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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 OSTI2018

Profiling and Predicting Application Performance on the Cloud

Cloud providers continue to expand and diversify their collection of leasable resources to meet the needs of an increasingly wide range of applications. While this flexibility is a key benefit of the cloud, it also creates a complex landscape in which users are faced with many resource choices for a given application. Suboptimal selections can both degrade performance and increase costs. Given the rapidly evolving pool of resources, it is infeasible for users alone to select instance types; instead, automated methods are needed to simplify and guide resource provisioning. Here we present a method for the automatic prediction of application performance on arbitrary cloud instances. We combine offline and online profiling approaches, using historical data gathered from non-cloud environments and targeted profiling runs on cloud environments to create a composite application model that can predict run times on a given cloud instance type for a given input data size. We demonstrate average error of 17.2% across nine applications used in production bioinformatics workflows. Finally, we evaluate an experiment design approach to explore the trade-off between the cost of profiling and the accuracy of our models. Using this approach, with no prior knowledge, we show that using 4 selectively chosen experiments we can achieve performance within 30% of a model trained using all instance types.

Cloud profiling↗
DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗
DOE OSTI2022

Multireference Methods are Realistic and Useful Tools for Modeling Catalysis

Abstract Highly correlated systems, in particular those that include transition metals, are ubiquitous in catalysis. The significant static correlation found in such systems is often poorly accounted for using Kohn Sham density functional theory methods, as they are single determinantal in nature. Applications to catalysis of more rigorous and appropriate multiconfigurational methods have been reported in select instances, but their use remains rare. We discuss obstacles that hinder the routine application of multireference (MR) wave function theoretical calculations to catalytic systems and the current state of the art with respect to removing those obstacles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

LDEF polymeric materials: A summary of Langley characterization

The NASA Long Duration Exposure Facility (LDEF) enabled the exposure of a wide variety of materials to the low earth orbit (LEO) environment. This paper provides a summary of research conducted at the Langley Research Center into the response of selected LDEF polymers to this environment. Materials examined include graphite fiber reinforced epoxy, polysulfone, and additional polyimide matrix composites, films of FEP Teflon, Kapton, several experimental high performance polyimides, and films of more traditional polymers such as poly(vinyl toluene) and polystyrene. Exposure duration was either 10 months or 5.8 years. Flight and control specimens were characterized by a number of analytical techniques including ultraviolet-visible and infrared spectroscopy, thermal analysis, scanning electron and scanning tunneling microscopy, x-ray photoelectron spectroscopy, and, in some instances, selected solution property measurements. Characterized effects were found to be primarily surface phenomena. These effects included atomic oxygen-induced erosion of unprotected surfaces and ultraviolet-induced discoloration and changes in selected molecular level parameters. No gross changes in molecular structure or glass transition temperature were noted. The intent of this characterization is to increase our fundamental knowledge of space environmental effects as an aid in developing new and improved polymers for space application. A secondary objective is to develop benchmarks to enhance our methodology for the ground-based simulation of environmental effects so that polymer performance in space can be more reliably predicted.

Young, Philip R.↗
DOE OSTI2022

X 17 boson and the H 3 ( p , e + e – ) He 4 and He 3 ( n , e + e – ) He 4 processes: A theoretical analysis

The present work deals with e + – e – pair production in the four-nucleon system. We first analyze the process as a purely electromagnetic one in the context of a state-of-the-art approach to nuclear strong-interaction dynamics and nuclear electromagnetic currents, derived from chiral effective field theory ( χ EFT ) . Next, we examine how the exchange of a hypothetical low-mass boson would impact the cross section for such a process. We consider several possibilities, that this boson is either a scalar, pseudoscalar, vector, or axial particle. The ab initio calculations use exact hyperspherical-harmonics methods to describe the bound state and low-energy spectrum of the A = 4 continuum, and they fully account for initial state interaction effects in the 3 + 1 clusters. While electromagnetic interactions are treated to high orders in the chiral expansion, the interactions of the hypothetical boson with nucleons are modeled in leading-order χ EFT (albeit, in some instances, selected subleading contributions are also accounted for). We also provide an overview of possible future experiments probing pair production in the A = 4 system at a number of candidate facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Gut microbial features can predict host phenotype response to protein deficiency

