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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Effective Field Theory Analysis and Active Neutron Veto Design for the Cryogenic Dark Matter Search

Astrophysical measurements and cosmological predictions suggest the exists of a large amount of matter in the universe that does not interact via electromagnetic forces. This non-luminescent matter, known dark matter, exists in halos that encompass and are within galaxies, including the Milky Way. Therefore, dark matter particles should should be directly detectable by experiments on Earth, such as the Super Cryogenic Dark Matter Search (SuperCDMS). Dark matter is assumed to be low mass (< 100 GeV/c2) and interact via the weak force using either a spin-independent or spin-dependent coupling. However, making incorrect assumptions about dark matter interactions can lead to misleading results. Because interactions with dark matter particles are rare, direct detection experiments must be able to shield for or reject backgrounds to very low levels. Low energy neutron backgrounds that make it to the detectors are especially dangerous, because they cannot be easily distinguished from the expected dark matter signal. Scintillator doped with a high neutron-capture cross-section material can be used to detect neutrons via their resulting gamma rays. Examples of such detectors using liquid scintillator have been successfully used in past high-energy physics (HEP) experiments. However, a liquid scintillator can leak and is not as amenable to modular or complex shapes as a solid scintillator. The light outputs and efficiencies of gadolinium-loaded polystyrene-based scintillators have been explored using a wide variety of gadolinium compounds with varying concentrations. Collection strategies using a wavelength shift- ing (WLS) fiber and silicon photomultipliers (SiPMs) were also evaluated as a possible neutron veto for an upgrade to SuperCDMS SNOLAB. The scattering of dark matter particles off nuclei in direct detection experiments can be described in terms of a multidimensional effective field theory (EFT). A new systematic analysis technique is developed using the EFT approach and Bayesian inference methods to exploit, when possible, the energy-dependent information of the detected events, experimental efficiencies, and backgrounds. Highly dimensional likelihoods are calculated over the mass of the weakly interacting massive particle (WIMP) and multiple EFT coupling coefficients, which can then be used to set limits on these parameters and choose models (EFT operators) that best fit the direct detection data. Expanding the parameter space beyond the standard spin-independent isoscalar cross section and WIMP mass reduces tensions between previously published experiments. Combining these experiments to form a single joint likelihood leads to stronger limits than when each experiment is considered on its own. Simulations using two nonstandard operators (O3 and O8) are used to test the proposed analysis technique in up to five dimensions and demonstrate the importance of using multiple likelihood projections when determining constraints on WIMP mass and EFT coupling coefficients. In particular, this shows that an explicit momentum dependence in dark matter scattering can be identified. CDMSlite Run 2 was a search for Weakly Interacting Massive Particles (WIMPs) with a cryogenic 600 g Germanium detector operated deep underground. It was operated in a mode optimizing sensitivity to WIMPs of relatively low mass, 2 - 20 GeV, while sacrificing background rejection. An EFT analysis of CDMSlite Run 2 data from SuperCDMS Soudan is presented here. A binned likelihood Bayesian analysis was performed on the data, optimizing over the parameters of EFT interactions and the recoil energy spectra due to the dominant Compton scattering and tritium backgrounds. Recoil energy regions within 5σ of known activation peaks were removed from the analysis. The Bayesian evidences of the resulting likelihoods show that CDMSlite Run 2 data is entirely consistent with the background models with no EFT interaction necessary. Upper limits on the WIMP mass and coupling coefficients amplitudes and phases are presented for each EFT operator.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Opinion: Making the Most of Colour Rendition Research

