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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Experimentally Known Properties of U-10Zr Alloys: A Critical Review

More than 14000 U-10Zr fuel rods (alloys of U with 10 wt% Zr) Zr were been irradiated in the Experimental Breeder Reactor II and Fast Flux Test Facility, with some irradiated to burnups of almost 20 at%. However, very little information about properties of un-irradiated U-10Zr alloys is available. This data is needed to help with interpretation of data from irradiated materials, develop and validate phase diagrams and models of U-Zr and more complex systems, develop and validate fuel-performance codes, design fuels for future fast reactors, and design future experimental investigations to fill in crucial gaps in knowledge. This paper provides a summary and critical review of the available experimental data on phases and phase diagrams, electrical properties, thermal expansion, thermal conductivity, heat capacity, mechanical properties, vapor pressures, and thermodynamic properties of un-irradiated U-10Zr alloys. As a result, much of this data was published before ~1970 and is available only in obscure reports.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Bayesian Treed Multivariate Gaussian Process with Adaptive Design: Application to a Carbon Capture Unit

Computer experiments (numerical simulations) are widely used in scientific research to study and predict the behavior of complex systems, which usually have responses consisting of a set of distinct outputs. The computational cost of the simulations at high resolution are often expensive and become impractical for parametric studies at different input values. To overcome these difficulties we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) in order to model and evaluate a multivariate process. A suitable choice of covariance function and the prior distributions facilitates the different Markov chain Monte Carlo (MCMC) movements. We utilize this model to sequentially sample the input space for the most informative values, taking into account model uncertainty and expertise gained. A simulation study demonstrates the use of the proposed method and compares it with alternative approaches. We apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit. The application presented in this paper is an important tool for research into carbon dioxide emissions from thermal power plants.

Konomi, Bledar A.↗
DOE OSTI2018

Wang–Landau approach to the simulation of water clusters

The Wang–Landau Monte Carlo method [Wang FG, Landau DP. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys Rev Lett. 2001;86:2050-2053; Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. Phys Rev E. 2001;64:056101] has been established as an effective computational approach for studying complex systems. In this study, we review the properties of 6 popular empirical water potentials, including a recently proposed explicit 3-body potential, obtained by applying the Wang–Landau method to the simulation of water clusters. Finally, comparing to numerous classical and first principle calculations, we show that several classical water models can provide a consistent description of the structural and thermodynamical properties of water clusters partially in agreement of ab initio results, although they were designed to reproduce macroscopic properties of water at ambient conditions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Mitigating the Threat of Nuclear-Weapon Proliferation via Nuclear-Submarine Programs

Brazil is building a nuclear-powered attack submarine, South Korea has in the past asserted its need for nuclear-powered attack submarines to deal with the threat of North Korean nuclear-armed submarine-launched ballistic missiles, and Iran has asserted more vaguely its need for naval nuclear propulsion. All three countries are non-weapon-state parties to the Nuclear Nonproliferation Treaty (NPT). Their interest in naval reactors brings with it two challenges to the NPT: 1) The safeguards “loophole” under which a country can withdraw nuclear materials from IAEA safeguards indefinitely for allowed “non-peaceful activities;” and 2) A justification for the acquisition of national uranium-enrichment facilities because the international suppliers of enrichment services have peaceful-use requirements. In fact, Brazil’s acquisition of enrichment was driven by its navy. It is argued, however, that nuclear-powered attack submarines have advantages over modern conventional attack submarines primarily in their ability to transit great distances at high speed whereas only a few countries have foreign military commitments requiring such capabilities. For defense of nearby waters against foreign navies, attack submarines are merely one component of a complex system of anti-submarine aircraft and surface vessels and underwater sensor networks. Conventional attack submarines are no less effective and much less costly than nuclear-powered attack submarines in this role.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

A low-order model of water vapor, clouds, and thermal emission for tidally locked terrestrial planets

In the spirit of minimal modeling of complex systems, we develop an idealized two-column model to investigate the climate of tidally locked terrestrial planets with Earth-like atmospheres in the habitable zone of M-dwarf stars. The model is able to approximate the fundamental features of the climate obtained from three-dimensional (3D) atmospheric general circulation model (GCM) simulations. One important reason for the two-column model's success is that it reproduces the high cloud albedo of the GCM simulations, which reduces the planet's temperature and delays the onset of a runaway greenhouse state. The two-column model also clearly illustrates a secondary mechanism for determining the climate: the nightside acts as a 'radiator fin' through which infrared energy can be lost to space easily. This radiator fin is maintained by a temperature inversion and dry air on the nightside, and plays a similar role to the subtropics on modern Earth. Since one-dimensional radiative-convective models cannot capture the effects of the cloud albedo and radiator fin, they are systematically biased toward a narrower habitable zone. We also show that cloud parameters are the most important in the two-column model for determining the day-night thermal emission contrast, which decreases and eventually reverses as the stellar flux increases. This reversal is important because it could be detected by future extrasolar planet characterization missions, which would suggest that the planet has Earth-like water clouds and is potentially habitable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

