Search NASA⌕ Search

SEARCH · Search NASA

Results for “performance proxy”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

Cation-Specific Electrokinetic Separations Using Prussian Blue Intercalation Reactions

In this work, we report a new approach for forming ion depletion zones (IDZs) using intercalation reactions at an electrochemically controlled Prussian blue (PB) film. The experiments described here were performed using a microfluidic device, wherein a charged fluorophore in solution was used as a proxy to monitor IDZ formation. In the presence of K + , intercalation reactions proceed readily to form an IDZ, and thus enrich the fluorophore up to 37-fold. In the presence of the larger hydrated Li + or tetrabutylammonium (TBA + ) ions, however, ion intercalation proceeded less quickly than with K + . Slower ion intercalation resulted in a weaker IDZ and correspondingly lower enrichment factors of eight- and six-fold, respectively. These results are significant because they show that PB intercalation reactions selectively form an IDZ. Accordingly, we anticipate that these findings will lead to a better understanding and further interesting applications of intercalation materials like PB for efficient and selective enrichment and separations.

99 GENERAL AND MISCELLANEOUS↗

Do solar cycles influence giant cell arteritis and rheumatoid arthritis incidence?

Our objective was to examine the influence of solar cycle and geomagnetic effects on the incidence of giant cell arteritis (GCA) and rheumatoid arthritis (RA). Methods: We used data from patients with GCA (1950-2004) and RA (1955-2007) obtained from population-based cohorts. Yearly trends in age-adjusted and sex-adjusted incidence were correlated with the F10.7 index (solar radiation at 10.7 cm wavelength, a proxy for the solar extreme ultraviolet radiation) and AL index (a proxy for the westward auroral electrojet and a measure of geomagnetic activity). Fourier analysis was performed on AL, F10.7, and GCA and RA incidence rates. Results: The correlation of GCA incidence with AL is highly significant: GCA incidence peaks 0-1 year after the AL reaches its minimum (ie, auroral electrojet reaches a maximum). The correlation of RA incidence with AL is also highly significant. RA incidence rates are lowest 5-7 years after AL reaches maximum. AL, GCA and RA incidence power spectra are similar: they have a main peak (periodicity) at about 10 years and a minor peak at 4-5 years. However, the RA incidence power spectrum main peak is broader (8-11 years), which partly explains the lower correlation between RA onset and AL. The auroral electrojets may be linked to the decline of RA incidence more strongly than the onset of RA. The incidences of RA and GCA are aligned in geomagnetic latitude. Conclusions: AL and the incidences of GCA and RA all have a major periodicity of about 10 years and a secondary periodicity at 4-5 years. Geomagnetic activity may explain the temporal and spatial variations, including east-west skewness in geographic coordinates, in GCA and RA incidence, although the mechanism is unknown. Lastly, the link with solar, geospace and atmospheric parameters need to be investigated. These novel findings warrant examination in other populations and with other autoimmune diseases.