After decades of debate, new methods for characterizing color rendition have been formalized, providing an improved system of tools to aid technology developers and lighting specifiers. Solid-state lighting, previously burdened by legacy measures based on outdated science, is positioned to take advantage of these new tools to provide tailored, efficient lighting that better meets the needs of specific architectural lighting applications. This transition requires new guidance, however, presenting an opportunity for researchers. There are several key methodological considerations specific to experiments investigating color rendition that will help build a base of guidance that can be widely applied. The importance of these ideas mostly stems from new knowledge gained in the last five years, but their origins date back much further. 1.The spectral power distribution must be adequately varied to stress the measure(s) of color rendition being evaluated. Often, this means varying the gamut shape—the pattern of shift across different hues—to probe weaknesses of hue-averaged characterizations, avoiding misleading conclusions resulting from limited variation in the stimuli. 2.Evaluating the role of chromaticity on color quality evaluations requires careful accommodation of chromatic adaptation to prevent a centering bias. Asking for delayed responses may be insufficient if chromaticity changes are apparent to participants, just as side-by-side viewing of different chromaticities may not produce results that can be reliably applied in the field. It is also critical that color rendition and chromaticity conditions are varied independently. 3.The stimulus being viewed is the interaction of light and objects’ spectral reflectances. This interaction should be quantified in order to assess how derived guidelines will apply measures using standardized color sample sets. Although light source characterizations are typically used to model responses, the objects and their presentation may strongly influence evaluations. 4.Context is important. Many factors—some investigated, others hypothesized—can influence subjective evaluations of color quality, including illuminance level, type of scene, object familiarity, color composition, viewing duration, and viewer traits. Although their influence on research results is unavoidable, the effects of these contextual factors must be carefully considered in developing guidance. Appropriate experimental design can often mitigate negative consequences arising from these considerations. At a minimum, it is crucial to understand how each of these considerations can influence experimental outcomes, as well as the interpretation and application of the results in practice. Consulting other experts prior to conducting an experiment can help ensure appropriate, up-to-date research methods will be used, so that the results will be novel and useful—and not rejected during peer review. There is an interesting feedback loop: technology development provides new research tools, which allow for refined research methods and new results that lead to more technology development. It is time to make a concerted, collective effort to improve color rendition research so that the full potential of solid-state lighting and new characterization methods can be realized, ensuring that high-efficiency, high-utility, high-appeal lighting systems become standard.

Royer, Michael P.↗
DOE OSTI2018

Compact Heat Exchanger Design and Testing for Advanced Reactors and Advanced Power Cycles

The goal of the proposed research is to demonstrate the thermal hydraulic performance of innovative surface geometries in compact heat exchangers used as intermediate heat exchangers (IHXs) and recuperators for the supercritical carbon dioxide (s-CO 2 ) Brayton cycle. Printed-circuit heat exchangers (PCHEs) are the primary compact heat exchangers of interest. The overall objectives are: To develop optimized PCHE designs for different working fluid combinations including helium to s-CO 2 , liquid salt to s-CO 2 , sodium to s-CO 2 , and liquid salt to helium; To experimentally and numerically investigate thermal performance, thermal stress and failure mechanism of PCHEs under various transients; and To study diffusion bonding techniques for elevated-temperature alloys and examine post-test material integrity of the PCHEs. The project objectives were accomplished by defining and executing five different tasks corresponding to these specific objectives. The first task involved a thorough literature review and a selection of IHX candidates with different surface geometries as well as a summary of prototypic operational conditions. The second task involved optimization of PCHE design with numerical analyses of thermal-hydraulic performances and mechanical integrity. The subsequent task dealt with the development of testing facilities and engineering design of PCHE to be tested in s-CO 2 fluid conditions. The next task involved experimental investigation and validation of the thermal-hydraulic performances and thermal stress distribution of prototype PCHEs manufactured with particular surface geometries. The last task involved an investigation of diffusion bonding process and posttest destructive testing to validate mechanical design methods adopted in the design process. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed s-CO 2 test loop (STL) facility and s-CO 2 test facility at University of Wisconsin – Madison (UW).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Electron-ion recombination in composite interactions in liquid xenon

The response of liquid xenon to various types of ionizing radiation has been extensively studied theoretically and experimentally. Recent progress in direct detection dark matter experiments highlights the significance of composite events, where multiple particles interact with xenon simultaneously and generate overlapping ionization signatures. In these events, recombination of electrons and ions associated with different primary particles leads to additional suppression of the ionization signal, introducing a new source of uncertainty in dark matter searches and Migdal effect studies. We developed a model to estimate the recombination enhancement for overlapping low-energy particle interactions. This method, which has minimal dependence on xenon microphysics and is primarily driven by existing experimental data, yields predictions that are consistent with available measurements of composite interactions. Furthermore, we demonstrate that the model predictions are robust against xenon microphysics assumptions.

charge↗
DOE OSTI2015

Multiscale simulations of hexa-peri-hexabenzocoronene and hexa-n-dodecyl-hexa-peri-hexabenzocoronene

