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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 OSTI2017

Is the 80 - 20 Principle a Friend or a Foe? Critical Remarks on the Dominant Flat Gradient - 17206

The well-known 80 - 20 principle, also known as the Pareto principle, describes the fact that usually 80 % of the result is achieved by 20 % of the effort, because many probability distributions are caused by a power-law. This principle is valued in the realm of project management and management in general because of the fast progress which generally takes place in the first fifth of the process. If the final result is not exactly specified or resources are spent wisely, 100 % of the planned result will be reached easily. However, in nuclear business safety is a principal parameter. Therefore precision is inevitable. In long-lasting projects the bad end of the Pareto-distribution is dominant: The very shallowly-inclined gradient at the end of the graph means that results at that point come at a very high effort and cost. The graph reaches the 100% only asymptomatically, meaning the initially projected results are never met. Especially if in earlier project phases the Pareto principle is applied very successfully, a lot of work is transferred to the future. For example, in decommissioning projects the waste often is badly characterized causing an enormous effort in the process of characterization and qualification. Especially if funding or time are limited, it is important to be aware of the asymmetric distribution of applied work and result. The 80 - 20 principle is a great advantage, if you start a project and respect the asymmetric distribution of applied resources and results in your project plan. Especially in long-lasting projects with changing perspectives, this asymmetric distribution may cause massive delays and expansion of costs, because small changes near the end cause large/big effort due to consequences on the existing data. If the proper goal of a project is not entirely known in the beginning, it is very probable that the last 5 - 10 % of the result will cause major financial threat and extensions of the estimated time frame. If funding or time are limited, it is almost impossible to readjust a project in the estimated frame of resources. In this case a complete restart of the project may be more reasonable than a long-lasting change management with uncertain results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

On the use of graph search techniques for the analysis of extreme-scale combustion simulation data

With the continuous increase in high performance computing capabilities, simulations are becoming ever larger and more complex, using bigger domains, tracking more variables, and producing more time steps. This increase in the ranges of spatial and temporal simulation scales results in data that presents significant challenges to as well as new opportunities for the visualization and data analysis community. For example, highly-localized, intermittent events (such as the formation of ignition kernels in turbulent combustion) may be caused by interactions between multiple variables across a series of time steps, making both their definition and their extraction difficult, particularly at scale. This paper introduces an intuitive framework to support the identification, characterization, and tracking of such complex, multivariate, temporally evolving events in large-scale simulations. In a pre-processing step, we use topological techniques to create a hierarchical family of feature definitions for each variable of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns in the ARG that characterize interesting events. We demonstrate the power of this approach by analyzing a large-scale turbulent combustion simulation.

McLendon, W. C.↗
DOE OSTI2018

When managers forage for pests: Implementing the functional response in pest management

In this work, we explore how the functional response framework can be implemented in pest management. Here, managers take the role of predators foraging on pests and facing monetary costs for survey and control in a spatial domain where the pest distribution and control strategy do not have to be random. To investigate this framework quantitatively, we simulated various management processes on different pest spatial distributions using a spatially-explicit individual-based model and Monte-Carlo simulations, and also confirmed some of the results analytically. By graphing the number of pests controlled versus pest density, we obtained management functional response curves. Whether the management functional response was shaped like a type I, type II or type III functional response depended on the management costs and the search area. Yet, the management spatial strategy and the pest spatial distribution had little effect on the functional response. We applied our model to the management of mountain pine beetle epidemic in Cypress Hills, Saskatchewan, Canada, with simulations matching the real number of attacked trees controlled by managers. We showed how to make an analogy between functional responses in predator–prey interactions and in human–pest interactions and thereby, apply insights from the functional response framework to pest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications

Traditional physical layer protocols (e.g. WiFi, WiMax, etc.) are well established and are often optimal in a wide variety of channel conditions including heterogenous links and in tactical communications. Unfortunately, this same optimality encourages ubiquity in wireless communication technology and enhances the potential for catastrophic cyber or physical attacks due to prolific knowledge of underlying physical layers. Any truly resilient communications protocol must be capable of immediate redeployment to meet quality of service (QoS) demands in a wide variety of possible channel media. This work proposes an approach to communications that is contrary to much traditional approaches in that processing blocks are generated real-time and only relevant to the particular channel medium being used. Rather than creating man-made ubiquitous blocks of signal processing, we examine using processing that is immediately expendable once it has been used. This is achieved through software-defined radios, and deep modulation, where system blocks are replaced with machine learning graphs that can be trained, used, and then discarded as needed. Simulation and experimental hardware show how deep modulation can converge to viable communications links, using the same machine intelligence, in vastly different channels.

