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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2019

Structures of neurexophilin–neurexin complexes reveal a regulatory mechanism of alternative splicing

Neurexins are presynaptic, cell-adhesion molecules that specify the functional properties of synapses via interactions with trans-synaptic ligands. Neurexins are extensively alternatively spliced at six canonical sites that regulate multifarious ligand interactions, but the structural mechanisms underlying alternative splicing-dependent neurexin regulation are largely unknown. Here, we determined high-resolution structures of the complex of neurexophilin-1 and the second laminin/neurexin/sex-hormone-binding globulin domain (LNS2) of neurexin-1 and examined how alternative splicing at splice site #2 (SS2) regulates the complex. Our data reveal a unique, extensive, neurexophilin–neurexin binding interface that extends the jelly-roll β-sandwich of LNS2 of neurexin-1 into neurexophilin-1. The SS2A insert of LNS2 augments this interface, increasing the binding affinity of LNS2 for neurexophilin-1. Taken together, our data reveal an unexpected architecture of neurexophilin–neurexin complexes that accounts for the modulation of binding by alternative splicing, which in turn regulates the competition of neurexophilin for neurexin binding with other ligands.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

Alternate-Fueled Combustion-Sector Emissions

In order to meet rapidly growing demand for fuel, as well as address environmental concerns, the aviation industry has been testing alternate fuels for performance and technical usability in commercial and military aircraft. Currently, alternate aviation fuels must satisfy MIL-DTL- 83133F(2008) (military) or ASTM D 7566- Annex(2011) (commercial) standards and are termed drop-in fuel replacements. Fuel blends of up to 50% alternative fuel blended with petroleum (JP-8), which have become a practical alternative, are individually certified on the market. In order to make alternate fuels (and blends) a viable option for aviation, the fuel must be able to perform at a similar or higher level than traditional petroleum fuel. They also attempt to curb harmful emissions, and therefore a truly effective alternate fuel would emit at or under the level of currently used fuel. This paper analyzes data from gaseous and particulate emissions of an aircraft combustor sector. The data were evaluated at various inlet conditions, including variation in pressure and temperature, fuel-to-air ratios, and percent composition of alternate fuel. Traditional JP-8+100 data were taken as a baseline, and blends of JP- 8+100 with synthetic-paraffinic-kerosene (SPK) fuel (Fischer-Tropsch (FT)) were used for comparison. Gaseous and particulate emissions, as well as flame luminosity, were assessed for differences between FT composition of 0%, 50%, and 100%. The data showed that SPK fuel (a FT-derived fuel) had slightly lower harmful gaseous emissions, and smoke number information corroborated the hypothesis that SPK-FT fuels are cleaner burning fuels.

Saxena, Nikita T.↗
NASA NTRS2013

Alternate-Fueled Combustor-Sector Emissions

In order to meet rapidly growing demand for fuel, as well as address environmental concerns, the aviation industry has been testing alternate fuels for performance and technical usability in commercial and military aircraft. In order to make alternate fuels (and blends) a viable option for aviation, the fuel must be able to perform at a similar or higher level than traditional petroleum fuel. They also attempt to curb harmful emissions, and therefore a truly effective alternate fuel would emit at or under the level of currently used fuel. This report analyzes data from gaseous and particulate emissions of an aircraft combustor sector. The data were evaluated at various inlet conditions, including variation in pressure and temperature, fuel-to-air ratios, and percent composition of alternate fuel. Traditional JP-8+100 data were taken as a baseline, and blends of JP-8+100 with synthetic-paraffinic-kerosene (SPK) fuel (Fischer-Tropsch (FT)) were used for comparison. Gaseous and particulate emissions, as well as flame luminosity, were assessed for differences between FT composition of 0, 50, and 100 percent. The data show that SPK fuel (an FT-derived fuel) had slightly lower harmful gaseous emissions, and smoke number information corroborated the hypothesis that SPK-FT fuels are cleaner burning fuels.