GAS OUTFLOWS IN SEYFERT GALAXIES: EFFECTS OF STAR FORMATION VERSUS AGN FEEDBACK

Large-scale, weakly collimated outflows are very common in galaxies with large infrared luminosities. In complex systems in particular, where intense star formation (SF) coexists with an active galactic nucleus (AGN), it is not clear yet from observations whether the SF, the AGN, or both are driving these outflows. Accreting supermassive black holes are expected to influence their host galaxies through kinetic and radiative feedback processes, but in a Seyfert galaxy, where the energy emitted in the nuclear region is comparable to that of the body of the galaxy, it is possible that stellar activity is also playing a key role in these processes. In order to achieve a better understanding of the mechanisms driving the gas evolution especially at the nuclear regions of these galaxies, we have performed high-resolution three-dimensional hydrodynamical simulations with radiative cooling considering the feedback from both SF regions, including supernova (Type I and II) explosions and an AGN jet emerging from the central region of the active spiral galaxy. We computed the gas mass lost by the system, separating the role of each of these injection energy sources on the galaxy evolution, and found that at scales within 1 kpc an outflow can be generally established considering intense nuclear SF only. The jet alone is unable to drive a massive gas outflow, although it can sporadically drag and accelerate clumps of the underlying outflow to very high velocities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Roadmap on photonic, electronic and atomic collision physics: I. Light–matter interaction

We publish three Roadmaps on photonic, electronic and atomic collision physics in order to celebrate the 60th anniversary of the ICPEAC conference. In Roadmap I, we focus on the light–matter interaction. In this area, studies of ultrafast electronic and molecular dynamics have been rapidly growing, with the advent of new light sources such as attosecond lasers and x-ray free electron lasers. In parallel, experiments with established synchrotron radiation sources and femtosecond lasers using cutting-edge detection schemes are revealing new scientific insights that have never been exploited. Relevant theories are also being rapidly developed. Target samples for photon-impact experiments are expanding from atoms and small molecules to complex systems such as biomolecules, fullerene, clusters and solids. This Roadmap aims to look back along the road, explaining the development of these fields, and look forward, collecting contributions from twenty leading groups from the field.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Infrared studies of gas phase and surface processes of the enhancement of catalytic methane decomposition by low temperature plasma

Catalyst enhancement by atmospheric pressure plasma is a recently emerging field of research that embodies a complex system of reactive species and how they interact with surfaces. In this work we use an atmospheric pressure plasma jet integrated with a nickel on Al 2 O 3 /SiO 2 support catalyst material to decompose methane gas by partial oxidation reaction. We use Fourier-transform Infrared spectroscopy (FTIR) analysis of the gas phase post reaction to measure the loss of methane and the production of CO, CO 2 , and H 2 O and diffuse reflectance Fourier-transform Infrared spectroscopy (DRIFTs) in situ analysis of the catalyst surface as a function of both catalyst temperature and plasma operating parameters. We find reduction of methane by both plasma alone, catalyst alone, and an increase when both plasma and catalyst were simultaneously used. The production of CO appears to be due primarily to the plasma source as it only appears above 2.5 W plasma dissipated power and decreases as catalyst temperature increases. CO 2 production is enhanced by having the catalyst at high temperature and H 2 O production depends on both plasma power and temperature. Using DRIFTs we find that both heating and plasma treatment remove absorbed water on the surface of the catalyst. Plasma treatment alone however leads to the formation of CO and another IR spectral feature at 1590 cm -1 , which may be attributed to carboxylate groups, bonded to the catalyst surface. These species exhibit a regime of plasma treatment where they are formed on the surface and where they are significantly removed from the catalyst surface. We see the formation of a new spectral feature at 995 cm -1 and discuss the behavior and possible origins of this feature. Furthermore, this research highlights the potential for plasma regeneration of catalyst materials as well as showing enhancement of the catalytic behavior under low temperature plasma treatment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Data-driven prediction of the output composition of an atmospheric pressure plasma jet

Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O 3 , N 2 O, and NO 2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. In conclusion, this methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Technology readiness assessment of magnetohydrodynamic stability control

The technology readiness level scale is revised with clear definitions for magnetohydrodynamic stability control technologies for magnetic confinement fusion. The definitions are in the form of a sentence with three dimensions, which are the system complexity, the test environment, and the cost. A clear and simple definition is sought after to make an assessment based on the evidence which is agreeable to the fusion energy development community. A sample technology readiness assessment (TRA) for the electron cyclotron current drive-based control of neoclassical tearing mode for ST Advanced Reactor, using the proposed scale, reveals that the risk of a TRA being misinterpreted is high, which can only be avoided with an understanding of the procedure described in this paper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Plasma dark matter direct detection