58 GEOSCIENCES↗

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

58 GEOSCIENCES↗

X-ray-selected galaxy groups in Boötes

We present the X-ray and optical properties of the galaxy groups selected in the Chandra X-Boötes survey. We used follow-up Chandra observations to better define the group sample and their X-ray properties. Group redshifts were measured from the AGN and Galaxy Evolution Survey spectroscopic data. We used photometric data from the NOAO Deep Wide Field Survey to estimate the group richness (N {sub gals}) and the optical luminosity (L {sub opt}). Our final sample comprises 32 systems at z < 1.75 with 14 below z = 0.35. For these 14 systems, we estimate velocity dispersions (σ {sub gr}) and perform a virial analysis to obtain the radii (R {sub 200} and R {sub 500}) and total masses (M {sub 200} and M {sub 500}) for groups with at least 5 galaxy members. We use the Chandra X-ray observations to derive the X-ray luminosity (L{sub X} ). We examine the performance of the group properties σ{sub gr}, L {sub opt}, and L{sub X} , as proxies for the group mass. Understanding how well these observables measure the total mass is important to estimate how precisely the cluster/group mass function is determined. Exploring the scaling relations built with the X-Boötes sample and comparing these with samples from the literature, we find a break in the L{sub X} -M {sub 500} relation at approximately M {sub 500} = 5 × 10{sup 13} M {sub ☉} (for M {sub 500} > 5 × 10{sup 13} M {sub ☉}, M{sub 500}∝L{sub X}{sup 0.61±0.02}, while for M {sub 500} ≤ 5 × 10{sup 13} M {sub ☉}, M{sub 500}∝L{sub X}{sup 0.44±0.05}). Thus, the mass-luminosity relation for galaxy groups cannot be described by the same power law as galaxy clusters. A possible explanation for this break is the dynamical friction, tidal interactions, and projection effects that reduce the velocity dispersion values of the galaxy groups. By extending the cluster luminosity function to the group regime, we predict the number of groups that new X-ray surveys, particularly eROSITA, will detect. Based on our cluster/group luminosity function estimates, eROSITA will identify ∼1800 groups (L{sub X} = 10{sup 41}-10{sup 43} erg s{sup –1}) within a distance of 200 Mpc. Since groups lie in large-scale filaments, this group sample will map the large-scale structure of the local universe.

79 ASTRONOMY AND ASTROPHYSICS↗

Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.

Amini, Shohreh↗

Multi-Purpose, Application-Centric, Scalable I/O Proxy Application

MACSio is a Multi-purpose, Application-Centric, Scalable I/O proxy application. It is designed to support a number of goals with respect to parallel I/O performance testing and benchmarking including the ability to test and compare various I/O libraries and I/O paradigms, to predict scalable performance of real applications and to help identify where improvements in I/O performance can be made within the HPC I/O software stack.

Miller, M.C.↗

The CHROMA Cloud-Top Pressure Retrieval Algorithm for the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission

This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

PACE↗

Multi-Purpose, Application-Centric, Scalable I/O Proxy Application

MACSio is a Multi-purpose, Application-Centric, Scalable I/O proxy application. It is designed to support a number of goals with respect to parallel I/O performance testing and benchmarking including the ability to test and compare various I/O libraries and I/O paradigms, to predict scalable performance of real applications and to help identify where improvements in I/O performance can be made within the HPC I/O software stack.

Miller, M. C.↗

Automated Design Space Exploration with Aspen

Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection of costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.

Spafford, Kyle L.↗

Blackcomb2: Hardware-Software Co-design for Nonvolatile Memory in Exascale Systems

The impending shift from DRAM and Flash to fast nonvolatile memory (NVM) provides a game-changing opportunity to rethink traditional system architectures; to address their energy inefficiencies; to leverage the new devices for greater performance; and to exploit the new capability of nonvolatility to enhance system resilience, even in the face of larger system scale and degraded reliability of components. Building on the accomplishments of the Blackcomb Project, funded in 2010, we propose to identify, evaluate, and optimize the most promising NVM hardware and software technologies, which are essential to provide the necessary memory capacity, performance, resilience, and energy efficiency in exascale systems. Capacity and energy are the key drivers. Today's pressing application requirements mandate exascale computing; the datasets and concurrency of these applications demand memory capacity that cannot be met by traditional memory technologies. Although charge-based DRAM will endure, experts foresee only modest DRAM capacity gains. And, as moving data between levels of the hierarchy, and storing and accessing data on disk consumes significant energy, a more energy efficient solution is required. We, therefore, posit that exascale systems will need high density, energy-efficient storage technologies, such as nonvolatile memory for access, transformation, and management of exascale data. To address these issues, in this renewal request, we propose continue the work of our vertically- integrated, focussed team, where our co-design is informed by proxies and interactions with DOE co-design centers. In software, we start by assuming that nonvolatility requires new abstractions and new infrastructure, allowing persistent objects, and delivering performance gains for _le systems and robustness through fast checkpointing. In architecture, we evaluate the tradeoffs of performance and energy by employing a scalable and accurate simulation system to analyze realistic proxy applications. Through this co-design process, we rethink the whole memory architecture, from the cell to the array to the device, with ECC, wearout management, and interfaces. Finally, we provide open-source software models of these NVM-based memory systems.

