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At least 541 records · Page 30

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NASA NTRS2017

The Effect of Firn-Aquifer Drainage on the Greenland Subglacial System or Subglacial Efficiency and Storage Modified by the Temporal Pattern of High-Elevation Meltwater Input

Ice flow in marginal region of the Greenland Ice Sheet dynamically responds to summer melting as surface meltwater is routed through the supraglacial hydrologic system to the bed of the ice sheet via crevasses and moulins. Given the expected increases in surface melt production and extent, and the potential for high elevation surface-to-bed connections, it is imperative to understand how meltwater delivered to the bed from different high-elevation supraglacial storage features affects the evolution of the subglacial hydrologic system and associated ice dynamics. Here, we use the two-dimensional subglacial hydrologic model, GLaDS, which includes distributed and channelized water flow, to test how the subglacial system of an idealized outlet glacier responds to cases of high-elevation firn-aquifer-type and supraglacial-lake-type englacial drainage over the course of 5 years. Model outputs driven by these high elevation drainage types are compared to steady-state model results, where the subglacial system only receives the 1980- 2016 mean MERRA-2 runoff via low-elevation moulins. Across all experiments, the subglacial hydrologic system displays inter-annual memory, resulting in multiyear declines in subglacial pressure during the onset of seasonal melting and growth of subglacial channels. The gradual addition of water in firn-aquifer-type drainage scenarios resulted in small increases in subglacial water storage but limited changes in subglacial efficiency and channelization. Rapid, supraglacial- lake-type drainage resulted in short-term local increases in subglacial water pressure and storage, which gave way to spatially extensive decreases in subglacial pressure and downstream channelization. These preliminary results suggest that the character of high-elevation englacial drainage can have a strong, and possibly outsized, control on subglacial efficiency throughout the ablation zone. Therefore, understanding both how high elevation meltwater is stored supraglacially and the probability of crevassing at high elevations will play an important role in how the subglacial system, proglacial discharge and ice motion will respond to future increases in surface melt production and runoff.

Andrews, Lauren C.↗

DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A phase-field model for non-isothermal phase transformation and plasticity in polycrystalline yttria-stabilized tetragonal zirconia

Here we propose an elastoplastic phase-field (PF) model to investigate the mechanics of tetragonal-to-monoclinic phase transformation (TMPT) and elastoplastic deformation of polycrystalline yttria-stabilized tetragonal zirconia (YSTZ). A Landau polynomial with non-vanishing chemical energy at the equilibrium temperature is introduced to account for the actual formation energies of the phases. The effects of different grain orientations, latent heat, and temperature on TMPT and deformation mechanisms are considered. The suppressive transformation effects of the grain boundaries (GBs) is modeled using an inhomogeneous kinetic coefficient in the bulk and GBs. The simulation results for single crystals demonstrate the capability of the model to reproduce the orientation-dependent compressive deformation of YSTZ similar to atomistic simulations and micropillar experiments. The single crystal with [100] crystallographic orientation along the loading direction (SC[100]) displays both TMPT and plasticity, SC[101] experiences only phase transformation, while SC[001] undergoes only plastic yielding. The TMPT induced by compressive loading exhibits shape memory effect (SME) below the equilibrium transformation temperature and pseudoelasticity above it, while the critical transformation stress increases with increasing loading temperature. The irrecoverable plastic strain is found to trap a part of the monoclinic phase, which prevents a complete reverse transformation. The polycrystalline cases also display SME and PE at low and high temperatures, respectively. Due to the orientation differences between grains and the stress concentrations at geometric nonlinearities, plastic deformation occurs in polycrystalline YSTZ for an applied load less than the yield stress. The results suggest a possible limitation of plasticity and an improvement of the shape recovery of YSTZ if one can control the orientation of the grains and/or increase the density of stacking faults at the GBs during material processing.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Self-biased magnetoelectric switching at room temperature in three-phase ferroelectric–antiferromagnetic–ferrimagnetic nanocomposites

Magnetoelectric systems could be used to develop magnetoelectric random access memory and microsensor devices. One promising system is the two-phase 3-1-type multiferroic nanocomposite in which a one-dimensional magnetic column is embedded in a three-dimensional ferroelectric matrix. However, it suffers from a number of limitations including unwanted leakage currents and the need for biasing with a magnetic field. Here we show that the addition of an antiferromagnet to a 3-1-type multiferroic nanocomposite can lead to a large, self-biased magnetoelectric effect at room temperature. Our three-phase system is composed of a ferroelectric Na 0.5 Bi 0.5 TiO 3 matrix in which ferrimagnetic NiFe 2 O 4 nanocolumns coated with antiferromagnetic p-type NiO are embedded. This system, which is self-assembled, exhibits a magnetoelectric coefficient of up to 1.38 × 10 –9 s m –1 , which is large enough to switch the magnetic anisotropy from the easy axis (K eff = 0.91 × 10 4 J m –3 ) to the easy plane (K eff = –1.65 × 10 4 J m –3 ).

