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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2017

Achieving algorithmic resilience for temporal integration through spectral deferred corrections

Spectral deferred corrections (SDC) is an iterative approach for constructing higher-order-accurate numerical approximations of ordinary differential equations. SDC starts with an initial approximation of the solution defined at a set of Gaussian or spectral collocation nodes over a time interval and uses an iterative application of lower-order time discretizations applied to a correction equation to improve the solution at these nodes. Each deferred correction sweep increases the formal order of accuracy of the method up to the limit inherent in the accuracy defined by the collocation points. In this paper, we demonstrate that SDC is well suited to recovering from soft (transient) hardware faults in the data. A strategy where extra correction iterations are used to recover from soft errors and provide algorithmic resilience is proposed. Specifically, in this approach the iteration is continued until the residual (a measure of the error in the approximation) is small relative to the residual of the first correction iteration and changes slowly between successive iterations. Here, we demonstrate the effectiveness of this strategy for both canonical test problems and a comprehensive situation involving a mature scientific application code that solves the reacting Navier-Stokes equations for combustion research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Prediction of Alpha-Particle-Immune Gate-All-Around Field-Effect Transistors (GAA-FET) Based SRAM Design

Alpha particles are known to be a major source of particles creating soft errors in semiconductor devices, such as content flipping in Static Random-Access Memory (SRAM). Recent advancements in transistor nodes have led to the introduction of Gate-All-Around Field Effect Transistors (GAA-FETs), which have better gate control, thus better electrostatics. Moreover, the introduction of bottom dielectric isolation (BDI) eliminates substrate leakage and thus is expected to enhance its radiation hardness. It is thus important to explore if one can design an SRAM that is completely radiation-hard to alpha particles. In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design an SRAM using GAA-FET technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LET max , for the alpha particle in Si and Si x Ge 1-x . Based on that, by de signing a sub-7nm GAA-FET-based SRAM with BDI, we show that the SRAM does not flip even if the particle strike is in the worst-case scenario for LET > LET max .

42 ENGINEERING↗
DOE OSTI2018

Multi-petascale highly efficient parallel supercomputer

A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaflop-scale includes node architectures based upon System-On-a-Chip technology, where each processing node comprises a single Application Specific Integrated Circuit (ASIC). The ASIC nodes are interconnected by a five dimensional torus network that optimally maximize the throughput of packet communications between nodes and minimize latency. The network implements collective network and a global asynchronous network that provides global barrier and notification functions. Integrated in the node design include a list-based prefetcher. The memory system implements transaction memory, thread level speculation, and multiversioning cache that improves soft error rate at the same time and supports DMA functionality allowing for parallel processing message-passing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Accelerated Nuclear Radiation Effects on the Raspberry Pi 3B+ and Pi 4

Raspberry Pi™ computers running Linux and embedded benchmarks are subjected to radiation testing in the neutron beam at LANSCE. The ARM® Cortex®-A53 in the Raspberry Pi 3B+ versus ARM® Cortex®-A72 in the Raspberry Pi 4, single-core versus multi-core, and small versus large array SBU cross sections are compared. Results for the A53 and the A72 are similar. The results of one process on one of four cores and four identical processes on four cores are presented. The results of the array size show that there is more going on than just an increase in size. Linux is helpful in relating some errors that would have been classified as SEFI to an upset in a single bit.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Validating Extreme Scale Resilience with Veracity (Final Project Report)

As semiconductor technology continues to scale to smaller dimensions and as power-consumption of processor chips continues to increase in importance, there is significant uncertainty about the reliability of future large-scale computers. This uncertainty impacts algorithm design and application development, as well as decisions about system operation policy and provisioning decisions. The Veracity endeavored to answer pertinent questions regarding system reliability and resilience with the goal of enabling reliable science on future leadership machines while keeping overprovisioning low.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Application health monitoring for extreme-scale resiliency using cooperative fault management

Resiliency is and will be a critical factor in determining scientific productivity on current and exascale supercomputers, and beyond. Applications oblivious to and incapable of handling transient soft and hard errors could waste supercomputing resources or, worse, yield misleading scientific insights. In this work, we introduce a novel application-driven silent error detection and recovery strategy based on application health monitoring. Our methodology uses application output that follows known patterns, as indicators of an application's health and knowledge that violation of these patterns could be indication of faults. Information from system monitors that report hardware and software health status is used to corroborate faults. Collectively, this information is used by a fault coordinator agent to take preventive and corrective measures by applying computational steering to an application between checkpoints. This cooperative fault management system uses the Fault Tolerance Backplane as a communication channel. The benefits of this framework are demonstrated with two real application case studies, molecular dynamics, and quantum chemistry simulations, on scalable clusters with simulated memory and I/O corruptions. Lastly, the developed approach is general and can be easily applied to other applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Celestial Quantum Error Correction. Part II. From qudits to celestial CFT

