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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Using SpF to Achieve Petascale for Legacy Pseudospectral Applications

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community. We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. Highlevel abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely inprocessor. The granularity of domain decomposition provided by SpF is only constrained by the datalocality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures. In this presentation, we will describe our experience in porting legacy pseudospectral models, MoSST and DYNAMO, to use SpF as well as present preliminary performance results provided by the improved scalability.

DYNAMO↗
DOE OSTI2020

Testing H 0 in Acoustic Dark Energy with Planck and ACT Polarization

The canonical acoustic dark energy model (cADE), which is based on a scalar field with a canonical kinetic term that rapidly converts potential to kinetic energy around matter radiation equality, alleviates the Hubble tension found in ΛCDM. Here, we show that it successfully passes new consistency tests in the CMB damping tail provided by the ACT data, while being increasingly constrained and distinguished from alternate mechanisms by the improved CMB acoustic polarization data from Planck. The best fit cADE model to a suite of cosmological observations, including the SH0ES H 0 measurement, has H 0 = 70.25 compared with 68.23 (km s –1 Mpc –1 ) in ΛCDM and a finite cADE component is preferred at the 2.8σ level. The ability to raise H0 is now mainly constrained by the improved Planck acoustic polarization data, which also plays a crucial role in distinguishing cADE from the wider class of early dark energy models. ACT and Planck TE polarization data are currently mildly discrepant in normalization and drive correspondingly different preferences in parameters. Improved constraints on intermediate scale polarization approaching the cosmic variance limit will be an incisive test of the acoustic dynamics of these models and their alternatives.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Bridging the Gap between Earth Science and Students: An Integrated Approach using NASA Earth Science Climate Data

Under the auspices of the Department of Education's No Child Left Behind (NCLB) Act, beginning in 2007 students will be tested in the science area. There are many techniques that educators can employ to teach students science. The use of authentic materials or in this case authentic data can be an engaging alternative to more traditional methods. An Earth science classroom is a great place for the integration of authentic data and science concepts. The National Aeronautics and Space Administration (NASA) has a wealth of high quality Earth science data available to the general public. For instance, the Atmospheric Science Data Center (ASDC) at NASA s Langley Research Center houses over 800 Earth science data sets related to Earth's radiation budget, clouds, aerosols and tropospheric chemistry. These data sets were produced to increase academic understanding of the natural and anthropogenic factors that influence global climate; however, a major hurdle in using authentic data is the size of the data and data documentation. To facilitate the use of these data sets for educational purposes, the Mentoring and inquirY using NASA Data on Atmospheric and Earth science for Teachers and Amateurs (MY NASA DATA) project has been established to systematically support educational activities at all levels of formal and informal education. The MY NASA DATA project accomplishes this by reducing these large data holdings to microsets that are easily accessible and explored by K-12 educators and students though the project's Web page. MY NASA DATA seeks to ease the difficulty in understanding the jargon-heavy language of Earth science. This manuscript will show how MY NASA DATA provides resources for NCLB implementation in the science area through an overview of the Web site, the different microsets available, the lesson plans and computer tools, and an overview of educational support mechanisms.

Alston, Erica J.↗
NASA NTRS2011

Long-Term Changes in Light Scattering in Chesapeake Bay Inferred from Secchi Depth, Light Attenuation, and Remote Sensing Measurements

The relationship between the Secchi depth (Z(sub SD)) and the diffuse attenuation coefficient for photosynthetically active radiation (K(sub d)(PAR)), and in particular the product of the two, Z(sub SD) X K(sub d)(PAR), is governed primarily by the ratio of light scattering to absorption. We analyzed measurements of Z(sub SD) and K(sub d)(PAR) at main stem stations in Chesapeake Bay and found that the Z(sub SD) X K(sub d)(PAR) product has declined at rates varying from 0.020 to 0.033 /yr over the 17 to 25 years of measurement, implying that there has been a long -term increase in the scattering-to-absorption ratio. Remote sensing reflectance at the green wavelength most relevant to Z(sub SD) and K(sub d)(PAR) in these waters, R(sub rs)(555), did not exhibit an increasing trend over the 10 years of available measurements. To reconcile the observations we constructed a bio-optical model to calculate Z(sub SD), K(sub d)(PAR), Z(sub SD) X K(sub d)(PAR), and R(sub rs)(555) as a function of light attenuating substances and their mass-specific absorption and scattering coefficients. When simulations were based exclusively on changes in concentrations of light attenuating substances, a declining trend in Z(sub SD) E K(sub d) entailed an increasing trend in R(sub rs)(555), contrary to observations. To simulate both decreasing Z(sub SD) X K(sub d)(PAR) and stationary R(sub rs)(555), it was necessary to allow for a declining trend in the ratio of backscattering to total scattering. Within our simulations, this was accomplished by increasing the relative proportion of organic detritus with high mass-specific scattering and low backscattering ratio. An alternative explanation not explicitly modeled is an increasing tendency for the particulate matter to occur in large aggregates. Data to discriminate between these alternatives are not available.

