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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Parameter Dependencies of Early-Stage Tangential Discontinuity-Driven Foreshock Bubbles in Local Hybrid Simulations

Foreshock bubbles (FBs) are significant foreshock transients that can accelerate particles and disturb the magnetosphere-ionosphere system. In the kinetic formation model, foreshock ions interact with the discontinuity by performing partial gyrations to generate currents that change the magnetic field topology around the discontinuity. However, how different foreshock ion properties affect the growth of the field variations is not well understood. Therefore, we use 2-D local hybrid simulations to study the effects of different foreshock ion distributions and properties on the growth of tangential discontinuity (TD)-driven FBs. We discover that for a gyrophase-bunched distribution with an initial phase where the guiding center is on the other side of the TD, the foreshock ions gyrate together across the TD, causing more foreshock ions to cross the TD and leading to a faster expansion of the structure than for a Maxwellian distribution. A ring distribution also yields higher expansion speeds because of the higher projected velocity into the new perpendicular direction. For Maxwellian distributions, there are positive and linear correlations of the FB expansion speeds with the initial foreshock ion densities, thermal speeds, parallel speeds, and sine of the TD magnetic shear angles. These parameter dependencies grow in strength as the structures evolve with time. The foreshock ion distributions and properties that lead to stronger currents produce more significant magnetic field variations and higher expansion speeds. Our study helps quantify the formation and expansion of FBs to forecast their space weather effects and contribution to shock acceleration.

Andrew Vu↗
DOE OSTI2020

Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1994

System identification of the Large-Angle Magnetic Suspension Test Fixture (LAMSTF)

The Large-Angle Magnetic Suspension Test Fixture (LAMSTF), a laboratory-scale research project to demonstrate the magnetic suspension of objects over wide ranges of attitudes, has been developed. This system represents a scaled model of a planned Large-Gap Magnetic Suspension System (LGMSS). The LAMSTF consists of a small cylindrical permanent magnet suspended element which is levitated above a planar array of five electromagnets mounted in a circular configuration. The cylinder is a rigid body and can be controlled to move in five independent degrees of freedom. Six position variables are sensed indirectly by using infrared light-emitting diodes and light-receiving phototransistors. The motion of the suspended cylinder is in general nonlinear and hence only the linear, time-invariant perturbed motion about an equilibrium state is considered. One of the main challenges in this project is the control of the suspended element over a wide range of orientations. An accurate dynamic model plays an essential role in controller design. The analytical model is first derived and open-loop characteristics discussed. The system is shown to be highly unstable and requires feedback control for system identification. Projection filters are first proposed to identify the state space model from closed-loop input/output test data in the time domain. This method is then extended to identify linear systems from the frequency test data. A canonical transformation matrix is also derived to transform the identified state space model into the physical coordinate. The LAMSTF system is stabilized by using a linear quadratic regulator (LQR) feedback controller for closed-loop identification. The rate information is obtained by calculating the back difference of the sensed position signals. Only the closed-loop random input/output data are recorded. Preliminary results from numerical simulations demonstrate that the identified system model is fairly accurate from either time domain or frequency-domain data. Experiments will be performed to validate the proposed closed-loop identification algorithms.

Huang, Jen-Kuang↗
NASA NTRS2024

Summary and Annotated Bibliography of Measurement Error Corrections with Potential Application in Future Quesst Mission Community Noise Studies

This document is motivated by likely needs of the Quesst mission community response tests, which will culminate in data collection and estimation of dose-response regression relationships for consideration by domestic and international aviation regulators. Furthermore, basic research questions evaluating interactions between rates of community annoyance, dose levels, and indicators of the presence of rattle, vibration, and startle hinge on hypothesis testing in the context of regression models. For a variety of reasons, noise doses may be known only imprecisely and may not reflect the actual level experienced by responding subjects. These differences between true dose and estimated dose, be they systematic or random, constitute covariate measurement error. Available statistics literature speaks to the impacts of measurement error on regression models, both in terms of bias in estimated coefficients and predicted values, and in terms of the loss of statistical power for hypothesis testing. Given the particulars of a categorical annoyance response variable and a continuous noise dose predictor variable subject to measurement error during testing, the emphasis of this report is on findings and methods pertinent to generalized linear (and mixed) models likely to be employed during the Quesst mission community tests. We reach the following conclusions: 1. Of four reviewed methods, structural Bayesian measurement error models and simulation extrapolation (SIMEX) may be the most readily applicable to Quesst mission community noise study objectives. 2. If warranted, a linear measurement model can help model systematic sources of measurement error that the classical measurement error does not. 3. For its ready implementation and small additional input requirements, simulation extrapolation may be ideally suited for addressing secondary research questions involving interactions between annoyance, noise dose, and other factors through hypothesis testing. 4. For their flexibility and ability to propagate uncertainty, structural Bayesian hierarchical models have great appeal for mission purposes; some care may be needed in developing appropriate probability models describing actual noise exposure during testing. An annotated bibliography logs additional papers and resources that may be of value to analysts in other projects and disciplines.

Dose-Response Model↗
DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗
NASA NTRS2002

Uncertainties in Estimates of the Risks of Late Effects from Space Radiation

The health risks faced by astronauts from space radiation include cancer, cataracts, hereditary effects, and non-cancer morbidity and mortality risks related to the diseases of the old age. Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Within the linear-additivity model, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain a Maximum Likelihood estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including ISS, lunar station, deep space outpost, and Mar's missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time, and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative objective's, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits.

