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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stopping Force Analysis of 235 U Elemental Fission Product Yields for $E_n$ = 0.11–92.4 MeV

We report most evaluated elemental fission product yield distributions are not experimentally measured. Instead, the majority of evaluated distributions are based on analytic expressions of the Zp-model for relevant cumulative yields. Here we report independent elemental fission product yield distributions of a 235 U target for incident neutron energies ranging from 0.11 MeV through 92.4 MeV. Atomic numbers are calculated by an approach that combines a 2E analysis with a stopping force analysis method, developed within this paper. These analyses are applied to more than 6.1 × 10 6 fission fragment ionization tracks captured within the NIFFTE (Neutron Induced Fission Fragment Tracking Experiment) collaboration fission time projection chamber (fissionTPC). A 3-Z resolution was obtained with the fissionTPC spatial and energy resolutions. Tabulated results are presented for the atomic yield and experimentally derived Zp values as a function of pre-neutron-emission fragment masses for the complete range of incident neutron energies. The stopping-force-derived Zp values tend to support the unchanged charge distribution theory within uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Approximating the particle distribution in rotating and tandem mirror traps

Steady-state distribution functions can be used to calculate stability conditions for modes, radiation energy losses and particle loss rates. Heuristic analytic approximations to these distributions can capture key behaviors of the true distributions such as the relative speeds of different transport processes while possessing computational advantages over their numerical counterparts. In this paper, we motivate and present a closed-form ana- lytic model for a distribution of particles in a centrifugal or tandem mirror. We find that our model outperforms other known models in approximating numerical steady- state simulations outside of a narrow range of low confining potentials. We demonstrate the model’s suitability in the high confining potential regime for applications such as loss-cone stability thresholds, fusion yields and available energy.

Li, G.X.↗
DOE OSTI2025

Efficient Measurement of Length Distribution of 1D Nanoparticles in Solution via Optical Polarimetry

The efficient measurement of the length distribution of nanotubes, nanowires, and other one-dimensional (1D) nanoparticles in solution is important to enable their incorporation into materials and devices and to optimize their processing for properties of interest, such as thermal/electrical conductivity or mechanical strength, in suspensions and composites. We report an electric-field (E-field)-assisted optical-polarimetry technique to measure the length distribution of ensembles of high-aspect-ratio particles in dilute suspension. The degree of alignment of polarizable 1D particles suspended in a fluid under Brownian motion explicitly depends on the E-field strength and the particle length. We show that it is possible to extract the length distribution of 1D nanoparticles suspended in an insulating fluid by applying a range of E-fields and using optical polarimetry to measure the corresponding alignment order parameter. Notably, the method is relatively insensitive to the diameter of the 1D particles, which can be poorly known or vary within a sample. The technique is validated with silver nanowires and carbon nanotubes of known lengths, as well as polymer-depletion-length-separated single-wall carbon nanotube samples with length distributions independently measured with analytical ultracentrifugation. Finally, we demonstrate the ability of the optical-polarimetry technique to quantify changes in the length distribution of ultranarrow, sub-nanometer-diameter single-wall carbon nanotubes under different types and durations of ultrasonication. Within its range of applicability (polarizable 1D nanoparticles in the 0.5 to 15 μm length range, constrained by the voltage stability of the media and the suspended particles), the E-field-assisted optical-polarimetry method is a particularly efficient and accurate method to measure the length distribution of nanowires and nanotubes in suspension.

1D nanoparticles↗
DOE OSTI2021

Relativistic flying laser focus by a laser-produced parabolic plasma mirror

Here, the question of electromagnetic field intensification towards the values typical for strong field quantum electrodynamics is of fundamental importance. One of the most promising intensification schemes is based on the relativistic-flying mirror concept, which shows that the electromagnetic radiation reflected by the mirror will be frequency upshifted by a factor of 4γ 2 (γ is the Lorentz factor of the mirror). In laser-plasma interactions, such a mirror travels with relativistic velocities through plasma and typically has a parabolic form, which is advantageous for light intensification. Thus, a relativistic-flying parabolic mirror reflects the counterpropagating radiation in the form of a focused and flying electromagnetic wave with a high frequency. The relativistic-flying motion of the laser focus makes the electric and magnetic field distributions of the focus complicated, and the mathematical expressions describing the field distributions of the focus become of fundamental interest. We present analytical expressions describing the field distribution formed by an ideal flying mirror which has a perfect reflectance over the entire surface and wavelength range. The peak field strength of an incident laser pulse with a center wavelength of λ 0 and an effective beam radius of w e is enhanced by a factor proportional to γ 3 (w e /λ 0 ) in the relativistic limit. Electron-positron pair production is investigated in the context of invariant fields based on the enhanced electromagnetic field. The pair production rate under the relativistic-flying laser focus is modified by the Lorentz γ-factor and the beam radius-wavelength ratio (w e /λ 0 ). We show that the electron-positron pairs can be created by colliding two counterpropagating relativistic-flying laser focuses in vacuum, each of which is formed when a 180 TW laser pulse is reflected by a relativistic-flying parabolic mirror with γ=12.2.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Fixed Source Sensitivity Calculations for Inertial Confinement Fusion Applications

