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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Study on advanced information processing system

Issues related to the reliability of a redundant system with large main memory are addressed. In particular, the Fault-Tolerant Processor (FTP) for Advanced Launch System (ALS) is used as a basis for our presentation. When the system is free of latent faults, the probability of system crash due to nearly-coincident channel faults is shown to be insignificant even when the outputs of computing channels are infrequently voted on. In particular, using channel error maskers (CEMs) is shown to improve reliability more effectively than increasing the number of channels for applications with long mission times. Even without using a voter, most memory errors can be immediately corrected by CEMs implemented with conventional coding techniques. In addition to their ability to enhance system reliability, CEMs--with a low hardware overhead--can be used to reduce not only the need of memory realignment, but also the time required to realign channel memories in case, albeit rare, such a need arises. Using CEMs, we have developed two schemes, called Scheme 1 and Scheme 2, to solve the memory realignment problem. In both schemes, most errors are corrected by CEMs, and the remaining errors are masked by a voter.

Shin, Kang G.↗
NASA NTRS1988

Simulation of proton-induced energy deposition in integrated circuits

A time-efficient simulation technique was developed for modeling the energy deposition by incident protons in modern integrated circuits. To avoid the excessive computer time required by many proton-effects simulators, a stochastic method was chosen to model the various physical effects responsible for energy deposition by incident protons. Using probability density functions to describe the nuclear reactions responsible for most proton-induced memory upsets, the simulator determines the probability of a proton hit depositing the energy necessary for circuit destabilization. This factor is combined with various circuit parameters to determine the expected error-rate in a given proton environment. An analysis of transient or dose-rate effects is also performed. A comparison to experimental energy-disposition data proves the simulator to be quite accurate for predicting the expected number of events in certain integrated circuits.

Fernald, Kenneth W.↗
DOE OSTI2023

Polarity effects on wake–up behavior of Al 0.94 B 0.06 N ferroelectrics

Wurtzite ferroelectric materials are promising candidates for energy-efficient memory technologies, particularly for applications requiring high operating temperatures. Asymmetric wake-up behaviors, in which the polarization reversal depends both on polarity and cycle number for the first few dozen cycles, must be better understood for reliable device operation. Here, the detailed analysis of the asymmetric wake-up behavior of thin film Al 0.94 B 0.06 N was performed combining time-resolved switching measurements with Rayleigh analysis, piezoelectric measurements, and etching experiments of progressively switched samples. Furthermore, the analysis shows that the gradual opening of the polarization hysteresis loops associated with wake-up is driven by a gradual increase in the domain-wall density and/or domain-wall mobility with electric field cycle to the polarity opposite to the growth polarity. The insights of this discovery will help to guide interface and polarity design in the eventual deployment of reliable devices based on these materials.

36 MATERIALS SCIENCE↗
NASA NTRS1928

The Effect of a Flap and Ailerons on the N.A.C.A. M-6 Airfoil Section

This report contains the results obtained at the Langley Memorial Aeronautical Laboratory on an N. A. C. A. M-6 airfoil, fitted with a flap and ailerons, and tested in the variable density wind tunnel at a density of 20 atmospheres. Airfoil characteristics are given for the model up to 48 degree angle of attack with the flap set at various angles, and also with the ailerons set at similar angles. The approximate lift distribution and the center of pressure variation along the span are determined with the model at 18 degree angle of attack and with the ailerons displaced at 20 degrees. Approximate rolling moment and yawing moment coefficients are determined for the various aileron settings. A comparison of the calculated angles of zero lift and the calculated lift and moment coefficiencies with those observed is given in the appendix.

Higgins, George J↗
NASA NTRS1993

Single event effects and laser simulation studies

The single event upset (SEU) linear energy transfer threshold (LETTH) of radiation hardened 64K Static Random Access Memories (SRAM's) was measured with a picosecond pulsed dye laser system. These results were compared with standard heavy ion accelerator (Brookhaven National Laboratory (BNL)) measurements of the same SRAM's. With heavy ions, the LETTH of the Honeywell HC6364 was 27 MeV-sq cm/mg at 125 C compared with a value of 24 MeV-sq cm/mg obtained with the laser. In the case of the second type of 64K SRAM, the IBM640lCRH no upsets were observed at 125 C with the highest LET ions used at BNL. In contrast, the pulsed dye laser tests indicated a value of 90 MeV-sq cm/mg at room temperature for the SEU-hardened IBM SRAM. No latchups or multiple SEU's were observed on any of the SRAM's even under worst case conditions. The results of this study suggest that the laser can be used as an inexpensive laboratory SEU prescreen tool in certain cases.

