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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Comprehensive Computational Study of Partial Lead Substitution in Methylammonium Lead Bromide

Impurities in semiconductors, for example, lead-based hybrid perovskites, have a major influence on their performance as photovoltaic (PV) light absorbers. While impurities could create harmful trap states that lead to nonradiative recombination of charge carriers and adversely affect PV efficiency, they could also potentially increase absorption via midgap energy levels that act as stepping stones for subgap photons or introduce charge carriers via doping. To unearth trends in impurity energy states, we use first principles density functional theory calculations to extensively study partial substitution of Pb in methylammonium lead bromide (MAPbBr 3 ), a representative lead-halide perovskite. Investigation of the density of states and energy levels related to the transition of the substitutional defect from one charge state to another reveals that several elements create midgap energy states in MAPbBr 3 . We machine learned trends and design rules from the computational data and discovered that a few easily computed properties of the bromide compounds of any element can be used to predict the energetics and energy levels of the substitutional defect related to that element. The calculated Fermi level-dependent defect formation energies lead to the observation that substitution by transition metals, Zr, Hf, Nb, and Sc, and group V element Sb can shift the equilibrium Fermi level and change the perovskite conductivity, as determined by the dominant intrinsic point defects. Finally, metal-substituted MAPbBr 3 compounds of Bi, Sc, Ni, and Zr were experimentally investigated, and while there was an improvement in the thin-film morphology and an enhancement in charge carrier lifetimes, no clear evidence of subgap absorption features owing to the substituent being incorporated in the MAPbBr 3 lattice could be seen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Magnetic phases of spatially modulated spin-1 chains in Rydberg excitons: Classical and quantum simulations

Here, in this work, we study the magnetic phases of a spatially modulated chain of spin-1 Rydberg excitons. Using the Density Matrix Renormalization Group (DMRG) technique, we study various magnetic and topologically nontrivial phases using both single-particle properties, such as local magnetization and quantum entropy, and many-body ones, such as pair-wise Néel and long-range string correlations. In particular, we investigate the emergence and robustness of the Haldane phase, a topological phase of anti-ferromagnetic spin-1 chains. Furthermore, we devise a hybrid quantum algorithm employing restricted Boltzmann machine to simulate the ground state of such a system that shows very good agreement with the results of exact diagonalization and DMRG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A practical and efficient approach for Bayesian quantum state estimation

Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank–Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optimizing the shape of photometric redshift distributions with clustering cross-correlations

We present an optimization method for the assignment of photometric galaxies to a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimization algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organizing map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimize the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimizes the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic Legacy Survey of Space and Time cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 percent to 16 percent.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Overview of design and analysis activities for the W7-X scraper element

The Wendelstein 7-X stellarator is in final stages of commissioning, and will begin operation in late 2015. In the first phase, the machine will operate with a limiter, and will be restricted to low power and short pulse. But in 2019, plans are for an actively cooled divertor to be installed, and the machine will operate in steady state at full power. Recently, plasma simulations have indicated that, in this final operational phase, a bootstrap current will evolve in certain scenarios. This will cause the sensitive ends of the divertor target to be overloaded beyond their qualified limit. A high heat flux scraper element (HHF-SE) has been proposed in order to take up some of the convective flux and reduce the load on the divertor. In order to examine whether the HHF-SE will be able to effectively reduce the plasma flux in the divertor region of concern, and to determine how the pumping effectiveness will be affected by such a component, it is planned to include a test divertor unit scraper element (TDU-SE) in 2017 during an earlier operational phase. Several U.S. fusion energy science laboratories have been involved in the design, analysis (structural and thermal finite element, as well as computational fluid dynamics), plasma simulation, planning, prototyping, and diagnostic development around the scraper element program (both TDU-SE and HHF-SE). As a result, this paper presents an overview of all of these activities and their current status.

42 ENGINEERING↗
DOE OSTI2022

A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring

The objective of this project is to develop a robust event diagnostics (RED) platform by integrating state-of-the-art tensor analytics and machine learning into real-time grid monitoring. The proposed platform can effectively analyze and discover the information hiding within the provided PMU data for effective real-time grid monitoring. The proposed RED platform provides a set of robust diagnostics tools for grid operation and management, including 1) data quality assessment, 2) data completion, 3) event detection, and 4) robust event classification. All the functionalities of the RED platform can help the operator to make informed decisions and respond in a timely manner. The developed RED platform will serve as an innovative advisory tool to reliably identify key events and discover new insights about the events and grid characteristics in the PMU data, and contribute to the efficient, safe, reliable operation and design of the nation’s electric system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Trustworthy Physics-Informed Deep Learning for Predictive Scientific Computing

