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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Micromagnetic simulations of spin-wave normal modes and the spin-transfer-torque driven magnetization dynamics of a ferromagnetic cross

We studied spin-transfer-torque (STT) switching of a cross-shaped magnetic tunnel junction in a recent report [Roy et al., J. Appl. Phys. 113, 223904 (2013)]. In that structure, the free layer is designed to have four stable energy states using the shape anisotropy of a cross. STT switching showed different regions with increasing current density. Here, we employ the micromagnetic spectral mapping technique in an attempt to understand how the asymmetry of cross dimensions and spin polarization direction of the injected current affect the magnetization dynamics. We compute spatially averaged frequency-domain spectrum of the time-domain magnetization dynamics in the presence of the current-induced STT term. At low currents, the asymmetry of polarization direction and that of the arms are observed to cause a splitting of the excited frequency modes. Higher harmonics are also observed, presumably due to spin-wave wells caused by the regions of spatially non-uniform effective magnetic field. The results could be used towards designing a multi-bit-per-cell STT-based random access memory with an improved storage density.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2024

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
NASA NTRSDate not supplied

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
NASA NTRSDate not supplied

Behavioral consequences of low dose radiation and sex differences in MCAT mouse model

Our study used 1-year old C57BL/6NJ male and female mice (astronaut-relevant age) that underwent exposure to 0.5 gray of gamma radiation and were euthanized 12 weeks after. In this study, we used an MCAT mouse model for mitochondrial ROS quenching, which overexpress human catalase. MCAT mice were shown to live longer and age better. Hence, in this study we determined whether quenching ROS in the mitochondria will mitigate the adverse effects of ionizing radiation exposure on spaceflight-relevant tissues. As part of the analysis, we have completed 5 different behavioral tests which focus on memory, physical stance, stress, anxiety, and other mission relevant behaviors. In the Neuro-score battery, performed after both 1and 8 weeks post IR we saw that all female groups had significantly higher scores compared to males. When comparing the baseline vs 8 weeks of radiation, we saw that all the male groups (including the sham) had lower neuro-score, pointing out to aging effect in addition to IR. In the female groups only the female IR group had lower neuro-score and the MCAT group was protected from this effect. In the Nestlet building test we saw similarly that only females were affected by radiation, having lower scores and this effect was mitigated in the MCAT animals as well. In the Catwalk test we saw that females were faster, had higher swing speed and stride length in all four paws. Males had higher stand, step cycle and max contact area. Aging is associated with slowing of gait speed, swing speed and shortening of stride length which we see in males, this is consistent with physical appearance where males look markedly older. In the Light-Dark Box test we saw that females were more frequently present in the light side and altered zones more frequently, pointing out to a more exploratory and less anxious pattern of behavior. Similarly, to what was detected in the Nest building and Neuro-score test, in the Barnes maze test, during the acquisition phase (learning) we saw that IR affected more the females who did not do better in the maze after 4 days. On the other hand, during the probe phase of the test (spatial memory) the females visited the target hole and the box quadrant more often, but also had more errors vs males, which points out to possible serial escape vs spatial escape strategy. Overall, we see that older females look physically better are faster and perform better almost in all behavioral tests compared to their male counterparts. On the other hand, they are more sensitive to low dose radiation in many cases, in some cases this effect was mitigated in the MCAT model pointing out to the importance of ROS in these stressors. In the near future we will focus on corelating these behavioral tests with molecular findings such as for example brain IHC, plasma and hippocampal cytokines in order to find specific biomarkers for behavioral deficits.

radiation↗
NASA NTRS2004

The effects of proton exposure on neurochemistry and behavior

Future space missions will involve long-term travel beyond the magnetic field of the Earth, where astronauts will be exposed to radiation hazards such as those that arise from galactic cosmic rays. Galactic cosmic rays are composed of protons, alpha particles, and particles of high energy and charge (HZE particles). Research by our group has shown that exposure to HZE particles, primarily 600 MeV/n and 1 GeV/n 56Fe, can produce significant alterations in brain neurochemistry and behavior. However, given that protons can make up a significant portion of the radiation spectrum, it is important to study their effects on neural functioning and on related performance. Therefore, these studies examined the effects of exposure to proton irradiation on neurochemical and behavioral endpoints, including dopaminergic functioning, amphetamine-induced conditioned taste aversion learning, and spatial learning and memory as measured by the Morris water maze. Male Sprague-Dawley rats received a dose of 0, 1.5, 3.0 or 4.0 Gy of 250 MeV protons at Loma Linda University and were tested in the different behavioral tests at various times following exposure. Results showed that there was no effect of proton irradiation at any dose on any of the endpoints measured. Therefore, there is a contrast between the insignificant effects of high dose proton exposure and the dramatic effectiveness of low dose (<0.1 Gy) exposures to 56Fe particles on both neurochemical and behavioral endpoints. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗
DOE OSTI2016

