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At least 1,279 records · Page 71

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DOE OSTI2021

Machine learning and algebraic approaches towards complete matter spectra in 4d F-theory

Motivated by engineering vector-like (Higgs) pairs in the spectrum of 4d F-theory compactifications, we combine machine learning and algebraic geometry techniques to analyze line bundle cohomologies on families of holomorphic curves. To quantify jumps of these cohomologies, we first generate 1.8 million pairs of line bundles and curves embedded in dP 3 , for which we compute the cohomologies. A white-box machine learning approach trained on this data provides intuition for jumps due to curve splittings, which we use to construct additional vector-like Higgs-pairs in an F-Theory toy model. We also find that, in order to explain quantitatively the full dataset, further tools from algebraic geometry, in particular Brill-Noether theory, are required. Using these ingredients, we introduce a diagrammatic way to express cohomology jumps across the parameter space of each family of matter curves, which reflects a stratification of the F-theory complex structure moduli space in terms of the vector-like spectrum. Furthermore, these insights provide an algorithmically efficient way to estimate the possible cohomology dimensions across the entire parameter space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Microfluidic Fabrication of Nanoparticles Based on Ethyl Acrylate-Functionalized Chitosan for Adsorption of Methylene Blue from Aqueous Solutions

The adsorption of nanoparticle (NP)-based adsorbents especially those made of polymers to remove dyes as toxic substances from pollutants is yet remained as a critical issue. The objective of this work was to prepare microfluidic (MF) fabrication of NPs based on ethyl acrylate (EA)-functionalized chitosan (CS) for methylene blue (MB) dye removal from aqueous solutions. For this purpose, a series of experiments such as FTIR, SEM alongside DLS, and UV–vis spectrophotometry were carried out to investigate the physicochemical properties of the samples. The FTIR spectra showed that the amine (–NH{sub 2}) and hydroxyl (–OH) groups on CS structure were replaced with EA, appropriately. The SEM and DLS results depicted that EA-functionalized CS NPs fabricated using MF technique had uniform shapes, and the diameters as small as 70 nm compared to those samples produced via bulk mixing (BM) method (~ 300 nm). Moreover, the surface charge of MF-assisted CS NPs after functionalization exhibited the lower zeta potential value (~ + 11) than that of samples based on BM (~ + 14) due to occurring the laminar flow through the micro-channels embedded in the MF chip, leading to an increase in the MB adsorption. Subsequently, at the optimized amounts of the dye concentration (89.12 mg L{sup −1}), the adsorbent concentration (1.38 g L{sup −1}), and pH (10.58) by means of Box-Behnken design (BBD) method, the maximum percentage of MB removal by the use of MF-fabricated EA-functionalized CS NPs was 98.4%. By fitting the obtained adsorption data to the isotherm models such as Lagmuir, Freundlich, and Temkin, it has been found that the adsorption mechanism followed the Langmuir model (maximum capacity of the adsorption 384.61 mg/g), which assumed a monolayer coverage of MB molecules as adsorbates over a homogeneous adsorbent surface.

36 MATERIALS SCIENCE↗

DOE OSTI2017

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Hohman, Frederick M.↗

NASA NTRS1977

Indoor thermal performance evaluation of the SEPCO air collector

The procedures used and the results obtained during the evaluation test program on the Solaron solar air collector, model EF-212, under simulated conditions for comparison with data collected in outdoor tests on the same collector are given. The test article was a single glazed collector with a nonsensitive absorber plate, aluminum box frame, and one inch isocyanurate foam insulation.

Source record↗

NASA NTRS1978

Investigation of turbulent diffusion in the extreme lower atmosphere

Turbulent diffusion in the extreme lower layer of the atmosphere (up to 5 m) has been investigated. Turbulent flow was simulated under dry, stable and calm conditions by means of a 3.04 m diameter fan installed at a field site situated on flat grassland. The ambient wind was continuously monitored by means of a cup anemometer placed outside the wake, and the temperature distribution was measured by four thermometers placed on an 18 m tower, also outside the wake. Balloons and red smoke were used to visualize the wake flow and investigate the predominant sizes of turbulent eddies and their streamwise behavior. The mean and turbulent velocities along the turbulence line were measured using an array of hot-wire anomometers. Results provide substantial verification of a recently proposed model (Koper and Sadeh, 1975; Koper et al., 1978) relating the Lagrangian to the Eulerian turbulent velocity autocorrelation. In this model the Lagrangian autocorrelation is given by a domain integral over a set of ordinary Eulerian autocorrelations acquired simultaneously at all points within the flow field in question, which is viewed as a turbulence 'box'.

