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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inhibition of Micro-pitting by Tribofilm-Forming ZrO 2 Nanocrystal Lubricant Additives: A Micro-pitting Rig and Transmission Electron Microscope Study

The drive to reduce fuel consumption in transportation has encouraged the emergence of low viscosity lubricants to reduce viscous losses in the engine, drivetrain, and other components. However, viscosity reduction increases the risk of surface damage, thus motivating the development of new anti-wear (AW) additives. Capped ZrO 2 nanocrystals (NCs) in base oils have been shown to form AW tribofilms within microscale sliding contacts. However, the potential of ZrO 2 NCs to protect surfaces subjected to rolling-sliding contact from macroscale damage, such as micro-pitting, remains unexplored. Here, we explore the ability of ZrO 2 NCs to form protective tribofilms under harsh conditions using a micro-pitting rig (MPR), consisting of a three ring-on-roller configuration. The experiments were conducted in polyalphaolephin (PAO) base oil, with and without 5 nm diameter ZrO 2 NCs, at two levels of slide-to-roll ratio (SRR) (30% and 0%) and at variable test durations up to long durations (119 h). MPR results showed the use of ZrO 2 NCs gives rise to the formation of a tribofilm covering the roller surfaces and decreases the initiation and propagation of micro-pits compared to tests in pure PAO base stock. Transmission electron microscopy (TEM) performed on focused ion beam (FIB) milled cross-sectional samples of the roller surfaces revealed the growth of dense and 50–100 nm thick ZrO 2 -based tribofilms independent of (SRR), indicating the potential for robust micro-pitting fatigue protection. Nevertheless, small cracks localized within the near surface region of the roller tested at the most severe conditions (30% SRR and 119 h) were observed. The initiation of these cracks was directly related to the presence of manganese sulphide (MnS) inclusions in the steel, revealed using TEM combined with energy dispersive spectroscopy (EDS). Furthermore, the results highlight the benefits of the protective tribofilms formed by ZrO 2 NCs and suggest approaches for further optimizing their use.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ionic Liquids as Extreme Pressure Additives for Bearing Steel Applications

The protection of steel surfaces from wear under extreme pressure conditions is of major importance in several industries as it provides better performance and longer life of machinery. The motivation for this work was to study the lubrication of steel by ionic liquids (ILs), which have recently emerged as greener alternatives to commercial lubricants and additives. Three ILs based on sulfur-containing anions, used as 2-wt% additives in polyethylene glycol base oil (MW 200; PEG 200), were tested in the lubrication of ASTM 52100 bearing steel contacts in extreme pressure conditions (under mixed lubrication with a Hertzian pressure of 1.12 GPa) using a mini traction machine (MTM). Due to the poor resistance to corrosion of bearing steel, a semi-ester of succinic acid derivative corrosion inhibitor (Lanxess RC 4801) was added to the mixtures at a 1 wt% concentration. The ILs 1-hexyl-methylimidazolium trifluoromethanesulfonate ([C 6 mim][TfO]) and 1-hexyl-4-picolinium trifluoromethanesulfonate ([C 6 -4-pic][TfO]) revealed promising results in terms of surface protection of bearing steel. In contrast, 4-picolinium hydrogen sulfate ([4-picH][HSO 4 ]) as 2-wt% additive to PEG 200 + 1% RC 4801 did not show any improvement in wear performance compared to neat PEG 200 + 1% RC 4801. PEG 200 + 2% [C 6 mim][TfO] + 1%RC 4801 allowed for a decrease in wear up to ~ 76% and PEG 200 + 2% [C 6 -4-pic][TfO] + 1%RC 4801 up to ~ 46% when compared with neat PEG 200 + 1% RC 4801. Optical microscopy images suggest the formation of an adsorbed layer, which was further supported by chemical analysis via x-ray photoelectron spectroscopy (XPS) data for [C6mim][TfO].

friction↗
DOE OSTI2021

Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation

Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei Guo's 60th birthday with a review of the ODE Method and its recent evolution. The method has been regarded as a technique for algorithm analysis. It is argued that this viewpoint is backwards: The original stochastic approximation method was surely motivated by an ODE, and tools for analysis came much later (based on establishing robustness of Euler approximations). The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time, following by careful approximation to obtain a practical recursive algorithm. While these methods are usually presented in a stochastic setting, this is not a prerequisite. In fact, recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo. Subject to conditions, the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert. The conditions are not universal, but theory suggests alternatives to achieve acceleration. The theory is illustrated with applications to gradient-free optimization, and policy gradient algorithms for reinforcement learning.