42 ENGINEERING↗

DOE OSTI2025

Sheath transitions in a cylindrical filament discharge: Axisymmetric 1D3V PIC-MCC simulations

We present the first nonplanar hot cathode discharge simulations that capture the role of the trapped-ions plasma, elucidating new phenomena unobservable in planar geometric discharges. A discharge struck between a single emitting wire filament cathode and a bounding anode is simulated in cylindrical geometry using an axisymmetric (radial) particle-in-cell Monte-Carlo collisions code. Operating the discharge near its ionization energy threshold can lead to the formation of a two plasma mode (TPM). One plasma forms in the conventional upstream region through electron impact ionization of background neutrals. A second plasma, whose global effect on the discharge was not previously well understood, forms downstream through the trapping of cold ions in the potential well of the filament’s virtual cathode, a process enabled by ion-neutral charge exchange collisions. Three space charge regions intersperse the electrode gap—an emissive sheath between the cathode filament and trapped-ions plasma, a double layer between the two plasmas, and a classical sheath between the upstream plasma and the outer anode. Simulations exhibit mode transitions and quenching instabilities that transform the discharge between the TPM and other single-plasma sheath modes that include classical (temperature-limited), space charge limited, and inverse (anode glow) modes. The transitions are explained via “aid-and-compete” dynamics wherein the growth of one plasma enhances growth in the other while concurrently exhibiting expansion dynamics antagonistic to each other. The system exhibits strong hysteresis memory during the mode transitions. Improved understanding and control of these sheath mode transitions are expected to benefit plasma applications with hot cathodes.

Electrical hysteresis↗

DOE OSTI2024

Unconventional Quantum Advantages for Computation (U-QuAC)

While quantum computing offers the promise of exponential advantages, limited quantum speedups are known, especially for practical applications. To open new avenues for quantum advantages, we propose Unconventional Quantum Advantages for Computation (U-QuACs), with respect to unconventional resources such as space (number of bits or quantum bits of memory required to solve a problem), accuracy of solution, communication, or energy consumption. We focus on space-efficient quantum algorithms, where we seek to design algorithms that solve a problem using much less space than the total size of the input. A natural setting in which space is critical is the streaming model of computation, where the input data arrives sequentially in pieces that must each be processed individually. Streaming is motivated by a variety of problems including analysis of internet traffic or social networks. We design the first exponential quantum space advantage for a natural streaming problem, which also constitutes the first quantum advantage for approximating a discrete optimization problem, albeit with respect to space.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Two–Photon Polymerized Shape Memory Microfibers: A New Mechanical Characterization Method in Liquid

Two-photon polymerization (TPP) is widely used to create 3D micro- and nanoscale scaffolds for biological and mechanobiological studies, which often require the mechanical characterization of the TPP fabricated structures. To satisfy physiological requirements, most of the mechanical characterizations need to be conducted in liquid. However, previous characterizations of TPP fabricated structures are all conducted in air due to the limitation of conventional micro- and nanoscale mechanical testing methods. In this study, a new experimental method is reported for testing the mechanical properties of TPP-printed microfibers in liquid. The experiments show that the mechanical behaviors of the microfibers tested in liquid are significantly different from those tested in air. By controlling the TPP writing parameters, the mechanical properties of the microfibers can be tailored over a wide range to meet a variety of mechanobiology applications. In addition, it is found that, in water, the plasticly deformed microfibers can return to their predeformed shape after tensile strain is released. The shape recovery time is dependent on the size of microfibers. The experimental method represents a significant advancement in mechanical testing of TPP fabricated structures and may help release the full potential of TPP fabricated 3D tissue scaffolds for mechanobiological studies.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗

NASA NTRS2003

The Newell Test Should Commit to Diagnosing Dysfunctions

"Conceptual coordination" analysis bridges connectionism and symbolic approaches by posting a "process memory" by which categories are physically coordinated (as neural networks) in time. Focusing on dysfunctions and odd behaviors like slips reveals the function of consciousness, especially taken-for-granted constructive processes, different from conventional programming constructs. Newell strongly endorsed identifying architectural limits; the heuristic of "diagnose unusual behaviors" will provide targets of opportunity that greatly strengthens the Newell Test.

