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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Causes of fuel–ablator mix inferred from modeling of monochromatic time-gated radiography of OMEGA cryogenic implosions

Here, we present evidence, in the context of OMEGA cryogenic target implosions, that laser imprint, known to be capable of degrading laser-direct-drive target performance, plays a major role in generating fuel–ablator mix. OMEGA cryogenic target implosions show a performance boundary correlated with acceleration-phase shell stability; for sufficiently low adiabats (where the adiabat is the ratio of the pressure to the Fermi pressure) and high in-flight aspect ratios (IFAR's), the neutron-weighted shell areal density and neutron yield relative to the clean simulated values sharply decline. Direct evidence of Rayleigh–Taylor fuel–ablator mixing was previously obtained using a Si Heα backlighter driven by an ~20-ps short pulse generated by OMEGA EP. The shadow cast by the shell shortly prior to stagnation, as diagnosed using backlit radiographs, shows a softening near the limb, which is evidence of an ablator–fuel mix region for a low-adiabat implosion (α ~ 1.9, IFAR = 14) but not for a moderate adiabat implosion (α ~ 2.5, IFAR = 10). We find good agreement between experimental and synthetic radiographs in simulations that model laser imprint and account for uncertainty in the initial ablator thickness. Furthermore, we further explore the role of other mechanisms such as classical instability growth at the fuel–ablator interface, species concentration diffusion, and long-wavelength drive and target asymmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1979

On the origin of chondrules

A new mechanism for primordial melting of chondrules based upon heating by relativistic electrons accelerated by reconnecting magnetic field lines is discussed. This mechanism is free of the creation-annihilation problem of collisions used for heating. The basic requirements for reconnection are an interplanetary magnetic field with regions of reversals. An early magnetic field is inferred from the paleomagnetism of meteorites and the requirements of solar spin-down, while the field reversals upon which the reconnection is based are a common property of the present epoch solar wind.

Sonett, C. P.↗
NASA NTRS1986

Catastrophic disruption of asteriods and satellites; Proceedings of the International Workshop, Pisa, Italy, July 30-August 2, 1985

Theoretical, numerical, and experimental investigations of the violent disruption of asteroids or planetary satellites are discussed in reviews and reports. Topics examined include acceleration techniques and results of experiments simulating catastrophic fragmentation events; laboratory simulations of catastrophic impact; scaling laws for the catastrophic collisions of asteroids; asteroid collisional history, the origin of the Hirayama families, and disruption of small satellites; and the implications of the inferred compositions of a steroids for their collisional evolution. Diagrams, graphs, tables, and a summary of the discussion at the workshop are provided.

Davis, D. R.↗
NASA NTRSDate not supplied

Quantum-accelerated Global Constraint Filtering

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the all different constraint and the global cardinality constraint (gcc). The subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search.

Quantum algorithms↗
DOE OSTI2026

Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation

Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$ galaxy-galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $Ω_{m}$ from lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $Λ$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $Ω_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.

Jarugula, Sreevani [Fermilab] (ORCID:0000000253867↗
DOE OSTI2022

A Framework for Neural Network Inference on FPGA-Centric SmartNICs

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication, thereby mitigating the von Neumann bottleneck. Existing work, however, has been generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs, but most control tasks are left to the CPUs. This leads to frequent waiting, inferior performance, and scaling challenges. In this work, we propose a new distributive data-centric computing framework, named FCsN, for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture, FCsN allows the complete detaching of kernel execution, control logic, system scheduling, and network communication to the SmarNICs. This boosts performance by: (i) avoiding the control dependency with CPUs and (ii) supporting streaming kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network applications including graph neural networks; as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with the support of FCsN can achieve, on average, 144 speedups over the MPI-based standard CPU baselines.

Guo, Anqi↗
DOE OSTI2023

Laser Wakefield Accelerator modelling with Variational Neural Networks

A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

43 PARTICLE ACCELERATORS↗
NASA NTRS1994

Laser-Induced Fluorescence Helps Diagnose Plasma Processes

Technique developed to provide in situ monitoring of rates of ion sputter erosion of accelerator electrodes in ion thrusters also used for ground-based applications to monitor, calibrate, and otherwise diagnose plasma processes in fabrication of electronic and optical devices. Involves use of laser-induced-fluorescence measurements, which provide information on rates of ion etching, inferred rates of sputter deposition, and concentrations of contaminants.

