Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distributed algorithms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

On a theory of stability for nonlinear stochastic chemical reaction networks

We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Using Poisson-regularized inversion of Bremsstrahlung emission to extract full electron energy distribution functions from x-ray pulse-height detector data

We measured Electron Energy Distribution Functions (EEDFs) from below 200 eV to over 8 keV and spanning five orders-of-magnitude in intensity, produced in a low-power, RF-heated, tandem mirror discharge in the PFRC-II apparatus. The EEDF was obtained from the x-ray energy distribution function (XEDF) using a novel Poisson-regularized spectrum inversion algorithm applied to pulse-height spectra that included both Bremsstrahlung and line emissions. The XEDF was measured using a specially calibrated Amptek Silicon Drift Detector (SDD) pulse-height system with 125 eV FWHM at 5.9 keV. Finally, the algorithm is found to out-perform current leading x-ray inversion algorithms when the error due to counting statistics is high.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

DYNAMICALLY EXCITED OUTER SOLAR SYSTEM OBJECTS IN THE HUBBLE SPACE TELESCOPE ARCHIVE

We present the faintest mid-ecliptic latitude survey in the second part of Hubble Space Telescope archival search for outer solar system bodies. We report the discovery of 28 new trans-Neptunian objects and one small centaur (R {approx} 2 km) in the band 5 Degree-Sign -20 Degree-Sign off the ecliptic. The inclination distribution of these excited objects is consistent with the distribution derived from brighter ecliptic surveys. We suggest that the size and inclination distribution should be estimated consistently using suitable surveys with calibrated search algorithms and reliable orbital information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

i- flow: High-dimensional integration and sampling with normalizing flows

In many fields of science, high-dimensional integration is required. Numerical methods have been developed to evaluate these complex integrals. We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows. Normalizing flows are machine-learned, bijective mappings between two distributions. i-flow can also be used to sample random points according to complicated distributions in high dimensions. We compare i-flow to other algorithms for high-dimensional numerical integration and show that i-flow outperforms them for high dimensional correlated integrals. The i-flow code is publicly available on gitlab at https://gitlab.com/i-flow/i-flow.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The Architecture of Metabolism Maximizes Biosynthetic Diversity in the Largest Class of Fungi

Abstract Ecological diversity in fungi is largely defined by metabolic traits, including the ability to produce secondary or “specialized” metabolites (SMs) that mediate interactions with other organisms. Fungal SM pathways are frequently encoded in biosynthetic gene clusters (BGCs), which facilitate the identification and characterization of metabolic pathways. Variation in BGC composition reflects the diversity of their SM products. Recent studies have documented surprising diversity of BGC repertoires among isolates of the same fungal species, yet little is known about how this population-level variation is inherited across macroevolutionary timescales. Here, we applied a novel linkage-based algorithm to reveal previously unexplored dimensions of diversity in BGC composition, distribution, and repertoire across 101 species of Dothideomycetes, which are considered the most phylogenetically diverse class of fungi and known to produce many SMs. We predicted both complementary and overlapping sets of clustered genes compared with existing methods and identified novel gene pairs that associate with known secondary metabolite genes. We found that variation among sets of BGCs in individual genomes is due to nonoverlapping BGC combinations and that several BGCs have biased ecological distributions, consistent with niche-specific selection. We observed that total BGC diversity scales linearly with increasing repertoire size, suggesting that secondary metabolites have little structural redundancy in individual fungi. We project that there is substantial unsampled BGC diversity across specific families of Dothideomycetes, which will provide a roadmap for future sampling efforts. Our approach and findings lend new insight into how BGC diversity is generated and maintained across an entire fungal taxonomic class.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

True Load Balancing for Matricized Tensor Times Khatri-Rao Product

MTTKRP is the bottleneck operation in algorithms used to compute the CP tensor decomposition. For sparse tensors, utilizing the compressed sparse fibers (CSF) storage format and the CSF-oriented MTTKRP algorithms is important for both memory and computational efficiency on distributed-memory architectures. Existing intelligent tensor partitioning models assume the computational cost of MTTKRP to be proportional to the total number of nonzeros in the tensor. However, this is not the case for the CSF-oriented MTTKRP on distributed-memory architectures. We outline two deficiencies of nonzero-based intelligent partitioning models when CSF-oriented MTTKRP operations are performed locally: failure to encode processors' computational loads and increase in total computation due to fiber fragmentation. We focus on existing fine-grain hypergraph model and propose a novel vertex weighting scheme that enables this model encode correct computational loads of processors. We also propose to augment the fine-grain model by fiber nets for reducing the increase in total computational load via minimizing fiber fragmentation. In this way, the proposed model encodes minimizing the load of the bottleneck processor. In conclusion, parallel experiments with real-world sparse tensors on up to 1024 processors prove the validity of the outlined deficiencies and demonstrate the merit of our proposed improvements in terms of parallel runtimes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated nuclear cloud feature extraction from film