Clancey, William J.↗

NASA NTRS2004

Lightweight, Self-Deploying Foam Antenna Structures

Lightweight, deployable antennas for a variety of outer-space and terrestrial applications would be designed and fabricated according to the concept of cold hibernated elastic memory (CHEM) structures, according to a proposal. Mechanically deployable antennas now in use are heavy, complex, and unreliable, and they utilize packaging volume inefficiently. The proposed CHEM antenna structures would be simple and would deploy themselves without need for any mechanisms and, therefore, would be more reliable. The proposed CHEM antenna structures would also weigh less, could be packaged in smaller volumes, and would cost less, relative to mechanically deployable antennas. The CHEM concept was described in two prior NASA Tech Briefs articles: "Cold Hibernated Elastic Memory (CHEM) Expandable Structures" (NPO-20394), Vol. 23, No. 2 (February 1999), page 56; and "Solar Heating for Deployment of Foam Structures" (NPO-20961), Vol. 25, No. 10 (October 2001), page 36. To recapitulate from the cited prior articles: The CHEM concept is one of utilizing opencell foams of shape-memory polymers (SMPs) to make lightweight, reliable, simple, and inexpensive structures that can be alternately (1) compressed and stowed compactly or (2) expanded, then rigidified for use. A CHEM structure is fabricated at full size from a block of SMP foam in its glassy state [at a temperature below the glass-transition temperature (Tg) of the SMP]. The structure is heated to the rubbery state of the SMP (that is, to a temperature above Tg) and compacted to a small volume. After compaction, the structure is cooled to the glassy state of the SMP. The compacting force can then be released and the structure remains compact as long as the temperature is kept below Tg. Upon subsequent heating of the structure above Tg, the simultaneous elastic recovery of the foam and its shape-memory effect cause the structure to expand to its original size and shape. Once thus deployed, the structure can be rigidified by cooling below Tg. Once deployed and rigidified, the structure could be heated and recompacted. In principle, there should be no limit on the achievable number of compaction/deployment/ rigidification cycles. Thus far, several different designs of a 3.5-m-long CHEM conical corrugated horn antenna have been analyzed (see figure). A small CHEM structural antenna model was fabricated and a thin, electrically conductive layer of aluminum was deposited on the inner surface of the model. This structural model was then subjected to the compaction and deployment treatments described above to demonstrate the feasibility of a CHEM corrugated horn antenna.

Sokolowski, Witold↗

DOE OSTI2023

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short-Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1990

A nonlinear viscoelastic approach to durability predictions for polymer based composite structures

Current industry approaches for the durability assessment of metallic structures are briefly reviewed. For polymer based composite structures, it is suggested that new approaches must be adopted to include memory or viscoelastic effects which could lead to delayed failures that might not be predicted using current techniques. A durability or accelerated life assessment plan for fiber reinforced plastics (FRP) developed and documented over the last decade or so is reviewed and discussed. Limitations to the plan are outlined and suggestions to remove the limitations are given. These include the development of a finite element code to replace the previously used lamination theory code and the development of new specimen geometries to evaluate delamination failures. The new DCB model is reviewed and results are presented. Finally, it is pointed out that new procedures are needed to determine interfacial properties and current efforts underway to determine such properties are reviewed. Suggestions for additional efforts to develop a consistent and accurate durability predictive approach for FRP structures is outlined.

Brinson, Hal F.↗

NASA NTRS1991

A nonlinear viscoelastic approach to durability predictions for polymer based composite structures

Brinson, Hal F.↗

NASA NTRS2012

Deformation and Phase Transformation Processes in Polycrystalline NiTi and NiTiHf High Temperature Shape Memory Alloys

The deformation and transformation mechanisms of polycrystalline Ni49.9Ti50.1 and Ni50.3Ti29.7Hf20 (in at.%) shape memory alloys were investigated by combined experimental and modeling efforts aided by an in situ neutron diffraction technique at stress and temperature. The thermomechanical response of the low temperature martensite, the high temperature austenite phases, and changes between these two states during thermomechanical cycling were probed and reported. In the cubic austenite phase, stress-induced martensite, deformation twinning and slip processes were observed which helped in constructing a deformation map that contained the limits over which each of the identified mechanisms was dominant. Deformation of the monoclinic martensitic phase was also investigated where the microstructural changes (texture, lattice strains, and phase fractions) during room-temperature deformation and subsequent thermal cycling were compared to the bulk macroscopic response. When cycling between these two phases, the evolution of inelastic strains, along with the shape setting procedures were examined and used for the optimization of the transformation properties as a function of deformation levels and temperatures. Finally, this work was extended to the development of multiaxial capabilities at elevated temperatures for the in situ neutron diffraction measurements of shape memory alloys on the VULCAN Diffractometer at Oak Ridge National Laboratory.