Saxena, Nikita T.↗
NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗
DOE OSTI2026

Tabulated data for Lignin-Derived Phenolic Compounds and Water Are Effective Cosolvents for Reductive Catalytic Fractionation

Reductive catalytic fractionation (RCF) is an effective lignin-first biorefining method to extract lignin as a stabilized oil from lignocellulosic biomass. To realize RCF at scale, process modeling has shown that minimizing the use of exogenous organic solvents is critical. To this end, here we investigate the ability of lignin-derived monomers to act as either solvents or cosolvents for RCF. We begin by examining the influence of lignin-derived aromatic compounds (4-propylguaiacol, 4-propylphenol, and propylbenzene) on RCF monomer yields and subsequently extend our analysis to mixtures of 4-propylguaiacol and either methanol or water. We demonstrate that 4-propylguaiacol is an effective solvent for lignin extraction and depolymerization during RCF, especially when used in combination with water as a cosolvent. Cosolvent mixtures of 4-propylguaiacol and water enable up to 81% lignin extraction, monomer yields up to 25 wt %, and postreaction phase separation. However, unlike methanol, water as a cosolvent fails to inhibit aromatic ring hydrogenation when conducted over Ru/C as a catalyst, potentially leading to excess hydrogen consumption in a process utilizing this approach. Nonetheless, these results suggest a promising strategy for eliminating external organic solvents from RCF by utilizing mixtures of lignin-derived compounds and water as alternative extraction solvents. This is the tabulated data for the paper

biorefining↗
NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗
NASA NTRS1997

Out-of-Core Streamline Visualization on Large Unstructured Meshes

It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms.

Ueng, Shyh-Kuang↗
DOE OSTI2018

Modeling I/O Performance Variability Using Conditional Variational Auto Encoders

Storage system performance modeling is crucial for efficient use of heterogeneous shared resources on leadership class computers. Variability in application performance, particularly variability arising from concurrent applications sharing I/O resources, is a major hurdle in the development of accurate performance models. We adopt a deep learning approach based on conditional variational auto encoders (CVAE) for I/O performance modeling, and use it to quantify performance variability. We illustrate our approach using the data collected on Edison, a production supercomputing system at the National Energy Research Scientific Computing Center (NERSC). The CVAE approach is investigated by comparing it to a previously proposed sensitivity-based Gaussian process (GP) model. We find that the CVAE model performs slightly better than the GP model in cases where training and testing data come from different applications, since CVAE can inherently leverage the whole data from multiple applications whereas GP partitions the data and builds separate models for each partition. Hence, the CVAE offers an alternative modeling approach that does not need preprocessing; it has enough flexibility to handle data from a wide variety of applications without changing the inference approach.

I/O perfornance variability↗
DOE OSTI2020

Implications of Non-Bingham Rheology

One of the key challenges now facing the US Department of Energy (DOE) is the fate of radioactive waste remaining from World War II and the Cold War which is stored underground in tanks some 75 feet in diameter and over 30 feet tall. Over time, the waste has segmented into multiple layers with sludges and slurries at the bottom with salt crust layers often at the top and liquid in between. DOE’s current official baseline plans call for remaining sludges and slurries to be removed from the tanks and converted into a more stable glass waste form. Minimizing worker exposure to radiation drives DOE to use slurry processing techniques to suspend, mobilize, transport, mix, and process the waste. Therefore, a clear and quantitative understanding of Hanford waste rheology is essential for the success of the DOE mission. Historically much of the waste has been characterized using Eugene Bingham’s model more than a century old that provides a straight line fit to higher shear rate data with the intercept suggesting a yield stress and the slope providing the consistency. Yet, Bingham fits overestimate the shear stress at a given shear rate for low to intermediate shear rates, exactly the range of shear rates typically encountered in pipe flow, where shear rates peak along the pipe wall and vanish in the center. This model produces a fictitious yield stress for some of the wastes that do not exhibit yield phenomena. While over estimating the yield stress may be prudent, safe, and conservative for some applications (e.g., pump sizing to ensure that pumps can handle yield stresses), over estimating the rheology may be inaccurate and non-conservative for other applications (e.g., eroding settled particle beds). Therefore, this paper evaluates the slurry rheology of Hanford and Savannah River wastes using a more modern rheological model that fits the full range of experimental data. Although a bias has been recognized and alternative models proposed, the magnitude of this bias and the implications for tank waste have only been qualitatively suggested. The purpose of this paper is to evaluate quantitatively implications of the poor quality of fit between a Bingham model for rheology and experimental data at modest shear rates. We first demonstrate the magnitude of the bias between the data and the Bingham extrapolation. We then evaluate quantitatively the velocity profile under laminar conditions. This analysis shows that the bias may be large (hundreds of percent or more) at modest shear rates and that modest shear rates dominate pipe velocity profiles.