learning and adaptive systems in artificial intell↗
DOE OSTI2020

Solving Recent Challenges for Wrought Ni-Base Superalloys

This paper reviews the status of technology in design and manufacture of new wrought 21 polycrystalline Ni-base superalloys for critical engineering applications. There is a strong 22 motivation to develop new alloys that are capable of operating at higher temperatures to 23 realise improvements in thermal efficiency, which are necessary to achieve environmental 24 targets for reduced emissions of harmful green-house gases. From the aerospace sector, 25 the development of new powder metallurgy and ingot metallurgy alloys are discussed for 26 disk rotor and static applications. New compositions for powder metallurgy contain about 27 50-55% of gamma prime (γ') strengthening precipitates to ensure components operate 28 successfully at temperatures up to 788°C (1450°F). In contrast, new compositions for ingot 29 metallurgy aim to occupy a design space in temperature capability between Alloy 718 and 30 current powder alloys that are in-service, and show levels of γ' of about 30-44%. The focus 31 in developing these alloys was design for manufacturability. To complement the aerospace 32 developments, a review of work to understand the suitability of candidate alloys for multiple 33 applications in Advanced-Ultra Supercritical (AUSC) power plants has been undertaken by 34 Detrois, Jablonski and Hawk from the National Energy Technology Laboratory. In these 35 power plants, steam temperatures are required to reach 700-760°C. The common thread is 36 to develop alloys that demonstrate a combination of high temperature properties, which are 37 reliant on both the alloy composition and microstructure and can be produced readily at the 38 right price. For the AUSC applications, the emphasis is on high temperature strength, long 39 term creep life, phase stability, oxidation resistance and robust welding for fabrications. 40 Whereas for powder disk rotors in aircraft engines, the priority is enhanced resistance to 41 time dependent crack growth, phase stability and resistance to environmental damage, while 42 extending the current strength levels, which are shown by existing alloys, to higher 43 temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Emerging Opportunities in Manufacturing Bulk Soft-Magnetic Alloys for Energy Applications: A Review

Abstract Soft-magnetic alloys exhibit exceptional functional properties that are beneficial for a variety of electromagnetic applications. These alloys are conventionally manufactured into sheet or bar forms using well-established ingot metallurgy practices that involve hot- and cold-working steps. However, recent developments in process metallurgy have unlocked opportunities to directly produce bulk soft-magnetic alloys with improved, and often tailorable, structure–property relationships that are unachievable conventionally. The emergence of unconventional manufacturing routes for soft-magnetic alloys is largely motivated by the need to improve the energy efficiency of electromagnetic devices. In this review, literature that details emerging manufacturing approaches for soft-magnetic alloys is overviewed. This review covers (1) severe plastic deformation, (2) recent advances in melt spinning, (3) powder-based methods, and (4) additive manufacturing. These methods are discussed in comparison with conventional rolling and bar processing. Perspectives and recommended future research directions are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. Lastly, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2025

A New Method for Polishing Laminated Dissimilar Metals

Here, this paper presents a novel method for preparing metallographic samples of laminated dissimilar metals, specifically a layered structure of zirconium (Zr), vanadium (V), and tungsten (W), which pose a challenge due to their differing crystal structures and hardness levels. The work was motivated by the need to develop a cladding for tungsten targets in spallation neutron sources, where a zirconium face is applied over an interlayer of either vanadium or titanium to prevent the formation of brittle intermetallic compounds such as ZrW 2 . Traditional polishing techniques prove inadequate because each constituent metal requires a different polishing approach based on its unique material properties. The methodology outlined includes a sequential process involving conductive epoxy mounting, successive grinding and polishing with specific abrasives, and a final chemical-mechanical polish using a colloidal silica-based solution. Optical microscopy confirmed that this multi-step process successfully produced scratch-free surfaces and minimized surface relief across the different metal interfaces. Although developed for refractory metals, this approach shows promise for other layered structures comprised of transition metals, offering a meticulous yet effective technique for high-quality metallographic sample preparation.