97 MATHEMATICS AND COMPUTING↗

Performance of the Belle II Conditions Database

The Belle II experiment at KEK observed its first collisions in the summer of 2018. Processing the large amounts of data that will be produced requires conditions data to be readily available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. This was accomplished by relying on industry-standard tools and methods: the conditions database is built as an HTTP REST service using tools such as Swagger for the API interface development, Payara for the Java EE application server, and Squid for the caching proxy. This article presents the design of the Belle II conditions database production environment as well as details about the capabilities and performance during both Monte Carlo campaigns and data reprocessing.

Wood, Lynn S.↗

Studying CPU and memory utilization of applications on Fujitsu A64FX and Nvidia Grace Superchip

ARM-based manycore CPU architectures are well-positioned to provide the rising memory throughput requirements of modern data intensive scientific applications in High Performance Computing (HPC). The Fujitsu A64FX CPU platform is based on the ARM v8.2A architecture, and is the processor of the flagship Japanese supercomputer - "Fugaku", which was previously ranked as the #1 supercomputer in the world according to the Top500 list. The Nvidia Grace superchip features 144 Neoverse V2 cores based on the ARMv9 architecture with 4x128b SVE2, providing exceptional computational power. The chip supports up to 480GB of memory, making it ideal for AI, machine learning, and scientific computing workloads. In this paper, we conduct a thorough performance exploration of a variety of parallel bandwidth-sensitive benchmarks and applications compiled with the native Fujitsu compiler on a Fugaku A64FX compute node and ARM (LLVM) Compiler on an NVIDIA Grace superchip compute node, engaging all the computational cores per cluster using OpenMP multithreading (assuming the cores can drive the available bandwidth). Our ultimate goals are to study the resource utilization of scientific applications and benchmarks on A64FX and Grace superchip, considering graph application scenarios ( GAP Benchmark suite) and eleven appli- cation proxies from the Rodinia heterogeneous benchmark suite (considering domains such as Data Mining, Bioinformatics, Fluid Dynamics, Pattern Recognition, etc.). Through exhaustive performance monitoring, we quantify the resource utilization of diverse OpenMP-based HPC applications on both the Fujitsu A64FX and the Nvidia Grace Superchip platforms.

benchmarking, Performance Analysis, High performan↗

Effect of boronization on plasma-facing graphite surfaces and its correlation with the plasma behavior in NSTX-U

Boronization is a Plasma Facing Component (PFC) conditioning technique widely used in tokamak machines. The National Spherical Torus Experiment-Upgrade (NSTX-U) applied this conditioning, using a plasma glow with a deuterated Trimethyl-boron (d-TMB) and He mixture. The use of boronization during the campaign improved the plasma performance, allowing longer plasma discharges and H-mode access. The chemical state of an ATJ graphite sample, used as a proxy for the NSTX-U PFCs, was monitored in-situ using the Materials Analysis Particle Probe (MAPP) diagnostic and X-ray Photoelectron Spectroscopy (XPS). The XPS data showed a progressive rise (from + fluence increased. Filterscopes were used to measure the light emitted by oxygen impurities in the plasma near the surface of the PFC. An increase in the registered magnitude of the OII line, normalized to the Dγ intensity, was observed as the concentration of O on the ATJ surface increased. The plasma performance was found to be strongly correlated to oxygen impurity concentrations at the plasma edge and on the PFC surface, as measured by the discharge length and access to the H-mode regime. In this work, we present a quantitative analysis of the evolution of the chemistry of the ATJ surface, and the oxygen presence in the plasma-material interface, and report relevant plasma parameters observed during the same period of time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗

Initial Laboratory Measurements Probing Hydrogen Interactions with Eagle Ford Shale and Pyrite: Potential Implications for Subsurface Hydrogen Storage

