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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Towards the island of stability with relativistic energy density functionals

Relativistic energy density functionals (REDF) provide a complete and accurate, global description of nuclear structure phenomena. Modern semi-empirical functionals, adjusted to the nuclear matter equation of state and to empirical masses of deformed nuclei, are applied to studies of shapes of superheavy nuclei. The theoretical framework is tested in a comparison to empirical masses, quadrupole deformations, and energy barriers of actinide nuclei. The model is used in a self-consistent mean-field calculation of spherical, axial and triaxial shapes of superheavy nuclei, alpha-decay energies and lifetimes. The effect of explicit treatment of collective correlations is analyzed in calculations that consistently use a collective Hamiltonian model based on REDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Gaussian-process generative model for the QCD equation of state

We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian process regression method. We impose first-principles theoretical constraints from lattice quantum chromodynamics and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian process regression model to vary freely near the phase transition region, we generate random smooth crossover equations of state with different speeds of sound that do not rely on specific parametrizations. Here, we explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗
DOE OSTI2020

Longitudinal Wobbling Motion in Au 187

The rare phenomenon of nuclear wobbling motion has been investigated in the nucleus 187Au. A longitudinal wobbling-bands pair has been identified and clearly distinguished from the associated signature-partner band on the basis of angular distribution measurements. Theoretical calculations in the framework of the particle rotor model are found to agree well with the experimental observations. This is the first experimental evidence for longitudinal wobbling bands where the expected signature partner band has also been identified, and establishes this exotic collective mode as a general phenomenon over the nuclear chart.

150 ≤ A ≤ 189↗
DOE OSTI2024

Paradigm for approaching the forbidden spontaneous phase transition in the one-dimensional Ising model at a fixed finite temperature

The Ising model describes collective behaviors such as phase transitions and critical phenomena in various physical, biological, economical, and social systems. It is well known that spontaneous phase transition at finite temperature does not exist in the Ising model with short-range interactions in one dimension. Yet, little is known about whether this forbidden phase transition can be approached arbitrarily closely—at fixed finite temperature. Here I use symmetry analysis of the transfer matrix to reveal the existence of spontaneous ultranarrow phase crossover (UNPC) at finite temperature in one class of one-dimensional Ising models on decorated two-leg ladders, in which the crossover temperature T 0 is determined solely by on-rung interactions and decorations, while the crossover width 2 δ T is independently, exponentially reduced ( δ T = 0 means a genuine phase transition) by on-leg interactions and decorations. These findings establish a simple ideal paradigm for realizing an infinite number of one-dimensional Ising systems with spontaneous UNPC at desirable T 0 , which would be characterized in routine laboratory measurements as a genuine first-order phase transition with large latent heat thanks to the ultranarrow δ T (say less than one nanokelvin), paving a way to push the limit in our understanding of phase transitions and the dynamical actions of frustration arbitrarily close to the forbidden regime. Published by the American Physical Society 2024

1-dimensional systems↗
DOE OSTI2015

SU-E-T-630: Predictive Modeling of Mortality, Tumor Control, and Normal Tissue Complications After Stereotactic Body Radiotherapy for Stage I Non-Small Cell Lung Cancer

Purpose: While rates of local control have been well characterized after stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC), less data are available characterizing survival and normal tissue toxicities, and no validated models exist assessing these parameters after SBRT. We evaluate the reliability of various machine learning techniques when applied to radiation oncology datasets to create predictive models of mortality, tumor control, and normal tissue complications. Methods: A dataset of 204 consecutive patients with stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) at the University of Pennsylvania between 2009 and 2013 was used to create predictive models of tumor control, normal tissue complications, and mortality in this IRB-approved study. Nearly 200 data fields of detailed patient- and tumor-specific information, radiotherapy dosimetric measurements, and clinical outcomes data were collected. Predictive models were created for local tumor control, 1- and 3-year overall survival, and nodal failure using 60% of the data (leaving the remainder as a test set). After applying feature selection and dimensionality reduction, nonlinear support vector classification was applied to the resulting features. Models were evaluated for accuracy and area under ROC curve on the 81-patient test set. Results: Models for common events in the dataset (such as mortality at one year) had the highest predictive power (AUC = .67, p < 0.05). For rare occurrences such as radiation pneumonitis and local failure (each occurring in less than 10% of patients), too few events were present to create reliable models. Conclusion: Although this study demonstrates the validity of predictive analytics using information extracted from patient medical records and can most reliably predict for survival after SBRT, larger sample sizes are needed to develop predictive models for normal tissue toxicities and more advanced machine learning methodologies need be consider in the future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Development of a bayesian belief network model for the software reliability assessment of nuclear digital I and C safety systems - 206

