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NASA NTRS2011

Empirical Tests of the Predicted Footprint for Uncontrolled Satellite Reentry Hazards

A number of statistical tools have been developed over the years for assessing the risk of reentering object to human populations. These tools make use of the characteristics (e.g., mass, material, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. Because this information is used in making policy and engineering decisions, it is important that these assumptions be tested using empirical data. This study uses the latest database of known uncontrolled reentry locations measured by the United States Department of Defense. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors in the final stages of reentry - including the effects of gravitational harmonics, the effects of the Earth s equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and possibly change the probability of reentering over a given location. In this paper, the measured latitude and longitude distributions of these objects are directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.

Matney, Mark↗

NASA NTRS2011

Statistical Issues for Uncontrolled Reentry Hazards Empirical Tests of the Predicted Footprint for Uncontrolled Satellite Reentry Hazards

A number of statistical tools have been developed over the years for assessing the risk of reentering objects to human populations. These tools make use of the characteristics (e.g., mass, material, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. Because this information is used in making policy and engineering decisions, it is important that these assumptions be tested using empirical data. This study uses the latest database of known uncontrolled reentry locations measured by the United States Department of Defense. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors in the final stages of reentry - including the effects of gravitational harmonics, the effects of the Earth s equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and possibly change the probability of reentering over a given location. In this paper, the measured latitude and longitude distributions of these objects are directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.

Matney, Mark↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

DOE OSTI2024

Complete the Planned FY24 Design Parameters Development at INL to Support the Drafting of the A709 Near-Term Code Case

This report presents the thermophysical properties measurements conducted in the Fiscal Year 2024 to extend the database up to 1000?. The new values are compared against properties of high Molybdenum steels in the ASME Section II Part D tables. This report also shows the comparison between new measurements against the previously measured values in literature and provides recommended thermophysical properties for A709 code case. These values will support the thermophysical property tables in near term code case of A709.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Emerging opportunities for hybrid perovskite solar cells using machine learning

While there are several bottlenecks in hybrid organic–inorganic perovskite (HOIP) solar cell production steps, including composition screening, fabrication, material stability, and device performance, machine learning approaches have begun to tackle each of these issues in recent years. Different algorithms have successfully been adopted to solve the unique problems at each step of HOIP development. Specifically, high-throughput experimentation produces vast amount of training data required to effectively implement machine learning methods. Here, we present an overview of machine learning models, including linear regression, neural networks, deep learning, and statistical forecasting. Experimental examples from the literature, where machine learning is applied to HOIP composition screening, thin film fabrication, thin film characterization, and full device testing, are discussed. These paradigms give insights into the future of HOIP solar cell research. As databases expand and computational power improves, increasingly accurate predictions of the HOIP behavior are becoming possible.

Hering, Abigail R. (ORCID:0000000270806953)↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

The objective of this work is to provide a valuable database from controlled experiments for validating computational models. Recently, coarse-grained techniques such as particle-in-cell (PIC) or coarse-grained discrete element modeling (DEM) have gained popularity due to their computational efficiency while modeling large-scale systems; however, the influence of model parameters and their sensitivities at different geometric scales and flow conditions remain to be analyzed. These datasets are critical for the multiphase flow research community to assess predictive capability of modeling techniques as well as elucidate the hydrodynamic behavior in these systems. This study performed fluidization experiments using three different test sections with internal diameters of 2.5, 4, and 6 in. The operating conditions, bed material, and range of flow velocities at the inlet were constant in all the units, which were not hydrodynamically scaled. Glass beads having a Sauter Mean Diameter of 332 μm were used. Superficial velocity was varied from 2.97 to 5.35 times the minimum fluidization velocity and the initial static bed height was 0.1524 m. The order in which the experiments were performed was randomized and replicates were included to provide uncertainty in measurements. Statistics of differential pressure and bed height from these tests were reported. Future plans include validating PIC methodology in the open-source software, MFiX (Multiphase Flow with Interphase Exchanges) using results from this study. This could further be extended to determine optimal model parameters using inverse techniques such as deterministic calibration or Bayesian inference.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