Beattie, J. R.↗
NASA NTRS1998

Mapping the Solar Wind from its Source Region into the Outer Corona

Knowledge of the radial variation of the plasma conditions in the coronal source region of the solar wind is essential to exploring coronal heating and solar wind acceleration mechanisms. The goal of the present proposal is to determine as many plasma parameters in that region as possible by coordinating different observational techniques, such as Interplanetary Scintillation Observations, spectral line intensity observations, polarization brightness measurements and X-ray observations. The inferred plasma parameters are then used to constrain solar wind models.

Esser, Ruth↗
DOE OSTI2020

Geophysical Methods for Stratigraphic Identification

The goal of this work is to identify geophysical methods that can be used to delineate (in 3D) a large-scale paleochannel that has been inferred to exist between the 200 West and 200 East Areas of the Hanford Site’s Central Plateau . This paleochannel is of particular importance because it will significantly impact flow and contaminant transport from 200 West to 200 East. Its high permeability can both accelerate contaminant transport and lower contaminant concentrations through spreading. Information about the spatial extent and structure of such features is needed to inform subsurface flow and contaminant transport models that support remedy decisions at Hanford.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2003

Coupling the Solar-Wind/IMF to the Ionosphere through the High Latitude Cusps

Magnetic merging is a primary means for coupling energy from the solar wind into the magnetosphere-ionosphere system. The location and nature of the process remain as open questions. By correlating measurements form diverse locations and using large-scale MHD models to put the measurements in context, it is possible to constrain out interpretations of the global and meso-scale dynamics of magnetic merging. Recent evidence demonstrates that merging often occurs at high latitudes in the vicinity of the cusps. The location is in part controlled by the clock angle in the interplanetary magnetic field (IMF) Y-Z plane. In fact, B(sub Y) bifurcated the cusp relative to source regions. The newly opened field lines may couple to the ionosphere at MLT locations of as much as 3 hr away from local noon. On the other side of noon the cusp may be connected to merging sites in the opposite hemisphere. In face, the small convection cell is generally driven by opposite hemisphere merging. B(sub X) controls the timing of the interaction and merging sites in each hemisphere, which may respond to planar features in the IMF at different times. Correlation times are variable and are controlled by the dynamics of the tilt of the interplanetary electric field phase plane. The orientation of the phase plane may change significantly on time scales of tens of minutes. Merging is temporally variable and may be occurring at multiple sites simultaneously. Accelerated electrons from the merging process excite optical signatures at the foot of the newly opened field lines. All-sky photometer observations of 557.7 nm emissions in the cusp region provide a "television picture" of the merging process and may be used to infer the temporal and spatial variability of merging, tied to variations in the IMF.

Maynard, Nelson C.↗
NASA NTRS1987

Titan's atomic nitrogen torus - Inferred properties and consequences for the Saturnian aurora

A population of warm, approximately keV N(-) ions furnishing the greater part of the mass and energy input to the Titan magnetosphere, at a power level of 2 x 10 to the 11th W, is generated by ionization of Titan's nitrogen torus by both solar EUV photons and magnetospheric electron impact. Magnetospheric electrons also accelerate pickup ions to keV energies by means of lower hybrid emissions, on a time scale comparable to the plasma residence time. It is presently suggested that the aurora is excited by ion and electron precipitation from the outer magnetosphere; the energetics in question can be accounted for entirely on the basis of the titanogenic plasma supply proper to a corotation-dominated magnetosphere.

