SEARCH · Search NASA

Results for “Acceleration methods”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

NASA NTRS1993

Preconditioning and the limit to the incompressible flow equations

The use of preconditioning methods to accelerate the convergence to a steady state for both the incompressible and compressible fluid dynamic equations are considered. The relation between them for both the continuous problem and the finite difference approximation is also considered. The analysis relies on the inviscid equations. The preconditioning consists of a matrix multiplying the time derivatives. Hence, the steady state of the preconditioned system is the same as the steady state of the original system. For finite difference methods the preconditioning can change and improve the steady state solutions. An application to flow around an airfoil is presented.

Turkel, E.↗

NASA NTRS2023

Relativistic Electron Beams Accelerated By an Interplanetary Shock

Context: Collisionless shock waves have long been considered to be among the most prolific particle accelerators in the universe. Shocks alter the plasma they propagate through, and often exhibit complex evolution across multiple scales. Interplanetary (IP) traveling shocks have been recorded in situ for over half a century and act as a natural laboratory for experimentally verifying various aspects of large-scale collisionless shocks. A fundamentally interesting problem in both heliophysics and astrophysics is the acceleration of electrons to relativistic energies (> 300 keV) by traveling shocks. Aims: The reason for an incomplete understanding of electron acceleration at IP shocks is due to scale-related challenges and a lack of instrumental capabilities. This Letter presents the first observations of field-aligned beams of relativistic electrons upstream of an IP shock, observed thanks to the instrumental capabilities of Solar Orbiter. This study presents the characteristics of the electron beams close to the source and contributes to the understanding of their acceleration mechanism. Methods: On 25 July 2022, Solar Orbiter encountered an IP shock at 0.98 AU. The shock was associated with an energetic storm particle event, which also featured upstream field-aligned relativistic electron beams observed 14 min prior to the actual shock crossing. The distance of the beam’s origin was investigated using a velocity dispersion analysis (VDA). Peak-intensity energy spectra were analyzed and compared with those obtained from a semi-analytical fast-Fermi acceleration model. Results: By leveraging Solar Orbiter’s high temporal resolution Energetic Particle Detector (EPD), we successfully showcase an IP shock’s ability to accelerate relativistic electron beams. Our proposed acceleration mechanism offers an explanation for the observed electron beam and its characteristics, while we also explore the potential contributions of more complex mechanisms.

acceleration of particles↗

DOE OSTI2021

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1977

Lifetimes of fiber composites under sustained tensile loading

Results are presented for a study intended to summarize lifetime data on several fiber/epoxy composite materials subjected to sustained uniaxial tensile loading, to report preliminary results of an accelerated test method for predicting the life of simple composites, and to describe related work in progress on pressure vessels and other filament-wound structures. The lifetime performance of the tested composites was compared by plotting the percent of ultimate strength (applied fiber stress normalized with respect to fiber failure stress in a composite) versus lifetime. In terms of performance in long-term tensile applications, the tested composites are ranked in the following order: graphite/epoxy, Be wire/epoxy, Aramid/epoxy, and S-glass/epoxy. The accelerated test using temperature and stress to simulate the passage of time proves to be encouraging, at least in the case of the Aramid/epoxy composite. The potential of a statistical analysis based on Weibull distribution analyses or a power law relationship is demonstrated.

Chiao, T. T.↗

DOE OSTI2025

Analysis of semivolatile organics in liquid radioactive residue using sorptive stir bar and solvent back-extraction

Current methods for semivolatiles analysis in radioactive samples can produce large volumes of radioactive solvent residue. A method utilizing stir-bar sorptive extraction has been explored in this work for its applicability to radioactive waste samples. This low solvent analytical method may accelerate remediation, minimize hazardous solvent waste, and reduce exposure risk to workers. Organic compounds (polyaromatic hydrocarbons, chlorinated aromatics, and phenolic compounds) were chosen as surrogates for common Liquid Waste System (LWS) contaminants at Savannah River Site (Aiken, SC). Stir-bar extraction parameters (extraction time, matrix modification, and effective pH range) and solvent back extraction parameters (solvent type, volume, and extraction time) were optimized experimentally for the chosen compounds. Affinity of the stir-bar extraction polymer to radionuclides Cs-137 and Am-241 was observed to determine radionuclide concentration effects. The stir-bar method achieved mean recovery of 100 ± 0.7% (1σ), relative to 114 ± 7% using solvent extraction, while reducing weekly method hands-on time by 93.4% and solvent volume consumption by 99.3%. Sensitivity was improved by 378% in simulated tank waste and 278% in real-world LWS matrix, relative to solvent extraction. This work has produced a safe and optimized method for the low solvent analysis of organics in legacy radioactive tank waste by stir-bar sorptive extraction.