Rheology, Radioactive waste↗
NASA NTRS2002

A Measurement of the Non-Thermal X-Ray Emission of the Supernova Remnant RX J1713.7-3946

The ALP grant was used to support our analysis of the RXTE, ASCA, and ROSAT data for the supernova remnant RX J 1713.7-3946 and to publicize the results of the analysis. This remnant is one of only two whose X-ray emission is dominated by non-thermal emission and that have been detected at Texas energies. Previous X-ray studies failed to find evidence of thermal X-ray emission. For the first time, we have found such evidence for emission from the center of the remnant. The results of our analysis indicate that the average density of the material into which the remnant has expanded is very low. The lack of evidence of thermal emission from the bright northwestern rim of the remnant places an extremely strong constraint on the amount of Texas emission that can be produced by the interaction of very-high energy cosmic-ray protons accelerated in the remnant with other protons in the remnant. This constraint is inconsistent with the published claim that the Texas emission from the rim is due to the decay of neutral plans produced by the collision of Texas protons with material in the remnant. We offer an alternative interpretation of the gamma-ray data which is consistent with all of the available data: the gamma-ray emission is produced by inverse Compton emission from very-high energy electrons. In summary the grant has helped us solve two mysteries about the remnant. The results of this analysis will be published soon in the Astrophysical Journal.

Source record↗
DOE OSTI2019

Federal Emergency Management Agency Mobile Housing Unit Running Gear Testing (Final Report)

