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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Near-wall modelling of compressible turbulent flows

Work was carried out to extend the near-wall models formulated for the incompressible Reynolds stress equations to compressible flows. The idea of splitting the compressible dissipation function into a solenoidal part that is not sensitive to changes of compressibility indicators and a compressible part that is directly affected by these changes is adopted. This means that all models involving the dissipation rate could be expressed in terms of the solenoidal dissipation rate and an equation governing its transport could be formulated to close the set of compressible Reynolds stress equations. The near-wall modelling of the dissipation rate equation is investigated and its behavior near a wall is studied in detail using k-epsilon closure. It is found that all existing modelled equations give the wrong behavior for the dissipation rate near a wall. Improvements are suggested and the resultant behavior is found to be in good agreement with near-wall data. Furthermore, the present modified k-epsilon closure is used too calculate a flat plate boundary layer and the results are compared with four existing k-epsilon closures. These comparisons show that all closures tested give essentially the same flow properties, except in a region very close to the wall. In this region, the present k-epsilon closure calculations are in better agreement with measurements and direct simulation data; in particular, the behavior of the dissipation rate.

So, Ronald M. C.↗
DOE OSTI2024

Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain

ABSTRACT Recently, a surge of interest has been given to quantifying epistemic uncertainty (EU), the reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the binary classification setting, as the posterior expected value of the empirical gain in accuracy between the current prediction and the optimal prediction. In order to validate the performance of our EU estimator, we introduce an experimental procedure where we take an existing dataset, remove a set of points, and compare the estimated EU with the observed change in accuracy. Through real and simulated data experiments, we demonstrate the effectiveness of our proposed EU estimator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Temperature Effects on Interdiffusion of Al and U-Mo under Irradiation

A high-energy Xe ion irradiation experiment was conducted to investigate the temperature dependence of interdiffusion in bilayer Al-UMo samples under irradiation. The amount of interdiffusion achieved at a fixed dose with the increase of temperature showed a clear transition at 175 degrees C (with an estimated error in the range of ± 10 degrees C) from temperature-independent to temperature-dependent behavior. The activation energy derived from the curve of interdiffusion quantity vs. irradiation temperature is 0.77 ± 0.16 eV. This information has been utilized to understand the temperature effect on the interdiffusion process that occurred at the interfaces of U-Mo particles and the Al matrix in U-Mo/Al dispersion fuels, whose magnitude significantly impacts the fuel's performance. Although this temperature effect was deemed important, it cannot be examined directly using in-pile irradiation data, as fuel temperatures cannot be measured in reactor irradiation and are highly correlated with fission rate and thermal conductivity evolution. To connect the knowledge accumulated from ion irradiation with in-pile irradiation data, simulation of a full-sized U-Mo/Al dispersion fuel plate irradiated in the FUTURE test in the BR2 reactor was performed with the Dispersion Analysis Research Tool (DART), a dispersion fuel performance code. DART is equipped with an interaction or interdiffusion layer (IL) growth correlation formulated to describe the temperature dependence of ion mixing results. The agreement between calculated and measured fuel meat constituent volume fractions and swelling data demonstrated that the temperature effect on in-pile Al-UMo interdiffusion is well captured with the correlation. In this case, the fitted activation energy is 0.70 eV. Considering the uncertainties associated with the ion irradiation data, the activation energy obtained from in-pile data fitting is in accord with that from ion irradiation results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Direct structural retrieval from gas-phase ultrafast diffraction data using a genetic algorithm

Ultrafast scattering techniques such as ultrafast electron diffraction and ultrafast x-ray diffraction have been utilized to elucidate the structural dynamics, reaction intermediates, and final products in molecular reactions following photoexcitation. The time-dependent structures are typically not directly retrieved from the experimental data, but they rely on comparison with calculations. The genetic algorithm (GA), a global optimization strategy, can be used to retrieve the molecular structures directly from diffraction patterns without any theoretical input. However, the robustness of the GA with respect to real experimental conditions such as a limited momentum transfer range, noise, and artifacts has not been studied in detail. In this work, we characterize the performance of the GA with simulated data that mimic realistic experimental conditions. We have developed and implemented a variant of the GA specific to diffraction measurements which performs better in the presence of imperfect data compared to the standard implementation of the GA. We demonstrate this method with both synthetic data and experimental ultrafast electron diffraction data on the UV-induced photodissociation of trifluoroiodomethane (C⁢F 3⁡ I) molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Prong Segmentation using Point Set Transformers in Multiple View Neutrino Detectors

NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.

Liu, Jiaxi [UC, Irvine]↗
NASA NTRS1985

Remote Sensing of Coastal Wetlands Biomass Using Thematic Mapper Wavebands

Spectral data, simulating thematic mapper bands 3, 4 and 5 were gathered in salt and brackish marshes using a hand-held radiometer. Simple regression models were developed equating spectral radiance indicies with total live biomass for S. alterniflora in a salt marsh and for a variety of plant species in a brackish marsh. Models were then tested and compared to harvest estimates of biomass. In the salt marsh, biomass estimates from spectral data were similar to harvest biomass estimates during most of the growing season. Estimates of annual net aerial primary productivity calculated from spectral data were within 21% of production estimated from harvest data. During August, biomass estimates from spectral data in the brackish marsh were similar to biomass estimated by harvesting techniques but not always comparable at other times in the growing season.

