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At least 415 records · Page 23

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NASA NTRS1980

Numerical simulation of plasma insulator interactions in space. Part 2: Dielectric effects

Any process which may be modeled statistically for a simple plasma particle may be modeled by the particle in cell technique. The success of the calculation is then dependent on having a large enough number of particles that the statistical treatment is meaningful. Thus it is possible to include the effects of secondary emission, backscattering, charge sticking and possibly dielectric breakdown, photoemission, and spallation. The first plasma dielectric interaction included in the computer code for simulating plasma insulator interactions is secondary electron emission. A calculated current density vs. voltage curve is presented and compared to an experimental curve.

Chaky, R. C.↗

NASA NTRS2023

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗

DOE OSTI2021

Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks

Restricted Boltzmann machines (RBMs) are simple statistical models defined on a bipartite graph which have been successfully used in studying more complicated many-body systems, both classical and quantum. In this work, we exploit the representation power of RBMs to provide an exact decomposition of many-body contact interactions into one-body operators coupled to discrete auxiliary fields. This construction generalizes the well known Hirsch's transform used for the Hubbard model to more complicated theories such as pionless effective field theory in nuclear physics, which we analyze in detail. Finally, we also discuss possible applications of our mapping for quantum annealing applications and conclude with some implications for RBM parameter optimization through machine learning.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Diabatic paths through the scission point in nuclear fission

An outstanding problem in the theory of nuclear fission is to understand the Hamiltonian dynamics at the scission point. In this work the fissioning nucleus is modeled in self-consistent mean-field theory as a set of generator coordinate method (GCM) configurations passing through the scission point. In contrast to previous methods, the configurations are constructed in the Hartree-Fock approximation with axially symmetric mean fields and using the K -partition numbers as additional constraints. The goal of this work is to find paths through the scission point where the overlaps between neighboring configurations are large. A measure of distance along the path is proposed that is insensitive to the division of the path into short segments. For most of the tested K partitions two shape degrees of freedom are adequate to define smooth paths. However, some of the configurations and candidate paths have sticking points where there are substantial changes in the many-body wave function, especially if quasiparticle excitations are present. The excitation energy deposited in fission fragments arising from thermal excitations in the pre-scission configurations is determined by tracking orbital occupation numbers along the scission paths. This allows us to assess the validity of the well-known scission-point statistical model, in which the scission process is assumed to be fully equilibrated up to the separated fission fragments. The nucleus 236 U is taken as a representative example in the calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Radiological Characterisation of VLLW Produced at High-Energy Particle Accelerators - 19118

In this paper we describe the characterisation method of very-low-level radioactive waste (VLLW) produced at the European Organization for Nuclear Research (CERN). The method is based on the calculation of a preliminary radionuclide inventory using either analytical or Monte Carlo codes, on the measurement of γ-emitters and on the quantification of pure-α, pure-β and low-energy X-ray emitters, called difficult-to-measure (DTM) radionuclides, using the so-called scaling factor (SF) technique. The first stage of the characterisation process is the calculation of the radionuclide inventory, which is a list of radionuclides that can potentially be produced via the interaction of primary and secondary particles with the surrounding media. Once the preliminary radionuclide inventory is obtained, the γ-emitting radionuclides are measured via γ-ray spectrometry on each package of the waste population. The major γemitter, called key nuclide (KN), is also identified. The scaling factor method estimates the activity of DTM radionuclides by checking for a consistent and repeated relationship between the key nuclide and the activity of the difficult to measure radionuclides from samples. Between 2016 and 2018, several hundred samples were collected at CERN and over 1000 radiochemical analyses were performed to estimate robust statistics to quantify scaling factors for the most common DTM radionuclides, such as H-3, Fe-55 and Ni-63. This paper illustrates the sample collection strategy, the tests performed to choose the most appropriate statistical models describing the behaviour of scaling factors and a complete quantification of the uncertainties associated with the characterisation process. Finally, we present the results obtained during the characterisation of over 2000 m{sup 3} radioactive metals and cables disposed of at the French National repository for VLLW. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Photoneutron reaction cross section measurements on Mo 94 and Zr 90 relevant to the p -process nucleosynthesis

The photodisintegration cross sections for the 94 Mo(γ,n) and 90 Zr(γ,n) reactions have been experimentally investigated with quasi-monochromatic photon beams at the High Intensity γ-ray Source (HIγS) facility of the Triangle Universities Nuclear Laboratory (TUNL). The energy dependence of the photoneutron reaction cross sections was measured with high precision from the respective neutron emission thresholds up to 13.5 MeV. Here, these measurements contribute to a broader investigation of nuclear reactions relevant to the understanding of the p-process nucleosynthesis. The results are compared with the predictions of Hauser-Feshbach statistical model calculations using two different models for the dipole γ-ray strength function. Lastly, the resulting 94 Mo(γ,n) and 90 Zr(γ,n) photoneutron stellar reaction rates as a function of temperature in the typical range of interest for the p-process nucleosynthesis show how sensitive the photoneutron stellar reaction rate can be to the experimental data in the vicinity of the neutron threshold.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1981

