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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

On-Line Robust Modal Stability Prediction using Wavelet Processing

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.

Brenner, Martin J.↗
NASA NTRS2017

An Improved SEL Test of the ADV212 Video Codec

Single-event effect (SEE) test data is presented on the Analog Devices ADV212. Focus is given to the test setup used to improve data quality and validate single-event latchup (SEL) protection circuitry.

Single-event effect (SEE); microelectronic devices↗
NASA NTRS2017

An Improved SEL Test of the ADV212 Video Codec

Single-event effect (SEE) test data is presented on the Analog Devices ADV212. Focus is given to the test setup used to improve data quality and validate single-event latch-up (SEL) protection circuitry.

Single-event effect (SEE); microelectronic devices↗
NASA NTRS2019

Attitude control system for the Mars Cube One spacecraft

The paper begins with a discussion of the hardware that was selected for the two systems as well as descriptions of the interface between the attitude control and propulsion systems and the interface between these systems and the rest of the spacecraft’s command and data handling system. Next, the paper summarizes the different types of tests that were performed at the system and spacecraft levels. Test data is included for some of these tests which helped define the methods by which the spacecraft is operated in space. Lastly, the paper lists a series of lessons-learned for developing attitude control and propulsion systems for interplanetary CubeSats.

Krajewski, Joel↗
DOE OSTI2022

Bridging the length scales on mechanical property evaluation (Final Report)

The development of small-scale mechanical testing in combination with microstructural investigation is of great interest to the nuclear materials community for both materials development and monitoring applications. Dramatically reducing the sample sizes to reduce radioactivity and obtaining mechanical properties of irradiated samples is truly intriguing. Moreover, such studies promise a range of benefits including cost reduction, fundamental insight in structure-property relationships, increased statistics on less sample material, and reinvestigation of prior irradiated and tested reactor samples while simultaneously enabling the generation of mechanical test data on ion beam irradiated materials with limited penetration depths. Small scale materials testing on sub-sized samples has been studied for several decades, though it has only been after the development of micro-testing based on Focused Ion Beam (FIB) sample manufacturing in that orders of magnitude smaller samples could really be investigated in a quantitative manner. In recent years, small scale mechanical testing techniques at a number of length scales has been developed for both unirradiated and irradiated (ion and neutron) materials. Technological advances made in this field have enabled ex-situ and in-situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) examination, thus leading to more accurate measurements as well as additional mechanistic information. A recent review of the benefits of these techniques show that these techniques are at a stage to tackle multi-scale ranges of materials investigations and can be utilized to obtain fundamental science-based understanding of nuclear materials. Considering the tremendous advances made, one can see how small-scale mechanical testing techniques combined with modeling can enable true small scale to bulk scale mechanical property correlations. However, for the engineering community to adapt this approach fully, one needs to demonstrate that a) that these techniques can produce results with high fidelity and reproducibility, b) generate engineering stress-strain data that one can utilize to understand bulk behavior, and c) generate new insight into relevant phenomena fostering the true understanding of radiation damage and microstructure in materials for nuclear applications. It is the objective of this proposal to bridge the length scale between macro- and micro- scale mechanical testing of unirradiated and irradiated materials. This involves the development and demonstration of procedures for multi-scale mechanical testing that enable high fidelity reproducibility of data and the generation bulk property data from small-scale mechanical tests. Through this, the proposal aims to enhance the confidence in the obtained data at the smaller length scales and enhance the insight provided from these techniques for bulk scale applications on both unirradiated and irradiated materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1982

Extraction of model performance from wall data in a 2-dimensional transonic flexible walled test section

Data obtained from the boundary of a test section provides information on the model contained within it. A method for extracting some of this data in two dimensional testing is described. Examples of model data are included on lift, pitching moment and wake displacement thickness. A FORTRAN listing is also described, having a form suitable for incorporation into the software package used in the running of such a test section.

Goodyer, M. J.↗
NASA NTRS1979

Orbiter data reduction complex data processing requirements for the OFT mission evaluation team (level C)

This document addresses requirements for post-test data reduction in support of the Orbital Flight Tests (OFT) mission evaluation team, specifically those which are planned to be implemented in the ODRC (Orbiter Data Reduction Complex). Only those requirements which have been previously baselined by the Data Systems and Analysis Directorate configuration control board are included. This document serves as the control document between Institutional Data Systems Division and the Integration Division for OFT mission evaluation data processing requirements, and shall be the basis for detailed design of ODRC data processing systems.