Anderson, Adam L↗
DOE OSTI2025

Clustering at Massive Scale

ClaMS provides hierarchical clustering technology for use on massive, high-dimensional datasets that require distributed memory for processing. The algorithm employed is inspired by the popular HDBSCAN algorithm but makes use of computational kernels better suited for distributed computing. ClaMS is built on scalable nearest neighbor graph construction, metric forest completion, and approximate minimum spanning tree techniques.

Stanley, ThomasA [Lawrence Livermore National Labo↗
DOE OSTI2019

Development of Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

Ambient mass spectrometry is being investigated as a new method for rapidly characterizing trace inorganic species, including nuclear materials. SRNL is currently developing electrospray ionization (ESI) and paper spray ionization (PSI) methods for mass spectrometry for the detection of trace inorganic compounds JEOL AccuTOF™-DART ® LP 4G mass spectrometer. Sample analysis generates a complex three-dimensional data set with 10 2 -10 3 individual mass spectra spread temporally. To improve the quality and ease of data extraction from this amount of information, a simple data analysis pipeline written in R/R Studio has been improved and expanded upon. Specifically, the software now has header inputs to set base input/output filenames, isotopes of interest, and a list of concentrations analyzed on the instrument. Using list-based structures to enable batch processing of many concentrations at once, it then loads each file, extracts the relevant intensities for masses of interest from each spectrum, and iterates through each concentration to generate graphs of internal standard & analyte correlations, measured isotope ratios, and total ion chromatograms. It then filters the data to remove points at background levels or at the detector saturation limit. Using this filtered data, the pipeline re-creates the graphs to reveal high-quality information from a low-quality raw dataset. All graphs of the R software then output to PowerPoint format. Additionally, faster 3D visualization functionality was added to the pipeline for viewing the entire data file produced by the instrument as an intensity map. With these data analysis tools ready, samples of strontium nitrate/yttrium nitrate as an analyte/internal standard combination were measured on the AccuTOF. From the filtered data, calibration curves were prepared and limits of detection (LOD) of 1 ppb for ESI-MS and 20 ppb for PSI-MS were obtained. The quality available from the calibration curves and the low limits of detection demonstrate the value of the instrument/analysis pipeline system in characterizing trace inorganics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

BBMap: A Fast, Accurate, Splice-Aware Aligner

Alignment of reads is one of the primary computational tasks in bioinformatics. Of paramount importance to resequencing, alignment is also crucial to other areas - quality control, scaffolding, string-graph assembly, homology detection, assembly evaluation, error-correction, expression quantification, and even as a tool to evaluate other tools. An optimal aligner would greatly improve virtually any sequencing process, but optimal alignment is prohibitively expensive for gigabases of data. Here, we will present BBMap [1], a fast splice-aware aligner for short and long reads. We will demonstrate that BBMap has superior speed, sensitivity, and specificity to alternative high-throughput aligners bowtie2 [2], bwa [3], smalt, [4] GSNAP [5], and BLASR [6].

09 BIOMASS FUELS↗
DOE OSTI2018

Managing Rich Metadata in High-Performance Computing Systems Using a Graph Model

High-performance computing (HPC) systems generate huge amounts of metadata about different entities such as jobs, users, and files. Existing systems can efficiently record and manage part of these metadata, mainly the POSIX metadata of data files (e.g., file size, name, and permissions mode). But another important set of metadata, referred to as "rich" metadata in this study, which record not only wider range of entities (e.g., running processes and jobs) but also more complex relationships between them, are mostly missing in current HPC systems. Yet such rich metadata are critical for supporting many advanced data management functions such as identifying data sources and parameters behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. To uniformly and efficiently manage the rich metadata generated in HPC systems, We propose to utilize a graph model in this study. We identify the key challenges of implementing such a graph-based HPC rich metadata management system and present GraphMeta, a graph-based rich metadata management system designed and optimized for HPC platforms, to tackle these challenges. In conclusion, extensive evaluations on both synthetic and real HPC metadata workloads show its advantages in both performance and scalability compared with existing solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

rNets: a standalone package to visualize reaction networks

In the study of chemical processes, visualizing reaction networks is pivotal for identifying crucial compounds and transformations. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity. Alternatives capable of leading with complexity include graph methods, but they are not user-friendly, lacking simplicity and modularity, which hinders their integration with widely-used research software. This work introduces rNets an innovative tool designed for the efficient visualization of reaction networks with a user-friendly interface, modularity, and seamless integration with existing software packages. The effectiveness of rNets is demonstrated through its application in analyzing three catalytic reactions, showcasing its potential to significantly enhance research both in homogeneous and heterogeneous catalysis fields. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.