Gallegos, Charles L.↗
DOE OSTI2025

A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS

The recent development of the physics-aware Mass-Conserving Long Short-Term Memory network (MC-LSTM) provides an alternative to other data-driven Deep Learning (DL) models in hydrology. Mass-Conserving Long Short-Term Memory incorporates mass conservation directly into the LSTM architecture. Despite the theoretical advancements, studies have reported a surprisingly limited performance of the MC-LSTM in streamflow simulation. We hypothesize that such a limitation is due to the unrealistic mass conservation scheme in MC-LSTM, which overlooks unobserved incoming water fluxes beyond precipitation. As an attempt to verify this hypothesis, we propose a Mass Conservation Relaxed LSTM (MCR-LSTM), which incorporates a bi-directional mass relaxation (MR) component to account for potential incoming water fluxes beyond precipitation. We train and test the proposed MCR-LSTM model across 531 watersheds in the contiguous United States (CONUS) against three baseline models: the Sacramento Soil Moisture Accounting, LSTM, and MC-LSTM. Our results show that MCR-LSTM outperforms MC-LSTM despite its underperformance compared to LSTM. Specifically, MCR-LSTM's advantage over MC-LSTM is mainly seen in the Plains and Western U.S., where the newly incorporated MR component better simulates water loss and suggests the likely existence of additional incoming water fluxes beyond precipitation, respectively. The novelty and contribution of this study are twofold: firstly, it introduces an alternative physics-aware DL tool (i.e., MCR-LSTM) in hydrology with higher accuracy in specific regions compared to MC-LSTM. Secondly, it provides a diagnosis of regions where strict, precipitation-based mass conservation constraints may be unrealistic in streamflow simulation.

deep learning↗
NASA NTRS1997

Regulation of body fluid volume and electrolyte concentrations in spaceflight

Despite a number of difficulties in performing experiments during weightlessness, a great deal of information has been obtained concerning the effects of spaceflight on the regulation of body fluid and electrolytes. Many paradoxes and questions remain, however. Although body mass, extracellular fluid volume, and plasma volume are reduced during spaceflight and remain so at landing, the changes in total body water are comparatively small. Serum or plasma sodium and osmolality have generally been unchanged or reduced during the spaceflight, and fluid intake is substantially reduced, especially during the first of flight. The diuresis that was predicted to be caused by weightlessness, has only rarely been observed as an increased urine volume. What has been well established by now, is the occurrence of a relative diuresis, where fluid intake decreases more than urine volume does. Urinary excretion of electrolytes has been variable during spaceflight, but retention of fluid and electrolytes at landing has been consistently observed. The glomerular filtration rate was significantly elevated during the SLS missions, and water and electrolyte loading tests have indicated that renal function is altered during readaptation to Earth's gravity. Endocrine control of fluid volumes and electrolyte concentrations may be altered during weightlessness, but levels of hormones in body fluids do not conform to predictions based on early hypotheses. Antidiuretic hormone is not suppressed, though its level is highly variable and its secretion may be affected by space motion sickness and environmental factors. Plasma renin activity and aldosterone are generally elevated at landing, consistent with sodium retention, but inflight levels have been variable. Salt intake may be an important factor influencing the levels of these hormones. The circadian rhythm of cortisol has undoubtedly contributed to its variability, and little is known yet about the influence of spaceflight on circadian rhythms. Atrial natriuretic peptide does not seem to play an important role in the control of natriuresis during spaceflight. Inflight activity of the sympathetic nervous system, assessed by measuring catecholamines and their metabolites and precursors in body fluids, generally seems to be no greater than on Earth, but this system is usually activated at landing. Collaborative experiments on the Mir and the International Space Station should provide more of the data needed from long-term flights, and perhaps help to resolve some of the discrepancies between U.S. and Russian data. The use of alternative methods that are easier to execute during spaceflight, such as collection of saliva instead of blood and urine, should permit more thorough study of circadian rhythms and rapid hormone changes in weightlessness. More investigations of dietary intake of fluid and electrolytes must be performed to understand regulatory processes. Additional hormones that may participate in these processes, such as other natriuretic hormones, should be determined during and after spaceflight. Alterations in body fluid volume and blood electrolyte concentrations during spaceflight have important consequences for readaptation to the 1-G environment. The current assessment of fluid and electrolyte status during weightlessness and at landing and our still incomplete understanding of the processes of adaptation to weightlessness and readaptation to Earth's gravity have resulted in the development of countermeasures that are only partly successful in reducing the postflight orthostatic intolerance experienced by astronauts and cosmonauts. More complete knowledge of these processes can be expected to produce countermeasures that are even more successful, as well as expand our comprehension of the range of adaptability of human physiologic processes.