This study concerns atomistic and coarse-grained molecular dynamics simulations of two disk-shaped molecules in the crystalline phase. The coarse-grained models were developed by applying the Iterative Boltzmann Inversion method which is a systematic coarse-graining method. In particular, a set of radial distribution functions and intramolecular distributions are reproduced at the coarse-grained level. Before applying coarse-graining, a reliable atomistic model was developed to reproduce the main experimental properties of these molecules. The crystalline phases are analyzed in terms of the Saupe ordering tensor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Radial Inertia Effect on Dynamic Compressive Response of Polymeric Foam Materials

Polymeric foams have been extensively used in shock isolation applications because of their superior shock or impact energy absorption capability. However, as a type of soft condensed matter, the highly nonlinear, heterogeneous, and dissipative behavior of polymeric foams may result in an ineffective mitigation or isolation to shock/blast loading. To meet certain desired shock mitigation or isolation requirements, the polymeric foams need to be experimentally characterized to obtain their intrinsic material response. However, radial inertia during dynamic compression has become a severe issue and needs to be fully understood. In this study, we developed an analytical method to calculate the additional stress induced by radial inertia in a polymeric foam specimen. The radial inertia is generally caused by Poisson’s effect and associated with three different mechanisms – axial strain acceleration, large deformation, and Poisson’s ratio change. In conclusion, the effect of Poisson’s ratio change during deformation on radial inertia was specifically investigated for hyperelastic foam materials, and verified with experimental results obtained from Kolsky compression bar tests on a silicone foam.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization

Here, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We select six of the most complex PolyBench benchmarks and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat-3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170,368 different configurations. We find that the Floyd-Warshall benchmark did not benefit from autotuning. To cope with this issue, we provide some compiler option solutions to improve the performance. Then we present loop autotuning without a user's knowledge using a simple mctree autotuning framework to further improve the performance of the Floyd-Warshall benchmark. We also extend the ytopt autotuning framework to tune a deep learning application.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Drop weight impact measurements of HE sensitivity: modified detection methods

High explosives small-scale sensitivity testing has been a hallmark of safety screening since WWII. Sensitivity testing was once crude and simple; broom sticks were used to scrape explosives on the floor while experimenters would look, listen, and smell for signs of a reaction. Since then, a wide variety of testing apparatus have been developed to explore the effects of different stimuli on explosives. In concert with the development of the machines themselves, the reaction detection methods have also evolved. This paper’s focus is on the Los Alamos National Laboratory’s (LANL) drop weight impact machine and reaction detection methods.

42 ENGINEERING↗
DOE OSTI2016

Digital image analysis to quantify carbide networks in ultrahigh carbon steels

A method has been developed and demonstrated to quantify the degree of carbide network connectivity in ultrahigh carbon steels through digital image processing and analysis of experimental micrographs. It was shown that the network connectivity and carbon content can be correlated to toughness for various ultrahigh carbon steel specimens. The image analysis approach first involved segmenting the carbide network and pearlite matrix into binary contrast representations via a grayscale intensity thresholding operation. Next, the carbide network pixels were skeletonized and parceled into braches and nodes, allowing the determination of a connectivity index for the carbide network. Intermediate image processing steps to remove noise and fill voids in the network are also detailed. The connectivity indexes of scanning electron micrographs were consistent in both secondary and backscattered electron imaging modes, as well as across two different (50 × and 100 ×) magnifications. Results from ultrahigh carbon steels reported here along with other results from the literature generally showed lower connectivity indexes correlated with higher Charpy impact energy (toughness). A deviation from this trend was observed at higher connectivity indexes, consistent with a percolation threshold for crack propagation across the carbide network. - Highlights: • A method for carbide network analysis in steels is proposed and demonstrated. • ImageJ method extracts a network connectivity index from micrographs. • Connectivity index consistent in different imaging conditions and magnifications. • Impact energy may plateau when a critical network connectivity is exceeded.

36 MATERIALS SCIENCE↗
DOE OSTI2014

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Fast Reactor Methods Development Plan

Fast reactor design and safety analysis methods (computer codes) have been under development for approximately sixty years. During the first forty years, code development was driven by the immediate needs of operational facilities such as Experimental Breeder Reactors I and II and the Fast Flux Test Facility. Development was further driven by programmatic needs to support advanced reactor programs such as the Clinch River Breeder Reactor Project and the Advanced Liquid Metal Reactor Program. By the early 1990s, design and safety analysis codes had been well established to addressed the unique characteristics of fast reactors. During the subsequent twenty years, however, development was nearly stagnant as a result of U.S. Government policy changes that abruptly canceled nearly all of the fast reactor research in the United States. As a result, the state of fast reactor design and safety analysis methods has deteriorated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Experimental evaluation of neural probe’s insertion induced injury based on digital image correlation method