Hardisky, M. A.↗
NASA NTRS2016

Tracking Blade Tip Vortices for Numerical Flow Simulations of Hovering Rotorcraft

Blade tip vortices generated by a helicopter rotor blade are a major source of rotor noise and airframe vibration. This occurs when a vortex passes closely by, and interacts with, a rotor blade. The accurate prediction of Blade Vortex Interaction (BVI) continues to be a challenge for Computational Fluid Dynamics (CFD). Though considerable research has been devoted to BVI noise reduction and experimental techniques for measuring the blade tip vortices in a wind tunnel, there are only a handful of post-processing tools available for extracting vortex core lines from CFD simulation data. In order to calculate the vortex core radius, most of these tools require the user to manually select a vortex core to perform the calculation. Furthermore, none of them provide the capability to track the growth of a vortex core, which is a measure of how quickly the vortex diffuses over time. This paper introduces an automated approach for tracking the core growth of a blade tip vortex from CFD simulations of rotorcraft in hover. The proposed approach offers an effective method for the quantification and visualization of blade tip vortices in helicopter rotor wakes. Keywords: vortex core, feature extraction, CFD, numerical flow visualization

Numerical↗
NASA NTRS1988

Utilizing remote sensing of thematic mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

The land-water interface of coastal marshes may influence the production of estuarine-dependent fisheries more than the area of these marshes. To test this hypothesis, a spatial model was created to explore the dynamic relationship between marshland-water interface and level of disintegration in the decaying coastal marshes of Louisiana's Barataria, Terrebonne, and Timbalier basins. Calibrating the model with Landsat Thematic Mapper satellite imagery, a parabolic relationship was found between land-water interface and marsh disintegration. Aggregated simulation data suggest that interface in the study area will soon reach its maximum and then decline. A statistically significant positive linear relationship was found between brown shrimp catch and total interface length over the past 28 years. This relationship suggests that shrimp yields will decline when interface declines, possibly beginning about 1995.

Browder, Joan A.↗
DOE OSTI2014

Structure transitions induced by the Hall term in homogeneous and isotropic magnetohydrodynamic turbulence

Hall effects on local structures in homogeneous, isotropic, and incompressible magnetohydrodynamic turbulence are studied numerically. The transition of vortices from sheet-like to tubular structures induced by the Hall term is found, while the kinetic energy spectrum does not distinguish the two types of structures. It is shown by the use of the sharp low-pass filter that the transition occurs not only in the scales smaller than the ion skin depth but also in a larger scale. The transition is related with the forward energy transfer in the spectral space. Analyses by the use of the sharp low-pass filter show that the nonlinear energy transfer associated with the Hall term is dominated by the forward transfer and relatively local in the wave number space. A projection of the simulation data to a Smagorinsky-type sub-grid-scale model shows that the high wave number component of the Hall term may possibly be replaced by the model effectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties (CRADA 511)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and ESI North America Inc. (“ESI” or “Participant”) to apply computation and data analytics to the challenge of light weighting with a focus on the battery enclosures of electric vehicles (EVs). EVs use heavy batteries to increase range and power. A complex-shaped battery enclosure is required to meet a host of challenging performance requirements. The ability to virtually develop composite parts such as battery enclosure with tailored properties to meet required performance will be highly valuable to the automotive industry. However, efficient simulation of composite-manufacturing processes remains a challenging issue since simulation involves multiscale models in space and time, highly non-linear and anisotropic behavior, strongly coupled multi-physics, and complex geometries. This work will advance the state of the art by reducing the computational burden of composite optimization by using simulation data from a limited number of configurations off-line and then developing a reduced order model (ROM) using data analytics and machine learning (ML). Develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts.

42 ENGINEERING↗
DOE OSTI2018

Assessing the Implementation of an SDM at Sandia and LLNL

This report assesses the proposed implementation of a Scientific Data Management system that could be co-developed by LLNL and SNL and installed at each lab. In the current view, the systems would not be connected to each other. This report examines the different collaboration possibilities and status of planning and implementation of the teams at LLNL and SNL. The report uses identified requirements from previous work that has been done and documented in [1]. In that work, a set of simulation data management requirements was developed during a series of meetings, starting from requirements developed by Sandia Laboratory. These requirements are expected to evolve, and it is recommended that these requirements be revisited and updated for the following reasons: 1- Advancement in the computing hardware and software which leads to more processing power and better data movement speeds. 2- Advancement in computer algorithms which creates opportunities for better data management and analysis. 3- The need to expand and add to the capabilities of the SDM tools due to advancement in the applications software and due to new requirements by the scientific community. This report builds upon the experience from the work that has been done by some of the team members and the current SDM development efforts at LLNL and Sandia.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1991

Improved visualization of flow field measurements

A capability is proposed that makes it feasible to apply to measured flow field data the visualization tools developed to display numerical solutions for computational fluid dynamic problems. The measurement monitor surface (MMS) methodology was used for the analysis of flow field measurements within a low-aspect-ratio transonic axial-flow fan rotor acquired with two-dimensional laser anemometry. It is shown that the MMS method may be utilized to generate input for the multidimensional processing and analytical tools developed for numerical flow field simulation data. Thus an experimenter utilizing an interactive graphics program could illustrate scalar quantities such as Mach number by profiles, contour lines, carpet plots, and surfaces employing various color intensities. Also, flow directionality can be shown by the display of vector fields and particle traces.

