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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Nature of the nonequilibrium phase transition in the non-Markovian driven Dicke model

The Dicke model famously exhibits a phase transition to a superradiant phase with a macroscopic population of photons and is realized in multiple settings in open quantum systems. In this paper, we study a variant of the Dicke model where the cavity mode is lossy due to the coupling to a Markovian environment while the atomic mode is coupled to a colored bath. We analytically investigate this model by inspecting its low-frequency behavior via the Schwinger-Keldysh field theory and carefully examine the nature of the corresponding superradiant phase transition. Integrating out the fast modes, we can identify a simple effective theory allowing us to derive analytical expressions for various critical exponents including the dynamical exponent. We find excellent agreement with previous numerical results when the non-Markovian bath is at zero temperature; however, contrary to these studies, our low-frequency approach reveals that the same exponents govern the critical behavior when the colored bath is at finite temperature unless the chemical potential is zero. Furthermore, we show that the superradiant phase transition is classical in nature, while it is genuinely nonequilibrium. In this work, we derive a fractional Langevin equation and conjecture the associated fractional Fokker-Planck equation that captures the system's long-time memory as well as its nonequilibrium behavior. Finally, we consider finite-size effects at the phase transition and identify the finite-size scaling exponents, unlocking a rich behavior in both statics and dynamics of the photonic and atomic observables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Enhanced ferroelectricity in ultrathin films grown directly on silicon

Ultrathin ferroelectric materials could potentially enable low-power logic and nonvolatile memories. As ferroelectric materials are made thinner, however, the ferroelectricity is usually suppressed. Size effects in ferroelectrics have been thoroughly investigated in perovskite oxides—the archetypal ferroelectric system. Perovskites, however, have so far proved unsuitable for thickness scaling and integration with modern semiconductor processes. In this paper we report ferroelectricity in ultrathin doped hafnium oxide (HfO 2 ), a fluorite-structure oxide grown by atomic layer deposition on silicon. We demonstrate the persistence of inversion symmetry breaking and spontaneous, switchable polarization down to a thickness of one nanometre. Our results indicate not only the absence of a ferroelectric critical thickness but also enhanced polar distortions as film thickness is reduced, unlike in perovskite ferroelectrics. This approach to enhancing ferroelectricity in ultrathin layers could provide a route towards polarization-driven memories and ferroelectric-based advanced transistors. This work shifts the search for the fundamental limits of ferroelectricity to simpler transition-metal oxide systems—that is, from perovskite-derived complex oxides to fluorite-structure binary oxides—in which ‘reverse’ size effects counterintuitively stabilize polar symmetry in the ultrathin regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1999

Vibration Analysis and Control of Flexible Beam by Using Smart Damping Structures

The temperature effects on frequency, loss factor and control of a flexible beam with a constrained viscoelastic layer and shape memory alloy layer (SMA) are discussed. It is shown that the temperature in the SMA (actuation) layer is very important in the determination of frequency and loss factor of such a structure. The effects of damping layer shear modulus and damping layer height as affected by the temperature are also discussed. As temperature plays such an important role, it is, therefore, imperative to evaluate temperature effects on the control of the system as well. Results with and without active control are discussed.

Chen, Q.↗
NASA NTRSDate not supplied

Chimeric Mouse Models for Space Radiation Risk Investigations

Assessment of human health risks associated with space radiation exposure is based largely on the knowledge learned from studies in which animals, mostly rodents, are exposed to high-LET radiation on the ground. It has been recognized that translation of animal results to meaningful implications for human disease can be challenging, particularly for certain risk categories such as the high-LET radiation effects in the central nervous system (CNS). Considering limitations in utilizing non-human primates and clinical studies in humans, chimeric animals can potentially bridge the knowledge gap between rodents and humans. In a chimeric animal, a specific organ or a cell type is replaced with respective human cells that are functional. A number of chimeric mouse models have been developed in the medical research community to study human diseases, and some of the models can potentially be used for NASA applications. For instance, mice engrafted with human hepatocytes, which have been used in studies of genotoxicity from carcinogen exposures, can be used for quantification of space radiation damage. A chimeric brain model, which was shown to perform superiorly in memory and cognitive tests, can also be a candidate for studying the CNS effects of radiation. It has also been reported that mice engrafted with human hematopoietic progenitor cells were exposed to X-rays and high-LET Si ions to investigate the radiation effects in the immune system. In a pilot study, we use PXB mice whose livers contain >90% human cells. These mice are exposed to gamma rays for investigations of DNA damage and transcriptomics changes in the humanized livers. Results obtained from PXB mice will be compared non-engrafted control animals from the same background strain that are exposed to identical conditions. The aim of the study is to determine whether chimeric mouse models are suitable for investigations of space radiation risks.