This project has developed powerful trustworthy physics-informed deep learning (TPiDL) models and methods to fundamentally enhance the scale and power of computational modeling in the scientific and engineering domains. Deep learning (DL) has radically advanced the state-of-the-art in machine learning, computer vision, natural language processing, and also scientific computing. Nevertheless, progress has been driven almost entirely by empirical observations, hacks, and tricks. Under the support of this project, the graph operator learning tools and advanced trustworthy physical informed neural networks have been developed. In addition, stochastic gradient replica-exchange Markov Chain Monte Carlo (MCMC) sampling algorithms have been designed to quantify the uncertainties and speed up the training of large-scale neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

Swain, P. H.↗
NASA NTRS1982

Characterization of silicon-gate CMOS/SOS integrated circuits processed with ion implantation

The procedure used to generate MEBES masks and produce test wafers from the 10X Mann 1600 Pattern Generator Tape using existing CAD utility programs and the MEBES machine in the RCA Solid State Technology Center are described. The test vehicle used is the MSFC-designed SC102 Solar House Timing Circuit. When transforming the Mann 1600 tapes into MEBES tapes, extreme care is required in order to obtain accurate minimum linewidths when working with two different coding systems because the minimum grid sizes may be different for the two systems. The minimum grid sizes are 0.025 mil for MSFC Mann 1600 and 0.02 mil for MEBES. Some snapping to the next grid is therefore inevitable, and the results of this snapping effect are significant when submicron lines are present. However, no problem was noticed in the SC102 circuit because its minimum linewidth is 0.3 mil (7.6 microns). MEBES masks were fabricated and wafers were processed using the silicon-gate CMOS/SOS and aluminum-gate COS/MOS processing.

Woo, D. S.↗
NASA NTRS1999

Parallel Tetrahedral Mesh Adaptation with Dynamic Load Balancing

The ability to dynamically adapt an unstructured grid is a powerful tool for efficiently solving computational problems with evolving physical features. In this paper, we report on our experience parallelizing an edge-based adaptation scheme, called 3D_TAG. using message passing. Results show excellent speedup when a realistic helicopter rotor mesh is randomly refined. However. performance deteriorates when the mesh is refined using a solution-based error indicator since mesh adaptation for practical problems occurs in a localized region., creating a severe load imbalance. To address this problem, we have developed PLUM, a global dynamic load balancing framework for adaptive numerical computations. Even though PLUM primarily balances processor workloads for the solution phase, it reduces the load imbalance problem within mesh adaptation by repartitioning the mesh after targeting edges for refinement but before the actual subdivision. This dramatically improves the performance of parallel 3D_TAG since refinement occurs in a more load balanced fashion. We also present optimal and heuristic algorithms that, when applied to the default mapping of a parallel repartitioner, significantly reduce the data redistribution overhead. Finally, portability is examined by comparing performance on three state-of-the-art parallel machines.

Oliker, Leonid↗
NASA NTRS2000

A Comparison of Three Programming Models for Adaptive Applications

We study the performance and programming effort for two major classes of adaptive applications under three leading parallel programming models. We find that all three models can achieve scalable performance on the state-of-the-art multiprocessor machines. The basic parallel algorithms needed for different programming models to deliver their best performance are similar, but the implementations differ greatly, far beyond the fact of using explicit messages versus implicit loads/stores. Compared with MPI and SHMEM, CC-SAS (cache-coherent shared address space) provides substantial ease of programming at the conceptual and program orchestration level, which often leads to the performance gain. However it may also suffer from the poor spatial locality of physically distributed shared data on large number of processors. Our CC-SAS implementation of the PARMETIS partitioner itself runs faster than in the other two programming models, and generates more balanced result for our application.