Benafan, Othmane↗

DOE OSTI2024

Ferroelectric AlBN films by molecular beam epitaxy

We report the properties of molecular beam epitaxy deposited AlBN thin films on a recently developed epitaxial nitride metal electrode, Nb2N. While a control AlN thin film exhibits standard capacitive behavior, distinct ferroelectric switching is observed in the AlBN films with increasing Boron mole fraction. The measured remnant polarization Pr∼15μC/cm2 and coercive field Ec∼ 1.45 MV/cm in these films are smaller than those recently reported on films deposited by sputtering, due to incomplete wake-up, limited by current leakage. Because AlBN preserves the ultrawide energy bandgap of AlN compared to other nitride hi-K dielectrics and ferroelectrics, and it can be epitaxially integrated with GaN and AlN semiconductors, its development will enable several opportunities for unique electronic, photonic, and memory devices.

Physics↗

DOE OSTI2025

Implementing Directive-Based Deferred Execution for Effective Network Aggregation

Remote direct memory access technology provides an efficient mechanism for one-sided communication that can be leveraged to implement a distributed shared memory programming model. However, when applications generate large numbers of small, irregular messages, network congestion often arises. Existing solutions address this small message problem by facilitating message aggregation but typically require disruptive code transformations that detract from the algorithmic intent of applications, or can be limited by dependent operations on aggregated data between synchronisation points. A solution is to use a directive-assisted approach that enables compilers to transform code dependent on aggregated communication for deferred execution. This paper presents an algorithm that a compiler can use to implement and optimise deferred execution for code dependent on aggregated data, based on an "aggregation context" extension for the OpenSHMEM partitioned global address space library. This new capability addresses a key challenge of message aggregation, allowing its full potential to reduce network congestion and enhance programmability to be realised.

Welch, Aaron [ORNL]↗

DOE OSTI2024

Bridging the Gap Between LLMs and LNS with Dynamic Data Format and Architecture Codesign

Deep neural networks (DNNs) have achieved tremendous success in the past few years. However, their training and inference demand exceptional computational and memory resources. Quantization has been shown as an effective approach to mitigate the cost, with the mainstream data types reduced from FP32 to FP16/BF16 and recently FP8 in the latest NVIDIA H100 GPUs. With increasingly aggressive quantization, however, the conventional floating-point formats suffer from limited precision in representing numbers around zero. Recently, NVIDIA demonstrated the potential of using a Logarithmic Number System (LNS) for the next generation of tensor cores. While LNS mitigates the hurdles in representing small numbers, in this work we observed a mismatch between LNS and the emerging Large Language Models (LLM), where LLM exhibits significant outliers when directly adopting the LNS format. In this paper, we present a data-format/architecture codesign to bright this gap. On the format side, we propose a dynamic LNS format to flexibly represent outliers at a higher precision, by exploiting asymmetry in the LNS representation and identifying outliers through a per-vector basis. On the architecture side, for demonstration, we realize the dynamic LNS format in a systolic array, which can handle the irregularity of the outliers at runtime. We implement our approach on an Alveo U280 FPGA as a prototype. Experimental results show that our design can effectively handle the outliers and resolve the mismatch between LNS and LLM, contributing to an accuracy improvement of 15.4% and 16% over the floating-point and the original LNS baselines, using four state-of-the-art LLM models. Our observation and design lay a solid foundation for the large-scale adoption of the LNS format in the next-generation deep learning hardware.

Haghi, Pouya↗

DOE OSTI2023

HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems

Quantum circuit simulations are applied in more and more circum- stances as the quantum computing community becomes broader. It helps researchers to evaluate quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilize either CPU or GPU to store and calculate the state vector, which results in resource starvation. Moreover, the maximum number of qubits supported by the simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeneous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benefits the data movement between CPU and GPU.

Zhang, Boyuan↗