A numerical code library was developed for the radiation transport code MCNP6.3 to calculate generalized response sensitivity coefficients for fixed source neutron transport problems with applications to inertial confinement fusion (ICF) experiments. The new MCNP6.3 dependency is used to generate a novel time convolution response that represents a neutron time-of-flight (nToF) signal. The traditional suite of macroscopic cross-section sensitivities and constrained fixed source probability distribution sensitivities are available for both the standard and the new response tallies in this library. However, novel sensitivity coefficients for the constrained hyperparameters of analytic fixed source probability distributions are emphasized in this work for their connection to ICF neutron transport models. Particularly, advanced Monte Carlo methods are developed for calculating the sensitivity of a nToF signal to perturbations in an ICF plasma’s ion temperature and burn history as well as perturbations in the target liner mass density and the shape parameters of the nToF detector’s impulse response function. Together, these capabilities form an advanced suite of computational tools that can be used to analyze and extract information from any ICF experimental platform.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The 3D clustering of Lyman Alpha Emitters measured with DESI

We present a clustering analysis of Lyman-$α$ emitters (LAEs) using spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) of candidates selected from the Blanco/DECam Intermediate-Band Imaging Survey (IBIS). We measure the two-point correlation function and the power spectrum, including cross-correlations with DESI quasars. Using both analytical and halo occupation distribution (HOD) simulation-based modeling, we find a linear bias of $b \sim 2.31$--$2.62$ for LAEs over the redshift range $2.26 < z < 3.41$. The analytical modeling also provides constraints on the strength of radiative transfer effects, while the HOD analysis characterizes the LAE-halo connection across multiple models. Finally, we quantify the magnitude of non-perturbative clustering effects such as Fingers of God in the LAE population, providing essential input for the accurate modeling of LAE-based cosmological analyses in forthcoming high-redshift surveys such as DESI-II.

Ebina, H. [UC, Berkeley; LBL, Berkeley] (ORCID:000↗
DOE OSTI2021

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo parameters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy–halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher order statistics, such as cumulants of the density field, help us to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Analytical Model for the Higher Order Moments of Midlatitude Atmospheric Temperature Distributions

Observed distributions of atmospheric temperature are non-Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid-latitude temperature variability based on symmetric advection from a non-symmetric background temperature profile. We then use this model to derive analytical expressions for the higher order moments of temperature distributions. Our results show that nonzero skewness and kurtosis arise due to the nonlinearity of the time-mean meridional temperature profile. The analytical model matches an idealized Held-Suarez atmospheric model, indicating nonlinearity of time-mean temperature in latitude is the dominant contribution to nonzero skewness and kurtosis in synoptic temperature variations. Model analysis further shows decrease in higher order moments due to climate change come roughly equally from changes in mixing length and changes in the background temperature profiles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantum sensing effect of electron tunneling in DQD/analyte complex

We investigate electron tunneling between quantum dots and molecules to propose a quantum sensor. This sensor consists of double quantum dots (DQD) with energy levels specifically tailored to mirror those of the target analyte. By analyzing the spectral distribution of electron localizations in the DQD system, we can delineate the analyte’s spectrum and deduce its composition by comparing it with a reference sample. To understand electron tunneling dynamics within the DQD/analyte complex, we performed three-dimensional computational modeling applying the effective potential approach to the InAs/GaAs heterostructure. In this modeling, we mimicked the analyte spectrum by utilizing a quantum well characterized by a quasi-discrete spectrum. Our calculations reveal the inherent potential of utilizing this method as a highly sensitive and selective sensor.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Electric Distribution System Planning with DERs - High-level Assessment of Tools and Methods

This document addresses analytical tools and methods to support electric distribution system planning with distributed energy resources (DERs) and grid modernization, drawing on leading-edge research and examples from around the United States. In this report, tools include computer models and software, as well as other analytical aids and practices that support distribution system planning. At its core, distribution system planning is about supporting investment decisions. Enhanced and integrated distribution system planning allows utilities to move from a passive reactive role to smart proactive planning with respect to DERs. Distribution system planning can increase transparency around utility distribution system investments before they are brought to the regulatory body for cost recovery, can support deferring traditional infrastructure investments, and can provide information and incentives to customers and third parties about where DERs can help the grid. A growing number of states are beginning to consider a comprehensive distribution system planning process that addresses the costs and benefits of DERs, reflects more choices coming from the customer/third-party domains, is integrated with resource and transmission planning, and incorporates planning scenarios that reflect the dynamic and changing nature of the electricity system

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Stochastic Transport Model for the Cumulative Number of Fissions and Deposited Fission Energy