Newberry, Daniel [Oak Ridge National Laboratory (O↗
DOE OSTI2021

Quantum space, quantum time, and relativistic quantum mechanics

We treat space and time as bona fide quantum degrees of freedom on an equal footing in Hilbert space. Motivated by considerations in quantum gravity, we focus on a paradigm dealing with linear, first-order Hamiltonian and momentum constraints that lead to emergent features of temporal and spatial translations. Unlike the conventional treatment, we show that Klein-Gordon and Dirac equations in relativistic quantum mechanics can be unified in our paradigm by applying relativistic dispersion relations to eigenvalues rather than treating them as operator-valued equations. With time and space being treated on an equal footing in Hilbert space, we show symmetry transformations to be implemented by unitary basis changes in Hilbert space, giving them a stronger quantum mechanical footing. Global symmetries, such as Lorentz transformations, modify the decomposition of Hilbert space; and local symmetries, such as U(1) gauge symmetry are diagonal in coordinate basis and do not alter the decomposition of Hilbert space. Here, we briefly discuss extensions of this paradigm to quantum field theory and quantum gravity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

60 years of science in ICF: from conception to scientific breakeven on the National Ignition Facility

The recent achievements of a burning plasma, fusion ignition, and scientific energy gain with deuterium-tritium (DT) fuel at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) represents a major milestone in the development of inertial confinement fusion (ICF) and all of fusion research. In these experiments, fuel pressures well in excess of hundreds of GBars were achieved in the compressed fuel, and robust alpha heating of the fuel, far in excess of the energy provided by the implosion, were demonstrated for the first time. These achievements occurred 60 years after the inception of ICF and the first laser demonstration, and were made possible by more than five decades of research at laser facilities around the world. Advances in laser technology both in wavelength and precision, motivated by improved understanding of laser-plasma interaction physics and the demands of targets; improvements in target fabrication inspired by the need to control and minimize hydrodynamic instabilities in the implosion; and multi-dimensional simulations and diagnostics have been critical to this achievement. This paper will summarize the scientific and technical advances, the surprises, and the challenges that had to be overcome to achieve these goals.

fusion↗
DOE OSTI2021

Charged Particle Tracking via Edge-Classifying Interaction Networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high-energy particle physics. In particular, particle tracking data are naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges, given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN’s excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

accelerator physics↗
DOE OSTI2022

A Common Tracking Software Project

Abstract The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using GEANT4 . Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigating the Chinese postman problem on a quantum annealer

The recent availability of quantum annealers has fueled a new area of information technology where such devices are applied to address practically motivated and computationally difficult problems with hardware that exploits quantum mechanical phenomena. D-Wave annealers are promising platforms to solve these problems in the form of quadratic unconstrained binary optimization. Here we provide a formulation of the Chinese postman problem that can be used as a tool for probing the local connectivity of graphs and networks. We treat the problem classically with a tabu algorithm and simulated annealing, and using a D-Wave device. The efficiency of quantum annealing with respect to the simulated annealing has been demonstrated using the optimal time to solution metric. We systematically analyze computational parameters associated with the specific hardware. Our results clarify how the interplay between the embedding due to limited connectivity of the Chimera graph, the definition of logical qubits, and the role of spin-reversal controls the probability of reaching the expected solution

36 MATERIALS SCIENCE↗
DOE OSTI2023

A semi-agnostic ansatz with variable structure for variational quantum algorithms

Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The stability of single-atom catalysts in thermocatalysis

Abstract Single-atom catalysts (SACs) featuring 100% atom utilization and uniform isolated active sites have been receiving sustainable attention over the last decade, as they offer exceptional performance in various catalytic applications. The motivation behind studying SACs is to develop highly active, selective, and stable catalysts for industrial applications with desirable economic and ecological benefits. Despite their promising potential, SACs face challenges related to stability, which need to be addressed for practical implementation. In this mini-review we discuss the existing stability issues of SACs, and summarize the deactivation mechanism and behaviors, protective strategies, and regeneration methods of SACs. We highlighted the challenges and prospects of future SACs study, aiming to pave the way for their widespread application in industrial processes.

Chae, Sudong↗