A holographic CFT description of asymptotically flat spacetimes inherits vacuum degeneracies and IR divergences from its gravitational dual. We devise a Quantum Error Correcting (QEC) framework to encode both effects as correctable fluctuations on the CFT dual. The framework is physically motivated by embedding a chain of qudits in the so-called Klein spacetime and then taking a continuum 𝑁 → ∞ limit. At finite 𝑁 the qudit chain 1) enjoys a discrete version of celestial symmetries and 2) supports a Gottesman-Kitaev-Preskill (GKP) code. The limit results in hard states with quantized BMS hair in the celestial torus forming the logical subspace, robust under errors induced by soft radiation. Technically, the construction leverages the recently studied 𝑤 1+∞ hierarchy of soft currents and its realization from a sigma model in twistor space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Fault Tolerant Decoding of QLDPC-GKP Codes with Circuit Level Soft Information

Concatenated bosonic-stabilizer codes have recently gained prominence as promising candidates for achieving low-overhead fault-tolerant quantum computing in the long term. In such systems, analog information obtained from the syndrome measurements of an inner bosonic code is used to inform decoding for an outer code layer consisting of a discrete-variable stabilizer code such as a surface code. The use of Quantum Low-Density Parity Check (QLDPC) codes as an outer code is of particular interest due to the significantly higher encoding rates offered by these code families, leading to a further reduction in overhead for large-scale quantum computing. Recent works have investigated the performance of QLDPC-GKP codes in detail, and the use of analog information from the inner code significantly boosts decoder performance. However, the noise models assumed in these works are typically limited to depolarizing or phenomenological noise. In this paper, we investigate the performance of QLDPC-GKP concatenated codes under circuit-level noise, based on a model introduced by Noh et al. in the context of the surface-GKP code. To demonstrate the performance boost from analog information, we investigate three scenarios: (a) decoding without soft information, (b) decoding with precomputed error probabilities but without real-time soft information, and (c) decoding with real-time soft information obtained from round-to-round decoding of the inner GKP code. Results show minimal improvement between (a) and (b), but a significant boost in (c), indicating that real-time soft information is critical for concatenated decoding under circuit-level noise. We also study the effect of measurement schedules with varying depths and show that using a schedule with minimum depth is essential for obtaining reliable soft information from the inner code.

Borah, Shantom K. [Arizona U. (main)]↗
DOE OSTI2022

Soft Syndrome Decoding of Quantum LDPC Codes for Joint Correction of Data and Syndrome Errors

Quantum errors are primarily detected and corrected using the measurement of syndrome information which itself is an unreliable step in practical error correction implementations. Typically, such faulty or noisy syndrome measurements are modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this "soft" or analog information without the conventional discretization step to benefit the iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation (BP) decoders are modified to utilize the syndrome-soft information to successfully correct both data and syndrome errors simultaneously, without repeated measurements. We demonstrate the advantages of extracting the soft information from the syndrome in our improved decoders, not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families, but also for faster convergence of iterative decoders. In particular, the new BP decoder with noisy syndrome performs as good as the standard BP decoder under ideal syndrome.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Feasibility of MRI-only treatment planning for proton therapy in brain and prostate cancers: Dose calculation accuracy in substitute CT images

