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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model’s predictions and estimate the growth of uncertainties in the region far from measurements.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRSDate not supplied

QuantifyML: How Good is My Machine Learning Model?

We present QuantifyML, which applies model counting to assess the learn ability, safety, and robustness of machine learning models. Typically, the efficacy of machine learning models is determined by computing their accuracy statistically on test datasets. However, this may be misleading, if the test data is not representative of the problem that is being studied. With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into aC program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise countsw.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, ex-pressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the safety and robustness of trained models.

Deep Neural Networks↗

NASA NTRS2018

High-precision Method for Cyclic Loading of Small-Animal Vertebrae to Assess Bone Quality

One potentially important bone quality characteristic is the response of bone to cyclic (repetitive) mechanical loading. In small animals, such as in rats and mice, cyclic loading experiments are particularly challenging to perform in a precise manner due to the small size of the bones and difficult-to-eliminate machine compliance. Addressing this issue, we developed a precise method for ex vivo cyclic compressive loading of isolated mouse vertebral bodies. The method has three key characteristics: 3D-printed support jigs for machining plano-parallel surfaces of the tiny vertebrae; pivotable loading platens to ensure uniform contact and loading of specimen surfaces; and specimen-specific micro-CT-based finite element analysis to measure stiffness to prescribe force levels that produce the same specified level of strain for all test specimens. To demonstrate utility, we measured fatigue life for three groups (n = 5-6 per group) of L5 vertebrae of C57BL/6J male mice, comparing our new method against two methods commonly used in the literature. We found reduced scatter of the mechanical behavior for this new method compared to the literature methods. In particular, for a controlled level of strain, the standard deviation of the measured fatigue life was up to 5-fold lower for the new method (F-ratio = 4.9; p < 0.01). The improved precision for this new method for biomechanical testing of small-animal vertebrae may help elucidate aspects of bone quality.

Bone quality↗

DOE OSTI2022

Continuous-time probabilistic models for longitudinal electronic health records

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The role of complementary shape in protein dimerization

Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. Furthermore, a machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as—to our knowledge—no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Design and Analysis of an Advanced Three-Point Bend Test Approach for Miniature Irradiated Disk Specimens

Candidate tungsten armor materials in a magnetic confinement fusion device must be able to withstand thermal variation that leads to internal stresses caused by the impinging heat load. In addition, the thermomechanical properties of these materials are degraded by irradiation-induced defect accumulation. Fission reactor–based irradiation data are used to predict the fusion neutron damage and property change. This study examines the motivation and design of a custom-designed three-point bend test for neutron-irradiated disk specimens that are 3 mm in diameter to be able to define the flexural strength of advanced tungsten materials, alloys, and composites—and to the extent that embrittlement occurs after neutron irradiation. The theory provided shows a calculation for the flexural deflection and shear deflection due to the small-geometry constraints. A finite element deformation analysis is performed to evaluate the mechanical stress field of disk bend specimens. The stress values above 80% of the maximum stress are concentrated in 2.4 mm of the 3.0-mm length of the centerline across the tungsten disk diameter. A bend test fixture has been designed and fabricated to enable testing of these specimens with precisely engineered tolerance and minimal machine compliance. This fixture will be able to be placed inside a universal testing frame at elevated temperatures for the mechanical property evaluation of future neutron-irradiated disk specimens.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The PAU Survey: Photometric redshifts using transfer learning from simulations

In this paper, we introduce the DEEPZ deep learning photometric redshift (photo-z) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. DEEPZ reduces the σ68 scatter statistic by 50 percent at iAB = 22.5 compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-z scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Hitting the Thermal Target for Leptophilic Dark Matter at Future Lepton Colliders

We study future lepton collider prospects for testing predictive models of leptophilic dark matter (DM) candidates with a thermal origin. We calculate experimental milestones for testing the parameter space compatible with freeze-out and the associated collider signals at past, present, and future facilities. This analysis places new limits on such models by leveraging the utility of lepton colliders. At 𝑒+⁢𝑒− machines, we make projections using precision 𝑍 -pole observables from 𝑒+⁢𝑒−→ℓ+⁢ℓ−+𝐸 signatures at large electron-positron collider and future projections for future circular collider (ee) in these channels. Additionally, a muon collider could also probe new thermal relic parameter space in this scenario via 𝜇+⁢𝜇−→𝑋+𝐸 , where 𝑋 is any easily identifiable standard model object. Collectively, these processes can probe much of the parameter space for which DM direct annihilation to ℓ+⁢ℓ− yields the observed relic density in Higgs-like models with mass-proportional couplings to charged leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1980

