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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Making statistical inferences about software reliability

Failure times of software undergoing random debugging can be modelled as order statistics of independent but nonidentically distributed exponential random variables. Using this model inferences can be made about current reliability and, if debugging continues, future reliability. This model also shows the difficulty inherent in statistical verification of very highly reliable software such as that used by digital avionics in commercial aircraft.

Miller, Douglas R.↗
NASA NTRS1978

Information theory and the earth's density distribution

The present paper argues for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia leads to a density distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

Rubincam, D. P.↗
DOE OSTI2024

A simulation study of the ability to detect power distribution perturbations in the texas A&M TRIGA reactor with self-powered neutron detectors

Given the variety of ways that nuclear reactor core power may be perturbed, reactor operators and developers are keen on understanding the accuracy and convergence time during which perturbations in reactor power distribution may be synthesized (i.e., inferred) from an array of in-core radiation detectors. A simulation study was conducted as described herein using a highly detailed model of the Texas A&M Training, Research, Isotopes, General Atomics Reactor, in which an array of self-powered neutron detectors (SPNDs) was considered for input to the power synthesis methodology. The core power synthesis is conducted using a point-based iterative method with an iterative loop built in to ensure working equation consistency. The forward problem of SPND response to simulated perturbations in reactor power was solved for Gaussian peak-type perturbations in the reactor power distribution. These perturbations varied in variance, amplitude, and core location to assess their impact on synthesis error and to determine the number of iterations required for convergence. A relation between the unique resolvability limit and perturbation width was identified such that the maximum synthesis error increased rapidly when the peak width went beneath this limit (a width approximating half the reactor’s fuel pin-to-pin pitch); this resolvability limit is specific to the SPND configuration and fuel segmentation considered herein. The synthesis error increased linearly with perturbation peak amplitude, whereas the convergence time increased nonlinearly. Perturbations located closer to the center of the core were synthesized more accurately, albeit with a higher number of required iterations. These findings provide a qualitative and quantitative understanding of the accuracy and speed at which different types of spatial power perturbations can be resolved in light-water reactors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Adapting Reinforcement Learning Treatment Policies Using Limited Data to Personalize Critical Care

We report reinforcement learning (RL) demonstrates promise for developing effective treatment policies in critical care settings. However, existing RL methods often require large and comprehensive patient data sets and do not readily lend themselves to settings in which certain patient subpopulations are severely underrepresented. In this study, we develop a new method, noisy Bayesian policy updates (NBPU), for selecting high-performing reinforcement learning–based treatment policies for underrepresented patient subpopulations using limited observations. Our method uses variational inference to learn a probability distribution over treatment policies based on a reference patient subpopulation for which sufficient data are available. It then exploits limited data from an underrepresented patient subpopulation to update this probability distribution and adapts its recommendations to this subpopulation. We demonstrate our method’s utility on a data set of ICU patients receiving intravenous blood anticoagulant medication. Our results show that NBPU outperforms state-of-the-art methods in terms of both selecting effective treatment policies for patients with nontypical clinical characteristics and predicting the corresponding policies’ performance for these patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Applying Bayesian inference and deterministic anisotropy to retrieve the molecular structure ∣Ψ(R)∣2 distribution from gas-phase diffraction experiments

Abstract Currently, our general approach to retrieving molecular structures from ultrafast gas-phase diffraction heavily relies on complex ab initio electronic or vibrational excited state simulations to make conclusive interpretations. Without such simulations, inverting this measurement for the structural probability distribution is typically intractable. This creates a so-called inverse problem. Here we address this inverse problem by developing a broadly applicable method that approximates the molecular frame structure ∣Ψ( R , t )∣ 2 distribution independent of these complex simulations. We retrieve the vibronic ground state ∣Ψ( R )∣ 2 for both simulated stretched NO 2 and measured N 2 O. From measured N 2 O, we observe 40 mÅ coordinate-space resolution from 3.75 Å −1 reciprocal space range and poor signal-to-noise, a 50X improvement over traditional Fourier transform methods. In simulated NO 2 diffraction experiments, typical to high signal-to-noise levels predict 100–1000X resolution improvements, down to 0.1 mÅ. By directly measuring the width of ∣Ψ( R )∣ 2 , we open ultrafast gas-phase diffraction capabilities to measurements beyond current analysis approaches. This method has the potential to effectively turn gas-phase ultrafast diffraction into a discovery-oriented technique to probe systems that are prohibitively difficult to simulate.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1993