Honglu Wu↗
NASA NTRSDate not supplied

Chimeric Mouse Models for Space Radiation Risk Investigations

Assessment of human health risks associated with space radiation exposure is based largely on the knowledge learned from studies in which animals, mostly rodents, are exposed to high-LET radiation on the ground. It has been recognized that translation of animal results to meaningful implications for human disease can be challenging, particularly for certain risk categories such as the high-LET radiation effects in the central nervous system (CNS). Considering limitations in utilizing non-human primates and clinical studies in humans, chimeric animals can potentially bridge the knowledge gap between rodents and humans. In a chimeric animal, a specific organ or a cell type is replaced with respective human cells that are functional. A number of chimeric mouse models have been developed in the medical research community to study human diseases, and some of the models can potentially be used for NASA applications. For instance, mice engrafted with human hepatocytes, which have been used in studies of genotoxicity from carcinogen exposures, can be used for quantification of space radiation damage. A chimeric brain model, which was shown to perform superiorly in memory and cognitive tests, can also be a candidate for studying the CNS effects of radiation. It has also been reported that mice engrafted with human hematopoietic progenitor cells were exposed to X-rays and high-LET Si ions to investigate the radiation effects in the immune system. In a pilot study, we use PXB mice whose livers contain >90% human cells. These mice are exposed to gamma rays for investigations of DNA damage and transcriptomics changes in the humanized livers. Results obtained from PXB mice will be compared non-engrafted control animals from the same background strain that are exposed to identical conditions. The aim of the study is to determine whether chimeric mouse models are suitable for investigations of space radiation risks.

Honglu Wu↗
DOE OSTI2025

Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and Its Scalability for High-Density 3-D Ferroelectric Capacitor Memory

In this work, we present a comprehensive experimental and modeling study on the scaling of vertical 2T-nC ferroelectric random access memory (FeRAM) hybrid cells, comprising n metal-ferroelectric–metal (MFM) capacitors, to demonstrate a high-performance and high-density 3-D capacitor memory. Our contributions include: 1) successful process integration of vertical 2T-3C FeRAM cells by stacking MFM structures on top of Si CMOS transistors; 2) experimental validation of memory cell functionality, confirming the feasibility of the vertical 2T-nC FeRAM architecture; 3) an analysis of scaling effects on parasitic capacitance in densely integrated 3-D arrays, using 3-D technology computer-aided design (TCAD) simulations; 4) exploration of aggressive stacking of write bitlines (WBLs) to enhance memory density, where ferroelectric linear capacitance ( C FE ) enables self-boosted inhibition under the V W /2 scheme, but renders the V W /3 scheme ineffective due to intolerable write disturbances; and 5) assessment of horizontal scaling, revealing significant increases in read disturbances caused by interplane capacitance between adjacent WBLs ( C Z ). This work represents an early exploration into the potential of 2T-nC FeRAM as a scalable and efficient 3-D memory solution.

42 ENGINEERING↗
DOE OSTI2018

Repairing large cracks and reversing fatigue damage in structural metals

Self-healing materials represent a paradigm shift from traditional materials development, enabling intrinsic repair of functionality (such as strength) after a catastrophic failure as opposed to part replacement. Several healing mechanisms have been demonstrated in polymeric and ceramic materials, but few in metallic systems. This study demonstrates a novel liquid-assisted self-healing metal-matrix composite (MMC) designed to be capable of over 90% strength recovery after a healing cycle. The aluminum (Al)-based matrix is reinforced with continuous nickel-titanium (NiTi) shape-memory alloy (SMA) reinforcements. Using a tailored heat treatment for healing, a paired effect of crack closure from the SMA reinforcement and partial liquefaction of the matrix occurs. These effects result in a compressive force across the crack surface, and, coupled with increased diffusion rates from the liquefied matrix, produces consolidation and healing in the composite structure. This work provides experimental and computational evidence for the healing mechanism under both tensile and fatigue conditions.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Single Event Effects in Highly Scaled Devices for Space Applications