42 ENGINEERING↗

DOE OSTI2020

Generic FEPs Catalogue and Salt Knowledge Archive

This report describes the development of a comprehensive catalogue of generic features, events, and processes (FEPs) that are potentially important for the post-closure performance of a repository for high-level radioactive waste (HLW) and spent nuclear fuel (SNF) in salt (halite) host rock. The FEPs and other supporting information have been entered into a “SaltFEP” Database. The generic salt repository FEPs include consideration of relevant FEPs from a number of U.S., Dutch, German, and international FEP lists and should be a suitable starting point for any repository program in salt host rock. The salt FEP catalogue and database employ a FEP classification matrix approach that is based on the concept that a FEP is typically a process or event acting upon or within a feature. The FEP matrix provides a two-dimensional structure consisting of a Features/Components axis that defines the “rows” and a Processes/Events axis that defines the “columns” of the matrix. The design of the FEP classification matrix is consistent with repository performance assessment – the Features/Components axis is organized vertically to generally correspond to the direction of potential radionuclide migration (from the waste to the biosphere) and the Processes/Events axis is designed to represent the common two-way couplings between thermal processes and other processes (such as thermal-mechanical or thermal-hydrologic processes). Related FEPs can be easily identified – related FEPs will typically be grouped in a single matrix cell or aligned along a common row (Feature/Component) or column (Process/Event). The online SaltFEP database can be downloaded from www.saltfep.org. It contains the FEP matrix, the FEPs, and the associated processes for each FEP. It provides a starting point to create and document site-specific individual FEPs. Furthermore, the FEP matrix is connected to the Salt Knowledge Archive (SKA), a database of about 20,000 references and documents representing the historical knowledge on radioactive disposal in salt. This work is the result of an ongoing collaboration between researchers in the U.S., the Netherlands, and Germany, and supports the NEA Salt Club Mandate. It builds upon prior work which is documented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1998

NASA's Space Environments and Effects (SEE) Program: The Pursuit of Tomorrow's Space Technology

A hazard to all spacecraft orbiting the earth and exploring the unknown in deep space is the existence of a harsh and ever changing environment with its subsequent effects. Some of these environmental hazards, such as plasma, extreme thermal excursions, meteoroids, and ionizing radiation result from natural sources, whereas others, such as orbital debris and neutral contamination are induced by the presence of spacecraft themselves. The subsequent effects can provide damaging or even disabling effects on spacecraft, its materials, and its instruments. In partnership with industry, academia, and other government agencies, National Aeronautics & Space Administration's (NASA's) Space Environments & Effects (SEE) Program defines the space environments and advocates technology development to accommodate or mitigate these harmful environments on the spacecraft. This program provides a very comprehensive and focused approach to understanding the space environment, to define the best techniques for both flight and ground-based experimentation, to update the models which predict both the environments and the environmental effects on spacecraft, and finally to ensure that this information is properly maintained and inserted into spacecraft design programs. This paper will provide an overview of the Program's purpose, goals, database management and technical activities. In particular, the SEE Program has been very active in developing improved ionizing radiation models and developing related flight experiments which should aid in determining the effect of the radiation environment on modern electronics.

Pearson, Steven D.↗

DOE OSTI2024

Determining the effects of U/Pu ratio on subsolidus phase transitions in U-Pu-Zr metallic fuel alloys

Here, ternary alloys consisting primarily of uranium, plutonium, and zirconium (U-Pu-Zr) are among the leading candidate fuel systems considered for fast spectrum nuclear reactors. Despite historical operation data from the testing of U-Pu-Zr rods in the Experimental Breeder Reactor-II, considerable uncertainty about the evolution of phases and microstructure across the ternary composition space exists. Due to sluggish kinetics and other difficulties in handling metal actinide specimens, quantitative measurements of phase-transitions in U-Pu-Zr alloys remain sparse in scientific literature, with most investigators reporting either phase-transition temperatures or phase identification data, but not both from the same specimens. The purpose of this paper is to critically compare experimental and calculated phase transition data and correlate with the microstructure and phase characterization data of as-cast and annealed U-Pu-Zr alloys. Phase transition peaks were measured using differential scanning calorimetry in the subsolidus regions (723-948 K) of three ternary U-Pu-Zr alloys with the same zirconium concentration but various U/Pu ratios. Overlapping peaks were deconvoluted using a Frazier-Suzuki peak fitting algorithm, and the critical peak temperatures and enthalpies were calculated. In general, increasing concentrations of Pu were associated with enhanced thermal stability of the body-centered cubic γ phase upon both heating and cooling. Experimental phase transition temperatures in this study tended to agree well with the predictions of the established ternary phase diagrams and other reported phase transition temperatures in literature. Additionally, the TAF-ID thermodynamic database was used to calculate a U-Pu-40 at.% Zr pseudobinary diagram as well as ternary diagrams from 773 to 973 K. The equilibrium phase transition temperatures tended to be considerably lower than measured peak temperatures upon both heating and cooling. Recommendations for improving the quality of data in future U-Pu-Zr characterization studies are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗

DOE OSTI2023

FY23 Status of Quality Assurance Plan for Out-of-Pile Test Data

The DOE Advanced Reactor Technology program has supported recovery and preservation of legacy metallic fuel data collected as part of the US fast reactor program, recognizing it as essential to development and licensing activities for advanced fast reactors. Databases were established as organized collections of experimental records and data generated from in-pile experiments at EBRII, FFTF, and TREAT as well as related out-of-pile examinations of irradiated fuels. The Out-of-Pile Transient Database (OPTD), includes records of over 150 out-of-pile furnace transient tests on metallic fuels conducted at Argonne’s Alpha-Gamma Hot Cell Facility to evaluate their transient performance and characterize fuel/cladding interaction. The database is accessible to registered users from US universities, laboratories, and nuclear industry. Because the data in OPTD has not been formally qualified, its applicability and ability to support licensing activities is limited. This report outlines progress and plans to quality assure data in OPTD, maximizing its impact for model validation and verification as well as qualification of fuels for safe and effective use in advanced reactor designs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2003

The Dependence of Target Properties Upon Fresh Crater Morphologies on Mars

Viking and Mars Orbiter Camera (MOC) images for approx. 900 "fresh" complex craters were chosen for inspection from a database of 1599 complex craters for which physical attributes have been obtained using the Mars Orbiter Laser Altimeter (MOLA). Preliminary analysis revealed that many of the complex craters have been significantly modified by various processes of denudation, as well as partial or complete infilling with sediments +/- impact melt +/- lava, considerably shallowing many of the craters. This study confirms that the majority of the 894 sampled complex craters are affected by such processes. In 474 cases (52%), no central peaks were observed owing to erosion or burial. Of the remaining craters with visible central peaks, 159 of the freshest craters were selected for further study. These were chosen based on: the absence of significant infilling materials; the presence of a sharp rim and, typically; the preservation of the finer scale morphologic features of the associated ejecta blanket. These craters were used in order to assess the association between the target type and various central peak morphologies, ejecta types and crater depth/diameter relations.

Whitehead, J.↗

NASA NTRS2018

Toolbox for Research and Exploration (TREX): Investigations of Fine-Grained Materials on Small Bodies

The Toolbox for Research and Exploration (TREX) is a NASA SSERVI (Solar System Exploration Research Virtual Institute) node. TREX (trex.psi.edu) aims to decrease risk to future missions, specifically to the Moon, the Martian moons, and near- Earth asteroids, by improving mission success and assuring the safety of astronauts, their instruments, and spacecraft. TREX studies will focus on characteristics of the fine grains that cover the surfaces of these target bodies - their spectral characteristics and the potential resources (such as H2O) they may harbor. TREX studies are organized into four Themes (Laboratory- Studies, Moon-Studies, Small-Bodies Studies, and Field-Work). In this presentation, we focus on the work targeted by the Small-Bodies Theme. The Small-Bodies' Theme delves into several topics, many which overlap or are synergistic with the other TREX Themes. The main topics include photometry, spectral modeling, laboratory simulations of space weathering processes relevant to asteroids, the assembly of an asteroid regolith database, the dichotomy between nuclear and reflectance spectroscopy, and the dynamical evolution of asteroids and the implications for the retention of volatiles.