Koper, C. A., Jr.↗

NASA NTRS1981

Aerodynamic drag reduction tests on a box-shaped vehicle

The intent of the present experiment is to define a near optimum value of drag coefficient for a high volume type of vehicle through the use of a boattail, on a vehicle already having rounded front corners and an underbody seal, or fairing. The results of these tests will constitute a baseline for later follow-on studies to evaluate candidate methods of obtaining afterbody drag coefficients approaching the boattail values, but without resorting to such impractical afterbody extensions. The current modifications to the box-shaped vehicle consisted of a full and truncated boattail in conjunction with the faired and sealed underbody. Drag results from these configurations are compared with corresponding wind tunnel results of a 1/10 scale model. Test velocities ranged up to 96.6 km/h (60 mph) and the corresponding Reynolds numbers ranged up to 1.3 x 10 to the 7th power based on the vehicles length which includes the boattail. A simple coast-down technique was used to define drag.

Peterson, R. L.↗

NASA NTRS1989

Conjugating binary systems for spacecraft thermal control. Appendix C: Aeroassist Flight Experiment Carrier Vehicle preliminary stress analysis

An assessment of the static strength of the Aeroassist Flight Experiment (AFE) Carrier Vehicle is presented. The Carrier Vehicle is the structural component which provides the mounting platform for the experiments, on-board computers, batteries, and other black boxes. In addition, the Solid Rocket Motor (SRM), the Thrusters, and the Aerobrake are all attached directly to the Carrier Vehicle. The basic approach in this analysis was to develop a NASTRAN Finite Element Model as a parallel effort to the preliminary design, and to use the internal loads from this model to perform the stress analysis. The NASTRAN method of Inertial Relief was employed. This method involves either specifying a set of CG (center of gravity) accelerations or applying forces at the CG and representing the Carrier Vehicle and all its mounted devices with the proper stiffness and mass properties.

Ingram, J. E.↗

NASA NTRS2011

NASA Tech Briefs, October 2011

Topics covered include: Laser Truss Sensor for Segmented Telescope Phasing; Qualifications of Bonding Process of Temperature Sensors to Deep-Space Missions; Optical Sensors for Monitoring Gamma and Neutron Radiation; Compliant Tactile Sensors; Cytometer on a Chip; Measuring Input Thresholds on an Existing Board; Scanning and Defocusing Properties of Microstrip Reflectarray Antennas; Cable Tester Box; Programmable Oscillator; Fault-Tolerant, Radiation-Hard DSP; Sub-Shot Noise Power Source for Microelectronics; Asynchronous Message Service Reference Implementation; Zero-Copy Objects System; Delay and Disruption Tolerant Networking MACHETE Model; Contact Graph Routing; Parallel Eclipse Project Checkout; Technique for Configuring an Actively Cooled Thermal Shield in a Flight System; Use of Additives to Improve Performance of Methyl Butyrate-Based Lithium-Ion Electrolytes; Li-Ion Cells Employing Electrolytes with Methyl Propionate and Ethyl Butyrate Co-Solvents; Improved Devices for Collecting Sweat for Chemical Analysis; Tissue Photolithography; Method for Impeding Degradation of Porous Silicon Structures; External Cooling Coupled to Reduced Extremity Pressure Device; A Zero-Gravity Cup for Drinking Beverages in Microgravity; Co-Flow Hollow Cathode Technology; Programmable Aperture with MEMS Microshutter Arrays; Polished Panel Optical Receiver for Simultaneous RF/Optical Telemetry with Large DSN Antennas; Adaptive System Modeling for Spacecraft Simulation; Lidar-Based Navigation Algorithm for Safe Lunar Landing; Tracking Object Existence From an Autonomous Patrol Vehicle; Rad-Hard, Miniaturized, Scalable, High-Voltage Switching Module for Power Applications; and Architecture for a 1-GHz Digital RADAR.