GC-MS↗

NASA NTRS2010

Serrating Nozzle Surfaces for Complete Transfer of Droplets

A method of ensuring the complete transfer of liquid droplets from nozzles in microfluidic devices to nearby surfaces involves relatively simple geometric modification of the nozzle surfaces. The method is especially applicable to nozzles in print heads and similar devices required to dispense liquid droplets having precise volumes. Examples of such devices include heads for soft printing of ink on paper and heads for depositing droplets of deoxyribonucleic acid (DNA) or protein solutions on glass plates to form microarrays of spots for analysis. The main purpose served by the present method is to ensure that droplets transferred from a nozzle have consistent volume, as needed to ensure accuracy in microarray analysis or consistent appearance of printed text and images. In soft printing, droplets having consistent volume are generated inside a print head, but in the absence of the present method, the consistency is lost in printing because after each printing action (in which a drop is ejected from a nozzle), a small residual volume of liquid remains attached to the nozzle. By providing for complete transfer of droplets (and thus eliminating residual liquid attached to the nozzle) the method ensures consistency of volume of transferred droplets. An additional benefit of elimination of residue is prevention of cross-contamination among different liquids printed through the same nozzle a major consideration in DNA microarray analysis. The method also accelerates the printing process by minimizing the need to clean a printing head to prevent cross-contamination. Soft printing involves a hydrophobic nozzle surface and a hydrophilic print surface. When the two surfaces are brought into proximity such that a droplet in the nozzle makes contact with the print surface, a substantial portion of the droplet becomes transferred to the print surface. Then as the nozzle and the print surface are pulled apart, the droplet is pulled apart and most of the droplet remains on the print surface. The basic principle of the present method is to reduce the liquid-solid surface energy of the nozzle to a level sufficiently below the intrinsic solid-liquid surface energy of the nozzle material so that the droplet is not pulled apart and, instead, the entire droplet volume becomes transferred to the print surface. In this method, the liquid-solid surface energy is reduced by introducing artificial surface roughness in the form of micromachined serrations on the inner nozzle surface (see figure). The method was tested in experiments on soft printing of DNA solutions and of deionized water through 0.5-mm-diameter nozzles, of which some were not serrated, some were partially serrated, and some were fully serrated. In the nozzles without serrations, transfer was incomplete; that is, residual liquids remained in the nozzles after printing. However, in every nozzle in which at least half the inner surface was serrated, complete transfer of droplets to the print surface was achieved.

Kim, Chang-Jin "CJ"↗

DOE OSTI2023

Nanopatterned electron beams for temporal coherence and deterministic phase control of x-ray free-electron lasers

A method includes accelerating an electron bunch along a direction of propagation to a relativistic energy and partitioning the electron bunch by transmitting the electron bunch through a grating at the relativistic energy. The grating includes a plurality of alternating narrow portions and wide portions. The narrow portions have a first thickness in a direction substantially parallel to the direction of propagation of the electron bunch, and the wide portions have a second thickness in the direction substantially parallel to the direction of propagation of the electron bunch. The second thickness is greater than the first thickness. The method also includes generating a pulse of light using the partitioned electron bunch.

Graves, William↗

DOE OSTI2023

Calendar life of lithium metal batteries: Accelerated aging and failure analysis

Lithium metal batteries (LMBs) are prime candidates for next-generation energy storage devices characterized by their remarkable energy storage capabilities. Despite the importance of understating the calendar aging impacts in LMBs, cycle life and calendar life have received inconsistent attention across various research and development stages. For acceptance into an application, especially electric vehicles, batteries are required to have sufficient calendar life to ensure performance over the life of vehicles which often experience extended periods of inactivity. In this study, an in-depth exploration into the calendar aging of LMB (Li/ Li[Ni 0.8 Mn 0.1 Co 0.1 ]O 2 in pouch cell format) is conducted. Specifically, the impact of calendar aging factors, state-of-charge (SOC), temperature, pressure, and operating conditions (open-circuit voltage, OCV and constant voltage, CV) on the surface morphology and thickness change rate in lithium metal anode is investigated. Notably, the results show that storing LMB pouch cells at an OCV, 80 % SOC, 25 °C, and 10 psi external pressure leads to a <1% reduction in capacity over 18 months. The results also suggest routes which can be utilized in the future for accelerated aging without altering the primary degradation modes. The results also show an accelerated calendar aging at high temperatures (45 °C), 100 % SOC, and CV suggesting they may serve as key accelerated aging methods for future studies. During calendar aging, pressure mitigates dendrite growth and extends calendar life. In conclusion, the insights from this study reinforce the viability of LMBs as a compelling solution for addressing increased energy storage needs in real-world applications.