Barbosa, D. D.↗
DOE OSTI2020

Design of a multichannel photonic crystal dielectric laser accelerator

To be useful for most scientific and medical applications, compact particle accelerators will require much higher average current than enabled by current architectures. For this purpose, we propose a photonic crystal architecture for a dielectric laser accelerator, referred to as a multi-input multi-output silicon accelerator (MIMOSA), that enables simultaneous acceleration of multiple electron beams, increasing the total electron throughput by at least 1 order of magnitude. To achieve this, we show that the photonic crystal must support a mode at the Γ point in reciprocal space, with a normalized frequency equal to the normalized speed of the phase-matched electron. We show that the figure of merit of the MIMOSA can be inferred from the eigenmodes of the corresponding infinitely periodic structure, which provides a powerful approach to design such devices. Additionally, we extend the MIMOSA architecture to electron deflectors and other electron manipulation functionalities. These additional functionalities, combined with the increased electron throughput of these devices, permit all-optical on-chip manipulation of electron beams in a fully integrated architecture compatible with current fabrication technologies, which opens the way to unconventional electron beam shaping, imaging, and radiation generation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Bayesian calibration of bubble size dynamics applied to CO 2 gas fermenters

To accelerate the scale-up of gaseous CO 2 fermentation reactors, computational models need to predict gas-to-liquid mass transfer which requires capturing the bubble size dynamics, i.e. bubble breakup and coalescence. However, the applicability of existing models beyond air–water mixtures remains to be established. Here, an inverse modeling approach, accelerated with a neural network surrogate, calibrates the breakup and coalescence closure models, that are used in class methods for population balance modeling (PBM). The calibration is performed based on experimental results obtained in a CO 2 -air–water-coflowing bubble column reactor. Bayesian inference is used to account for noise in the experimental dataset and bias in the simulation results. To accurately capture gas holdup and interphase mass transfer, the results show that the breakage rate needs to be increased by one order of magnitude. In conclusion, the inferred model parameters are then used on a separate configuration and shown to also improve bubble size distribution predictions.

09 BIOMASS FUELS↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Cosmic-ray transport in the galactic magnetosphere

It is advantageous to regard cosmic rays as the constitutent particles of the Galactic radiation belts and cosmic ray energization as a consequence of inward radial diffusion in the quasi-dipolar Galactic magnetosphere. This process occurs in addition to Fermi acceleration. The purpose of this work is to explore a magnetospheric explanation for the elevation of Galactic charged particles to cosmic ray energies. The magnetosphere that is of interest in this context is not a planetary magnetosphere but a galactic magnetosphere entirely analogous to those inferred from radio observations of distant galaxies. It is the magnetosphere of the Milky Way. Cosmic rays are (by this interpretation) the charged particles that constitute the radiation belts of the Galactic magnetosphere. Thus, the mechanism by which charged particles attain cosmic-ray energies is presumable the mechanism by which radiation-belt particles attain high energies in more familiar magnetosphere, i.e., the radial diffusion associated with magnetic disturbances that contain spectral power resonant with the azimuthal drift of the particles.

Schulz, M.↗
NASA NTRS1992

Numerical studies of convective transport associated with crystal growth in mirrogravity

Information on numerical studies of convective transport associated with crystal growth in microgravity is given in viewgraph form. Inferences drawn from the research are that protein crystals seem to grow at much larger relative supersaturations than small molecule crystals; growth rate is limited by attachment kinetics more than by transport; attachment kinetics are apparently influenced by convective flows, although the mechanism is uncertain; acceleration levels required to achieve D/L growth within Space Station Freedom specifications may not be achievable on manned vehicles; and D/L transport can help minimize incorporation of impurities, but growth cessation cannot be explained.

Ramachandran, N.↗
DOE OSTI2024

Harnessing ML Privacy by Design Through Crossbar Array Non-idealities

Deep Neural Networks (DNNs), handling computeand data-intensive tasks, often utilize accelerators like Resistiveswitching Random-access Memory (RRAM) crossbar for energyefficient in-memory computation. Despite RRAM’s inherent nonidealities causing deviations in DNN output, this study transforms the weakness into strength. By leveraging RRAM non-idealities, the research enhances privacy protection against Membership Inference Attacks (MIAs), which reveal private information from training data. RRAM non-idealities disrupt MIA features, increasing model robustness and revealing a privacy-accuracy tradeoff. Empirical results with four MIAs and DNNs trained on different datasets demonstrate significant privacy leakage reduction with a minor accuracy drop (e.g., up to 2.8% for ResNet-18 with CIFAR-100).

artificial intelligence↗