This paper discusses single-event upset (SEU) in memories and microprocessors that are the "drivers" of highly scaled commercial integrated circuits. Despite the decrease in critical charge that occurs for highly scaled CMOS devices, recent test data has shown that SEU rates are actually somewhat lower for scaled devices compared to older devices with larger feature size. Hard errors, which are increasingly important for memories, are discussed along with conventional soft errors. Functional errors in memories and microprocessors are particularly significant, and tend to dominate the response of highly scaled devices from an application standpoint. Predictions for future devices are made using the Semiconductor Industry Roadmap along with recent modeling and radiation test results.

microelectronics↗
DOE OSTI2022

Phoebe: a high-performance framework for solving phonon and electron Boltzmann transport equations

Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors, and energy conversion devices. Computational modeling can accurately predict material properties but, in order to be reliable, requires accurate descriptions of electron and phonon states and their interactions. While first-principles methods are capable of describing the energy spectrum of each carrier, using them to compute transport properties is still a formidable task, both computationally demanding and memory intensive, requiring integration of fine microscopic scattering details for estimation of macroscopic transport properties. To address this challenge, we present Phoebe—a newly developed software package that includes the effects of electron–phonon, phonon–phonon, boundary, and isotope scattering in computations of electrical and thermal transport properties of materials with a variety of available methods and approximations. This open source C++ code combines MPI-OpenMP hybrid parallelization with GPU acceleration and distributed memory structures to manage computational cost, allowing Phoebe to effectively take advantage of contemporary computing infrastructures. We demonstrate that Phoebe accurately and efficiently predicts a wide range of transport properties, opening avenues for accelerated computational analysis of complex crystals.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Radiation effects on microelectronics and future space missions

This paper briefly reviews the three basic radiation effect mechanisms, and how they interrupt the functionality of currently available non-volatile memory technologies. This paper also presents a very general overview of the radiation environments expected in future space exploration missions. Unfortunately, these environments will be very harsh, from a radiation standpoint, and thus a significant effort is required to develop non-volatile technologies that will meet future mission requirements.

radiation effects↗
DOE OSTI2016

On-the-Fly Treatment of Temperature Dependent Cross Sections in the Unresolved Resonance Regions in RMC Code

Monte Carlo method can provide high fidelity neutronics analysis of different nuclear reactors, owing to its advantages of the flexible geometry modeling and the use of continuous-energy nuclear cross sections. It can also be coupled with thermal-hydraulics codes to consider the feedback. For thermal reactors such as PWR and HTGR, two main temperature effects should be considered. The first is the effect of thermal motion of target nuclei in resolved resonance energy regions, which is known as Doppler effect. The second is the thermal scattering and bound effect in thermal energy regions. The traditional approach of pre-generated cross sections has difficulty in memory footprint for detailed temperature modelling in multi-physics calculations. Recently, on-the-fly (OTF) technique has been proposed in order to reduce the memory consumption for both resolved resonance energy and thermal energy. For fast reactors calculations, the temperature dependence of cross sections in the unresolved resonance energy regions is also important. However, less attention was paid to the unresolved resonance regions compared with resolved resonance energy and thermal energy. In this paper, the on-the-fly temperature treatment of cross sections in unresolved resonance regions was proposed and developed in RMC codes. The results show that the on-the-fly treatment has high efficiency and satisfactory fidelity. The on-the-fly treatment of unresolved resonance region was also combined with the target motion sampling (TMS) method for resolved resonance energy. The formats of libraries were changed, in which the probability tables were separated from the cross sections. Then the interpolation of probability table was performed on-the-fly during the transport routine. The results show that the on-the-fly treatment has high efficiency and satisfactory fidelity. The on-the-fly treatment of unresolved resonance region was also combined with the target motion sampling (TMS) method for resolved resonance energy, showing that TMS method is effective and accurate for cross sections treatment in resolved resonance energy regions. The memory saving of the on-the-fly treatment is also considerable. For the high-fidelity 'transport-burnup-thermal-hydraulics' coupling calculation, the memory requirement is 60 Mb. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A learning-based measurement framework for traffic matrix inference in software defined networks