The evidence for clumpy accretion in the Herbig Ae star HR 5999

Analysis of IUE high- and low-dispersion spectra of the young Herbig Ae star HR 5999 (HD 144668) covering 1978-1992 revealed dramatic changes in the Mg II h and k (2795.5, 2802.7 A) emission profiles, changes in the column density and distribution in radial velocity of accreting gas, and flux in the Ly(alpha), O I, and C IV emission lines, which are correlated with the UV excess luminosity. Variability in the spectral type inferred from the UV spectral energy distribution, ranging from A5 IV-III in high state to A7 III in the low state, was also observed. The trend of earlier inferred spectral type with decreasing wavelength and with increasing UV continuum flux has previously been noted as a signature of accretion disks in lower mass pre-main sequence stars (PMS) and in systems undergoing FU Orionis-type outbursts. Our data represent the first detection of similar phenomena in an intermediate mass (M greater than or equal to 2 solar mass) PMS star. Recent IUE spectra show gas accreting toward the star with velocities as high as plus 300 km/s, much as is seen toward beta Pic, and suggest that we also view this system through the debris disk. The absence of UV lines with the rotational broadening expected given the optical data (A7 IV, V sini=180 plus or minus 20 km/s for this system) also suggests that most of the UV light originates in the disk, even in the low continuum state. The dramatic variability in the column density of accreting gas, is consistent with clumpy accretion, such as has been observed toward beta Pic, is a hallmark of accretion onto young stars, and is not restricted to the clearing phase, since detectable amounts of accretion are present for stars with 0.5 Myr less than t(sub age) less than 2.8 Myr. The implications for models of beta Pic and similar systems are briefly discussed.

Perez, M. R.↗
DOE OSTI2023

The cosmic waltz of Coma Berenices and Latyshev 2 (Group X)

Context. Open clusters (OCs) are fundamental benchmarks where theories of star formation and stellar evolution can be tested and validated. Coma Berenices (Coma Ber) and Latyshev 2 (Group X) are the second and third OCs closest to the Sun, making them excellent targets to search for low-mass stars and ultra-cool dwarfs. In addition, this pair will experience a flyby in 10–16 Myr, making it a benchmark to test pair interactions of OCs. Aims. We aim to analyse the membership, luminosity, mass, phase-space (i.e. positions and velocities), and energy distributions for Coma Ber and Latyshev 2 and test the hypothesis of the mixing of their populations at the encounter time. Methods. We developed a new phase-space membership methodology and applied it to Gaia data. With the recovered members, we inferred the phase-space, luminosity, and mass distributions using publicly available Bayesian inference codes. Then, with a publicly available orbit integration code and members’ positions and velocities, we integrated their orbits 20 Myr into the future. Results. In Coma Ber, we identified 302 candidate members distributed in the core and tidal tails. The tails are dynamically cold and asymmetrically populated. The stellar system called Group X is made of two structures: the disrupted OC Latyshev 2 (186 candidate members) and a loose stellar association called Mecayotl 1 (146 candidate members), and both of them will fly by Coma Ber in 11.3 ± 0.5 Myr and 14.0 ± 0.6 Myr, respectively, and each other in 8.1 ± 1.3 Myr. Conclusions. We study the dynamical properties of the core and tails of Coma Ber and also confirm the existence of the OC Latyshev 2 and its neighbour stellar association Mecayotl 1. Although these three systems will experience encounters, we find no evidence supporting the mixing of their populations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