Effect of heavy metal layer thickness on spin-orbit torque and current-induced switching in Hf|CoFeB|MgO structures

We study the heavy metal layer thickness dependence of the current-induced spin-orbit torque (SOT) in perpendicularly magnetized Hf|CoFeB|MgO multilayer structures. The damping-like (DL) current-induced SOT is determined by vector anomalous Hall effect measurements. A non-monotonic behavior in the DL-SOT is found as a function of the thickness of the heavy-metal layer. The sign of the DL-SOT changes with increasing the thickness of the Hf layer in the trilayer structure. As a result, in the current-driven magnetization switching, the preferred direction of switching for a given current direction changes when the Hf thickness is increased above ∼7 nm. Although there might be a couple of reasons for this unexpected behavior in DL-SOT, such as the roughness in the interfaces and/or impurity based electric potential in the heavy metal, one can deduce a roughness dependence sign reversal in DL-SOT in our trilayer structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Hydrogen doping in HfO{sub 2} resistance change random access memory

The structures and energies of hydrogen-doped monoclinic hafnium dioxide were calculated using density-functional theory. The electronic interactions are described within the LDA + U formalism, where on-site Coulomb corrections are applied to the 5d orbital electrons of Hf atoms and 2p orbital electrons of the O atoms. The effects of charge state, defect-defect interactions, and hydrogenation are investigated and compared with experiment. It is found that hydrogenation of HfO{sub 2} resistance-change random access memory devices energetically stabilizes the formation of oxygen vacancies and conductive vacancy filaments through multiple mechanisms, leading to improved switching characteristic and device yield.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2025

Aluminum Boron Nitride Ferroelectric Field-Effect Transistors With ZnO Semiconductor Channel

The discovery of ferroelectricity in hafnium zirconium oxide (HfxZr 1−x O2) and related fluorite materials has spurred interest in ferroelectric devices suitable for integration with silicon integrated circuits (ICs), especially those that can be embedded in the back-end-of-line (BEOL) process. More recently, ferroelectricity has been found in wurtzite aluminum nitride-based materials, such as scandium and boron-doped aluminum nitride (Al 1−x ScxN and Al 1−x BxN). Although these materials currently have undesirably large coercive electric fields, and small breakdown electric field-to-coercive electric field ratio, their low processing temperatures and large remanent polarization offer intriguing possibilities for device applications. Furthermore, we report ferroelectric field-effect transistors (FeFETs) with a 15 nm thick Al0.88B0.12N layer and an 11 nm ZnO semiconductor channel, achieving a memory window >1 V and switching voltages ( V switch ) <±10 V.

42 ENGINEERING↗
DOE OSTI2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

ALD-Derived WO 3– x Leads to Nearly Wake-Up-Free Ferroelectric Hf 0.5 Zr 0.5 O 2 at Elevated Temperatures

Breaking the memory wall in advanced computing architectures will require complex 3D integration of emerging memory materials such as ferroelectrics─either within the back-end-of-line (BEOL) of CMOS front-end processes or through advanced 3D packaging technologies. Achieving this integration demands that memory materials exhibit high thermal resilience, with the capability to operate reliably at elevated temperatures, such as 125°C, due to the substantial heat generated by front-end transistors. However, silicon-compatible HfO 2 -based ferroelectrics tend to exhibit antiferroelectric-like behavior in this temperature range, accompanied by a more pronounced wake-up effect, posing significant challenges to their thermal reliability. Here, we report that by introducing a thin tungsten oxide (WO 3–x ) layer─known as an oxygen reservoir─and carefully tuning its oxygen content, ultrathin Hf 0.5 Zr 0.5 O 2 (5 nm) films can be made robust against the ferroelectric-to-antiferroelectric transition at elevated temperatures. This approach not only minimizes polarization loss in the pristine state but also effectively suppresses the wake-up effect, reducing the required wake-up cycles from 10 5 to only 10 at 125°C, a qualifying temperature for back-end memory integrated with front-end logic, as defined by the JEDEC standard. First-principles density functional theory (DFT) calculations reveal that WO 3 enhances the stability of the ferroelectric orthorhombic phase (o-phase) at elevated temperatures by increasing the tetragonal-to-orthorhombic phase energy gap and promoting favorable phonon mode evolution, thereby supporting o-phase formation under both thermodynamic and kinetic constraints.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ligand Substituent Effects on the Electronic Properties of Lindqvist‐Type Polyoxometalate Multi‐Level‐Switches in the Gas Phase, Solution and on Surfaces