NASA Discipline Number 18-10↗
DOE OSTI2024

Alternating conduction and convection drying of paper – an experimental analysis with a continuous data acquisition approach

In conventional multi-cylinder drying of paper and board, both conductive drying from steam-heated dryer cylinders and convective drying by flowing air over the paper surface in the pockets are used. Conductive drying from steam-heated drying cylinders is a critical component in providing the necessary thermal energy to paper and board as they dry. Steam temperature and internal and external resistances at the contacting surface are critical process parameters influencing the conductive drying process. An experimental setup was developed to study the alternating conductive and convective drying of paper and board. Paper sheet moisture, temperature, and temperature distribution within the heated platen and the instantaneous heat flux as the sheet was being dried were measured. The instantaneous heat flux, contact heat transfer coefficient, and drying rates were determined as drying proceeds. Experimental results, as well as comparisons to literature and commercial data, are presented. The conductive heat transfer coefficients determined were compared to traditional correlations normally used in the modeling of paper drying. Similarly, the convective heat and mass transfer coefficients are also determined and compared to literature data. In addition to the evaluation of alternating conductive and convective drying characteristics of paper and board, the potential inclusion of auxiliary energy components will also be included. Experimental results from the conduction and convection drying system are presented. Furthermore, this data will be useful in process development, intensification of manufacturing processes, and modeling and simulation of paper drying processes.

42 ENGINEERING↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Life-Cycle Greenhouse Gas Emissions and Human Health Trade-Offs of Organic Waste Management Strategies

Waste-to-energy systems can play an important role in diverting organic waste from landfills. However, real-world waste management can differ from idealized practices, and emissions driven by microbial communities and complex chemical processes are poorly understood. This study presents a comprehensive life-cycle assessment, using reported and measured data, of competing management alternatives for organic municipal solid waste including landfilling, composting, dry anaerobic digestion (AD) for the production of renewable natural gas (RNG), and dry AD with electricity generation. Landfilling is the most greenhouse gas (GHG)-intensive option, emitting nearly 400 kg CO 2e per tonne of organic waste. Composting raw organics resulted in the lowest GHG emissions, at -41 kg CO 2e per tonne of waste, while upgrading biogas to RNG after dry AD resulted in -36 to -2 kg CO 2e per tonne. Monetizing the results based on social costs of carbon and other air pollutant emissions highlights the importance of ground-level NH3 emissions from composting nitrogen-rich organic waste or post-AD solids. However, better characterization of material-specific NH 3 emissions from landfills and land-application of digestate is essential to fully understand the trade-offs between alternatives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Defect-driven anomalous transport in fast-ion conducting solid electrolytes

We report solid-state ionic conduction is a key enabler of electrochemical energy storage and conversion. The mechanistic connections between material processing, defect chemistry, transport dynamics, and practical performance are of considerable importance, but remain incomplete. Here, inspired by studies of fluids and biophysical systems, we re-examine anomalous diffusion in the iconic two-dimensional fast-ion conductors, the β- and β"-aluminas. Using large-scale simulations, we reproduce the frequency dependence of alternating-current ionic conductivity data. We show how the distribution of charge-compensating defects, modulated by processing, drives static and dynamic disorder, which lead to persistent sub-diffusive ion transport at macroscopic timescales. We deconvolute the effects of repulsions between mobile ions, the attraction between the mobile ions and charge-compensating defects, and geometric crowding on ionic conductivity. Our quantitative framework based on these model solid electrolytes connects their atomistic defect chemistry to macroscopic performance with minimal assumptions and enables mechanism-driven 'atoms-to-device' optimization of fast-ion conductors.

25 ENERGY STORAGE↗
DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hierarchical top-down bottom-up calibration with consideration for uncertainty and inter-scale discrepancy of Peierls stress of bcc Fe

A hierarchical multiscale model of plasticity in single crystal bcc Fe, framed at the mesoscale pertaining to a statistically representative set of dislocations for each slip system, relies on specification of a set of parameters which are informed using both bottom-up (BU) (atomistic) and top-down (TD) (experimental) information. We term this specification process a 'connection' between BU and TD pathways to inform the mesoscale model. The connection is considered in the presence of error, uncertainty, and discrepancy between the models. Furthermore, we expand upon a previously developed reconciled TD and BU calibration method to account for anticipated discrepancy between information from different length scales. The results of a previously formulated likelihood-based connection test of the multiscale model suggest a 'missing link' may be responsible for part of the inter-scale discrepancy. In this case, this link is assumed to be a relation between the unit-process (single dislocation line) of coordinated kink-pair nucleation on a screw dislocation segment and the many-body dislocation process which manifests in the onset of experimentally observed plastic deformation in a single crystal. A physics-informed discrepancy layer is formulated to improve the connection in the presence of additional persistent uncertainties. This physics-informed hypothesis testing is demonstrated as an alternative to fully data-driven search methods, particularly applicable to datasets of limited size that are often encountered in such multiscale material modeling applications. Discrete dislocation dynamics simulations are used to investigate the effect of the Peierls stress on straight screw dislocations and in the operation of Frank–Read sources. Discussion concerns the critical role of uncertainty quantification in providing a basis for this form of incremental hypothesis testing.