Since the digital instrumentation and control systems are expected to play an important role for the safety systems in nuclear power plants (NPPs), the need has emerged to not only establish a basis for incorporating software behavior into digital I and C system reliability models, but also to quantify the failure probability of the software used in NPP digital protection systems. In this study, a Bayesian belief network (BBN) model is developed to quantitatively assess software reliability by estimating the number of faults in a software program considering its software development life cycle (SDLC). The model structure and parameters are established based on the information applicable to NPP safety-related systems and the evidence used to construct and quantify the BBN model was collected from three stages of expert elicitation. The software failure probability is estimated from the number of residual defects in a software program at the end of SDLC phase. As a case study, the BBN model was applied to quantify the software reliability of a typical digital protection software having the size of 50 function points and having the Medium development and validation and verification (V and V) qualities. The developed model can be applied to estimate the failure probability for both developing and deployed safety-related NPP software, and such results can be used to evaluate the quality of the digital I and C systems in addition to estimating potential reactor risk due to software failure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2005

Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.

Remington, Roger W.↗
DOE OSTI2024

Collective excitations and low-energy ionization signatures of relativistic particles in silicon detectors

Abstract Solid-state detectors with a low energy threshold have several applications, including searches of non-relativistic halo dark-matter particles with sub-GeV masses. When searching for relativistic, beyond-the-Standard-Model particles with enhanced cross sections for small energy transfers, a small detector with a low energy threshold may have better sensitivity than a larger detector with a higher energy threshold. In this paper, we calculate the low-energy ionization spectrum from high-velocity particles scattering in a dielectric material. We consider the full material response including the excitation of bulk plasmons. We generalize the energy-loss function to relativistic kinematics, and benchmark existing tools used for halo dark-matter scattering against electron energy-loss spectroscopy data. Compared to calculations commonly used in the literature, such as the Photo-Absorption-Ionization model or the free-electron model, including collective effects shifts the recoil ionization spectrum towards higher energies, typically peaking around 4–6 electron-hole pairs. We apply our results to the three benchmark examples: millicharged particles produced in a beam, neutrinos with a magnetic dipole moment produced in a reactor, and upscattered dark-matter particles. Our results show that the proper inclusion of collective effects typically enhances a detector’s sensitivity to these particles, since detector backgrounds, such as dark counts, peak at lower energies.

Physics↗
DOE OSTI2016

Modeling fuels and fire effects in 3D: Model description and applications

Scientists and managers critically need ways to assess how fuel treatments alter fire behavior, yet few tools currently exist for this purpose. In this work, we present a spatially-explicit-fuel-modeling system, FuelManager, which models fuels, vegetation growth, fire behavior (using a physics-based model, FIRETEC), and fire effects. FuelManager's flexible approach facilitates modeling fuels across a wide range of detail. Large trees or shrubs with specific coordinates are modeled as individual “Plants”, while understory plants are modeled as collections of plants called “LayerSets”. Both Plants and LayerSets contain various fuel particles (leaves, needles, twigs) with various properties including shape, size and surface area to volume ratio. A wide range of vegetation and treatments can be modeled, analyzed quantitatively and visualized in a 3D viewer. We describe the modeling approach and demonstrate fuel modeling at different levels of detail, fuel treatment and fire effects capabilities. Lastly, detailed model equations are provided in the Appendices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Disentangling contributions to small-system collectivity via scans of light nucleus-nucleus collisions