Abstract Although the intrinsic electronic properties of polyoxometalates (POMs) can be greatly influenced by modifying them with organic substituents, their resistive switching behavior on surfaces dependent on the organic substituents remains largely unexplored. In this work, we assessed the importance of electron‐withdrawing and electron‐donating ligand substituents on the material properties of a series of hybrid Lindqvist‐type hexavanadates TBA 2 [V 6 O 13 ((OCH 2 ) 3 CCH 2 OH) 2 ] (TBA 2 V 6 ‐OH), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CMe) 2 ] (TBA 2 V 6 ‐Me), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 Cl) 2 ] (TBA 2 V 6 ‐Cl), and TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 ‐OOCC 10 H 15 ) 2 ] (TBA 2 V 6 ‐Ad) as potential resistive random‐access memory (ReRAM) components. Compared to their redox behavior in solution, changing the ligand substituents on surfaces results in no significant effect on the potential and, thus, no effect on the resistance steps in the current‐voltage profiles. However, while the current‐voltage characteristics do not change, the peripheral metal‐free substituents in the trisalkoxide framework of Lindqvist‐type hexavanadate molecules influence the adsorption and switching stability of these POMs on gold. This work highlights the noticeable differences between hexavanadate's redox properties in solution (which follow the trend observed in the gas phase) and hexavanadate's resistive switching properties on conducting surfaces. Importantly, their multi‐state switching behavior is not significantly altered by the different type of substituent at the periphery of the trisalkoxo ligands.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How arsenic makes amorphous GeSe a robust chalcogenide glass for advanced memory integration

The 3D integration technology in semiconductor fabrication requires a key component, the ovonic threshold switching (OTS) selector, to suppress the current leakage. The As doped amorphous (a-) GeSe glass is a commercialized OTS material in 3D phase-change memory, but the understanding of such a doping mechanism is still inadequate. Here we systematically explore the effect of As doping on the structural, bonding, and dynamics properties of a-GeAsSe using ab initio molecular dynamics simulations. The results reveal that As atoms form strong bonds with both Ge and Se atoms. The distorted octahedral structures and the 5-fold rings linked by atoms are increased. All of these structural features lead to a more disordered configuration. Moreover, as atoms have notably slowed down the atomic mobility, rendering a-GeAsSe a high stability. Overall, our studies offer insightful understanding of As-doping in OTS materials, paving the way for the design and application of advanced selector devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Magnetization dynamics driven by displacement currents across a magnetic tunnel junction

Understanding the high-frequency transport characteristics of magnetic tunnel junctions (MTJs) is crucial for the development of fast-operating spintronics memories and radio frequency devices. Here, we present the study of a frequency-dependent capacitive current effect in Co Fe B / Mg O -based MTJs and its influence on magnetization dynamics using a time-resolved magneto-optical Kerr effect technique. In our device, operating at gigahertz frequencies, we find a large displacement current of the order of mA, which does not break the tunnel barrier of the MTJ. Importantly, this current generates an Oersted field and spin-orbit torque, inducing magnetization dynamics. Our discovery holds promise for building robust MTJ devices operating under high current conditions, also highlighting the significance of capacitive impedance in high-frequency magnetotransport techniques. Published by the American Physical Society 2024

Safeer, C. K. (ORCID:0000000323969730)↗
DOE OSTI2016

Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing Techniques

Exascale systems promise the potential for computation at unprecedented scales and resolutions, but achieving exascale by the end of this decade presents significant challenges. A key challenge is due to the very large number of cores and components and the resulting mean time between failures (MTBF) in the order of hours or minutes. Since the typical run times of target scientific applications are longer than this MTBF, fault tolerance techniques will be essential. An important class of failures that must be addressed is process or node failures. While checkpoint/restart (C/R) is currently the most widely accepted technique for addressing processor failures, coordinated, stable-storage-based global C/R might be unfeasible at exascale when the time to checkpoint exceeds the expected MTBF. This paper explores transparent recovery via implicitly coordinated, diskless, application-driven checkpointing as a way to tolerate process failures in MPI applications at exascale. The discussed approach leverages User Level Failure Mitigation (ULFM), which is being proposed as an MPI extension to allow applications to create policies for tolerating process failures. Specifically, this paper demonstrates how different implementations of application-driven in-memory checkpoint storage and recovery compare in terms of performance and scalability. We also experimentally evaluate the effectiveness and scalability of the Fenix online global recovery framework on a production system -the Titan Cray XK7 at ORNL- and demonstrate the ability of Fenix to tolerate dynamically injected failures using the execution of four benchmarks and mini-applications with different behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Optimal Execution of Co-analysis for Large-scale Molecular Dynamics Simulations

The analysis of scientific simulation data enables scientists to derive insights from their simulations. This analysis of the simulation output can be performed at the same execution site as the simulation using the same resources or can be done at a different site. The optimal output frequency is challenging to decide and is often chosen empirically. We propose a mathematical formulation for choosing the optimal frequency of data transfer for analysis and the feasibility of performing the analysis, under the given resource constraints such as network bandwidth, disk space, available memory, and computation time. We propose formulations for two cases of co-analysis -- local and remote. We consider various analyses features such as computation time, input data and memory requirement, importance of the analysis and minimum frequency required for performing the analysis. We demonstrate the effectiveness of our approach using molecular dynamics applications on the Mira and Edison supercomputers.

analysis↗