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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
NASA NTRS2019

Evaluation of Classifier Complexity for Delay Tolerant Network Routing

The growing popularity of small cost effective satellites (SmallSats, CubeSats, etc.) creates the potential for a variety of new science applications involving multiple nodes functioning together or independently to achieve a task, such as swarms and constellations. As this technology develops and is deployed for missions in Low Earth Orbit and beyond, the use of delay tolerant networking (DTN) techniques may improve communication capabilities within the network. In this paper, a network hierarchy is developed from heterogeneous networks of SmallSats, surface vehicles, relay satellites and ground stations which form an integrated network. There is a tradeoff between complexity, flexibility, and scalability of user defined schedules versus autonomous routing as the number of nodes in the network increases. To address these issues, this work proposes a machine learning classifier based on DTN routing metrics. A framework is developed which will allow for the use of several categories of machine learning algorithms (decision tree, random forest and deep learning) to be applied to a dataset of historical network statistics, which allows for the evaluation of algorithm complexity versus performance to be explored. We develop the emulation of a hierarchical network, consisting of tens of nodes which form a cognitive network architecture. CORE (Common Open Research Emulator) is used to emulate the network using bundle protocol and DTN IP neighbor discovery.

Dudukovich, Rachel↗
NASA NTRS1988

Neural networks with hierarchically correlated patterns

The Hopfield model of neural networks is extended to allow for the storage and retrieval of hierarchically correlated patterns. The overlaps between these patterns form an ultrametric tree. Intermediate states, which serve as ancestors for the following levels, are generated at each level of the tree. The states belonging to each level are stored, by a modified learning rule, in a series of identical networks, one for each level. The retrieval of a particular pattern is preceded and assisted by the successive retrieval of its ancestors. The performance of this scheme is studied analytically and numerically.

Gutfreund, H.↗
DOE OSTI2022

ACORN Review: NPK fertilizer use in loblolly pine plantations: Who are we really feeding?

Optimizing loblolly pine (Pinus taeda L.) productivity using fertilizers and various site management practices has been a goal of foresters for decades. Nitrogen (N), phosphorus (P), and potassium (K) are the three most operationally applied fertilizers to loblolly pine silviculture and are of primary importance to their total productivity. Fertilizer recommendations for N, P, and K in loblolly pine are primarily made on abiotic factors such as site and soil characteristics, while the biological factors controlling nutrient uptake are typically overlooked in the production and optimization of these stands. Arguably the most important of these biological factors are the diverse ectomycorrhizal fungal (ECM) communities that colonize the fine roots of almost all loblolly pine trees. The mantle formed by ECM fungi on short-root tips presents a barrier for direct apoplastic uptake of N, P, and K from soil solution by pine roots. In well-colonized roots, the tree is dependent on symplastic fungal transport of N, P, and K foraged from the soil by the extraradical hyphal network. This raises the question: Who are we really feeding if the ECM fungi are the ones assimilating most of the tree's total nutritional requirements? Considering multiple species of ECM fungi can inhabit a single root system, many questions remain regarding the drivers of colonization, why some species are more efficient at taking up and exchanging nutrients with their hosts than others, and why certain fertilizers directly affect the morphology of ECM growth. The purposes of this review here are (1) to explore how the most commonly commercially applied macronutrients, N, P, and K, affect the relationship between loblolly pine and ECM communities, and (2) to propose future directions to investigate, preserve, and manipulate these interactions in pine plantations to optimize productivity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Efficient Probabilistic Diagnostics for Electrical Power Systems

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Mengshoel, Ole J.↗
DOE OSTI2024

Graph Analytics on Jellyfish topology

Because large unstructured datasets is important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the “best” network topologies for irregular communication, unstructured graph-based interconnects can be more suitable. We analyze three popular graph workloads – clustering, pattern enumeration, and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, varying the routing algorithms. Using packet-level simulations, we demonstrate up to 60% improvement in communication time with Jellyfish due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.

Graph Analytics, network topology, interconnect, H↗
DOE OSTI2021

Living on the edge: A continental-scale assessment of forest vulnerability to drought

Globally, forests are facing an increasing risk of mass tree mortality events associated with extreme droughts and higher temperatures. Hydraulic dysfunction is considered a key mechanism of drought-triggered dieback. By leveraging the climate breadth of the Australian landscape and a national network of research sites (Terrestrial Ecosystem Research Network), we conducted a continental-scale study of physiological and hydraulic traits of 33 native tree species from contrasting environments to disentangle the complexities of plant response to drought across communities. Here, we found strong relationships between key plant hydraulic traits and site aridity. Leaf turgor loss point and xylem embolism resistance were correlated with minimum water potential experienced by each species. Across the data set, there was a strong coordination between hydraulic traits, including those linked to hydraulic safety, stomatal regulation and the cost of carbon investment into woody tissue. These results illustrate that aridity has acted as a strong selective pressure, shaping hydraulic traits of tree species across the Australian landscape. Hydraulic safety margins were constrained across sites, with species from wetter sites tending to have smaller safety margin compared with species at drier sites, suggesting trees are operating close to their hydraulic thresholds and forest biomes across the spectrum may be susceptible to shifts in climate that result in the intensification of drought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗
DOE OSTI2020

Site-level Foliar C, N, delta13C data from samples collected during field survey associated with NEON AOP survey, East River, CO 2018