Pressure data for four analytically defined arrow wings in supersonic flow

In order to provide experimental data for comparison with newly developed finite difference methods for computing supersonic flows over aircraft configurations, wind tunnel tests were conducted on four arrow wing models. The models were machined under numeric control to precisely duplicate analytically defined shapes. They were heavily instrumented with pressure orifices at several cross sections ahead of and in the region where there is a gap between the body and the wing trailing edge. The test Mach numbers were 2.36, 2.96, and 4.63. Tabulated pressure data for the complete test series are presented along with selected oil flow photographs. Comparisons of some preliminary numerical results at zero angle of attack show good to excellent agreement with the experimental pressure distributions.

Townsend, J. C.↗

NASA NTRS1989

Error-Compensated Telescope

Proposed reflecting telescope includes large, low-precision primary mirror stage and small, precise correcting mirror. Correcting mirror machined under computer control to compensate for error in primary mirror. Correcting mirror machined by diamond cutting tool. Computer analyzes interferometric measurements of primary mirror to determine shape of surface of correcting mirror needed to compensate for errors in wave front reflected from primary mirror and commands position and movement of cutting tool accordingly.

Meinel, Aden B.↗

NASA NTRS1994

Making Precise Antenna Reflectors For Millimeter Wavelengths

In improved method of fabrication of precise, lightweight antenna reflectors for millimeter wavelengths, required precise contours of reflecting surfaces obtained by computer numberically controlled machining of surface layers bonded to lightweight, rigid structures. Achievable precision greater than that of older, more-expensive fabrication method involving multiple steps of low- and high-temperature molding, in which some accuracy lost at each step.

Sharp, G. Richard↗

NASA NTRS1996

Turbines For Optomechanical Energy Conversion

Class of proposed optomechanical energy-conversion machines exploit multiple reflections from precise, high-reflectivity mirrors, some of which fixed to turbinelike rotors that spin at high speeds. Basic idea to increase frequencies of photons (and thus energy of light) by repeated Doppler-shifting reflections from moving mirrors. Gain in optical energy in such reflection proportional to speed of mirror, while loss function of imperfections of mirror.

Dick, G. John↗

NASA NTRS1990

Catamaran Nets

West Coast Netting, Inc.'s net of Hyperester twine, is made of three strands of fiber twisted together by a company-invented sophisticated twisting machine and process that maintain precisely the same tension on each strand. The resulting twine offers higher strength and improved abrasion resistance. The technology that created the Hyperester supertwine has found spinoff applications, first as an extra-efficient seine for tuna fishing, then as a capture net for law enforcement agencies. The newest one is as a deck for racing catamarans. Hyperester twine net has been used on most of the high performance racing catamarans of recent years, including the America's Cup Challenge boats. They are tough and hold up well in the continual exposure to sunlight and saltwater.

Source record↗

NASA NTRS1981

Laser Balancing

Mechanical Technology, Incorporated developed a fully automatic laser machining process that allows more precise balancing removes metal faster, eliminates excess metal removal and other operator induced inaccuracies, and provides significant reduction in balancing time. Manufacturing costs are reduced as a result.

Source record↗

NASA NTRS2007

Fabrication of Submillimeter Axisymmetric Optical Components

It is now possible to fashion transparent crystalline materials into axisymmetric optical components having diameters ranging from hundreds down to tens of micrometers, whereas previously, the smallest attainable diameter was 500 m. A major step in the fabrication process that makes this possible can be characterized as diamond turning or computer numerically controlled machining on an ultrahigh-precision lathe.

Grudinin, Ivan↗

NASA NTRS2011

Micro-Slit Collimators for X-Ray/Gamma-Ray Imaging

A hybrid photochemical-machining process is coupled with precision stack lamination to allow for the fabrication of multiple ultra-high-resolution grids on a single array substrate. In addition, special fixturing and etching techniques have been developed that allow higher-resolution multi-grid collimators to be fabricated. Building on past work of developing a manufacturing technique for fabricating multi-grid, high-resolution coating modulation collimators for arcsecond and subarcsecond x-ray and gamma-ray imaging, the current work reduces the grid pitch by almost a factor of two, down to 22 microns. Additionally, a process was developed for reducing thin, high-Z (tungsten or molybdenum) from the thinnest commercially available foil (25 microns thick) down to approximately equal to 10 microns thick using precisely controlled chemical etching

Appleby, Michael↗