Dark matter in spiral galaxies like the Milky Way may take the form of a dark plasma. Hidden sector dark matter charged under an unbroken U(1)' gauge interaction provides a simple and well defined particle physics model realising this possibility. The assumed U(1)' neutrality of the Universe then implies (at least) two oppositely charged dark matter components with self-interactions mediated via a massless 'dark photon' (the U(1)' gauge boson). In addition to nuclear recoils such dark matter can give rise to keV electron recoils in direct detection experiments. In this context, the detailed physical properties of the dark matter plasma interacting with the Earth is required. This is a complex system, which is here modelled as a fluid governed by the magnetohydrodynamic equations. These equations are numerically solved for some illustrative examples, and implications for direct detection experiments discussed. In particular, the analysis presented here leaves open the intriguing possibility that the DAMA annual modulation signal is due primarily to electron recoils (or even a combination of electron recoils and nuclear recoils). The importance of diurnal modulation (in addition to annual modulation) as a means of probing this kind of dark matter is also emphasised.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Caustics of 1/r{sup n} binary gravitational lenses: from galactic haloes to exotic matter

We investigate the caustic topologies for binary gravitational lenses made up of two objects whose gravitational potential declines as 1/r{sup n}. With n<1 this corresponds to power-law dust distributions like the singular isothermal sphere. The n>1 regime can be obtained with some violations of the energy conditions, one famous example being the Ellis wormhole. Gravitational lensing provides a natural arena to distinguish and identify such exotic objects in our Universe. We find that there are still three topologies for caustics as in the standard Schwarzschild binary lens, with the main novelty coming from the secondary caustics of the close topology, which become huge at higher n. After drawing caustics by numerical methods, we derive a large amount of analytical formulae in all limits that are useful to provide deeper insight in the mathematics of the problem. Our study is useful to better understand the phenomenology of galaxy lensing in clusters as well as the distinct signatures of exotic matter in complex systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Optimization of 3D controlled ELM-free state with recovered global confinement for KSTAR with n = 1 resonant magnetic field perturbation

Mitigation of deleterious heat flux from edge-localized modes (ELMs) on fusion reactors is often attempted with 3D perturbations of the confining magnetic fields. However, the established technique of resonant magnetic perturbations (RMPs) also degrades plasma performance, complicating implementation on future fusion reactors. Here, we introduce an adaptive real-time control scheme on the KSTAR tokamak as a viable approach to achieve an ELM-free state and simultaneously recover high-confinement (βN ~ 1.91, β p ~ 1.53, and H 98 ~ 0.9), demonstrating successful handling of a volatile complex system through adaptive measures. We show that, by exploiting a salient hysteresis process to adaptively minimize the RMP strength, stable ELM suppression can be achieved while actively encouraging confinement recovery. This is made possible by a self-organized transport response in the plasma edge which reinforces the confinement improvement through a widening of the ion temperature pedestal and promotes control stability, in contrast to the deteriorating effect on performance observed in standard RMP experiments. These results establish the real-time approach as an up-and-coming solution toward an optimized ELM-free state, which is an important step for the operation of ITER and reactor-grade tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Permanent magnets for ELM suppression in tokamaks: feasibility and operational compatibility

Permanent magnets can provide static magnetic fields without power supplies or feed lines, offering a simpler alternative to conventional electromagnets in tokamaks. This work examines permanent magnet arrays (PMAs) for edge localized mode (ELM) control in double-null (DN) configurations, where traditional RMP coils exhibit limited effectiveness. Linear plasma response calculations using IPEC assess high-field-side (HFS) permanent magnet placement, benchmarking performance against DIII-D low-field-side (LFS) internal coils (I-coils) while evaluating engineering constraints. Modeling results demonstrate that HFS permanent magnet configurations generate HFS plasma response amplitudes more than 5 times larger than conventional I-coil systems. While other resonant response metrics show comparable or reduced response relative to I-coils, the combination of conventional RMP systems and permanent magnets is expected to provide enhanced performance. Operational impact assessments of persistent magnetic fields, including sideband field effects and startup/ramp-up compatibility, reveal acceptable performance within established operational boundaries. This analysis establishes PMAs as a technically viable approach for DN ELM control with reductions in system complexity, motivating further experimental validation on existing tokamak facilities.

3D magnetic perturbation↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Filtering higher-order datasets

Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset's structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections

Abstract In many fields of science and engineering, we frequently encounter experiments or simulations datasets that describe the behavior of complex systems and uncovering human interpretable patterns between their inputs and outputs via exploratory data analysis is essential for building intuition and facilitating discovery. Often, we resort to 2D embeddings for examining these high-dimensional relationships (e.g. dimensionality reduction). However, most existing embedding methods treat the dimensions as coordinates for samples in a high-dimensional space, which fail to capture the potential functional relationships, and the few methods that do take function into consideration either only focus on linear patterns or produce non-linear embeddings that are hard to interpret. To address these challenges, we proposed function preserving projections (FPP), which construct 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns between the domain and the range of a high-dimensional function. The intuition here is that humans are good at understanding potentially non-linear patterns in 2D but unable to interpret non-linear mapping from high-dimensional space to 2D. Therefore, we should restrict the projection to linear but not the pattern we are seeking. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in extremely large datasets that could not be processed with existing dimension reduction methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Coincident learning for unsupervised anomaly detection of scientific instruments

Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, F ^ β , out of analogy to the supervised classification F β statistic. CoAD uses F ^ β to train an anomaly detection algorithm on unlabeled data , based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.

43 PARTICLE ACCELERATORS↗