Shan, Hong-Zhang↗
NASA NTRS2003

Magnetized Target Fusion in Advanced Propulsion Research

The Magnetized Target Fusion (MTF) Propulsion lab at NASA Marshall Space Flight Center in Huntsville, Alabama has a program in place that has adopted to attempt to create a faster, lower cost and more reliable deep space transportation system. In this deep space travel the physics and development of high velocity plasma jets must be understood. The MTF Propulsion lab is also in attempt to open up the solar system for human exploration and commercial use. Fusion, as compared to fission, is just the opposite. Fusion involves the light atomic nuclei combination to produce denser nuclei. In the process, the energy is created by destroying the mass according to the distinguished equation: E = mc2 . Fusion energy development is being pursued worldwide as a very sustainable form of energy that is environmentally friendly. For the purposes of space exploration fusion reactions considered include the isotopes of hydrogen-deuterium (D2) and tritium (T3). Nuclei have an electrostatic repulsion between them and in order for the nuclei to fuse this repulsion must be overcome. One technique to bypass repulsion is to heat the nuclei to very high temperatures. The temperatures vary according to the type of reactions. For D-D reactions, one billion degrees Celsius is required, and for D-T reactions, one hundred million degrees is sufficient. There has to be energy input for useful output to be obtained form the fusion To make fusion propulsion practical, the mass, the volume, and the cost of the equipment to produce the reactions (generally called the reactor) need to be reduced by an order of magnitude or two from the state-of-the-art fusion machines. Innovations in fusion schemes are therefore required, especially for obtaining thrust for propulsive applications. Magnetized target fusion (MTF) is one of the innovative fusion concepts that have emerged over the last several years. MSFC is working with Los Alamos National Laboratory and other research groups in studying the underlying principles involved in MTF. Magnetized Target Fusion is an attempt to combine MCF (magnetic confinement fusion) for energy confinement and ICF (inertial confinement fusion) for efficient compression heating and wall free containment of the fusing plasma. It also seeks to combine the best features to these two main commonplace approaches to fusion.

Cylar, Rashad↗
DOE OSTI2023

Methods for the Automated Determination of Sustained Maximum Amplitudes in Oscillating Signals

Machine condition monitoring has been proven to reduce machine downtime and increase productivity. The state-of-the-art research uses vibration monitoring for tasks such as maintenance and tool wear prediction. A less explored aspect is how vibration monitoring might be used to monitor equipment sensitive to vibration. In a manufacturing environment, one example of where this might be needed is in monitoring the vibration of optical linear encoders used in high-precision machine tools and coordinate measuring machines. Monitoring the vibration of sensitive equipment presents a unique case for vibration monitoring because an accurate calculation of the maximum sustained vibration is needed, as opposed to extracting trends from the data. To do this, techniques for determining sustained peaks in vibration signals are needed. Here, this work fills this gap by formalizing and testing methods for determining sustained vibration amplitudes. The methods are tested on simulated signals based on experimental data. Results show that processing the signal directly with the novel Expire Timer method produces the smallest amounts of error on average under various test conditions. Additionally, this method can operate in real-time on streaming vibration data.

Industrial Internet of Things↗
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
DOE OSTI2024

Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision Earthquake Locations

Abstract On 18 March 2020, a magnitude 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States. Using a dense geophone deployment and machine learning (ML), an additional several thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deeper portion of the Salt Lake segment of the Wasatch fault—part of a large range-bounding fault system thought to be capable of generating an Mw 7.2 earthquake. However, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunately, the catalog locations and lack of focal mechanisms for this subset of aftershocks provide only a crude constraint on the true fault structure. To better illuminate fault structure, we relocate the ML-generated catalog with a range of magnitudes from −2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, (2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events. Results of the relocation suggest a simpler, minimally listric Wasatch fault geometry, contrary to what has been previously proposed. We also find that analysis of the focal mechanisms and waveform similarity indicates minimal event similarity throughout the Magna sequence, suggesting a highly complex and heterogeneous rupture zone, as opposed to rupture on a single plane. These findings suggest an increased seismic hazard due to the overall shallowness of the earthquake sequence and highly varied rupture mechanisms.

Geochemistry & Geophysics↗
DOE OSTI2021

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Preparation of Mo-Re-C samples containing Mo{sub 7}Re{sub 13}C with the β-Mn-type structure by solid state reaction of planetary-ball-milled powder mixtures of Mo, Re and C, and their crystal structures and superconductivity

Mo-Re-C compounds containing Mo{sub 7}Re{sub 13}C with the β-Mn structure were synthesized with high-melting-temperature metals Mo, Re, and C powders using a conventional solid state method with a planetary ball milling machine instead of the arc melting method. Use of the ball milling machine was necessary to obtain Mo{sub 7}Re{sub 13}C with the β-Mn structure using the solid state method. Almost single-phase Mo{sub 7}Re{sub 13}C with a trace of impurity were obtained using the synthesis method. By XRF and lattice parameter measurements on the samples, Fe element existed in the compound synthesized using the planetary ball milling machine with a pot and balls made of steel, though Fe element was not detected in the compound synthesized using a pot and balls made of tungsten carbide. The former compound containg the Fe atom did not show superconductivity but the latter compound without the Fe atom showed superconductivity at 6.1 K. - Graphical abstract: Temperature dependence of the magnetic susceptibility measured under 10 Oe for the superconducting PBM-T samples without Fe element and non-superconducting PBM-S with Fe element. The inset is the enlarged view of the data for the PBM-S sample.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