A stochastic-dynamic model for global atmospheric mass field statistics

A model that yields the spatial correlation structure of atmospheric mass field forecast errors was developed. The model is governed by the potential vorticity equation forced by random noise. Expansion in spherical harmonics and correlation function was computed analytically using the expansion coefficients. The finite difference equivalent was solved using a fast Poisson solver and the correlation function was computed using stratified sampling of the individual realization of F(omega) and hence of phi(omega). A higher order equation for gamma was derived and solved directly in finite differences by two successive applications of the fast Poisson solver. The methods were compared for accuracy and efficiency and the third method was chosen as clearly superior. The results agree well with the latitude dependence of observed atmospheric correlation data. The value of the parameter c sub o which gives the best fit to the data is close to the value expected from dynamical considerations.

Ghil, M.↗

DOE OSTI2021

Use of Physics to Improve Solar Forecast: Physics-Informed Persistence Models for Simultaneously Forecasting GHI, DNI, and DHI

Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (< 1 hour). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM)'s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. Overall, the results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 hours up to 6 hours. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1971

A model for multiple-drop-impact erosion of brittle solids

A statistical model for the multiple-drop-impact erosion of brittle solids was developed. An equation for calculating the rate of erosion is given. The development is not complete since two quantities that are needed to calculate the rate of erosion with use of the equation must be assessed from experimental data. A partial test of the equation shows that it gives results that are in good agreement with experimental observation.

Engel, O. G.↗

NASA NTRS1991

Solar F10.7 radiation - A short term model for Space Station applications

A new method is described for statistically modeling the F10.7 component of solar radiation for 91-day intervals. The resulting model represents this component of the solar flux as a quasi-exponentially correlated, Weibull distributed random variable, and thereby demonstrates excellent agreement with observed F10.7 data. Values of the F10.7 flux are widely used in models of the earth's upper atmosphere because of its high correlation with density fluctuations due to solar heating effects. Because of the direct relation between atmospheric density and drag, a realistic model of the short term fluctuation of the F10.7 flux is important for the design and operation of Space Station Freedom. The method of modeling this flux described in this report should therefore be useful for a variety of Space Station applications.

Vedder, John D.↗

DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Long-term exposure to ambient air pollution (including PM1) and metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS)

Highlights: • Associations of ambient air pollution with metabolic syndrome were scarcely studied. • Data from 15,477 Chinese adults were analyzed. • Long-term exposure to air pollution was associated with metabolic syndrome. • Demographic and lifestyle factors modified the association between metabolic syndrome and air pollution. Little evidence exists about the effects of long-term exposure to ambient air pollution on metabolic syndrome (MetS). This study aimed to determine the association between long-term ambient air pollution and MetS in China. A total of 15,477 adults who participated in the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. MetS was defined based on the recommendation by the Joint Interim Societies. Exposure to air pollutants was assessed using data from monitoring stations and a spatial statistical model (including particles with diameters ≤ 1.0 µm (PM{sub 1}), ≤ 2.5 µm (PM{sub 2.5}), and ≤ 10 µm (PM{sub 10}), sulfur dioxide (SO{sub 2}), nitrogen dioxide (NO{sub 2}), and ozone (O{sub 3})). Two-level logistic regression analyses were utilized to assess the associations between air pollutants and MetS. The prevalence of MetS was 30.37%. The adjusted odds ratio of MetS per 10 µg/m{sup 3} increase in PM{sub 1}, PM{sub 2.5}, PM{sub 10}, SO{sub 2}, NO{sub 2}, and O{sub 3} were 1.12 (95% CI = 1.00–1.24), 1.09 (95% CI = 1.00–1.18), 1.13 (95% CI = 1.08–1.19), 1.10 (95% CI = 1.02–1.18), 1.33 (95% CI = 1.12–1.57), and 1.10 (95% CI = 1.01–1.18), respectively. Stratified analyses indicated that the above associations were stronger in participants with the demographic variables of males, < 50 years of age, and higher income, as well as with the behavioral characteristics of smoking, drinking, and consuming sugar-sweetened soft drinks frequently. This study indicates that long-term exposure to ambient air pollutants may increase the risk of MetS, especially among males, the young to middle aged, those of low income, and those with unhealthy lifestyles.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Peak Oxygen Uptake during and after Long-duration Space Flight