Cucinotta, F. A.↗
DOE OSTI2026

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗
NASA NTRS2004

Uncertainties in estimates of the risks of late effects from space radiation

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗
DOE OSTI2020

Materials Data on GaCuO2 by Materials Project

CuGaO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Cu1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Cu–O bond lengths are 1.87 Å. Ga3+ is bonded to six equivalent O2- atoms to form edge-sharing GaO6 octahedra. All Ga–O bond lengths are 2.02 Å. O2- is bonded to one Cu1+ and three equivalent Ga3+ atoms to form a mixture of distorted corner and edge-sharing OGa3Cu tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on GaAgO2 by Materials Project

AgGaO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Ag1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Ag–O bond lengths are 2.13 Å. Ga3+ is bonded to six equivalent O2- atoms to form edge-sharing GaO6 octahedra. All Ga–O bond lengths are 2.02 Å. O2- is bonded to one Ag1+ and three equivalent Ga3+ atoms to form a mixture of distorted edge and corner-sharing OGa3Ag tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlAuO2 by Materials Project

AuAlO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Au1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Au–O bond lengths are 2.15 Å. Al3+ is bonded to six equivalent O2- atoms to form edge-sharing AlO6 octahedra. All Al–O bond lengths are 1.94 Å. O2- is bonded to one Au1+ and three equivalent Al3+ atoms to form a mixture of distorted corner and edge-sharing OAl3Au tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on InAuO2 by Materials Project

AuInO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Au1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Au–O bond lengths are 2.08 Å. In3+ is bonded to six equivalent O2- atoms to form edge-sharing InO6 octahedra. All In–O bond lengths are 2.21 Å. O2- is bonded to one Au1+ and three equivalent In3+ atoms to form a mixture of distorted edge and corner-sharing OIn3Au tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on InAgO2 by Materials Project

AgInO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Ag1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Ag–O bond lengths are 2.12 Å. In3+ is bonded to six equivalent O2- atoms to form edge-sharing InO6 octahedra. All In–O bond lengths are 2.21 Å. O2- is bonded to one Ag1+ and three equivalent In3+ atoms to form a mixture of distorted edge and corner-sharing OIn3Ag tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on PrCr2Si2C by Materials Project

PrCr2Si2C crystallizes in the tetragonal P4/mmm space group. The structure is three-dimensional. Pr3+ is bonded in a distorted linear geometry to eight equivalent Si4- and two equivalent C4- atoms. All Pr–Si bond lengths are 3.10 Å. Both Pr–C bond lengths are 2.70 Å. Cr+4.50+ is bonded in a distorted linear geometry to four equivalent Si4- and two equivalent C4- atoms. All Cr–Si bond lengths are 2.47 Å. Both Cr–C bond lengths are 2.00 Å. Si4- is bonded in a 9-coordinate geometry to four equivalent Pr3+, four equivalent Cr+4.50+, and one Si4- atom. The Si–Si bond length is 2.53 Å. C4- is bonded to two equivalent Pr3+ and four equivalent Cr+4.50+ atoms to form corner-sharing CPr2Cr4 octahedra. The corner-sharing octahedral tilt angles are 0°.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on PrSi2Mo2C by Materials Project

PrMo2Si2C crystallizes in the tetragonal P4/mmm space group. The structure is three-dimensional. Pr3+ is bonded in a distorted linear geometry to eight equivalent Si4- and two equivalent C4- atoms. All Pr–Si bond lengths are 3.23 Å. Both Pr–C bond lengths are 2.72 Å. Mo+4.50+ is bonded to four equivalent Si4- and two equivalent C4- atoms to form a mixture of distorted face, edge, and corner-sharing MoSi4C2 octahedra. The corner-sharing octahedral tilt angles are 0°. All Mo–Si bond lengths are 2.60 Å. Both Mo–C bond lengths are 2.12 Å. Si4- is bonded in a 9-coordinate geometry to four equivalent Pr3+, four equivalent Mo+4.50+, and one Si4- atom. The Si–Si bond length is 2.42 Å. C4- is bonded to two equivalent Pr3+ and four equivalent Mo+4.50+ atoms to form corner-sharing CPr2Mo4 octahedra. The corner-sharing octahedral tilt angles are 0°.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Cu2SO4 by Materials Project

Cu2SO4 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Cu1+ is bonded in a distorted linear geometry to four equivalent O2- atoms. There are two shorter (1.89 Å) and two longer (2.68 Å) Cu–O bond lengths. S6+ is bonded in a tetrahedral geometry to four equivalent O2- atoms. All S–O bond lengths are 1.50 Å. O2- is bonded in a 2-coordinate geometry to two equivalent Cu1+ and one S6+ atom.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on SrLiN by Materials Project

LiSrN crystallizes in the tetragonal P4_2/mmc space group. The structure is three-dimensional. Li1+ is bonded in a linear geometry to two equivalent N3- atoms. Both Li–N bond lengths are 1.97 Å. Sr2+ is bonded to four equivalent N3- atoms to form a mixture of edge and corner-sharing SrN4 tetrahedra. All Sr–N bond lengths are 2.65 Å. N3- is bonded to two equivalent Li1+ and four equivalent Sr2+ atoms to form a mixture of edge and corner-sharing NSr4Li2 octahedra. The corner-sharing octahedra tilt angles range from 0–63°.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlCuO2 by Materials Project

CuAlO2 crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Cu1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Cu–O bond lengths are 1.88 Å. Al3+ is bonded to six equivalent O2- atoms to form edge-sharing AlO6 octahedra. All Al–O bond lengths are 1.93 Å. O2- is bonded to one Cu1+ and three equivalent Al3+ atoms to form a mixture of distorted edge and corner-sharing OAl3Cu tetrahedra.

36 MATERIALS SCIENCE↗