Purpose: The application of neural probes in clinic has been challenged by probes’ short lifetime when implanted into brain tissue. The primary goal is to develop an evaluation system for testing brain tissue injury induced by neural probe’s insertion using microscope based digital image correlation method. Methods: A brain tissue phantom made of silicone rubber with speckle pattern on its surface was fabricated. To obtain the optimal speckle pattern, mean intensity gradient parameter was used for quality assessment. The designed testing system consists of three modules: (a) load module for simulating neural electrode implantation process; (b) data acquisition module to capture micrographs of speckle pattern and to obtain reactive forces during the insertion of the probe; (c) postprocessing module for extracting tissue deformation information from the captured speckle patterns. On the basis of the evaluation system, the effects of probe wedge angle, insertion speed, and probe streamline on insertion induced tissue injury were investigated. Results: The optimal quality speckle pattern can be attained by the following fabrication parameters: spin coating rate—1000 r/min, silicone rubber component A: silicone rubber component B: softener: graphite = 5 ml: 5 ml: 2 ml: 0.6 g. The probe wedge angle has a significant effect on tissue injury. Compared to wedge angle 40° and 20°, maximum principal strain of 60° wedge angle was increased by 40.3% and 87.5%, respectively; compared with a relatively higher speed (500 μm/s), the maximum principle strain within the tissue induced by slow insertion speed (100 μm/s) was increased by 14.3%; insertion force required by probe with convex streamline was smaller than the force of traditional probe. Based on the experimental results, a novel neural probe that has a rounded tip covered by a biodegradable silk protein coating with convex streamline was proposed, which has both lower insertion and micromotion induced tissue injury. Conclusions: The established evaluation system has provided a simulation environment for testing brain tissue injury produced by various insertion conditions. At the same time, it eliminates the adverse effect of biological factors on tissue deformation during the experiment, improving the repeatability of measurement results. As a result, the evaluation system will provide support on novel neural probe design that can reduce the acute tissue injury during the implantation of the probe.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Reactor antineutrino flux and anomaly

Reactor antineutrinos have played a significant role in establishing the standard model of particle physics and the theory of neutrino oscillations. In this article, we review the reactor antineutrino flux and in particular the reactor antineutrino anomaly (RAA) coined over a decade ago. RAA refers to a deficit of the measured antineutrino inverse beta decay rates at very short-baseline reactor experiments compared to the theoretically improved predictions (i.e. the Huber–Mueller model). Since the resolution of several previous experimental anomalies have led to the discovery of non-zero neutrino mass and mixing, many efforts have been invested to study the origin of RAA both experimentally and theoretically. The progress includes the observation of discrepancies in antineutrino energy spectrum between data and the Huber–Mueller model, the re-evaluation of the Huber–Mueller model uncertainties, the potential isotope-dependent rate deficits, and the better agreement between data and new model predictions using the improved summation method. Importantly, these developments disfavor the hypothesis of a light sterile neutrino as the explanation of RAA and supports the deficiencies of Huber–Mueller model as the origin. Looking forward, more effort from both the theoretical and experimental sides is needed to fully understand the root of RAA and to make accurate predictions of reactor antineutrino flux and energy spectrum for future discoveries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Development of ML FPGA Filter for Particle Identification and Tracking in Real Time

Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. Here we introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Tip-induced domain growth on the non-polar cuts of lithium niobate single-crystals

Currently ferroelectric materials with designed domain structures are considered as a perspective material for new generation of photonic, data storage and data processing devices. Application of external electric field is the most convenient way of the domain structure formation. Lots of papers are devoted to investigation of the domain kinetics on polar surface of crystals while the forward growth remains one of the most mysterious stages due to lack of experimental methods allowing to study it. Here we performed tip-induced polarization reversal on X- and Y-non-polar cuts in single-crystal of congruent lithium niobate allows us to study the forward growth with high spatial resolution. The revealed difference in the shape and length of domains induced on X- and Y-cuts is beyond previously developed theoretical approaches used for the theoretical consideration of the domains growth at non-polar ferroelectric surfaces. Lastly, to explain experimental results we used kinetic approach with anisotropy of screening efficiency along different crystallographic directions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