Source record↗

DOE OSTI2018

Studying language evolution in the age of big data

The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated, data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight declaring worthy prior commitments and interesting hypotheses in need of comparison.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1994

A hydrodynamic approach to cosmology: The mixed dark matter cosmological scenario

We compute the evolution of spatially flat, mixed cold and hot dark matter models containing both baryonic matter and two kinds of dark matter. Hydrodynamics is treated with a highly developed Eulerian hydrodynamic code (see Cen 1992). A standard particle-mesh (PM) code is also used in parallel to calculate the motion of the dark matter components. We adopt the following parameters: h equivalent to (sub 0)/100 km/s Mpc(exp -1) = 0.5, OMEGA(sub C) = 0.3, and OMEGA(sub B) = 0.06, with amplitude of the perturbation spectrum fixed by the Cosmic Background Explorer Satellite (COBE) Dark Matter Radiation (DMR) measurements (Smoot et al. 1992) being sigma (sub 8) = 0.67. Four different boxes are simulated with box sizes of L = (64, 16, 4, 1) h(exp -1) Mpc, respectively, the two small boxes providing good resolution but little valid information due to absence of large-scale power. We use 128(exp 3) approximate 10(exp 6.3) baryonic cells, 128(exp .3) cold dark matter particles, and 2 x 128(exp 3) hot dark matter particles. In addition to the dark matter we follow separately six baryonic species (H, H(+), He, He(+), He(++), e(-)) with allowance for both (nonequilibrium) collisional and radiative ionization in every cell. The background radiation field is also followed in detail with allowance made for both continuum and line processes, to allow nonequilibrium heating and cooling processes to be followed in detail. The mean final Zeldovich-Sunyaev y parameter is estimated to be y Bar = (5.4 + or - 2.7) x 10(exp -7) below currently attainable observations, with a rms fluctuation of approximately delta bar y = (0.6 + or - 3.0) x 10(exp -7) on arcminute scales. The rate of galaxy formation peaks at an even later epoch (z approximate 0.3) than in the standard (OMEGA = 1, sigma sub 8 = 0.67) cold dark matter (CDM) model (z approximate 0.5) and, at a redshift of z = 4, is nearly a factor of 100 lower than for the CDM model with the same value of sigma sub 8. With regard to mass function, the smallest objects are stabilized against collapse by thermal energy: the mass-weighted mass spectrum has a broad peak in the vicinity of M(sub B) = 10(exp 9.5) solar mass with a reasonable fit to the Schechter luminosity function if the ratio of baryon mass to blue light is approximately 4. In addition, one very large PM simulation was made in a box with size (320 h(exp - 1) Mpc) containing 3 x 200(exp 3) = 10(exp 7.4) particles. Utilizing this simulation we find that the model yields a cluster mass function which is about a factor of 4 higher than observed, but a cluster-cluster correlation length marginally lower than observed, but that both are closer to observations than in the (COBE) normalized CDM model. The one-dimensional pairwise velocity dispersion is 605 + or - 8 km/s at 1/h separation, lower than that of the DCM model normalized to COBE, but still significant higher than observations (Davis & Peebles 1983). A plausible velocity bias b(sub v) = 0.8 + or - 0.1 on this scale will reduce but not remove the discrepancy. The velocity auto-correlat ion function has a coherence length of 40/h Mpc, which is somewhat lower than the observed counterpart. In all these respects the model would be improved by decreasing the cold fraction of the dark OMEGA(sub CDM)/ (OMEGA(sub CDM) + OMEGA(sub HDB). But formation of galaxies and clusters of galaxies is much later in this model than in COBE-normalized CDM, perhaps too late. To improve on these constraints a larger ratio of OMEGA(sub CDM)/ (OMEGA(sub CDM) + OMEGA(sub HDM)) is required than the value of 0.67 adopted here. It does not seem possible to find a value for this ratio which would satisfy all tests. Overall, the model is similar both on large and intermediate scales to the standard CDM model normalized to the same value of sigma(sub B), but the problem with regard to late formation of galaxies is more severe in this model than in that CDM model. Adding hot dark matter, significantly improves the ability of the COBE-normalized CDM scenario to fit existing observations, but the model is in fact not as good as the CDM model with the same sigma(sub 8) and is still probably unsatisfactory with regard to several critical tests.

Cen, Renyue↗

DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

BraggNN : fast X-ray Bragg peak analysis using deep learning

X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Design, Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) v0.1.0

The DISPATCHES software will enable the design and optimization of Integrated Energy Systems (IES). The platform will support hybridizing existing generation facilities (such as nuclear and coal plants) as well as greenfield hybrid designs that incorporate advanced technologies (such as Advanced Modular Reactors and Coal FIRST), and it will enable the consideration of multiple technology options with different time constants. The design of the IES will be optimized for operation within the bulk power system via energy market signals (day ahead, real-time, and ancillary services) obtained from a production cost model. The goal of DISPATCHES is to identify flexible, dynamic designs that are responsive to grid market signals resulting in highly efficient, cost effective tightly coupled hybrid energy systems that can act as a "black box" to the grid to provide increased flexibility and resiliency.

