DTCs in models with more realistic interactions. In this work, by combining the latest generation of IBM quantum processors with state-of-the-art tensor network methods, we demonstrate the existence of a DTC in a two-dimensional system governed by anisotropic Heisenberg interactions. We uncover a rich phase diagram encompassing spin-glass, ergodic, and time-crystalline phases, and identify the interplay of initialization, interaction anisotropy, and driving protocols in stabilizing the DTC phase. By extending the study of Floquet matter beyond simplified models, we lay the groundwork for exploring how driven systems bridge the gap between quantum coherence and emergent non-equilibrium thermodynamics.

Phase transitions and critical phenomena↗
NASA NTRS1994

The Solid Surface Combustion Experiment Aboard the USML-1 Mission

AA Experimental results from the five experiments indicate that flame spread rate increases with increasing ambient oxygen content and pressure. An experiment was conducted aboard STS-50/USML-1 in the solid Surface Combustion Experiment (SSCE) hardware for flame spread over a thin cellulosic fuel in a quiescent oxidizer of 35% oxygen/65% nitrogen at 1.0 atm. pressure in microgravity. The USML-1 test was the fourth of five planned experiments for thin fuels, one performed during each of five Space Shuttle Orbiter flights. Data that were gathered include gas- and solid-phase temperatures and motion picture flame images. Observations of the flame are described and compared to theoretical predictions from steady and unsteady models that include flame radiation from CO2 and H2O. Experimental results from the five esperiments indicate that flame spread rate increases with increasing ambient oxygen content and pressure. The brightness of the flame and the visible soot radiation also increase with increasing spread rate. Steady-state numerical predictions of temperature and spread rate and flame structure trends compare well with experimental results near the flame's leading edge while gradual flame evolution is captured through the unsteady model.

Robert A Altenkirch↗
NASA NTRSDate not supplied

Controls for Electrified Aircraft Propulsion

Advanced electrified aircraft propulsion (EAP) concepts with integrated power, propulsion, and thermal systems require the development of equally sophisticated controllers to fly safe and efficient missions. Hybrid electric aircraft utilize electric machines mechanically coupled to the engine shafts to extract and insert power for a variety of purposes. Additionally, electric machines may drive propulsive fans pulling from a combination of on-board energy storage devices and engine extracted power. NASA’s investment in hybrid electric aircraft hardware-in-the-loop testing enables controls research on representative engine models using a novel emulation and scaling methodology. High-voltage, high-power electrical powertrain presents several technical risks at altitude. High-power requires high efficiency to minimize losses. Superconducting electric machines and power distribution research at NASA has identified key challenges with potential controls solutions. These risks necessitate the development of controllers robust to model uncertainty, disturbances, and fault conditions. System health management and fault detection schemes play an important role in a multi-layer controls approach that utilizes a supervisor which oversees inner loops for the highly coupled power, propulsion, and thermal systems. Such a supervisory controller enables coordinates energy transfer between engines, energy storage devices, electric machines, propulsors, and heat exchangers. Existing methods have shown an improvement in engine operability using the hybrid electric powertrain.

Halle E Buescher↗