Miles, Jeffrey Hilton↗
DOE OSTI2023

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. A series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

Kemp, Samuel↗
DOE OSTI2016

Capture zone area distributions for nucleation and growth of islands during submonolayer deposition

A fundamental evolution equation is developed to describe the distribution of areas of capture zones (CZs) associated with islands formed by homogeneous nucleation and growth during submonolayer deposition on perfect flat surfaces. This equation involves various quantities which characterize subtle spatial aspects of the nucleation process. These quantities in turn depend on the complex stochastic geometry of the CZ tessellation of the surface, and their detailed form determines the CZ area distribution (CZD) including its asymptotic features. For small CZ areas, behavior of the CZD reflects the critical island size, i. For large CZ areas, it may reflect the probability for nucleation near such large CZs. Predictions are compared with kinetic Monte Carlo simulation data for models with two-dimensional compact islands with i = 1 (irreversible island formation by diffusing adatom pairs) and i = 0 (adatoms spontaneously convert to stable nuclei, e.g., by exchange with the substrate).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Using Distributed Fiber-optic Strain Sensing to Estimate Generalized Modal Coordinates from Flight-test Data

A method for estimating the generalized modal coordinates of an aircraft during flight has been developed. The Fiber-optic Sensing System (FOSS) offers an efficient and cost-effective method of measuring the strain at thousands of points along the wings. The estimation of modal coordinates was implemented as a two-step process. First, a maximum likelihood method is used to estimate the statistical properties of the sensors and generalized modal coordinates. Second, the strain mode shapes from the finite element model are used along with the statistical properties from the first step to estimate the generalized modal coordinates over time. Using simulated data from the X-56A Multi-Utility Technology Testbed (MUTT), different methods of modal coordinate estimation were compared to demonstrate the benefits and weaknesses of each. These were compared against the exact solution and an ordinary least squares (a more traditional method) estimate. Modal coordinate estimation methods were then applied to flight-test data from the X-56A aircraft to show that the method continues to work well with actual test data. The new estimation method provides insights unavailable from more classical approaches.

Jeffrey Ouellette↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

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

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. Furthermore, a series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

TH-CD-201-07: Experimentally Investigating Proton Energy Deposition On the Microscopic Scale Using Fluorescence Nuclear Track Detectors

Purpose: In order to further understand the interplay between proton physics and radiobiology it is necessary to consider proton energy deposition on the microscopic scale. In this work we used Fluorescent Nuclear Track Detectors (FNTDs) to experimentally investigate proton energy deposition, track-by-track. Methods: We irradiated 8×4×0.5mm{sup 3} FNTD chips (Landauer Inc) at seven water depths along a pristine proton Bragg peak with range=12cm. After irradiation, the FNTDs were scanned using a confocal microscope (FV1200, Olympus) with a high-power red laser and an oil-immersion objective lens (UPLSAPO60XO, NA=1.35). 10 slice image stacks were acquired with a slice-thickness of 2µm at multiple positions across each FNTD. Image-based analyses of track radius and track “mass” (integrated signal intensity) were performed using trackpy. For comparison, Monte Carlo simulated data were obtained using TOPAS and TOPAS-nBio. Results: Excellent correlation was observed between median track mass and TOPAS dose-averaged linear energy transfer. The resolution of the imaging system was determined insufficient to detect a relationship between track radius and exposure depth. Histograms of track mass (i) displayed strong repeatability across positions within an FNTD and (ii) varied in peak position and shape as a function of depth. TOPAS-nBio simulations implemented on the nanometer scale using physics lists from GEANT4-DNA yielded energy deposition distributions for individual protons and electrons scored within a virtual FNTD. Good agreement was found between these simulated datasets and the FNTD track mass distributions. Conclusion: Robust experimental measurements of the integral energy deposited by individual proton tracks can be performed using FNTDs. Monte Carlo simulations offer an exceedingly powerful approach to the quantification of proton energy deposition on the microscopic scale, but whilst they have been well validated at the macroscopic level, their microscopic validation is far from complete. Our results demonstrate that FNTD-based study can play an important role in addressing this deficit. Tracy Underwood gratefully acknowledges the support of the European Commission under an FP7 Marie Curie International Outgoing Fellowship for Career Development (#630064).

60 APPLIED LIFE SCIENCES↗