Here, we propose an intelligent framework for Traffic Matrix (TM) inference in Software Defined Networks (SDN) where the Ternary Content Addressable Memory (TCAM) entries of switches are partitioned into two parts to: (1) effectively aggregate part of incoming flows for aggregate measurements, and (2) de-aggregate and directly measure the most informative flows for per-flow measurements. These measurements are then processed to effectively estimate the size of network flows. Under hard resource constraints of limited TCAM sizes, we show how to design the optimal and efficient-compressed flow aggregation matrices. We propose an optimal Multi-Armed Bandit (MAB) based algorithm to adaptively measure the most rewarding flows. We evaluate the performance of our framework using real traffic traces from different network environments and by considering two main applications: TM estimation and Heavy Hitter (HH) detection. Moreover, we have implemented a prototype of our framework in Mininet to demonstrate its effectiveness.

42 ENGINEERING↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2014

Three-terminal magnetic tunneling junction device with perpendicular anisotropy CoFeB sensing layer

We demonstrated read and write characteristics of a three terminal memory device with a perpendicular anisotropy-free layer of a strip of [Co/Ni] and a low-switching perpendicular-anisotropy CoFeB/MgO sensing layer. This new design of the cell results in a small cell area. The switching magnetic field of the sensing layer can be decreased by changing sputtering gas for the Ta-cap from Ar to Kr. An electron energy-loss spectroscopy analysis of the cross-section of the magnetic tunneling junction (MTJ) revealed that the boron content in CoFeB with a Kr-sputtered Ta-cap was smaller than that with an Ar-sputtered one. A change in resistance for the MTJ was observed that corresponded to the magnetic switching of the Co/Ni wire and its magnetoresistance ratio and critical current were 90% and 0.8 mA, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Engineering helimagnetism in MnSi thin films

Magnetic skyrmion materials have the great advantage of a robust topological magnetic structure, which makes them stable against the superparamagnetic effect and therefore a candidate for the next-generation of spintronic memory devices. Bulk MnSi, with an ordering temperature of 29.5 K, is a typical skyrmion system with a propagation vector periodicity of ∼18 nm. One crucial prerequisite for any kind of application, however, is the observation and precise control of skyrmions in thin films at room-temperature. Strain in epitaxial MnSi thin films is known to raise the transition temperature to 43 K. Here we show, using magnetometry and x-ray spectroscopy, that the transition temperature can be raised further through proximity coupling to a ferromagnetic layer. Similarly, the external field required to stabilize the helimagnetic phase is lowered. Transmission electron microscopy with element-sensitive detection is used to explore the structural origin of ferromagnetism in these Mn-doped substrates. Our work suggests that an artificial pinning layer, not limited to the MnSi/Si system, may enable room temperature, zero-field skyrmion thin-film systems, thereby opening the door to device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Superconducting neural networks with disordered Josephson junction array synaptic networks and leaky integrate-and-fire loop neurons

Fully coupled randomly disordered recurrent superconducting networks with additional open-ended channels for inputs and outputs are considered the basis to introduce a new architecture to neuromorphic computing in this work. Various building blocks of such a network are designed around disordered array synaptic networks using superconducting devices and circuits as an example, while emphasizing that a similar architectural approach may be compatible with several other materials and devices. A multiply coupled (interconnected) disordered array of superconducting loops containing Josephson junctions [equivalent to superconducting quantum interference devices (SQUIDs)] forms the aforementioned collective synaptic network that forms a fully recurrent network together with compatible neuron-like elements and feedback loops, enabling unsupervised learning. This approach aims to take advantage of superior power efficiency, propagation speed, and synchronizability of a small world or a random network over an ordered/regular network. Additionally, it offers a significant factor of increase in scalability. Here, a compatible leaky integrate-and-fire neuron made of superconducting loops with Josephson junctions is presented, along with circuit components for feedback loops as needed to complete the recurrent network. Several of these individual disordered array neural networks can further be coupled together in a similarly disordered way to form a hierarchical architecture of recurrent neural networks that is often suggested as similar to a biological brain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