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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Accelerometers in flow fields: a structural analysis of the chopped dummy in-pile tube

Four tests characterizing the structural response of the Chopped-Dummy In-Pile tube (CDIPT) experiment design were measured in the Hydro-Mechanical Fuel Test Facility (HMFTF). Four different test configurations were tried. These configurations tested the pressure drop and flow impact of various plate configurations and flow control orifices to be used later at different reactor power levels. Accelerometers were placed on the test vehicle and flow simulation housing. A total of five accelerometers were used with one on the top and bottom of the flow simulator and vehicle, and one on the outside of the flow simulator. Data were collected at a series of flow rates for 5 seconds each at an acquisition rate of 2 kHz for a Nyquist frequency of 1 kHz. The data were then analyzed using a Fast Fourier Transform (FFT) algorithm. The results show very coherent vibrations of the CDIPT experiment on the order of 50 Hz in frequency and 0.01 m/s{sup 2} in magnitude. The coherent vibrations, although small in magnitude pose a potential design problem if the frequencies coincide with the natural frequency of the fueled plates or test vehicle. The accelerometer data was integrated and combined to create a 3D trace of the experiment during the test. The merits of this data as well as further anomalies and artifacts are also discussed as well as their relation to the instrumentation and experiment design. (authors)

42 ENGINEERING↗
DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Coalescence kinetics in surfactant stabilized emulsions: Evolution equations from direct numerical simulations

Lattice Boltzmann simulations were used to study the coalescence kinetics in emulsions with amphiphilic surfactant, under neutrally buoyant conditions, and with a significant kinematic viscosity contrast between the phases (emulating water in oil emulsions). The 3D simulation domain was large enough (256 3rd power -- 10 7th power grid points) to obtain good statistics with droplet numbers ranging from a few thousand at early times to a few hundred near equilibrium. Increased surfactant contents slowed down the coalescence rate between droplets due to the Gibbs-Marangoni effect, and the coalescence was driven by a quasi-turbulent velocity field. The kinetic energy decayed at a relatively slow rate at early times, due to conversion of interfacial energy to kinetic energy in the flow during coalescence. Phenomenological, coupled differential equations for the mean droplet diameter D(t) and the number density nd(t) were obtained from the simulation data and from film draining theories. Local (in time) power law exponents for the growth of the mean diameter (and for the concomitant decrease of nd) were established in terms of the instantaneous values of the kinetic energy, coalescence probability, Gibbs elasticity, and interfacial area. The model studies indicated that true power laws for the growth of the droplet size and decrease of the number of droplets with time may not be justified, since the exponents derived using the phenomenological model were time dependent. In contrast to earlier simulation results for symmetric blends with surfactant, we found no evidence for stretched logarithmic scaling of the formD -- [ln (ct)]a for the morphology length, or exponential scalings associated with arrested growth, on the basis of the phenomenological model.

58 GEOSCIENCES↗
DOE OSTI2015

TU-G-210-00: Treatment Planning Strategies, Modeling, Control

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Iterative map-making with two-level preconditioning for polarized cosmic microwave background data sets: A worked example for ground-based experiments

Context. An estimation of the sky signal from streams of time ordered data (TOD) acquired by the cosmic microwave background (CMB) experiments is one of the most important steps in the context of CMB data analysis referred to as the map-making problem. The continuously growing CMB data sets render the CMB map-making problem progressively more challenging in terms of computational cost and memory in particular in the context of ground-based experiments with their operational limitations as well as the presence of contaminants. Aims. We study a recently proposed, novel class of the Preconditioned Conjugate Gradient (PCG) solvers which invoke two-level preconditioners in the context of the ground-based CMB experiments. We compare them against the PCG solvers commonly used in the map-making context considering their precision and time-to-solution. Methods. We compare these new methods on realistic, simulated data sets reflecting the characteristics of current and forthcoming CMB ground-based experiments. We develop a divide-and-conquer implementation of the approach where each processor performs a sequential map-making for a subset of the TOD. Results. We find that considering the map level residuals, the new class of solvers permits us to achieve a tolerance that is better than the standard approach by up to three orders of magnitude, where the residual level often saturates before convergence is reached. This often corresponds to an important improvement in the precision of the recovered power spectra in particular on the largest angular scales. The new method also typically requires fewer iterations to reach a required precision and therefore shorter run times are required for a single map-making solution. However, the construction of an appropriate two-level preconditioner can be as costly as a single standard map-making run. Nevertheless, if the same problem needs to be solved multiple times, for example, as in Monte Carlo simulations, this cost is incurred only once, and the method should be competitive, not only as far as its precision is concerned but also its performance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The Coupled Adjoint-State Equation in forward and inverse linear elasticity: Incompressible plane stress

A persistent challenge present in inverse or parameter estimation problems with interior data is how to deal with uncertainty in the boundary conditions employed in the forward or state model. In this work we focus on a linear plane stress inverse elasticity problem with measured displacement data where one component of the measured displacement field is known with considerably greater precision than the other. This situation is commonly encountered when the displacement field is measured using ultrasound or optical coherence tomography. We present a novel computational formulation in which no displacement or traction boundary conditions are assumed. The formulation results in coupling the state and adjoint equations, that are typically uncoupled when a well-posed state model is available. Two variants of residual-based stabilization are added. Our approach is applied to a simulated data set and experimental data from an ultrasound phantom.