Purpose: Magnetic resonance imaging (MRI) is increasingly used for radiotherapy target delineation, image guidance, and treatment response monitoring. Recent studies have shown that an entire external x-ray radiotherapy treatment planning (RTP) workflow for brain tumor or prostate cancer patients based only on MRI reference images is feasible. This study aims to show that a MRI-only based RTP workflow is also feasible for proton beam therapy plans generated in MRI-based substitute computed tomography (sCT) images of the head and the pelvis. Methods: The sCTs were constructed for ten prostate cancer and ten brain tumor patients primarily by transforming the intensity values of in-phase MR images to Hounsfield units (HUs) with a dual model HU conversion technique to enable heterogeneous tissue representation. HU conversion models for the pelvis were adopted from previous studies, further extended in this study also for head MRI by generating anatomical site-specific conversion models (a new training data set of ten other brain patients). This study also evaluated two other types of simplified sCT: dual bulk density (for bone and water) and homogeneous (water only). For every clinical case, intensity modulated proton therapy (IMPT) plans robustly optimized in standard planning CTs were calculated in sCT for evaluation, and vice versa. Overall dose agreement was evaluated using dose–volume histogram parameters and 3D gamma criteria. Results: In heterogeneous sCTs, the mean absolute errors in HUs were 34 (soft tissues: 13, bones: 92) and 42 (soft tissues: 9, bones: 97) in the head and in the pelvis, respectively. The maximum absolute dose differences relative to CT in the brain tumor clinical target volume (CTV) were 1.4% for heterogeneous sCT, 1.8% for dual bulk sCT, and 8.9% for homogenous sCT. The corresponding maximum differences in the prostate CTV were 0.6%, 1.2%, and 3.6%, respectively. The percentages of dose points in the head and pelvis passing 1% and 1 mm gamma index criteria were over 91%, 85%, and 38% with heterogeneous, dual bulk, and homogeneous sCTs, respectively. There were no significant changes to gamma index pass rates for IMPT plans first optimized in CT and then calculated in heterogeneous sCT versus IMPT plans first optimized in heterogeneous sCT and then calculated on standard CT. Conclusions: This study demonstrates that proton therapy dose calculations on heterogeneous sCTs are in good agreement with plans generated with standard planning CT. An MRI-only based RTP workflow is feasible in IMPT for brain tumors and prostate cancers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Accuracy of first-principles interatomic interactions and predictions of ferroelectric phase transitions in perovskite oxides: Energy functional and effective Hamiltonian

While first-principles density functional theory (DFT)-based models have been effective in capturing the physics of ferroelectric phase transitions in BaTiO 3 , PbTiO 3 , and KNbO 3 , quantitative estimates of the transition temperatures (T C ) have errors that are believed to originate from the errors in estimating lattice constants obtained within the local density approximation (LDA) and generalized gradient approximation (GGA) of DFT. The recently developed strongly constrained and appropriately normed (SCAN) meta-GGA functional has been shown to be quite accurate in the estimation of lattice constants. Here, we present a quantitative analysis of the estimates of ferroelectric ground-state properties of eight perovskite oxides and transition temperatures of BaTiO 3 , PbTiO 3 , and KNbO 3 obtained with molecular dynamics simulations using an effective Hamiltonian derived from the SCAN meta-GGA-based DFT. Relative to LDA, we find an improvement in the estimates of T C , which arises from the changes in the calculated strain-phonon, anharmonic coupling constants, and strength of ferroelectric instabilities, i.e., frequencies of the soft modes. We also assess the errors in T C originating from approximately integrating out the high-energy phonons during construction of the model Hamiltonian through estimates of the effects of fourth-order couplings between the soft mode and higher-energy modes of BaTiO 3 , PbTiO 3 , and KNbO 3 . We find that inclusion of these anharmonic couplings results in deeper double-well energy functions of ferroelectric distortions and further improvement in the estimates of transition temperatures. Consistently improved estimates of lattice constants and transition temperatures with the SCAN meta-GGA calculations augur well for their use in simulations of superlattices or heterostructures of perovskite oxides, in which the effects of lattice matching are critical.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Soft syndrome iterative decoding of quantum LDPC codes and hardware architectures

In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this “soft” or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.

Sabelhaus, Andrew P.↗
DOE OSTI2021

Encoder–decoder neural network for solving the nonlinear Fokker–Planck–Landau collision operator in XGC

An encoder–decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker–Planck–Landau collision operator. This is part of the governing equation in the massively parallel particle-in-cell code XGC, which is used to study turbulence in fusion energy devices. The neural network emphasizes physics-inspired learning, where it is taught to respect physical conservation constraints of the collision operator by including them in the training loss, along with the ℓ 2 loss. In particular, network architectures used for the computer vision task of semantic segmentation have been used for training. A penalization method is used to enforce the ‘soft’ constraints of the system and integrate error in the conservation properties into the loss function. During training, quantities representing the particle density, momentum and energy for all species of the system are calculated at each configuration vertex, mirroring the procedure in XGC. This simple training has produced a median relative loss, across configuration space, of the order of 10 –4 , which is low enough if the error is of random nature, but not if it is of drift nature in time steps. The run time for the current Picard iterative solver of the operator is O(n 2 ), where n is the number of plasma species. As the XGC1 code begins to attack problems including a larger number of species, the collision operator will become expensive computationally, making the neural network solver even more important, especially since its training only scales as O(n). Here, a wide enough range of collisionality has been considered in the training data to ensure the full domain of collision physics is captured. An advanced technique to decrease the losses further will be subject of a subsequent report. Eventual work will include expansion of the network to include multiple plasma species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Flexible Electronics toward Wearable Sensing