Aerobic capacity (VO2peak) previously has not been measured during or after long-duration spaceflight. PURPOSE: To measure VO2peak and submaximal exercise responses during and after International Space Station (ISS) missions. METHODS: Astronauts (9 M, 5 F: 49 +/- 5 yr, 175 +/- 7 cm, 77.2 +/- 15.1 kg, 40.6 +/- 6.4 mL/kg/min [mean +/-SD]) performed graded peak cycle tests ~90 days before spaceflight, 15 d (FD15) after launch and every ~30 d thereafter during flight, and 1 (R+1), 10 (R+10), and 30 d (R+30) after landing. Oxygen consumption (VO2) and heart rate (HR) were measured from rest to peak exercise, while cardiac output (Q), stroke volume (SV), and arterial-venous oxygen difference (a-vO2diff) were measured only during rest and submaximal exercise. Data were analyzed using mixed-model linear regression. Body mass contributed significantly to statistical models, and thus results are reported as modeled estimates for an average subject. RESULTS: Early inflight (FD15) VO2peak was 17% lower (95% CI = - 22%, -13%) than preflight. VO2peak increased during spaceflight (0.001 L/min/d, P = 0.02) but did not return to preflight levels. On R+1 VO2peak was 15% (95% CI = -19%, -10%) lower than preflight but recovered to within 2% of preflight by R+30 (95% CI = -6%, +3%). Peak HR was not significantly different from preflight at any time. Inflight submaximal VO2 and a-vO2diff were generally lower than preflight, but the Q vs. VO2 slope was unchanged. In contrast, the SV vs. VO2 slope was lower (P < 0.001), primarily due to elevated SV at rest, and the HR vs. VO2 slope was greater (P < 0.001), largely due to elevated HR during more intense exercise. On R+1 although the relationships between VO2 and Q, SV, and HR were not statistically different than preflight, resting and submaximal exercise SV was lower (P < 0.001), resting and submaximal exercise HR was higher (P < 0.002), and a-vO2diff was unchanged. HR and SV returned to preflight levels by R+30. CONCLUSION: In the average astronaut VO2peak was reduced during spaceflight and immediately after landing but factors contributing to lower VO2peak may be different during spaceflight and recovery. Maintaining Q while VO2 is reduced inflight may be suggestive of an elevated blood flow to vascular beds other than exercising muscles, but decreased SV after flight likely reduces Q at peak exertion.

Moore, Alan D., Jr.↗

DOE OSTI2025

First 𝛽-Delayed Two-Neutron Spectroscopy of the 𝑟-Process Nucleus 134 In and Observation of the 𝑖 13/2 Single-Particle Neutron State in 133 Sn

This manuscript reports on the direct observation of a 𝛽-delayed two-neutron emission in a study of 134 In at the ISOLDE Decay Station using neutron spectroscopy. We also report on the first measurement in 𝛽 − decay of the long-sought 13/2 + excited state in 133 Sn, attributed to be the neutron single-particle 𝑖 13/2 orbital. The observation of sequential neutron emission is used to extract the relative population of the 𝑖 13/2 state, which was found to be much smaller than the predictions of the statistical model. The experiment was possible because of the innovative use of a neutron array with neutron discrimination and interaction tracking capabilities. This is the first study of the details of the two-neutron emission for a nucleus, which belongs to the 𝑟-process path. Understanding 𝛽-delayed two-neutron emission probabilities is essential to validate models used in astrophysical 𝑟-process nucleosynthesis calculations. Observing two-neutron emissions in 𝛽 − decay paves the way for new experiments to study energy and angular correlations for 𝛽-delayed multineutron emitters.

Beta decay↗

DOE OSTI2019

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. Herein we introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Mental Models of Software Forecasting

The majority of software engineers resist the use of the currently available cost models. One problem is that the mathematical and statistical models that are currently available do not correspond with the mental models of the software engineers. In an earlier JPL funded study (Hihn and Habib-agahi, 1991) it was found that software engineers prefer to use analogical or analogy-like techniques to derive size and cost estimates, whereas curren CER's hide any analogy in the regression equations. In addition, the currently available models depend upon information which is not available during early planning when the most important forecasts must be made.

Cost Models↗

NASA NTRS2014

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

Active Learning↗

NASA NTRSDate not supplied

The effect of modeling dose uncertainty on low-boom community noise dose-response curves

In logistic dose-response modeling, failing to account for uncertainty in estimated doses can cause an artificial flattening or attenuation of the slope of the summary curve. In Lee et al. [J. Acoust. Soc. Am. 147(4), pp. 2222-2234 (2020)], data from two NASA low-amplitude sonic boom community noise survey tests were modeled using a Bayesian multilevel logistic regression (MLR) statistical model that assumed there was no uncertainty in the noise dose estimates. However, in these community tests, the noise dose uncertainty was estimated by Page et al. [NASA/CR-2014-218180 and NASA/CR-2020-220589/Volume I] using a leave-one-out method. In the current work, a term was added to extend the Bayesian MLR model to account for the estimated noise dose uncertainty quantified in the Page et al. analyses. This uncertainty term was included in two ways, either as classical or as Berkson uncertainty, and yield similar results. When the uncertainty is accounted for in the Bayesian MLR model, the dose-response curves become 5-10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). This result is encouraging for future X-59 community tests whose survey area will be sparsely populated with noise monitors.

X-59↗