Pablo-García, Sergio↗
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2014

Monomer-dimer problem on random planar honeycomb lattice

We consider the monomer-dimer (MD) problem on a random planar honeycomb lattice model, namely, the random multiple chain. This is a lattice system with non-periodic boundary condition, whose generating process is inspired by the growth of single walled zigzag carbon nanotubes. By applying algebraic and combinatorial techniques we establish a calculating expression of the MD partition function for bipartite graphs, which corresponds to the permanent of a matrix. Further, by using the transfer matrix argument we show that the computing problem of the permanent of high order matrix can be converted into some lower order matrices for this family of lattices, based on which we derive an explicit recurrence formula for evaluating the MD partition function of multiple chains and random multiple chains. Finally, we analyze the expectation of the number of monomer-dimer arrangements on a random multiple chain and the asymptotic behavior of the annealed MD entropy when the multiple chain becomes infinite in width and length, respectively.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

MAGNETIC VARIANCES AND PITCH-ANGLE SCATTERING TIMES UPSTREAM OF INTERPLANETARY SHOCKS

Recent observations of power-law time profiles of energetic particles accelerated at interplanetary shocks have shown the possibility of anomalous, superdiffusive transport for energetic particles throughout the heliosphere. Those findings call for an accurate investigation of the magnetic field fluctuation properties at the resonance frequencies upstream of the shock's fronts. Normalized magnetic field variances, indeed, play a crucial role in the determination of the pitch-angle scattering times and then of the transport regime. The present analysis investigates the time behavior of the normalized variances of the magnetic field fluctuations, measured by the Ulysses spacecraft upstream of corotating interaction region (CIR) shocks, for those events which exhibit superdiffusion for energetic electrons. We find a quasi-constant value for the normalized magnetic field variances from about 10 hr to 100 hr from the shock front. This rules out the presence of a varying diffusion coefficient and confirms the possibility of superdiffusion for energetic electrons. A statistical analysis of the scattering times obtained from the magnetic fluctuations upstream of the CIR events has also been performed; the resulting power-law distributions of scattering times imply long range correlations and weak pitch-angle scattering, and the power-law slopes are in qualitative agreement with superdiffusive processes described by a Levy random walk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs performed on hundreds of thousands of processors generating trillions of nonzeros in the output matrix. Distributed SpGEMM at this extreme scale faces two key challenges: (1) high communication cost and (2) inadequate memory to generate the output. Furthermore, we address these challenges with an integrated communication-avoiding and memory-constrained SpGEMM algorithm that scales to 262,144 cores (more than 1 million hardware threads) and can multiply sparse matrices of any size as long as inputs and a fraction of output fit in the aggregated memory. As we go from 16,384 cores to 262,144 cores on a Cray XC40 supercomputer, the new SpGEMM algorithm runs 10x faster when multiplying large-scale protein-similarity matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NuGraph2 with explainability: post-hoc explanations for geometric neural network predictions

With the growing popularity of artificial intelligence (AI) used for scientific applications, the ability of attribute a result to a reasoning process from the network is in high demand for robust scientific generalizations to hold. In this work we aim to motivate the need for and demonstrate the use of post-hoc explainability methods when applied to AI methods used in scientific applications. To this end, we introduce explainability add-ons to the existing graph neural network (GNN) for neutrino tagging, NuGraph2. The explanations take the form of a suite of techniques examining the output of the network (node classifications) and the edge connections between them, and probing of the latent space using novel general-purpose tools applied to this network. We show how none of these methods are singularly sufficient to show network ‘understanding’, but together can give insights into the processes used in classification. While these methods are tested on the NuGraph2 application, they can be applied to a broad range of networks, not limited to GNNs. The code for this work is publicly available on GitHub at https://github.com/voetberg/XNuGraph.

Voetberg, Margaret [Fermilab] (ORCID:0009000527154↗
DOE OSTI2013

Theory of magnetic field line random walk in noisy reduced magnetohydrodynamic turbulence

When a magnetic field consists of a mean part and fluctuations, the stochastic wandering of its field lines is often treated as a diffusive process. Under suitable conditions, a stable value is found for the mean square transverse displacement per unit parallel displacement relative to the mean field. Here, we compute the associated field line diffusion coefficient for a highly anisotropic 'noisy' reduced magnetohydrodynamic model of the magnetic field, which is useful in describing low frequency turbulence in the presence of a strong applied DC mean magnetic field, as may be found, for example, in the solar corona, or in certain laboratory devices. Our approach is nonperturbative, based on Corrsin's independence hypothesis, and makes use of recent advances in understanding factors that control decorrelation over a range of parameters described by the Kubo number. Both Bohm and quasilinear regimes are identified.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Neural Networks and Graph Algorithms with Next-Generation Processors

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

Hamilton, Kathleen↗
DOE OSTI2022

Do graph neural networks learn traditional jet substructure?

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs -- energetic particle clusters -- within a jet, when identifying jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