The observation of multiparticle azimuthal correlations in high-energy small-system collisions has led to intense debate on its origin and the possible coexistence from two competing theoretical scenarios: one based on initial-state intrinsic momentum anisotropy, and the other based on final-state interaction model (FSM) collective response to the collision geometry. To complement the previous scan of asymmetric collision systems ( p +Au, d +Au, and He+Au), we propose a scan of small symmetric collision systems at the Brookhaven National Laboratory Relativistic Heavy Ion Collider, such as C+C, O+O, Al+Al, and Ar+Ar$\sqrt{s_\text{NN}}$ = 0.2 TeV , to provide further insights in disentangling contributions from these two scenarios. These symmetric small systems have the advantage of providing a better controlled initial geometry dominated by the average shape of the overlap region as opposed to fluctuation-driven geometries in asymmetric systems. In this work, a transport model is employed to investigate the expected geometry response in the FSM scenario. Different trends of elliptic flow with increasing charge particle multiplicity are observed between symmetric and asymmetric systems, whereas triangular flow appears to show a similar behavior. Furthermore, a comparison of O+O collisions at $\sqrt{s_\text{NN}}$ = 0.2 TeV and at $\sqrt{s_\text{NN}}$ = 2.76 ₋ 7 TeV as proposed at the CERN Large Hadron Collider, provides a unique opportunity to disentangle the collision geometry effects at the nucleon level from those arising from subnucleon fluctuations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Pair-truncated shell-model analysis of nuclei around mass 130

Low-lying states for even-even, odd-mass, and doubly odd nuclei in the mass A{approx}130 region are systematically investigated using a pair-truncated shell model. In this model the collective nucleon pairs with angular momenta zero and two are the basic ingredients for even-even nuclei. Additional unpaired nucleons are added to the even-even core for a description of odd-mass and doubly odd nuclei. The effective interactions consist of single-particle energies and monopole and quadrupole pairing plus quadrupole-quadrupole interactions, whose strengths are assumed to be linearly changed as functions of the number of nucleons so as to describe the level schemes of the even-even and odd-mass nuclei. Energy levels of the low-lying collective states for even-even Xe, Ba, Ce, and Nd isotopes are reproduced very well along with intraband and interband B(E2) values, which simulate the typical features of the O(6) limit of the interacting boson model. For odd-mass and doubly odd nuclei, complicated level schemes and electromagnetic moments are in excellent agreement with the experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications

The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.

Bolme, David↗
DOE OSTI2016

Evaluation of Lower East Fork Poplar Creek Mercury Sources

This report summarizes a 3-year research project undertaken to better understand the nature and magnitude of mercury (Hg) fluxes in East Fork Poplar Creek (EFPC). This project addresses the requirements of Action Plan 1 in the 2011 Oak Ridge Reservation-wide Comprehensive Environmental Response, Compensation, and Liability Act Five Year Review (FYR). The Action Plan is designed to address a twofold 2011 FYR issue: (1) new information suggests mobilization of mercury from the upper and lower EFPC streambeds and stream banks is the primary source of mercury export during high-flow conditions, and (2) the current Record of Decision did not address the entire hydrologic system and creek bank or creek bed sediments. To obtain a more robust watershed-scale understanding of mercury sources and processes in lower EFPC (LEFPC), new field and laboratory studies were coupled with existing data from multiple US Department of Energy programs to develop a dynamic watershed and bioaccumulation model. LEFPC field studies for the project focused primarily on quantification of streambank erosion and an evaluation of mercury dynamics in shallow groundwater adjacent to LEFPC and potential connection to the surface water. The approach to the stream bank study was innovative in using imagery from kayak floats’ surveys from the headwaters to the mouth of EFPC to estimate erosion, coupled with detailed bank soil mercury analyses. The goal of new field assessments and modeling was to generate a more holistic and quantitative understanding of the watershed and the sources, flux, concentration, transformation, and bioaccumulation of inorganic mercury (IHg) and methylmercury (MeHg). Model development used a hybrid approach that dynamically linked a spreadsheet-based physical and chemical watershed model to a systems dynamics, mercury bioaccumulation model for key fish species. The watershed model tracks total Hg and MeHg fluxes and concentrations by examining upstream inputs, floodplain runoff, floodplain leaching, bank soil erosion, and periphyton matrix dynamics. The bioaccumulation model tracks the feeding, growth, and mercury assimilation of representative individual fish through their typical life span using key inputs of fish size, water temperature, and diet. The LEFPC watershed was divided into five modeling reaches, and fluxes and concentrations are assessed at this spatial scale. Following are the key findings of the field and laboratory studies and the watershed and bioaccumulation modeling: • The greatest flux of total mercury (HgT) in LEFPC is related to stormflow transport of Hg-contaminated solids entering the creek because of bank erosion in the upper reaches of the creek. • The second greatest flux originates from upper EFPC (Station 17 representing the exit stream sampling point near the boundary of the Y-12 Complex), and appears to control base flow fluxes. • The observed increase in MeHg concentration and flux from upstream to downstream is related primarily to instream methylation by periphyton and other biological activity. • A meaningful substantial reduction of the HgT flux in LEFPC would require addressing the flux of HgT originating from bank erosion and from Station 17. • Actions to reduce LEFPC floodplain leaching and runoff would not produce much of an impact on HgT or MeHg concentrations or fluxes unless other major sources are eliminated first. This project addresses the Action Plan goal to evaluate the role of LEFPC bank soil sources and to consider the entire EFPC hydrologic system. Model conclusions are dependent on the data available at the time of this assessment. However, a robust understanding and quantification for some mercury-related parameters and relationships is still lacking; there is a continued need for field data collection and modeling improvements. Model predictions should be viewed cautiously, with comparisons of the magnitude of predictions between scenarios being more valid than absolute predictions of concentrations or fluxes. With continued updates and refinement, the watershed-scale model can be a useful, valuable tool for future EFPC research prioritization, technology development, and remedial decision-making.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Proton production in relativistic heavy ion collisions; comparison with a thermodynamical model