Malnutrition remains a major health problem in low- and middle-income countries. During low protein intake, <0.67 g/kg/day, there is a loss of nitrogen (N 2 ) balance, due to the unavailability of amino acid for metabolism and unbalanced protein catabolism results. However, there are individuals, who consume the same low protein intake, and preserve N 2 balance for unknown reasons. A novel factor, the gut microbiota, may account for these N 2 balance differences. To investigate this, we correlated gut microbial profiles with the growth of four murine strains (C57Bl6/J, CD-1, FVB, and NIH-Swiss) on protein deficient (PD) diet. Results show that a PD diet exerts a strain-dependent impact on growth and N 2 balance as determined through analysis of urinary urea, ammonia and creatinine excretion. Bacterial alpha diversity was significantly ( P < 0.05, FDR) lower across all strains on a PD diet compared to normal chow (NC). Multi-group analyses of the composition of microbiomes (ANCOM) revealed significantly differential microbial signatures between the four strains independent of diet. However, mice on a PD diet demonstrated differential enrichment of bacterial genera including, Allobaculum (C57Bl6/J), Parabacteroides (CD-1), Turicibacter (FVB), and Mucispirillum (NIH-Swiss) relative to NC. For instance, selective comparison of the CD-1 (gained weight) and C57Bl6/J (did not gain weight) strains on PD diet also demonstrated significant pathway enrichment of dihydroorodate dehydrogenase, rRNA methyltransferases, and RNA splicing ligase in the CD-1 strains compared to C57Bl6/ J strains; which might account in their ability to retain growth despite a protein deficient diet. Taken together, these results suggest a potential relationship between the specific gut microbiota, N 2 balance and animal response to malnutrition.

Navarro, Guadalupe↗
DOE OSTI2017

Atomically Precise Membranes for the Separation of Hydrocarbons

Separations often account for a majority of process costs. This is because all traditional separation processes have inherent weaknesses that prevent the system from achieving perfect (or even near perfect in many instances) selectivity. These weaknesses result in large recycle streams and require multiple separation units in concert in order to produce a product clean enough for use or sale. An atomically precise membrane capable of allowing passage to only certain molecules would greatly improve process economics. We have synthesized atomically precise, triangular and rectangular-shaped, pore-containing macromolecules (channel molecules) using a unique, stiff, programmable molecular scaffold developed in Prof. Christian Schafmeister’s lab at Temple University. We synthesized porous triangular and rectangular macromolecules from our unique, poly-cyclic, shape-programmable, and functional group-programmable spiroligomer scaffolds. These macromolecules display functional groups pointing outward that will covalently cross-link with other functional groups on other copies of the molecule. The pores are of programmable size and shape to selectively allow some molecules and ions to pass through the pores while resisting others. These molecules were confirmed with H-NMR and HPLC/HRMS. Self-assembled layers of these molecules were fabricated with the use of a Langmuir trough where they were compressed to a mean area that is close to their corresponding macrocycle size (~0.5-1 nm 2 ). We have also demonstrated several strategies for supporting films of these selective membranes on polysulfone.

Amundsen, Ted↗
NASA NTRS1974

Segregation effects during solidification in weightless melts

The generalized problem of determining the temperature and solute concentration profiles during directional solidification of binary alloys with surface evaporation was mathematically formulated. Realistic initial and boundary conditions were defined, and a computer program was developed and checked out. The programs computes the positions of two moving boundaries, evaporation and solidification, and their velocities. Temperature and solute concentration profiles in the semiinfinite material body at selected instances of time are also computed.

Li, C.↗
NASA NTRS2000

Radar Observation of Large Attenuation in Convective Storms: Implications for the Dropsize Distribution

Airborne meteorological radars typically operate at attenuating wavelengths. The path integrated attenuation (PIA) can be estimated using the surface reference technique (SRT). In this method, an initial value is determined for the radar cross section of the earth surface in a rain-free area in relatively close proximity to the rain cloud. During subsequent observations of precipitation any decrease 'in the observed surface cross section from the reference value s assumed to be a result of the two-way attenuation along the propagation path. In this paper we present selected instances of high PIA observed over land by an airborne radar. The observations were taken in Brazil and Florida during TRMM (Tropical Rainfall Measurement Mission) field campaigns. We compared these observations with collocated and nearly simultaneous ground-based radar observations by an S-band radar that is not subject to significant attenuation. In this preliminary evaluation, a systematic difference in the attenuation in the two storms is attributed to a difference in the raindrop size distributions; this is supported by observations of ZDR (differential reflectivity).

