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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Structural Benchmark Testing for Stirling Convertor Heater Heads

The National Aeronautics and Space Administration (NASA) has identified high efficiency Stirling technology for potential use on long duration Space Science missions such as Mars rovers, deep space missions, and lunar applications. For the long life times required, a structurally significant design limit for the Stirling convertor heater head is creep deformation induced even under relatively low stress levels at high material temperatures. Conventional investigations of creep behavior adequately rely on experimental results from uniaxial creep specimens, and much creep data is available for the proposed Inconel-718 (IN-718) and MarM-247 nickel-based superalloy materials of construction. However, very little experimental creep information is available that directly applies to the atypical thin walls, the specific microstructures, and the low stress levels. In addition, the geometry and loading conditions apply multiaxial stress states on the heater head components, far from the conditions of uniaxial testing. For these reasons, experimental benchmark testing is underway to aid in accurately assessing the durability of Stirling heater heads. The investigation supplements uniaxial creep testing with pneumatic testing of heater head test articles at elevated temperatures and with stress levels ranging from one to seven times design stresses. This paper presents experimental methods, results, post-test microstructural analyses, and conclusions for both accelerated and non-accelerated tests. The Stirling projects use the results to calibrate deterministic and probabilistic analytical creep models of the heater heads to predict their life times.

Krause, David L.↗
NASA NTRS2004

Program Analyzes Radar Altimeter Data

A computer program has been written to perform several analyses of radar altimeter data. The program was designed to improve on previous methods of analysis of altimeter engineering data by (1) facilitating and accelerating the analysis of large amounts of data in a more direct manner and (2) improving the ability to estimate performance of radar-altimeter instrumentation and provide data corrections. The data in question are openly available to the international scientific community and can be downloaded from anonymous file-transfer- protocol (FTP) locations that are accessible via links from altimetry Web sites. The software estimates noise in range measurements, estimates corrections for electromagnetic bias, and performs statistical analyses on various parameters for comparison of different altimeters. Whereas prior techniques used to perform similar analyses of altimeter range noise require comparison of data from repetitions of satellite ground tracks, the present software uses a high-pass filtering technique to obtain similar results from single satellite passes. Elimination of the requirement for repeat-track analysis facilitates the analysis of large amounts of satellite data to assess subtle variations in range noise.

Vandemark, Doug↗
DOE OSTI2019

Production of isotopes using high power proton beams

The invention provides for a method for producing isotopes using a beam of particles from an accelerator, whereby the beam is maintained at between about 70 to 2000 MeV; and contacting a thorium-containing target with the particles. The medically important isotope .sup.225Ac is produced via the nuclear reaction (p,2p6n), whereby an energetic proton causes the ejection of 2 protons and 6 neutrons from a .sup.232Th target nucleus. Another medically important isotope .sup.213Bi is then available as a decay product. The production of highly purified .sup.211At is also provided.

Nolen, Jr., Jerry A.↗
DOE OSTI2019

Future High Energy Frontier Colliders

Colliders have been at the forefront of scientic discoveries in high-energy particle physics since the inception of the colliding beams method in the middle of the 20th century. The field of accelerators is very dynamic and many innovative concepts are currently being considered such future facilities as Higgs factories and energy frontier colliders beyond the LHC. Here we briefly overview leading proposals and studies towards the next generation colliders and discuss their major challenges as well as directions of corresponding accelerator R and D programs needed to address their cost and performance risks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

In Situ Plasma processing of SRF cuperconducting cavities at JLAB, 2024 Update

Jefferson Lab has an ongoing R&D program in plasma processing. The experimental program investi-gated processing using argon/oxygen and heli-um/oxygen gas mixtures. Plasma processing is a com-mon technique where the free oxygen produced by the plasma breaks down and removes hydrocarbons from surfaces. This increases the work function and reduces the secondary emission coefficient. The initial focus of the effort was processing C100 cavities by injecting RF power into the high order mode (HOM) coupler ports. We also developed the methods for establishing a plasma in C75 cryomodules where the RF power is injected via the fundamental power-coupler. Four C100 cryomodules were in situ processed in the CE-BAF accelerator in May 2023 with the cryomodules returning to an operational status in Sept. 2023. The overall operational energy gain for the four cryomod-ules was 49 MeV. Methods, systems and results from processing cryomodules in the CEBAF accelerator and vertical test results are presented. Current status and future plans are discussed.