Experimental results concerning proton production in nuclear collisions, obtained at Saturne with the Diogene 4 pi facility, are compared with the predictions of a thermodynamical model, using collective velocity distributions combined with a statistical thermodynamics in local rest frames. Experimental differential cross sections for alpha + nucleus and Neon + nucleus central collisions at incident energies between 200 and 800 MeV per nucleon are well reproduced by the model, for an angular range 30-110 degrees in the laboratory system. Extracted values of the temperatures are compared with those given by other authors.

NASA Program Biomedical Research↗
NASA NTRSDate not supplied

Implementing Geometric Surface Imperfections into Sandwich Composite Cylinder Finite Element Method Models

The buckling responses of certain cylindrical shell structures are extremely sensitive to geometric surface imperfections. The NASA Engineering and Safety Center (NESC) Shell Buckling Knockdown Factor Project (SBKF) is conducting research to develop analysis-based buckling design recommendations. Experiments are used to verify the analysis-based factors, but the sensitivity of the test articles to geometric imperfections requires implementing as-manufactured imperfections into high-fidelity finite element method models. Data collection methods such as structured light scanning are used for all geometric surface data used in this work. Common preprocessing and visualization steps used in SBKF are discussed, and steps on how surface scans are prepared for implementation into a finite element model is described. The Python Tool for Implementing Geometric Imperfections in Reduced Structures (Py_TIGIRS), written specifically for the use with SBKF, is briefly described and uses eight functions to extract, modify, and write geometric imperfections into Abaqus input files. Results of the pre-processing methods and results from Py_TIGIRS are provided and compared for Composite Test Article (CTA) 8.2B. Excellent agreement between the visualized scan data and the FEM-extracted geometry is demonstrated. A brief example of why geometric surface imperfections are significant in nonlinear numerical analyses for thin cylinders in axial compression is provided as motivation to use tools such as Py_TIGIRS. Future development of Py_TIGIRS including expansion to structures of arbitrary geometry is planned.

Sandwich structures↗
NASA NTRS1993

An information model for use in software management estimation and prediction

This paper describes the use of cluster analysis for determining the information model within collected software engineering development data at the NASA/GSFC Software Engineering Laboratory. We describe the Software Management Environment tool that allows managers to predict development attributes during early phases of a software project and the modifications we propose to allow it to develop dynamic models for better predictions of these attributes.

Li, Ningda R.↗
DOE OSTI2022

A Smart Alarm for the CEBAF Injector

We present initial results from a proof-of-concept ?smart alarm? for the CEBAF injector. Because of the injector's large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. Our approach leverages a trained neural network to not only identify an anomalous machine state, but also to identify the root-cause by pinpointing the specific element or region responsible. We developed an inverse model trained on data collected during normal operations. Using the inverse model, measurements from the machine are used to compute machine settings, which are then compared to EPICS setpoints. Instances when predictions differ from EPICS setpoints by a user-defined threshold are flagged as anomalies, and the user is alerted to the issue. We present the results of our data collection efforts, model training and performance, and initial performance metrics.

Tennant, C.↗