Hydrogen (H 2 ) has the potential to be a transformative technology as an enabler to a low-carbon future and promoter of renewable energy. When H 2 is injected and stored in the subsurface, it has the potential to interact with the caprock (usually shale) which overlies and seals the storage reservoir. Here, this study examines geochemical reactions or changes in surface morphology to Eagle Ford Shale, a proxy for caprock, upon exposure to H 2 at 50°C and 10.3 MPa. Reactions were also performed with N 2 to provide an experimental control. Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS), Atomic Force Microscopy (AFM), and Optical Photothermal-Infrared (O-PTIR) Spectroscopy were applied to quantify changes on the microscale and nanoscale level. Fluid chemistry changes were monitored with Ion Chromatography (IC) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Exposure of Eagle Ford Shale to H 2 gas alone did not result in any alterations to the shale chemically or any changes in the surface morphology. Exposure of Eagle Ford Shale to both H 2 and water as well as N 2 and water resulted in changes to the surface morphology because of gypsum dissolution and reprecipitation, thus indicating that H 2 is not necessary to promote changes. Pure pyrite was the most reactive with H 2 possibly resulting in a reduction to pyrrhotite. These initial studies suggest that the extent of reactions activated by hydrogen with caprock are minor under the temperature and pressure conditions that would represent underground hydrogen storage.

08 HYDROGEN↗

Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Li, He↗

From noise to information: The transfer function formalism for uncertainty quantification in reconstructing the nuclear density

The neutron distribution of neutron-rich nuclei provides critical information on the structure of finite nuclei and neutron stars. Parity violating experiments—such as PREX and CREX—provide a clean and largely model-independent determination of neutron densities. Such experiments, however, are challenging and expensive, which is why sound statistical arguments are required to maximize the information gained. We introduce a new framework, the “transfer function formalism,” aimed at uncertainty quantification, model selection, and experimental design in the context of neutron densities. The transfer functions (TFs) are built analytically by expressing the linear response of the objective function (e.g., χ2) to small perturbations of the data. Using the TF formalism, we are able to analyze the expected overall uncertainty—quantified in terms of bias and variance—of the mean square radius and interior density of 48 Ca and 208 Pb. Using relativistic mean field models as a proxy for the weak-charge density—and assuming that a total of five measurements could be performed on the weak form factor of 48 Ca and 208 Pb—we identify the optimal models and experimental locations that minimize the uncertainty in the extraction of the radius and interior density. We also explore the use of the TF formalism to understand the influence of prior distributions for the model parameters, as well as the optimization of model hyperparameters not constrained by the data. Here,we establish how the choice of experimental locations and the model that is used can have a significant impact on the final uncertainties of the extracted quantities of interest. For challenging experiments such as CREX and PREX, a proper quantification of such uncertainties is critical. We have demonstrated how the TF formalism provides several advantages for this type of analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Seasonal And Regional Differentiation Of Bio-Optical Properties Within The North Polar Atlantic

Using data collected during spring and summer seasons in the north polar Atlantic we examined the variability of the spectral absorption, a(lambda), and backscattering, b(sub b)(lambda), coefficients of surface waters and its relation to phytoplankton pigment concentration and composition. For a given chlorophyll a concentration (TChla), the concentrations of photosynthetic carotenoids (PSC), photoprotective carotenoids (PPC), and total accessory pigments (AP) were consistently lower in spring than in summer. The chlorophyll-specific absorption coefficients of phytoplankton and total particulate matter were also lower in spring, which can be partly attributed to lower proportions of PPC, PSC, and AP in spring. The spring values of the green-to-blue band ratio of the absorption coefficient were higher than the summer ratios. The blue-to-green ratios of backscattering coefficient were also higher in spring. The higher b(sub b) values and lower blue-to-green b(sub b) ratios in summer were likely associated with higher concentrations of detrital particles in summer compared to spring. Because the product of the green-to-blue absorption ratio and the blue-to-green backscattering ratio is a proxy for the blue-to-green ratio of remote-sensing reflectance, we conclude that the performance of ocean color band-ratio algorithms for estimating pigments in the north polar Atlantic is significantly affected by seasonal shifts in the relationships between absorption and TChla as well as between backscattering and TChla. Intriguingly, however, fairly good estimate of the particulate beam attenuation coefficient at 660 nm (potential measure of total particulate matter or particulate organic carbon concentration) can be obtained by applying a single blue-to-green band-ratio algorithm for both spring and summer seasons.

Stramska, Malgorzata↗