25 ENERGY STORAGE↗

DOE OSTI2020

Fast and Universal Kohn-Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma

Understanding many processes, e.g., fusion experiments, planetary interiors, and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter and hot dense plasma. This complex state of matter consists of a transient mixture of degenerate and nearly free electrons, molecules, and ions. This regime challenges both experiment and analytical modeling, necessitating predictive ab initio atomistic computation, typically based on quantum mechanical Kohn-Sham density functional theory (KS-DFT). However, cubic computational scaling with temperature and system size prohibits the use of DFT through much of the warm dense matter regime. A recently developed stochastic approach to KS-DFT can be used at high temperatures, with the exact same accuracy as the deterministic approach, but the stochastic error can converge slowly and it remains expensive for intermediate temperatures (< 50 eV). Here we have developed a universal mixed stochastic-deterministic algorithm for DFT at any temperature. This approach leverages the physics of KS-DFT to seamlessly integrate the best aspects of these different approaches. We demonstrate that this method significantly accelerated self-consistent field calculations for temperatures from 3 to 50 eV, while producing stable molecular dynamics and accurate diffusion coefficients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Role of Accelerated Burnup Irradiation Testing in Support of Accelerated Fuel Qualification

Accelerated fuel qualification has gained attention as a means to reduce the time needed to realize new nuclear fuel concepts and expand the operating windows of existing fuel forms. A key component of this approach is accelerated burnup irradiation testing. Although the concept of accelerated burnup has been familiar to the community for many decades, the specifics about how the increasing fission rate may be used as a qualification tool have not yet been elucidated. The present work provides a vision of how accelerated fission rate testing can enable accelerated fuel qualification. Technology readiness levels (TRLs) are reintroduced to demarcate the stages of traditional fuel qualification, and accelerated fuel qualification is presented in this context. The critical steps needed to achieve each TRL are reframed within the context of modern nuclear materials research and development, as revolutionary fuel concepts are more common than previous eras. The practical impacts of accelerated fuel qualification approaches as applied to contemporary fuel qualification efforts are illustrated. Examples are given to illustrate how accelerated burnup irradiations are being used currently and could be applied in the future to support qualification and licensure. Finally, outstanding challenges in the application of accelerated burnup methods to nuclear fuel qualification are summarized, with priority placed on understanding how fission rate impacts diffusion, microstructure evolution, and other critical mechanisms that dictate fuel performance.

Accelerated fuel qualification↗

DOE OSTI2022

Hybrid simulation of energetic particles interacting with magnetohydrodynamics using a slow manifold algorithm and GPU acceleration

The hybrid method combining particle-in-cell and magnetohydrodynamics can be used to study the interaction between energetic particles and global plasma modes. In this paper we introduce the M3D-C1-K code, which is developed based on the M3D-C1 finite element code solving the magnetohydrodynamics equations, with a newly developed kinetic module simulating energetic particles. The particle pushing is done using a new algorithm by applying the Boris pusher to the classical Pauli particles to simulate the slow-manifold of particle orbits, with long-term accuracy and fidelity. The particle pushing can be accelerated using GPUs with a significant speedup. The moments of the particles are calculated using the δƒ method, and are coupled into the magnetohydrodynamics simulation through pressure or current coupling schemes. Several linear simulations of magnetohydrodynamics modes driven by energetic particles have been conducted using M3D-C1-K with the δƒ method, including fishbone, toroidal Alfvén eigenmodes and reversed shear Alfvén eigenmodes. Good agreement with previous results from other eigenvalue, kinetic and hybrid codes have been achieved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Varela, Sebastian↗

NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗

DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗

DOE OSTI2023

Improving Stability of Triple-Cation Perovskite Solar Cells under High-Temperature Operation

Metal halide perovskite photovoltaic performance required for commercial technology encompasses both efficiency and stability. Advances in both these parameters have recently been reported; however, these strategies are often difficult to directly compare due to differences in perovskite composition, device architecture, fabrication methods, and accelerated stressors applied in stability tests. In particular, it is found that there is a distinct lack of elevated temperature, operational (light and bias) stability data. Furthermore, significant testing is required to understand the interactions when combinations are used (e.g., additives used with posttreatments). Herein, individual and combined additive, posttreatment, and contact layer strategies from recent literature reports under standardized operational stability tests of p-i-n CsMAFA perovskites at 70 degrees C are evaluated. Through analysis of over 1000 devices, it is concluded that the hole-transport layer (HTL) is the most significant component impacting elevated temperature operational stability. This analysis motivates future development of high-performance HTLs.

14 SOLAR ENERGY↗

DOE OSTI2023

A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.

97 MATHEMATICS AND COMPUTING↗