Kim, Q.↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

The Effects of Blood Glucose Levels on Cognitive Performance: A Review of the Literature

The purpose of this review paper is to discuss the research literature on the effects of blood glucose levels on executive and non-executive functions in humans. The review begins with a brief description of blood glucose, how it has been studied, previous syntheses of prior studies, and basic results regarding the role of blood glucose on cognitive functioning. The following sections describe work that investigated the effect of blood glucose on both non-executive and executive functions (e.g., sensory processing, psychomotor functioning, attention, vigilance, memory, language and communication, judgement and decision-making, and complex task performance). Within each section, summaries of the findings and challenges to the literature are included. Measurement conversions of blood glucose levels, blood glucose values, and associated symptoms are depicted. References to the types of tests used to investigate blood glucose and cognitive performance are provided. For more detailed descriptions of references within (and in addition to) this paper, an annotated bibliography is also provided. Several moderator variables including individual differences and contextual variables related to the effects of blood glucose levels on performance (e.g., age, gender, time of day, familiarity with the task and symptom awareness, expectancy effects, dose dependent effects, time dependent effects, task specific effects, rising and falling blood glucose levels, and speed and/or accuracy trade-offs) are addressed later in the paper. Some suggestions for future experimental methodologies are also made.

Feldman, Jolene↗
DOE OSTI2026

Block encoding of the three-dimensional heterogeneous Poisson equation with application to fracture flow

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of 𝑂⁡(𝑁 2/3 polylog 𝑁 ⋅log (1/𝜖)), outperforming the best classical methods (with run times of 𝑂⁡(𝑁⁢log 𝑁 ⋅log (1/𝜖))) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

58 GEOSCIENCES↗
NASA NTRS2010

Nonvolatile Memory Technology for Space Applications

This slide presentation reviews several forms of nonvolatile memory for use in space applications. The intent is to: (1) Determine inherent radiation tolerance and sensitivities, (2) Identify challenges for future radiation hardening efforts, (3) Investigate new failure modes and effects, and technology modeling programs. Testing includes total dose, single event (proton, laser, heavy ion), and proton damage (where appropriate). Test vehicles are expected to be a variety of non-volatile memory devices as available including Flash (NAND and NOR), Charge Trap, Nanocrystal Flash, Magnetic Memory (MRAM), Phase Change--Chalcogenide, (CRAM), Ferroelectric (FRAM), CNT, and Resistive RAM.

Oldham, Timothy R.↗
NASA NTRS1984

Utilization of Historic Information in an Optimisation Task

One of the basic components of a discrete model of motor behavior and decision making, which describes tracking and supervisory control in unitary terms, is assumed to be a filtering mechanism which is tied to the representational principles of human memory for time-series information. In a series of experiments subjects used the time-series information with certain significant limitations: there is a range-effect; asymmetric distributions seem to be recognized, but it does not seem to be possible to optimize performance based on skewed distributions. Thus there is a transformation of the displayed data between the perceptual system and representation in memory involving a loss of information. This rules out a number of representational principles for time-series information in memory and fits very well into the framework of a comprehensive discrete model for control of complex systems, modelling continuous control (tracking), discrete responses, supervisory behavior and learning.

Boesser, T.↗
DOE OSTI2020

Effect of Ion Irradiation on Amorphous and Crystalline Ge–Se and Their Application as Phase Change Temperature Sensor

Research on phase change materials is predominantly focused on their application as memory devices or for temperature control which requires low phase change temperature. The Ge–Se binary chalcogenide glass system with its wide glass-forming region is a potential candidate for high-temperature and high-radiation phase change applications. In this study, the concept of employing Ge x Se 100– x glasses to monitor high temperature (450–528 °C) using the phase change effect, is reported. Materials selection, device structure, and performance of prototype sensors are analyzed. In addition, the effect of heavy ion irradiation by Xe ions with energies of 200, 600, and 1000 keV (fluence ≈10 14 cm –2 ) on the Ge x Se 100– x ( x = 30, 33, 40) thin films and phase change devices is studied. The irradiation effect on the amorphous and crystalline structure of the thin films is evaluated by Raman spectroscopy and X-ray diffraction (XRD). Although the changes in the structural units of amorphous films are negligible, in crystalline films orthorhombic-GeSe 2 crystals are found to be most affected by irradiation and a new phase, orthorhombic GeSe is found in the thin films after irradiation. The performance of a sensor with an active film of Ge 40 Se 60 is also shown as an example.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2002

Brain signaling and behavioral responses induced by exposure to (56)Fe-particle radiation