Powers, T.↗
DOE OSTI2019

Plasma ignition and detection for in-situ cleaning of 1.3 GHz 9-cell cavities

Superconducting Radio Frequency (SRF) cavities performance preservation is crucial, from vertical test to accelerator operation. Field Emission (FE) is still one of the performance limiting factors to overcome and plasma cleaning has been proven successful by the Spallation Neutron Source (SNS), in cleaning field emitters and increasing the work function of Nb. A collaboration has been established between Fermi National Accelerator Laboratory (FNAL), SLAC National Accelerator Laboratory and Oak Ridge National Laboratory (ORNL) with the purpose of applying plasma processing to the Linac Coherent Light Source-II (LCLS-II) cavities, in order to minimize and overcome field emission without affecting the high Q of nitrogen-doped cavities. The cleaning for LCLS-II will follow the same plasma composition adopted at SNS, which allows in-situ processing of cavities installed in cryomodules from hydrocarbon contaminants. A novel method for plasma ignition has been developed at FNAL: a plasma glow discharge is ignited using high order modes to overcome limitations imposed by the fundamental power coupler, allowing in-situ cleaning for cavities in cryomodule. The plasma can be easily ignited and tuned in each of the cavity cells using low RF power. A method for plasma detection has been developed as well, which allows the detection of the plasma location in the cavity without the need of cameras at both cavity ends. The presented method can be applied to other multi-cell cavity designs, even for accelerators where the coupling for the fundamental modes at room temperature is very weak.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

The LCLS-II HE High Q and Gradient R&D Program

The LCLS-II HE project is a high energy upgrade to the superconducting LCLS-II linac. It consists of adding twenty additional 1.3 GHz cryomodules to the linac, with cavities operating at a gradient of 20.8 MV/m with a Q₀ of 2.7·10¹⁰. Performance of LCLS-II cryomodules has suggested that operations at this high of a gradient will not be achievable with the existing cavity recipe employed. Therefore a research program was developed between SLAC, Fermilab, Thomas Jefferson National Accelerator Facility, and Cornell University in order to improve the cavity processing method of the SRF cavities and reach the HE goals. This program explores the doping regime beyond what was done for LCLS-II and also has looked to further developed nitrogen-infusion. Here we will summarize the results from this R\&D program, showing significant improvement on both single-cell and 9-cell cavities compared with the original LCLS-II cavity recipe.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Generalized, Energy-conserving Numerical Simulations of Particles in General Relativity. II. Test Particles in Electromagnetic Fields and GRMHD

Observations of compact objects, in the form of radiation spectra, gravitational waves by LIGO/Virgo, and direct imaging with the Event Horizon Telescope, are currently the main information sources on plasma physics in extreme gravity. Modeling such physical phenomena Requires numerical methods that allow for the simulation of microscopic plasma dynamics in the presence of both strong gravity and electromagnetic fields. In Bacchini et al. (2018), we presented a detailed study of numerical techniques for the integration of free geodesic motion. In this paper, we extend the study by introducing electromagnetic forces in the simulation of charged particles in curved spacetimes. We extend the Hamiltonian energy-conserving method presented in Bacchini et al. to include the Lorentz force, and we test its performance compared to that of standard explicit Runge–Kutta and implicit midpoint rule schemes against analytic solutions. Then, we show the application of the numerical schemes to the integration of test particle trajectories in general relativistic magnetohydrodynamic (GRMHD) simulations by modifying the algorithms to handle grid-based electromagnetic fields. We test this approach by simulating ensembles of charged particles in a static GRMHD configuration obtained with the black hole accretion code (BHAC).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Anderson acceleration stability in NDA-accelerated k-eigenvalue problems