The evidence for clumpy accretion in the Herbig Ae star HR 5999

Analysis of IUE high- and low-dispersion spectra of the young Herbig Ae star HR 5999 (HD 144668) covering 1978-1992 has revealed dramatic changes in the Mg II h and k (2795.5, 2802.7 A) emission profiles, changes in the column density and distribution in radial velocity of accreting gas, and flux in the Ly(alpha), O I and C IV emission lines, which are correlated with the UV excess luminosity. We also observe variability in the spectral type inferred from the UV spectral energy distribution, ranging from A5 IV-III in high state to A7 III in the low state. The trend of earlier inferred spectral type with decreasing wavelength and with increasing UV continuum flux has previously been noted as a signature of accretion disks in lower mass pre-main sequence stars (PMS) and in systems undergoing FU Orionis-type outbursts. Our data represent the first detection of similar phenomena in an intermediate mass (M equal to or greater than 2 solar mass) PMS star. Recent IUE spectra show gas accreting toward the star with velocities as high as +300 km/s, much as is seen toward beta Pic, and suggest that we also view this system through the debris disk. The absence of UV lines with the rotational broadening expected given the optical data (A7 IV, upsilon sin i = 180 plus or minus 20 km/s) for this system also suggests that most of the UV light originates in the disk, even in the low continuum state. The dramatic variability in the column density of accreting gas, consistent with clumpy accretion, such as has been observed toward beta Pic, is a hallmark of accretion onto young stars, and is not restricted to the clearing phase, since detectable amounts of accretion are present for stars, and is not restricted to the clearing phase, since detectable amounts of accretion are present for stars with 0.5 less than t(sub age) less than 2.8 Myr. The implications for models of beta Pic and similar systems are briefly discussed.

Perez, M. R.↗
DOE OSTI2021

A Bayesian Approach to the Eagar–Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals

Here, this paper focuses on improving the melt pool geometry predictions and quantifying uncertainties using an adapted version of the Eagar–Tsai (E–T) model that incorporates temperature-dependent properties of the material as well as powder conditions. Additionally, Bayesian inference is employed to predict distributions for the E–T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results into the analysis. Monte Carlo uncertainty propagation is then used with these parameter distributions to estimate the melt pool depth and associated uncertainty. Our results for the 316L stainless steel suggest that both the absorptivity and powder bed porosity are strongly influenced by the laser power. In contrast, the scanning speed has only a marginal effect on both the absorptivity and powder bed porosity. We constructed a printability map using the Bayesian E–T model based on power-dependent input parameter values to demonstrate the merit of the approach. The Bayesian approach improved the accuracy in predicting the keyhole regions in the laser power-scan speed parameter space for the 316L stainless steel. Although applied to a specific adaptation of the E–T model, the method put forth can be extended to quantify uncertainties in other numerical models as well as in the estimation of unknown parameters.

316L Stainless Steel (SS).↗
NASA NTRS1979

Sensible and latent heating of the atmosphere as inferred from DST-6 data

The average distribution of convective latent heating, boundary layer sensible heat flux, and vertical velocity are determined for the winter 1976 DST period from GLAS model diagnostics. Key features are the regions of intense latent heating over Brazil, Central Africa, and Indonesia; and the regions of strong sensible heating due to air mass modification over the North Atlantic and North Pacific Oceans.