The stochastic theory of neutron transport is extended to describe the cumulative distribution of fission numbers and deposited fission energy in a multiplying assembly. Solutions for the probability distributions are obtained using analytical approximations and Monte Carlo simulation in lumped geometry and in symmetric homogeneous and heterogeneous spheres. The results show the development of a power-law tail in the steady state fission number and deposited energy distributions when the medium is critical, independent of the fission neutron multiplicity distribution and domain heterogeneity. In contrast, the asymptotic decay is faster than exponential in subcritical media due to rapid chain extinction and in supercritical media due to the increasing probability of chain divergence. Here, a formal asymptotic analysis of the problem in lumped geometry with an arbitrary fission neutron multiplicity confirms the existence of power-law tails at critical.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Establishing and Maintaining Engagement for a Distributional Equity Analysis [Slides]

The Distributional Equity Analysis (DEA) is an analytical framework that allows for the evaluation of the distributional equity of utility resource investments in combination with benefit-cost analysis. This guide serves as a resource for organizations to develop an engagement strategy for a DEA that is accessible, inclusive, and grounded in community interests. It is organized by the guiding principles of engagement for a DEA, summarized in the figure on the right, and offers a compendium of resources for further support and learning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Ex Machina Determination of Structural Correlation Functions

Determining the structural properties of condensed-phase systems is a fundamental problem in theoretical statistical mechanics. In this study, we present a machine learning method that is able to predict structural correlation functions with significantly improved accuracy in comparison with traditional approaches. The usefulness of this ex machina (from the machine) approach is illustrated by predicting the radial distribution functions of two paradigmatic condensed-phase systems, a Lennard-Jones fluid and a hard-sphere fluid, and then comparing those results to the results obtained using both integral equation methods and empirically motivated analytical functions. We find that application of the developed ex machina method typically decreases the predictive error by more than an order of magnitude in comparison with traditional theoretical methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Methods and systems for evaluating data transportability in distribution grids

This document describes systems and techniques for evaluating and improving distribution-grid data transportability. These systems and techniques allow engineers to quantify the data transportability of a communication system within or connected to a distribution grid, which represents an ability to transport in real-time telemetry from source locations (e.g., sensors in the distribution grid) to control mechanisms. Distribution engineers can use the sensor readings to perform grid analytics, control operating parameters, and operate protection systems. Distribution engineers can also use the transportability of the communication system to evaluate the observability of the distribution grid, which represents an ability to combine actual measurements and various types of computations (e.g., analytics, estimators, forecasters) from a system model. Distribution engineers can then generate a sensor allocation plan that indicates the number and location of sensors to maximize observability for a fixed sensor cost and/or minimize sensor cost for predetermined observability.

Taft, Jeffrey D.↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Vortex Structure of Head Bubble in Convective Cloud Starting Plume

Abstract A developing cumulus cloud (Cu) was modeled, and dynamic, thermodynamic, and microphysical properties of an ascending head bubble reproducing the upper part of a developing Cu were investigated. The data for analysis are taken from 10-m-resolution LES of trade wind Cu under BOMEX conditions. The detection of a rising bubble is carried out using wavelet filtering of the velocity fields and microphysical fields, while a low-frequency signal of the filtering is associated with the convective-scale structure of cloud. We substantiate and discuss the representation of the bubble as a vortex ring, and estimate the parameters of this vortex ring. The simplest Hill’s vortex was chosen as a model of a vortex ring inside cloud. Analytical approximations of the radial profiles of the vertical velocity and of conservative quantities (such as total water mixing ratio and liquid water potential temperature inside and outside the bubble) are obtained. The spatial structure of these quantities is investigated using analytical expressions. Analytical models for spatial distributions of liquid water content (LWC) and adiabatic fraction (AF) are also designed and analyzed. The results demonstrate the existence of a cloud core with high values of LWC and AF up to the height of 1800 m. The horizontally averaged value of the adiabatic fraction, calculated analytically using the Hill’s vortex concept, is evaluated as 0.39, which is the typical AF value in the upper parts of such Cu. The vertical profiles of different important quantities characterizing cloud structure are presented. The analysis performed in this study allows us to conclude that a rising vortex ring plays the dominating role in formation of the thermodynamic and microphysical structure of developing Cu. Significance Statement 1) Dynamic and thermodynamic fields of a developing cumulus cloud simulated by high-resolution LES with spectral bin microphysics are separated into convective and turbulent components by means of the wavelet technique. 2) The analysis of convective component of the cloud revealed the existence of a vortex ring at the developing stage of the cloud and evaluate its parameters. 3) The analysis performed in this study allows us to conclude that a rising vortex ring plays the important role in formation of the thermodynamic and microphysical structure of developing Cu. 4) The study provides a novel insight into the cloud–environment interaction. 5) The approximating equations describing the vortex ring can be usefully applied for developing new schemes of convective parameterization.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