Source record↗
DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗
DOE OSTI2018

Modeling dynamic fracture in granite under in situ conditions at high temperatures and pressures

Reproducing the in situ mechanical response of granite at a depth of a few kilometer under dynamic loading presents significant challenges. We have developed and calibrated a new thermo-mechanical model for granite using extensive experimental data from both quasi-static and dynamic test data. The model includes parameters which can be calibrated for a specific geologic setting. Using the model developed we have performed a series of 2D and 3D simulations to evaluate effects of in situ stress and temperature on the extent of damage surrounding underground explosions. We compare our results with available data for cavity size, plastic zone and peak velocity attenuation with range in granite.

58 GEOSCIENCES↗
DOE OSTI2022

Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as $\sqrt{T/N}$. When only K$\ll$T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to $\sqrt{K/N}$. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Emission factors of industrial boilers burning biomass-derived fuels

Boilers are combustion devices that provide process heat and are integral to many industrial facilities. Historically, outside of the pulp and paper industry, most boilers burned fossil fuels, although interest in decarbonization has been leading to an increased use of renewable fuels in boilers. These boilers, including those in the biorefineries, are often large sources of air pollutant emissions, and the characterization of these emissions is critical to obtaining air permits and ensuring protection of the surrounding air quality. Several industrial boilers and new biorefineries allow utilization of biomass-derived fuels (e.g. wastewater sludge, lignin, etc.) produced during their operation as a fuel for the boiler to meet process energy needs. However, there is limited empirical data on emission factors for the burning of unconventional fuels, such as solid-gas mixtures containing biomass residues. To fill this gap, we carry out a comprehensive data survey, collecting information on emission factors for boilers burning either a single or a mixture of solid and gaseous biomass-derived fuels. We review multiple hard-to-obtain and unconventional data sources, such as air permit applications, stack test data, and industry-sponsored data collection efforts, to compile emission factors for biomass-derived fuels. We then compare this data with wood residue emission factors from the U.S. Environmental Protection Agency’s AP-42 emission factor database. Our results indicate that the emission factors for boilers burning unconventional fuels vary widely depending on the fuel composition, boiler type, and fuel characteristics. Overall, we find that median emission factors of selected biomass-derived fuels are typically lower than those of wood residue boilers in AP-42. The information collected herein could be useful to permitting agencies and industries utilizing boilers who may want to reduce the carbon impact of their facilities by combusting biomass-derived wastes for process energy needs, for more accurate emission estimation for permitting.

09 BIOMASS FUELS↗
DOE OSTI2019

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗
DOE OSTI2025

Efficient near-field ptychography reconstruction using the Hessian operator

X-ray ptychography is a powerful and robust coherent imaging method providing access to the complex object and probe (illumination). Ptychography reconstruction is typically performed using first-order methods due to their computational efficiency. Higher-order methods, while potentially more accurate, are often prohibitively expensive in terms of computation. In this study, we present a mathematical framework for reconstruction using second-order information derived from an efficient computation of the bilinear Hessian and Hessian operator. The formulation is provided for Gaussian-based models, enabling the simultaneous reconstruction of the object, probe, and object positions. Synthetic data tests, along with experimental near-field ptychography data processing, demonstrate a ten-fold reduction in computation time compared to first-order methods. The derived formulas for computing the Hessians, along with the strategies for incorporating them into optimization schemes, are well-structured and easily adaptable to various ptychography problem formulations.

Carlsson, Marcus [Lund Univ. (Sweden)] (ORCID:0000↗
DOE OSTI2015

Structural Testing of the Blade Reliability Collaborative Effect of Defect Wind Turbine Blades

Two 8.3-meter (m) wind turbine blades intentionally constructed with manufacturing flaws were tested to failure at the National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL) south of Boulder, Colorado. Two blades were tested; one blade was manufactured with a fiberglass spar cap and the second blade was manufactured with a carbon fiber spar cap. Test loading primarily consisted of flap fatigue loading of the blades, with one quasi-static ultimate load case applied to the carbon fiber spar cap blade. Results of the test program were intended to provide the full-scale test data needed for validation of model and coupon test results of the effect of defects in wind turbine blade composite materials. Testing was part of the Blade Reliability Collaborative (BRC) led by Sandia National Laboratories (SNL). The BRC seeks to develop a deeper understanding of the causes of unexpected blade failures (Paquette 2012), and to develop methods to enable blades to survive to their expected operational lifetime. Recent work in the BRC includes examining and characterizing flaws and defects known to exist in wind turbine blades from manufacturing processes (Riddle et al. 2011). Recent results from reliability databases show that wind turbine rotor blades continue to be a leading contributor to turbine downtime (Paquette 2012).