Herman, G. F.↗
NASA NTRS2005

Energetic Electron Transport in the Inner Magnetosphere During Geomagnetic Storms and Substorms

We propose to examine the relationship of geomagnetic storms and substorms and the transport of energetic particles in the inner magnetosphere using measurements of the auroral X-ray emissions by PIXIE. PIXIE provides a global view of the auroral oval for the extended periods of time required to study stormtime phenomena. Its unique energy response and global view allow separation of stormtime particle transport driven by strong magnetospheric electric fields from substorm particle transport driven by magnetic-field dipolarization and subsequent particle injection. The relative importance of substorms in releasing stored magnetospheric energy during storms and injecting particles into the inner magnetosphere and the ring current is currently hotly debated. The distribution of particles in the inner magnetosphere is often inferred from measurements of the precipitating auroral particles. Thus, the global distributions of the characteristics of energetic precipitating particles during storms and substorms are extremely important inputs to any description or model of the geospace environment and the Sun-Earth connection. We propose to use PIXIE observations and modeling of the transport of energetic electrons to examine the relationship between storms and substorms.

McKenzie, D. L.↗
DOE OSTI2022

Non-universal stellar initial mass functions: large uncertainties in star formation rates at z ≈ 2–4 and other astrophysical probes

ABSTRACT We explore the assumption, widely used in many astrophysical calculations, that the stellar initial mass function (IMF) is universal across all galaxies. By considering both a canonical broken-power-law IMF and a non-universal IMF, we are able to compare the effect of different IMFs on multiple observables and derived quantities in astrophysics. Specifically, we consider a non-universal IMF that varies as a function of the local star formation rate, and explore the effects on the star formation rate density (SFRD), the extragalactic background light, the supernova (both core-collapse and thermonuclear) rates, and the diffuse supernova neutrino background. Our most interesting result is that our adopted varying IMF leads to much greater uncertainty on the SFRD at $z \approx 2-4$ than is usually assumed. Indeed, we find an SFRD (inferred using observed galaxy luminosity distributions) that is a factor of $\gtrsim 3$ lower than canonical results obtained using a universal IMF. Secondly, the non-universal IMF we explore implies a reduction in the supernova core-collapse rate of a factor of $\sim 2$, compared against a universal IMF. The other potential tracers are only slightly affected by changes to the properties of the IMF. We find that currently available data do not provide a clear preference for universal or non-universal IMF. However, improvements to measurements of the star formation rate and core-collapse supernova rate at redshifts $z \gtrsim 2$ may offer the best prospects for discernment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗
DOE OSTI2020

DiHydrogen

DiHydrogen is the second version of the Hydrogen fork of the well-known distributed linear algebra library, Elemental. DiHydrogen is a GPU-accelerated distributed multilinear algebra interface with a particular emphasis on the needs of the scalable distributed deep learning training and inference. DiHydrogen is part of the Livermore Big Artificial Neural Network (LBANN) software stack.

Maruyama, Naoya↗
DOE OSTI2020

Beam-ion Studies in NSTX and NSTX-U (Final Technical Report)

The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

An initial framework for the rapid qualification of long-term creep rupture strength via microstructural modeling

This report describes the development and testing of a new method for extrapolating short-term creep rupture test data to predict long-term rupture strength. The goal of this work is to reduce the time required to qualify new materials for nuclear service by reducing the lead time required for dedicated, long-term material testing to establish key long-term material properties. The new approach described here uses a physics-based model to predict the long-term creep rupture strength of 316H stainless steel using only short-term test data. The key idea is to use Bayesian inference to find the statistical distribution of the model parameters that best explain the short-term rupture data. Because the model is physics-based these parameters are all microstructural quantities that can be measured through detailed material characterization experiments. The Bayesian prior distributions provide a means for incorporating this characterization data into the final model to improve the accuracy of the long-term model predictions. However, where such data is not available the process still produces an accurate model based on an uniformed prior. Our hypothesis is that this approach more accurately extrapolates the short-term test data when compared to current, empirical methods. The report proves this hypothesis using actual long-term rupture data available for 316H, including tests with rupture times greater than 200,000 hours. The general approach developed here could be applied to other materials and other time-dependent material properties. Applying this new technique to develop long-term qualified material properties, potentially in conjunction with other accelerated qualification approaches like staggered qualification test programs, could greatly reduce the time required to qualify new materials for nuclear service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