Anderson acceleration (AA) has been used to improve the stability and convergence rate of multiphysics iterative methods for reactor analysis. Most applications studied assume a tightly converged solution for the different physics problems, and AA is usually applied to state variables like temperature, density, and heat generation rate. In this paper, we study the theoretical performance of AA in NDA-accelerated k-eigenvalue problems. The problems and algorithms studied are simplified from the coupled iteration scheme adopted by MPACT and many other high-fidelity whole-core reactor codes. Compared to previous analyses of AA for these iteration schemes, we study the case with a partially converged neutronics solution and possibly partially converged nonlinear diffusion acceleration (NDA)/coarse mesh finite difference (CMFD) solutions. We observe that the performance of the iteration scheme with AA is very sensitive to the initial guess and is affected by the partially converged CMFD solutions. When the NDA solution is fully converged, using AA cannot achieve the optimal convergence rate in large-sized problems. Conversely, if the NDA solution is partially converged, the iteration scheme with AA can diverge or converge extremely slowly. It is found that the loss of robustness for AA is due to the fact that it is applied to the iterative subspace of state variables rather than the fundamental unknowns of the governing equations. To improve the robustness, the scalar flux should also be considered in the implementation of AA. After considering the residuals of flux, we observe that the stability is regardless of the partial convergence of NDA solutions. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Progress in Normalizing Flows for 4d Gauge Theories

Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements: an architectural improvement referred to as learned active loops, and the application of correlated ensemble methods to QCD with N f = 2 dynamical fermions.

Abbott, Ryan [Massachusetts Institute of Technolog↗
NASA NTRS1974

Selection of appropriate test method for simulating steady-state spacecraft launch loads

In ground testing prior to launch the static loads portion of the launch profile may be simulated by either applying static loads directly to the spacecraft through an appropriate loading facility or by subjecting the spacecraft to acceleration loads on a centrifuge. The factors to be considered in selecting one of the two simulation methods are discussed, giving attention to the relative advantages of the two methods in each case.

Shockey, E. F.↗
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗
DOE OSTI2019

Low RF Power Plasma Ignition and Detection for In-Situ Cleaning of 1.3 GHz 9-Cell Cavities

Superconducting Radio Frequency (SRF) cavities performance preservation is crucial, from vertical test to accelerator operation. Field Emission (FE) is still one of the performance limiting factors to overcome and plasma cleaning has been proven successful by the Spallation Neutron Source (SNS), in cleaning field emitters and increasing the work function of Nb. A collaboration has been established between Fermi National Accelerator Laboratory (FNAL), SLAC National Accelerator Lab- oratory and Oak Ridge National Laboratory (ORNL) with the purpose of applying plasma processing to the Linac Coherent Light Source-II (LCLS-II) cavities, in order to minimize and overcome field emission without affecting the high Q of nitrogen-doped cavities. The cleaning for LCLS-II will follow the same plasma composition adopted at SNS, which allows in-situ processing of cavities installed in cryomod-ules from hydrocarbon contaminants. A novel method for plasma ignition has been developed at FNAL: a plasma glow discharge is ignited using high order modes to overcome limitations imposed by the fundamental power coupler, allowing in-situ cleaning for cavities in cryomodule. The plasma can be easily ignited and tuned in each of the cavity cells using low RF power. A method for plasma detection has been developed as well, which allows the detection of the plasma location in the cavity without the need of cameras at both cavity ends. The presented method can be applied to other multi-cell cavity designs, even for accelerators where the coupling for the fundamental modes at room temperature is very weak.

43 PARTICLE ACCELERATORS↗
NASA NTRS1975

Averaged initial Cartesian coordinates for long lifetime satellite studies

A set of initial Cartesian coordinates, which are free of ambiguities and resonance singularities, is developed to study satellite mission requirements and dispersions over long lifetimes. The method outlined herein possesses two distinct advantages over most other averaging procedures. First, the averaging is carried out numerically using Gaussian quadratures, thus avoiding tedious expansions and the resulting resonances for critical inclinations, etc. Secondly, by using the initial rectangular Cartesian coordinates, conventional, existing acceleration perturbation routines can be absorbed into the program without further modifications, thus making the method easily adaptable to the addition of new perturbation effects. The averaged nonlinear differential equations are integrated by means of a Runge Kutta method. A typical step size of several orbits permits rapid integration of long lifetime orbits in a short computing time.

Pines, S.↗
DOE OSTI2026

Accelerating multicanonical sampling with irreversibility

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2–4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards–Anderson spin glass, compared to traditional multicanonical sampling. In conclusion, the round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Abstract Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

59 BASIC BIOLOGICAL SCIENCES↗