42 ENGINEERING↗
DOE OSTI2015

TU-G-210-01: Modeling for Breast and Brain HIFU Treatment Planning

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Motion tomography via occupation kernels

The goal of motion tomography is to recover a description of a vector flow field using measurements along the trajectory of a sensing unit. In this paper, we develop a predictor corrector algorithm designed to recover vector flow fields from trajectory data with the use of occupation kernels developed by Rosenfeld et al. [9,10]. Specifically, we use the occupation kernels as an adaptive basis; that is, the trajectories defining our occupation kernels are iteratively updated to improve the estimation in the next stage. Initial estimates are established, then under mild assumptions, such as relatively straight trajectories, convergence is proven using the Contraction Mapping Theorem. We then compare the developed method with the established method by Chang et al. [5] by defining a set of error metrics. Here, we found that for simulated data, where a ground truth is available, our method offers a marked improvement over [5]. For a real-world example, where ground truth is not available, our results are similar results to the established method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

TU-G-210-02: TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-G-210-03: Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS Urvi Vyas – Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

PySpawn: Software for Nonadiabatic Quantum Molecular Dynamics

The ab initio multiple spawning (AIMS) method enables nonadiabatic quantum molecular dynamics simulations in an arbitrary number of dimensions, with potential energy surfaces provided by electronic structure calculations performed on-the-fly. However, the intricacy of the AIMS algorithm complicates software development, deployment on modern shared computer resources, and post-simulation data analysis. PySpawn is a nonadiabatic molecular dynamics software package that addresses these issues. Here, the program is designed to be easily interfaced with electronic structure software, and an interface to the TeraChem software package is described here. PySpawn introduces a task-based reorganization of the AIMS algorithm, allowing fine-grained restart capability and setting the stage for efficient parallelization in a future release. PySpawn includes a user-friendly and interactive Python analysis module that will enable novice users to painlessly adopt AIMS. As a demonstration of PySpawn’s simulation capability and analysis module, we report complete active space self-consistent field–based AIMS simulations of the 1,2- dithienyl-1,2-dicyanoethene molecule, a promising molecular photoswitch.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

SICLOPS - A system of computer programs for rectified mapping of airborne scanner imagery

This paper describes SICLOPS, a system of computer programs, which has been developed to perform geometrically corrected line-by-line mapping of airborne multispectral scanner data to ground coordinates and to estimate ground areas. As inputs, SICLOPS accepts aircraft attitude and positional information furnished by ancillary aircraft equipment, plus ground truth information giving the location, in map grid coordinates, of a number of recognizable landmarks on the ground. The geometric correction and mapping procedure locates the scan lines, or the pixels on each line, in terms of map grid coordinates. The area estimation procedure gives ground area for each pixel or for a predesignated parcel specified in map grid coordinates. The results of exercising the system with simulated data showed the uncorrected video and corrected imagery and produced area estimates accurate to better than 99.7%. Real input data, though prepared, have not yet been processed by the system.

Spencer, M. M.↗
NASA NTRS2008

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Tilton, James C.↗
NASA NTRSDate not supplied

The WFIRST CGI Integral Field Spectrograph: Requirements and Performance Predictions

The WFIRST coronagraphic instrument (CGI) will demonstrate exoplanet spectroscopy using an integral field spectrograph (IFS). The CGI IFS, being designed and built at Goddard Space Flight Center, has a spectral resolution of R50 and is designed to accommodate a 20% bandpass spanning 600- 970 nm. The IFS is principally targeting the abundance of Methane features, with the primary coronagraph band being centered around 770nm. Key to the performance estimates are the achievable signal-to-noise (SNR) ratios and the stability of the microspectra over the course of tens and hundreds of hours. As a technology demonstration for CGI, the ability to close a wavefront control loop around the IFS, maintain a stable dark hole, and provide time resolved data that simultaneously spans spatial and spectral dimensions are crucial demonstrations for future observatories. The IFS is optimized both for coronagraphs and science observations with a potential future starshade. We highlight how the long duration observations, and requirements for both starshades and coronagraphs drive the IFS requirements and the calibrations required both onorbit and on the ground. We also provide further detail on the optomechanical design, its stability based on thermal and structural predictions, anticipated performance, and operations concept of the CGI IFS. The impact of these performance metrics are projected into simulated data products, demonstrating cube extraction of noisy images and the subsequent planet spectrum that can be extracted from them. These demonstrations and performance predictions are key to future missions such as LUVOIR and HabEx, whose principal science case relies on efficient spectroscopy of exoplanets.

Tyler Groff↗
DOE OSTI2024

Subhourly Clipping Correction Model Comparison

This work will compare the Allen method and Walker method of accounting for subhourly inverter clipping power losses in hourly PV performance models. The Allen method uses a matrix lookup based on DNI clearness and clipping potential to assign a clipping correction loss at each simulation timestep. The Walker method models the PV DC power input to the inverter as a distribution over the hourly timestep and uses integration over the timestep to determine the amount of clipping that occurs within the timestep. Both these models have been recently implemented in the System Advisor Model's (SAM) open-source code, and will be applied to hourly SURFRAD datasets to analyze the subhourly clipping loss predicted by each model for different system designs and inverter loading conditions. Both models will be compared to "true" 1-minute SURFRAD data simulations to see their accuracy against more accurate 1-minute clipping correction loss predictions. This model comparisons will be investigated in more detail at the PVSC conference in Seattle, Washington June 2024.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2023

Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations

A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes.

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

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. Here, we also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