The Federal Emergency Management Agency (FEMA) stages manufactured housing units (MHUs) at key locations to be used during disasters when other housing accommodations cannot be arranged for individuals or families. Due to the unpredictability of natural disasters and the limitations of the manufactured housing industry to produce large numbers of MHUs in very short periods of time, it is necessary for FEMA to store a large number of units for many months or years. With these long storage periods come degradation issues with frames, tires, bearing, and braking systems. These types of issues are also of interest to the U.S. Department of Transportation (DOT) Federal Motor Carrier Safety Administration (FMCSA) because regulated carriers normally transport these units and 49 CFR 393.75 Tires specifies tire loading restrictions for manufactured homes. To compare the running gear (tires, brakes, drums, bearings, axles, springs, and spring mounting hardware) currently specified in the 2014 Rugged Based Performance Requirements (RBPR) and the newly recommended running gear specified in the upcoming revision to the RBPR, the Oak Ridge National Laboratory (ORNL) outlined a Longevity Test Plan, which focused on the assessment of component longevity (failure rates by component type). The Test Plan also studied the forces imparted to the MHU from the running gear from interaction with the roadway for both the running gear in the 2014 RBPR and the running gear specified in the upcoming RBPR revision. Since the assessment of component longevity and failures are also issues of concern for FMCSA, both agencies collaborated to support the research presented in this report. The tests and data collection were conducted at the Navistar Proving Grounds (NPG) in New Carlisle (near South Bend), Indiana, during the May-November 2018 period. Four alternatives for the suspension system of a typical three-bedroom MHU were considered in this test. Alternative 0 was the MHU that was selected from the FEMA staging area in Selma, Alabama. Empirical data had shown that, on average, this alternative experiences one tire failure every 150 miles (or 0.0067 tire failures per mile traveled). Before testing, Alternative 0 was equipped with similar running gear hardware, although in new condition and installed following the manufacturer’s specifications. This became the Baseline Unit, or Alternative 1. Another unit, also selected from the FEMA staging area in Selma, Alabama, was equipped with better axles (i.e., axles that were rated for 7,000lbs as opposed to 6,000lbs for the Baseline Unit) and better tires. This became the Upgrade Unit, or Alternative 2. At the end of the Longevity Test, the Upgrade Unit was mounted with radial tires. This became Alternative 3.Alternative 1 (Baseline Unit) had, on average, one tire failure every 251 miles (or 0.0040 tire failures per mile traveled). So, by simply following the correct manufacturer’s specifications for the assembly of the transportation system, a reduction of 41% in the per mile tire failure was achieved. In the same test, Alternative 2 (Upgrade Unit) showed, on average, one tire failure every 728 miles traveled (or 0.0014 tire failure s per mile traveled). When the Upgrade Unit was mounted with radial tires in the last part of the test, no tire failures were observed for the 2,400 miles tested. That is, in the case of the radial tires (Alternative 3) the tire failure was less than one per 2,400 miles traveled (or less than 0.0004 tire failures per mile traveled). The data collected for the Longevity Test showed that the maximum accelerations were registered in all cases at the center of the MHU in the vertical direction. The overall maximum was registered by the Baseline Unit, Alternative 1. If that value is normalized to 100%, then the Upgrade Unit (Alternative 2) showed, on average, 81% at the same location, and Alternative 3 (radial-tire test) 59%. That is, the upgraded transportation system showed a reduction of 19% and 41% in the vertical accelerations transmitted to MHU at its center when regular and radial tires, respectively, were used. As part of the Test Protocol, NPG conducted a visual inspection of the MHUs and transportation system at the end of each test day. ORNL reviewed all the inspection reports in an attempt to determine if there was any difference in the damage that the Baseline and Upgrade MHUs showed during the test. The Upgrade Unit always showed lower accelerations transmitted to the MHU than the Baseline Unit. In most cases, these differences were statistically significant. The FEMA inspection report, conducted by a FEMA certified inspector at the end of the tests, included a shorter list of damaged items for the Baseline Unit than for the Upgrade Unit, although similar type of damages was described for both units. This may be an indication that, although lower than those of the Baseline Unit, the accelerations transmitted by the Upgrade Unit were above the threshold at which MHU elements start to fail.

42 ENGINEERING↗
DOE OSTI2018

CHARACTERIZATION OF DIRECT ADDITIVE MANUFACTURED U3Si2 SURROGATE TO PREDICT U3Si2 MICROSTRUCTURES

The purpose of this research is to perform characterization exclusively on U3Si2 surrogate materials to predict the process envelope for future work with uranium compounds, considering potential microstructural properties. The surrogate material will be synthesized using a laser energy source. This characterization work will provide new property data to aid the concept development stages of fabricating U3Si2 by means of a novel INL-designed direct additive manufacturing methodology. This process not only seeks to potentially reduce production costs, but also to shorten the current silicide fuel fabrication process. U3Si2 has been downselected as a candidate for accident-tolerant fuel for commercial light-water reactors due to its improved thermal conductivity, higher atomic density, and fuel cycle economics when compared with UO2. The research efforts on this work are performed at Idaho National Laboratory (INL) in partnership with Westinghouse Electric. The initial stage of the surrogate research will include a thermodynamic study along with physical and mechanical property analyses to assist in the downselection of surrogate materials for initial experimentation. Initial characterization analyzed the microstructural effects to determine effectiveness of laser interaction on the surrogate materials. Additional characterization via scanning electron microscopy (SEM), transmission electron microscopy (TEM), and x-ray diffraction (XRD) may predict the end product morphology when working with uranium compounds. Complementary comparative studies were performed on the surrogate end products analyzing similarities in microstructural features present on the U3Si2 fuel fabricated by means of alternate methods. This stage provided novel microstructural data on surrogate silicide fuel fabrication by means of an additive manufacturing process (laser synthesis), and was crucial to confirm the formation of surrogate silicide fuels (Hf3Si2 and Zr3Si2) employing a laser synthesis.