42 ENGINEERING↗
DOE OSTI2016

Squeezing the halo bispectrum: a test of bias models

We study the halo-matter cross bispectrum in the presence of primordial non-Gaussianity of the local type. We restrict ourselves to the squeezed limit, for which the calculation are straightforward, and perform the measurements in the initial conditions of N-body simulations, to mitigate the contamination induced by nonlinear gravitational evolution. Interestingly, the halo-matter cross bispectrum is not trivial even in this simple limit as it is strongly sensitive to the scale-dependence of the quadratic and third-order halo bias. Therefore, it can be used to test biasing prescriptions. We consider three different prescription for halo clustering: excursion set peaks (ESP), local bias and a model in which the halo bias parameters are explicitly derived from a peak-background split. In all cases, the model parameters are fully constrained with statistics other than the cross bispectrum. We measure the cross bispectrum involving one halo fluctuation field and two mass overdensity fields for various halo masses and collapse redshifts. We find that the ESP is in reasonably good agreement with the numerical data, while the other alternatives we consider fail in various cases. This suggests that the scale-dependence of halo bias also is a crucial ingredient to the squeezed limit of the halo bispectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An Alternative Timing and Synchronization Approach for Situational Awareness and Predictive Analytics

Accurate and synchronized timing information is required by power system operators for controlling the grid infrastructure (relays, Phasor Measurement Units (PMUs), etc.) and determining asset positions. Satellite-based global positioning system (GPS) is the primary source of timing information. However, GPS disruptions today (both intentional and unintentional) can significantly compromise the reliability and security of our electric grids. A robust alternate source for accurate timing is critical to serve both as a deterrent against malicious attacks and as a redundant system in enhancing the resilience against extreme events that could disrupt the GPS network. To achieve this, we rely on the highly accurate, terrestrial atomic clock-based network for alternative timing and synchronization. In this paper, we discuss an experimental setup for an alternative timing approach. The data obtained from this experimental setup is continuously monitored and analyzed using various time deviation metrics. We also use these metrics to compute deviations of our clock with respect to the National Institute of Standards and Technologys (NIST) GPS data. The results obtained from these metric computations are elaborately discussed. Finally, we discuss the integration of the procedures involved, like real-time data ingestion, metric computation, and result visualization, in a novel microservices-based architecture for situational awareness.

Chinthavali, Supriya↗
DOE OSTI2020

Scalable Asynchronous Domain Decomposition Solvers

We discuss how parallel implementations of linear iterative solvers generally alternate between phases of data exchange and phases of local computation. Increasingly large problem sizes and more heterogeneous compute architectures make load balancing and the design of low latency network interconnects that are able to satisfy the communication requirements of linear solvers very challenging tasks. In particular, global communication patterns such as inner products become increasingly limiting at scale. We explore the use of asynchronous communication based on one-sided Message Passing Interface primitives in the context of domain decomposition solvers. In particular, a scalable asynchronous two-level Schwarz method is presented. We discuss practical issues encountered in the development of a scalable solver and show experimental results obtained on a state-of-the-art supercomputer system that illustrate the benefits of asynchronous solvers in load balanced as well as load imbalanced scenarios. Using the novel method, we can observe speedups of up to four times over its classical synchronous equivalent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Transcriptome Analysis of Four Arabidopsis thaliana Mediator Tail Mutants Reveals Overlapping and Unique Functions in Gene Regulation

The Mediator complex is a central component of transcriptional regulation in Eukaryotes. The complex is structurally divided into four modules known as the head, middle, tail and kinase modules, and in Arabidopsis thaliana , comprises 28-34 subunits. Here, we explore the functions of four Arabidopsis Medi-ator tail subunits, MED2, MED5a/b, MED16, and MED23, by comparing the impact of mutations in each on the Arabidopsis transcriptome. We find that these subunits affect both unique and overlapping sets of genes, providing insight into the functional and structural relationships between them. The mutants pri-marily exhibit changes in the expression of genes related to biotic and abiotic stress. We find evidence for a tissue specific role for MED23, as well as in the production of alternative transcripts. Together, our data help disentangle the individual contributions of these MED subunits to global gene expression and suggest new avenues for future research into their functions.

59 BASIC BIOLOGICAL SCIENCES↗