Domingue, D. L.↗

DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1987

The NESSUS finite element code

The objective of this development is to provide a new analysis tool which integrates the structural modeling versatility of a modern finite element code with the latest advances in the area of probabilistic modeling and structural reliability. Version 2.0 of the NESSUS finite element code was released last February, and is currently being exercised on a set of problems which are representative of typical Space Shuttle Main Engine (SSME) applications. NESSUS 2.0 allows linear elastostatic and eigenvalue analysis of structures with uncertain geometry, material properties and boundary conditions, which are subjected to a random mechanical and thermal loading environment. The NESSUS finite element code is a key component in a broader software system consisting of five major modules. NESSUS/EXPERT is an expert system under development at Southwest Research Institute, with the objective of centralizing all component-specific knowledge useful for conducting probabilistic analysis of typical Space Shuttle Main Engine (SSME) components. NESSUS/FEM contains the finite element code used for the structural analysis and parameter sensitivity evaluation of these components. The task of parametrizing a finite element mesh in terms of the random variables present is facilitated with the use of the probabilistic data preprocessor in NESSUS/PRE. An external database file is used for managing the bulk of the data generated by NESSUS/FEM.

Dias, J. B.↗

NASA NTRS2011

Asteroid (101955) 1999 RQ36: Spectroscopy from 0.4 to 2.4 Micrometer and Meteorite Analogs

We present reflectance spectra from 0.4 to 2.4 ?m of Asteroid (101955) 1999 RQ36, the target of the OSIRIS-REx spacecraft mission. The visible spectral data were obtained at the McDonald Observatory 2.1-m telescope with the ES2 spectrograph. The infrared spectral data were obtained at the NASA Infrared Telescope Facility using the SpeX instrument. The average visible spectrum is combined with the average near-infrared wavelength spectrum to form a composite spectrum. We use three methods to constrain the compositional information in the composite spectrum of Asteroid (101955) 1999 RQ36 (hereafter RQ36). First, we perform a least-squares search for meteorite spectral analogs using 15,000 spectra from the RELAB database. Three most likely meteorite analogs are proposed based on the least-squares search. Next, six spectral parameters are measured for RQ36 and their values are compared with the ranges in parameter values of the carbonaceous chondrite meteorite classes. A most likely meteorite analog group is proposed based on the depth of overlap in parameter values. The results of the least-squares search and the parametric comparisons point to CIs and/or CMs as the most likely meteorite analogs for RQ36, and COs and CHs as the least likely. RQ36 has a spectrally blue continuum slope that is also observed in carbonaceous chondrites containing magnetite. We speculate that RQ36 is composed of a CM1 -like material. Finally, we compare RQ36 to other B-type asteroids measured by Clark et al. (Clark, B.E. et al. [2010]. J. Geophys. Res. 115, E06005). The results of this comparison are inconclusive. RQ36 is comparable to Themis spectral properties in terms of its albedo, visible spectrum, and near-infrared spectrum from 1.1 to 1.45 micrometers. However, RQ36 is more similar to Pallas in terms of its near-infrared spectrum from 1.6 to 2.3 micrometers. Thus it is possible that B-type asteroids form a spectral continuum and that RQ36 is a transitional object, spectrally intermediate between the two end-members. This is particularly interesting because Asteroid 24 Themis was recently discovered to have H2O ice on the surface (Rivkin, A., Emery, J. [2010]. Nature 464, 1322 1323; Campins, H. et al. [2010a]. Nature 464, 1320 1321).

Clark, Beth Ellen↗

DOE OSTI2021

Predicting impurity spectral functions using machine learning

The Anderson Impurity Model (AIM) is a canonical model of quantum many-body physics. Here we investigate whether machine learning models, both neural networks (NN) and kernel ridge regression (KRR), can accurately predict the AIM spectral function in all of its regimes, from empty orbital, to mixed valence, to Kondo. To tackle this question, we construct two large spectral databases containing approximately 410 000 and 600 000 spectral functions of the single-channel impurity problem. We show that the NN models can accurately predict the AIM spectral function in all of its regimes, with pointwise mean absolute errors down to 0.003 in normalized units. We find that the trained NN models outperform models based on KRR and enjoy a speedup on the order of 10 5 over traditional AIM solvers. Finally, the required size of the training set of our model can be significantly reduced using farthest point sampling in the AIM parameter space, which is important for generalizing our method to more complicated multichannel impurity problems of relevance to predicting the properties of real materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