17 WIND ENERGY↗
DOE OSTI2018

BOP-301 and BOP-302R: Test Definitions and Analyses

Following the conclusion of the International Atomic Energy Agency (IAEA) coordinated research project (CRP) on analysis of two EBR-II tests, Argonne has continued its EBR-II analysis with additional tests from the Shutdown Heat Removal Test (SHRT) series. The tests selected for this follow-up analysis are BOP-301 and BOP-302R, two unprotected loss of heat of sink tests in which the intermediate sodium pump tripped without scramming the control rods or tripping the primary pumps. The IAEA CRP focused on the SHRT-17 and SHRT-45R tests, the most severe protected and unprotected loss of flow tests performed during the SHRT series. With the primary pumps tripping, flow rates decreased to levels where flowmeter uncertainty was very high. Accurately capturing the progressions of the two tests despite this uncertainty was one of the key modeling and simulation challenges during the CRP. BOP-301 and BOP-302R were selected because the primary sodium pumps did not trip for those tests. Primary flow rate measurements are known with greater certainty. Power and temperature discrepancies can be explored without concern that flow predictions match test measurements but not the actual flow rates from the tests. These two tests were performed during the same test window as SHRT-45R and had the same core load configuration. Therefore, core models created for analysis of SHRT-45R were easily adapted for analysis of BOP-301 and BOP-302R. Another reason for analyzing BOP-301 and BOP-302R is to analyze tests in which the core inlet temperature changed significantly, which did not occur during the loss of flow tests. The transient progression of the unprotected SHRT-45R test was driven by a decreasing core flow rate while the core inlet temperature remained relatively constant. For the BOP tests, the transient progression was driven by an increasing core inlet temperature while the core flow rate was stable. This report defines the BOP-301 and BOP-302R tests and provides a selection of measured test data. Initial predictions from SAS4A/SASSYS-1 simulations of these tests are then presented. Following these simulations, additional analysis was performed with the Dakota uncertainty quantification and optimization toolkit to determine which parameters have the greater impact on the transient results and the optimal values of the most important parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

The Results of 3-Point Flexure Testing of Irradiated AGC-1 Graphites Specimens

Here we report the test data from AGC-1 specimen 3-pt flexure testing. At this time all mechanical (flexural) testing of AGC-1 specimens has been completed. Individual force-extension curves are available as .txt files recorded electronically and are available from the author (burchelltd@ornl.gov). Typical curves are included for both the unirradiated and irradiated specimens. The flexure strength has been calculated for each specimen. This letter report meets Idaho National Laboratories statement on Work No. 10752 Rev. 3 deliverable 4.4B “Issue an electronic letter report communication indicating the status of AGC-1 mechanical testing to INL no later than March 27, 2015."

36 MATERIALS SCIENCE↗
DOE OSTI2018

Efficient Generalizable Deep Learning

Stochastic optimization is a fundamental field of research for machine learning. Stochastic gradient descent (SGD) and related methods provide a feasible means to train complicated prediction models over large datasets. SGD, however, does not explicitly address the problem of overfitting, which can lead to predictions that perform poorly on new data. This difference between loss performance on unseen testing data verses that of training data defines the generalization gap of a model. We introduce a new computational kernel called Stochastic Hessian Projection (SHP) that uses a maximum likelihood framework to simultaneously estimate gradient noise covariance and local curvature of the loss function. Our analysis illustrates that these quantities affect the evolution of parameter uncertainty and therefore generalizability. We show how these computations allow us to predict the generalization gap without requiring holdout data. Explicitly assessing this metric for generalizability during training may improve machine learning predictions when data is scarce and understanding prediction variability is critical.

42 ENGINEERING↗
DOE OSTI2020

Conservative Release Fraction Correlation for Spray Releases

Washington River Protection Solutions, LLC (WRPS) is planning to conduct repairs in the Liquid Effluent Retention Facility (LERF), and prior to conducting this work they will re-evaluate the hazard category designation for LERF. As part of this evaluation, WRPS needs estimates of the release fraction of potential accidental sprays, because a spray release is a potential bounding accident. Pacific Northwest National Laboratory previously developed a conservative correlation for the cumulative generation rate (volume per time) of aerosol droplets less than or equal to specific sizes from spray releases as a function of the spray pressure, orifice area, and droplet diameter. To obtain release fraction estimates from the conservative generation rate correlation, the correlation needs to be recast by dividing the aerosol generation rate by the flow rate of a spray. In the current study, a conservative release fraction correlation was developed from the conservative aerosol generation rate correlation developed previously by dividing the generation rate correlation by the flow rate of a spray using an orifice flow rate equation and then this correlation was adjusted to be conservative. Test data were also extrapolated to a lower pressure than the lowest pressure tested (100 psig) for the spray data used for developing the previous conservative generation rate correlation. The new conservative release fraction correlation was then used to predict release fractions of respirable droplets (droplets = 10 µm) for four specific pipes that are part of LERF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