Previous experiments have demonstrated that exposure to 56Fe-particle irradiation (1.5 Gy, 1 GeV) produced aging-like accelerations in neuronal and behavioral deficits. Astronauts on long-term space flights will be exposed to similar heavy-particle radiations that might have similar deleterious effects on neuronal signaling and cognitive behavior. Therefore, the present study evaluated whether radiation-induced spatial learning and memory behavioral deficits are associated with region-specific brain signaling deficits by measuring signaling molecules previously found to be essential for behavior [pre-synaptic vesicle proteins, synaptobrevin and synaptophysin, and protein kinases, calcium-dependent PRKCs (also known as PKCs) and PRKA (PRKA RIIbeta)]. The results demonstrated a significant radiation-induced increase in reference memory errors. The increases in reference memory errors were significantly negatively correlated with striatal synaptobrevin and frontal cortical synaptophysin expression. Both synaptophysin and synaptobrevin are synaptic vesicle proteins that are important in cognition. Striatal PRKA, a memory signaling molecule, was also significantly negatively correlated with reference memory errors. Overall, our findings suggest that radiation-induced pre-synaptic facilitation may contribute to some previously reported radiation-induced decrease in striatal dopamine release and for the disruption of the central dopaminergic system integrity and dopamine-mediated behavior.

NASA Discipline Radiation Health↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
DOE OSTI2025

Amorphous Indium Oxide Channel FEFETs With Write Voltage of 0.9 V and Endurance >10 12 for Refresh-Free Embedded Memory

This work presents, for the first time, a back-end-of-the-line (BEOL)-compatible W-doped indium oxide (IWO) ferroelectric field-effect transistor (FEFET) with a record-low operating voltage below 0.9 V and a write speed of 20 ns while achieving a transient read current window (CW) ratio ( I LVT /I HVT ) greater than 10 4 . The device also exhibits exceptional reliability characteristics such as: 1) measured bipolar write endurance up to 10 12 cycles; 2) a fast read speed of 50 ns; 3) read endurance surpassing 10 12 cycles; and 4) retention exceeding 10 4 s at 85 ∘ C. Furthermore, a physics-based numerical model has been developed to investigate the nanoscale characteristics of BEOL FEFET devices, leveraging nucleation-limited switching in HfO 2 ferroelectrics and dc characterization to extract material and channel parameters for accurate device simulation. The simulation uncovers the stochastic switching behavior of BEOL amorphous oxide semiconductor (AOS) FEFETs and demonstrates an intrinsic switching time as low as 1 ps, highlighting the potential of BEOL AOS FEFETs for ultrafast memory applications. These results establish AOS FEFETs as a compelling candidate for high-density embedded memory applications for last-level cache (LLC) (L4) in advanced CMOS technology nodes.

1-V ferroelectric field-effect transistor (FEFET)↗
DOE OSTI2024

EV Forecasting-Based Model Predictive Control for Distribution System Congestion Mitigation

The uncoordinated charging of electric vehicles (EVs) in time and space brings congestion issues to the distribution network. This paper proposes an EV charging demand forecasting-based model predictive control (MPC) method for distribution system congestion management. To effectively forecast the time-series EV station charging demand, a hybrid forecasting model that integrates the long short-term memory network (LSTM) and Transformer is proposed. The Transformer-LSTM model is trained using a one-year real historical charging dataset of EV stations to forecast future charging demand in 15-minute intervals. This informs the MPC for distribution network congestion management and minimization of PV curtailment. Numerical results carried out on the modified IEEE 123-bus distribution system demonstrate that the proposed method can effectively resolve line congestion issues through EV smart charging and PV curtailment while outperforming other benchmarks.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗
DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

AMReX v2024

The software framework, AMReX, supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the essential local descriptions of different physical processes in complex multiphysics algorithms. AMR uses a hierarchical representation of the solution at multiple levels of resolution where the solution on each level is defined on the union of data containers at that resolution. These data containers, which represent the solution over a logically rectangular subregion of the domain, can contain field data defined on a mesh, Lagrangian particles or combinations of both. In addition to these basic data types, AMReX supports a multilevel embedded boundary representation of complex geometry; linear solvers for cell-centered and nodal data; asynchronous I/O in a native format readable by ParaView, VisIt and yt; and interfaces to hypre and PETSc solvers. AMReX enables applications to run on distributed memory architectures with multicore CPUs and with GPU accelerators. AMReX uses a lightweight abstraction layer that effectively hides the details of the architecture from the application. The framework currently supports CUDA, HIP and SYCL for GPU acceleration and OpenMP for multi-core CPU architectures.

Almgren, Ann↗