42 ENGINEERING↗
NASA NTRS1977

Weather assessment and forecasting

Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.

Source record↗
NASA NTRS2020

Multibody Based Digital Astronaut Dynamics Simulation

BACKGROUND: This study provides the Software, Robotics, & Simulation Division at the NASA Johnson Space Center with a verification tool for multibody dynamics simulation requiring human motion. The motivation stems from current studies of several Vibration Isolation & Stabilization(VIS)system designs that attenuate the moments and forces which would be transmitted to a spacecraft during an exercise. A multibody dynamics model for a proposed VIS was available previously[1], therefore modeling of the VIS was not needed for this work. The interest here is in creating the multibody dynamics model of an astronaut in motion which may be utilized independently or while attached to a mechanism. An existing simulation [2] that utilizes OpenSim [3,4] and an in-house multibody dynamics package (MBDyn) [5] is used in order to verify the astronaut model. The main advantage this model will have over the existing simulation is that everything will be processed in one tool. METHODS AND RESULTS: Creating the simulation required; estimation of Body Segment Inertial Parameters (BSIP),a multibody model of the human-VIS system, joint acceleration profiles, and input files for MBDyn, which is used for this analysis. The scaling factors provided by Dumas et al. [6] are utilized in estimating the BSIP. Anthropometric data are used for estimating these parameters, the Anthropometric Survey of US Army Personnel (ANSUR II) [7] was the source. The astronaut model consists of 15 bodies, 14 joints and 32 degrees of freedom, with the dynamics topology generated using the center of mass locations and anthropometric data. MBDyn has an option for prescribed joint motion (PJM), which requires joint acceleration data as input. The joint angle data is first obtained from a motion capture system and then processed through code that has been created to generate approximate joint acceleration profiles. The topology tree for the astronaut model begins at the right foot up to the pelvis where there is one branch for going down the left leg and another for the torso. The torso branch leads to branches for the arms and a leaf body for the head/neck segment. For attachment to the VIS, the heel of the right foot is connected to the VIS platform through a fixed joint, resembling a foot restraint. The left foot does not attach to the platform in order to prevent a system with a closed loop. Topology and symmetry of the astronaut model were verified through kinematic analysis. Further verification of the forces and moments transmitted to the VIS were verified against the existing simulation. There was a satisfactory level of agreement when testing a simple motion, for example, rocking back and forth. Full exercise motions are to be tested soon. The main outcome has been a novel application of MBDyn for biomechanics modeling that is now available for dynamic simulations involving human motion. The estimation of BSIP was another useful result of this study, requiring only 15 inputs for generating mass properties of a theoretical astronaut model. Expansion on this work is possible by going through an alternative route in obtaining the joint motion data. Instead of high-tech and often expensive motion capture systems, an individual may watch videos with high focus and at a slow motion for each individual segment in order to determine the initial and final time and angle for that specific degree of freedom. Synthetic trajectories may also be created if there is no video reference available.

F N Matari↗
NASA NTRS1994

Automatic generation of efficient orderings of events for scheduling applications

In scheduling a set of tasks, it is often not known with certainty how long a given event will take. We call this duration uncertainty. Duration uncertainty is a primary obstacle to the successful completion of a schedule. If a duration of one task is longer than expected, the remaining tasks are delayed. The delay may result in the abandonment of the schedule itself, a phenomenon known as schedule breakage. One response to schedule breakage is on-line, dynamic rescheduling. A more recent alternative is called proactive rescheduling. This method uses statistical data about the durations of events in order to anticipate the locations in the schedule where breakage is likely prior to the execution of the schedule. It generates alternative schedules at such sensitive points, which can be then applied by the scheduler at execution time, without the delay incurred by dynamic rescheduling. This paper proposes a technique for making proactive error management more effective. The technique is based on applying a similarity-based method of clustering to the problem of identifying similar events in a set of events.

Morris, Robert A.↗
DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NCBI’s Virus Discovery Codeathon: Building “FIVE” —The Federated Index of Viral Experiments API Index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

59 BASIC BIOLOGICAL SCIENCES↗