Tian, Lin↗
NASA NTRS2016

PAH Spectroscopy: Past, Present and Future

Since their discovery in the 1970's, astronomers, astrophysicists and astrochemists have been intrigued by the nearly ubiquitous unidentified infrared emission (UIR) bands. In the 1980's, investigators determined the most probably source of these emissions was a family of molecules known as Polycyclic Aromatic Hydrocarbons or simply PAHs. In order to better understand these interstellar IR features and utilize them as chemical probes of the cosmos, laboratory spectroscopists have spent the last three decades investigating the spectroscopy of PAHs under astrophysically relevant conditions. This presentation will discuss the similarities and differences in the spectroscopic properties of PAHs as one goes from the Far to Mid to Near infrared wavelength regions and probe the changes observed in PAH spectra as they go from neutral to ionized molecules suspended in an inert gas matrix, to PAHs in a water ice matrix and as a thin film. In selected instances, the experimental results will be compared to theoretical values. The presentation will conclude with a discussion on the future directions of PAH spectroscopy.

spectroscopic properties of PAHs↗
DOE OSTI2018

Exploring Deep Learning and Sparse Matrix Format Selection

We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat; in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Enzymatic Routes to Designer Hemicelluloses for Use in Biobased Materials

Various enzymes can be used to modify the structure of hemicelluloses directly in vivo or following extraction from biomass sources, such as wood and agricultural residues. Generally, these enzymes can contribute to designer hemicelluloses through four main strategies: (1) enzymatic hydrolysis such as selective removal of side groups by glycoside hydrolases (GH) and carbohydrate esterases (CE), (2) enzymatic cross-linking, for instance, the selective addition of side groups by glycosyltransferases (GT) with activated sugars, (3) enzymatic polymerization by glycosynthases (GS) with activated glycosyl donors or transglycosylation, and (4) enzymatic functionalization, particularly via oxidation by carbohydrate oxidoreductases and via amination by amine transaminases. Thus, this Perspective will first highlight enzymes that play a role in regulating the degree of polymerization and side group composition of hemicelluloses, and subsequently, it will explore enzymes that enhance cross-linking capabilities and incorporate novel chemical functionalities into saccharide structures. These enzymatic routes offer a precise way to tailor the properties of hemicelluloses for specific applications in biobased materials, contributing to the development of renewable alternatives to conventional materials derived from fossil fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Selective Reinforcement to Enhance the Structural Performance of Metallic Compression Panels

An experimental and analytical investigation of the influence of selective reinforcement on metallic panels with cutouts was conducted. Selective reinforcement was shown to be a weight effective concept for increasing structural performance of panels with cutouts designed to carry loads into the post-buckled regime. For instance, a selectively reinforced aluminum panel under shear load exhibited a 68 percent increase in specific-buckling load as compared to a geometrically comparable unreinforced aluminum panel. In comparison, a quasi-isotropic carbon-fiber-reinforced-polymer composite panel only produced a 45 percent higher specific-buckling load than the same unreinforced aluminum panel. Selective reinforcement offers the potential to tailor structural response through local strengthening and stiffening the structure for a broad range of structural application.

Farley, Gary L.↗
DOE OSTI2022

Deep Active Learning for Solvability Prediction in Power Systems

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models. In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after all instances are labeled, active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. First, the IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments. Then, the Northeast Power Coordinating Council (NPCC) 140-bus system is used to validate the performance on large-scale power systems.

42 ENGINEERING↗
NASA NTRS1996

Satisfiability Test with Synchronous Simulated Annealing on the Fujitsu AP1000 Massively-Parallel Multiprocessor

Solving the hard Satisfiability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially difficult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchronous simulated annealing procedure, called Generalized Speculative Computation, which guarantees the same decision sequence as sequential simulated annealing. To demonstrate the performance of the parallel method, we have selected problem instances varying in size from 100-variables/425-clauses to 5000-variables/21,250-clauses. Experimental results on the AP1000 multiprocessor indicate that our approach can satisfy 99.9 percent of the clauses while giving almost a 70-fold speedup on 500 processors.

Sohn, Andrew↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