Carbon (C) and nitrogen (N) weight percent concentrations were obtained from the bulk foliar samples collected across East River, Washington Gulch, Slate River, and Coal Creek watersheds in Gunnison, Colorado during the summer of 2018. These samples were collected from sun-lit leaves of meadow, shrub, and tree vegetation types within 72 hours of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) hyperspectral survey. These samples were dried and then run on an elemental analyzer coupled to a mass spectrometer for C and N concentrations and delta13C. The intention of these data were to provide ground truth data to develop foliar nutrient models across the imaging spectroscopy domain. Additional associated site metadata and vegetation fractional cover information can be found in the associated data releases (DOI: 10.15485/1618130). Here we provide site level C, N, and delta 13C data in the CN_Results_Foliar.csv. All of these samples were also registered with IGSN codes, and the details from that registration with metadata for the samples is included in foliar_sample_igsn_metadata.csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗
NASA NTRS1984

Augmenting computer networks

Three methods of augmenting computer networks by adding at most one link per processor are discussed: (1) A tree of N nodes may be augmented such that the resulting graph has diameter no greater than 4log sub 2((N+2)/3)-2. Thi O(N(3)) algorithm can be applied to any spanning tree of a connected graph to reduce the diameter of that graph to O(log N); (2) Given a binary tree T and a chain C of N nodes each, C may be augmented to produce C so that T is a subgraph of C. This algorithm is O(N) and may be used to produce augmented chains or rings that have diameter no greater than 2log sub 2((N+2)/3) and are planar; (3) Any rectangular two-dimensional 4 (8) nearest neighbor array of size N = 2(k) may be augmented so that it can emulate a single step shuffle-exchange network of size N/2 in 3(t) time steps.

Bokhari, S. H.↗
DOE OSTI2021

Guaranteed Phase & Topology Identification in Three Phase Distribution Grids

We present a method for joint phase identification and topology recovery in unbalanced three phase radial networks using only voltage measurements. By recovering phases and topology jointly, we utilize all three phase voltage measurements and can handle networks where some buses have a subset of three phases. Our method is theoretically justified by a novel linearized formulation of unbalanced three phase power flow and makes precisely defined and reasonable assumptions on line impedances and load statistics. We validate our method on three IEEE test networks simulated under realistic conditions in OpenDSS, comparing our performance to the state of the art. In addition to providing a new method for phase and topology recovery, our intuitively structured linearized model will provide a foundation for future work in this and other applications.

42 ENGINEERING↗
DOE OSTI2022

Phylogenomic analysis of the hemp family (Cannabaceae) reveals deep cyto‐nuclear discordance and provides new insights into generic relationships

Abstract Cannabaceae are a relatively small family of angiosperms, but they include several species of huge economic and cultural significance: marijuana or hemp ( Cannabis sativa ) and hops ( Humulus lupulus ). Previous phylogenetic studies have clarified the most deep relationships in Cannabaceae, but relationships remain ambiguous among several major lineages. Here, we sampled 82 species representing all genera of Cannabaceae and utilized a new dataset of 90 nuclear genes and 82 chloroplast loci from Hyb‐Seq to investigate the phylogenomics of Cannabaceae. Nuclear phylogenetic analyses revealed a robust and consistent backbone for Cannabaceae. We observed nuclear gene‐tree conflict at several deep nodes in inferred species trees, also cyto‐nuclear discordance concerning the relationship between Gironniera and Lozanella and the relationships among Trema s.l. (including Parasponia ), Cannabis + Humulus , and Chaetachme + Pteroceltis . Coalescent simulations and network analyses suggest that observed deep cyto‐nuclear discordances were most likely to stem from incomplete lineage sorting (ILS); nuclear gene‐tree conflict might be caused by both ILS and gene flow between species. All genera of Cannabaceae were recovered as monophyletic, except for Celtis , which consisted of two distinct clades: Celtis I (including most Celtis species) and Celtis II (including Celtis gomphophylla and Celtis schippii ). We suggest that Celtis II should be recognized as the independent genus Sparrea based on both molecular and morphological evidence. Our work provides the most comprehensive and reliable phylogeny to date for Cannabaceae, enabling further exploration of evolutionary patterns across this family and highlighting the necessity of comparing nuclear with chloroplast data to examine the evolutionary history of plant groups.

Plant Sciences↗
NASA NTRS2002

Topology for efficient information dissemination in ad-hoc networking

In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

network topology↗
DOE OSTI2022

Context matters: Natural tree mortality can lead to neighbor growth release or suppression

Where competition suppresses tree growth, mortality of adjacent trees can release the surviving individuals, leading to a growth increase. However, primary forests are complicated systems, where trees interact in both competitive and facilitative ways mediated by their size, species, and the broad ecological context in which they grow. Thus, the magnitude and even direction of growth responses to the mortality of nearby trees may vary, which has implications for our understanding of community- and ecosystem-level dynamics following mortality events. Unfortunately, although many studies focus on the impacts of light availability and general crowding on tree growth, we know relatively little about the effects of naturally occurring mortality events on the growth of neighboring trees. To address this issue, we used 40 years of data from 15 permanent forest-monitoring plots in Mt Rainier old-growth forests, comparing observed to expected radial growth of individual trees following the death of their nearest neighbor. Although we found evidence of a general growth-release response, this was not universal among all trees, with small trees in particular exhibiting growth suppression (rather than release) following neighboring tree mortality. In addition to small size, growth-suppression was more likely if the dead neighbor was the same species, consistent with facilitative effects as mediated through belowground networks. At the stand level, the average growth release after nearest neighbor mortality was greatest in low-density stands with large trees, with elevation and community composition also playing a role. Decades more monitoring could reveal how long growth release (or suppression) is sustained by individual trees following neighboring mortality events, as well as potential response lags and the role of species identity in determining whether interactions with neighbors are competitive or facilitative. Nonetheless, our results suggest that although mature trees have competitive effects on their larger neighbors, they also have an important role in supporting the ingrowth of small trees. More broadly, we demonstrate that the nature of interactions between individual neighboring trees is highly context dependent.

54 ENVIRONMENTAL SCIENCES↗