Gunter, Daniel↗

NASA NTRS2008

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10(exp -8) Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust simulant processing capabilities: heating and cooling while stirring in order to degas and remove adsorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples.

Gaier, James R.↗

NASA NTRS2008

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10-8 Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust stimulant processing capabilities: heating and cooling while stirring in order to degas and remove absorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions, and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples

Gaier, James R.↗

NASA NTRS2010

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

Gaier, James R.↗

NASA NTRS2015

Test and Analysis Correlation for a Y-Joint Specimen for a Composite Cryotank

The Composite Cryotank Technology Demonstration (CCTD) project under NASA's Game Changing Development Program (GCDP) developed space technologies using advanced composite materials. Under CCTD, NASA funded the Boeing Company to design and test a number of element-level joint specimens as a precursor to a 2.4-m diameter composite cryotank. Preliminary analyses indicated that the y-joint in the cryotank had low margins of safety; hence the y-joint was considered to be a critical design region. The y-joint design includes a softening strip wedge to reduce localized shear stresses at the skirt/dome interface. In this paper, NASA-developed analytical models will be correlated with the experimental results of a series of positive-peel y-joint specimens from Boeing tests. Initial analytical models over-predicted the experimental strain gage readings in the far-field region by approximately 10%. The over-prediction was attributed to uncertainty in the elastic properties of the laminate and a mismatch between the thermal expansion of the strain gages and the laminate. The elastic properties of the analytical model were adjusted to account for the strain gage differences. The experimental strain gages also indicated a large non-linear effect in the softening strip region that was not predicted by the analytical model. This non-linear effect was attributed to delamination initiating in the softening strip region at below 20% of the failure load for the specimen. Because the specimen was contained in a thermally insulated box during cryogenic testing to failure, delamination initiation and progression was not visualized during the test. Several possible failure initiation locations were investigated, and a most likely failure scenario was determined that correlated well with the experimental data. The most likely failure scenario corresponded to damage initiating in the softening strip and delamination extending to the grips at final failure.

Mason, Brian H.↗

NASA NTRS2022

Microgreens Root Zone/Shoot Zone Partitioned Planting Box

To enable sustainable food production in future human exploration missions, plant growth is being studied by the Space Crop Production Team at KSC. Microgreens are good candidates for food supplements and contain specific nutrients that are lacking in the prepackaged diet, including vitamin C and vitamin K. Because they are densely sown, typical growth methods do not allow the ability to distinguish between the levels of evaporation from the rooting substrate and transpiration from the leaves. With larger plants, the root and shoot zone can be separated to distinguish these fluxes and accurate transpiration measurements of plant canopies are feasible. Furthermore, separation of the root and shoot zone may also be beneficial when harvesting microgreens in microgravity as it may reduce microbial contamination of the edible biomass by the roots, which have high microbial loads. Using a root and shoot separator box when harvesting may help with microbial contamination, but these tiny plants are challenging to handle in microgravity, so harvest management remains an open question. The innovation proposed here is a microgreen root/shoot partitioned planting box, which offers a solution to these challenges with accurate gas exchange measurements and a safe microgreen harvest in low gravity environments. Being able to measure transpiration of a microgreen canopy will be important for modeling plant growth in reduced gravity environments, so the first objective was to develop a planting unit with a seal between the root zone and the shoot zone. With an unsuitable harvesting technique, freshly harvested microgreens may add debris to the cabin, so the second objective was to test different harvesting techniques and management approaches associated with this innovation. These two objectives were pursued in parallel since many goals were the same: develop a planting unit that 1) separates the shoots from the roots, 2) allows acceptable germination rate, and 3) allows for seedlings to emerge and develop. What differed was the need to have a seal, which was only applicable for our gas exchange goal, and the need to have an embedded harvesting mechanism and bagging method which only applied to our harvesting goal. Testing of the various harvesting mechanisms and bagging methods was performed during a series of parabolic flights. All parabolic flight procedures took place inside a rented secondary containment chamber (e.g., glovebox) that was developed by the University of Louisville specifically for experiments involving fluids and other materials that may become airborne during reduced gravity flight. Three different harvesting methods and two different bagging collection methods were tested for microgreens. A third bagging method was initially tested but found to be unsuitable. Human factors were also taken into consideration, to identify which harvest and bagging collection methods would be easiest to use with favorable results in microgravity. Three parabolic flight tests were performed in total, one in November 2021 and two in December 2021.

Gioia Massa↗