Wearable sensors play a crucial role in realizing personalized medicine, as they can continuously collect data from the human body to capture meaningful health status changes in time for preventive intervention. However, motion artifacts and mechanical mismatches between conventional rigid electronic materials and soft skin often lead to substantial sensor errors during epidermal measurement. Because of its unique properties such as high flexibility and conformability, flexible electronics enables a natural interaction between electronics and the human body. Here, we summarize our recent studies on the design of flexible electronic devices and systems for physical and chemical monitoring. Material innovation, sensor design, device fabrication, system integration, and human studies employed toward continuous and noninvasive wearable sensing are discussed. A flexible electronic device typically contains several key components, including the substrate, the active layer, and the interface layer. The inorganic-nanomaterials-based active layer (prepared by a physical transfer or solution process) is shown to have good physicochemical properties, electron/hole mobility, and mechanical strength. Flexible electronics based on the printed and transferred active materials has shown great promise for physical sensing. For example, integrating a nanowire transistor array for the active matrix and a conductive pressure-sensitive rubber enables tactile pressure mapping; tactile-pressure-sensitive e-skin and organic light-emitting diodes can be integrated for instantaneous pressure visualization. Such printed sensors have been applied as wearable patches to monitor skin temperature, electrocardiograms, and human activities. In addition, liquid metals could serve as an attractive candidate for flexible electronics because of their excellent conductivity, flexibility, and stretchability. Liquid-metal-enabled electronics (based on liquid-liquid heterojunctions and embedded microchannels) have been utilized to monitor a wide range of physiological parameters (e.g., pulse and temperature). Despite the rapid growth in wearable sensing technologies, there is an urgent need for the development of flexible devices that can capture molecular data from the human body to retrieve more insightful health information. We have developed a wearable and flexible sweat-sensing platform toward real-time multiplexed perspiration analysis. An integrated iontophoresis module on a wearable sweat sensor could enable autonomous and programmed sweat extraction. A microfluidics-based sensing system was demonstrated for sweat sampling, sensing, and sweat rate analysis. Roll-to-roll gravure printing allows for mass production of high-performance flexible chemical sensors at low cost. These wearable and flexible sweat sensors have shown great promise in dehydration monitoring, cystic fibrosis diagnosis, drug monitoring, and noninvasive glucose monitoring. Future work in this field should focus on designing robust wearable sensing systems to accurately collect data from the human body and on large-scale human studies to determine how the measured physical and chemical information relates to the individual's specific health conditions. Further research in these directions, along with the large sets of data collected via these wearable and flexible sensing technologies, will have a significant impact on future personalized healthcare.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Energy storage has become more and more a limiting factor of today's sustainable energy applications, including electric vehicles and green electric grid based on volatile solar and wind sources. The pressing demand of developing high-performance electrochemical energy storage solutions, i.e., batteries, relies on both fundamental understanding and practical developments from both the academy and industry. The formidable challenge of developing successful battery technology stems from the different requirements for different energy-storage applications. Energy density, power, stability, safety, and cost parameters all have to be balanced in batteries to meet the requirements of different applications. Therefore, multiple battery technologies based on different materials and mechanisms need to be developed and optimized. Incisive tools that could directly probe the chemical reactions in various battery materials are becoming critical to advance the field beyond its conventional trial-and-error approach. Here, we present detailed protocols for soft X-ray absorption spectroscopy (sXAS), soft X-ray emission spectroscopy (sXES), and resonant inelastic X-ray scattering (RIXS) experiments, which are inherently elemental-sensitive probes of the transition-metal 3d and anion 2p states in battery compounds. We provide the details on the experimental techniques and demonstrations revealing the key chemical states in battery materials through these soft X-ray spectroscopy techniques.

25 ENERGY STORAGE↗