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2013

Commissioning of intensity modulated neutron radiotherapy (IMNRT)

Purpose: Intensity modulated neutron radiotherapy (IMNRT) has been developed using inhouse treatment planning and delivery systems at the Karmanos Cancer Center/Wayne State University Fast Neutron Therapy facility. The process of commissioning IMNRT for clinical use is presented here. Results of commissioning tests are provided including validation measurements using representative patient plans as well as those from the TG-119 test suite. Methods: IMNRT plans were created using the Varian Eclipse optimization algorithm and an inhouse planning system for calculation of neutron dose distributions. Tissue equivalent ionization chambers and an ionization chamber array were used for point dose and planar dose distribution comparisons with calculated values. Validation plans were delivered to water and virtual water phantoms using TG-119 measurement points and evaluation techniques. Photon and neutron doses were evaluated both inside and outside the target volume for a typical IMNRT plan to determine effects of intensity modulation on the photon dose component. Monitor unit linearity and effects of beam current and gantry angle on output were investigated, and an independent validation of neutron dosimetry was obtained. Results: While IMNRT plan quality is superior to conventional fast neutron therapy plans for clinical sites such as prostate and head and neck, it is inferior to photon IMRT for most TG-119 planning goals, particularly for complex cases. This results significantly from current limitations on the number of segments. Measured and calculated doses for 11 representative plans (six prostate/five head and neck) agreed to within -0.8 {+-} 1.4% and 5.0 {+-} 6.0% within and outside the target, respectively. Nearly all (22/24) ion chamber point measurements in the two phantom arrangements were within the respective confidence intervals for the quantity [(measured-planned)/prescription dose] derived in TG-119. Mean differences for all measurements were 0.5% (max = 7.0%) and 1.4% (max = 4.1%) in water and virtual water, respectively. The mean gamma pass rate for all cases was 92.8% (min = 88.6%). These pass rates are lower than typically achieved with photon IMRT, warranting development of a planar dosimetry system designed specifically for IMNRT and/or the improvement of neutron beam modeling in the penumbral region. The fractional photon dose component did not change significantly in a typical IMNRT plan versus a conventional fast neutron therapy plan, and IMNRT delivery is not expected to significantly alter the RBE. All other commissioning results were considered satisfactory for clinical implementation of IMNRT, including the external neutron dose validation, which agreed with the predicted neutron dose to within 1%. Conclusions: IMNRT has been successfully commissioned for clinical use. While current plan quality is inferior to photon IMRT, it is superior to conventional fast neutron therapy. Ion chamber validation results for IMNRT commissioning are also comparable to those typically achieved with photon IMRT. Gamma pass rates for planar dose distributions are lower than typically observed for photon IMRT but may be improved with improved planar dosimetry equipment and beam modeling techniques. In the meantime, patient-specific quality assurance measurements should rely more heavily on point dose measurements with tissue equivalent ionization chambers. No significant technical impediments are anticipated in the clinical implementation of IMNRT as described here.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Density-Equalizing Maps for Simply Connected Open Surfaces

Here in this paper, we are concerned with the problem of creating flattening maps of simply connected open surfaces in $\mathbb{R}^3$. Using a natural principle of density diffusion in physics, we propose an effective algorithm for computing density-equalizing maps with any prescribed density distribution. By varying the initial density distribution, a large variety of flattening maps with different properties can be achieved. For instance, area-preserving parameterizations of simply connected open surfaces can be easily computed. Experimental results are presented to demonstrate the effectiveness of our proposed method. Applications to data visualization and surface remeshing are explored.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Demand Response and Smart Buildings: A Survey of Control, Communication, and Cyber-Physical Security

In this paper, we perform a comprehensive survey of the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs); the power architecture with multibus characteristics; different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings; the communication technologies and network architectures; and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. Finally, the current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.

42 ENGINEERING↗
DOE OSTI2018

Investigation of an apodized imaged Hartmann wavefront sensor

Here, quantitative wavefront measurements are demonstrated using a Hartmann mask re-imaged onto a camera. The wavefront is reconstructed using standard algorithms applied to the difference of beamlet centroids determined from fluence distributions obtained for two different longitudinal locations of the mask. The wavefront of the optical wave in the object plane is measured independently of imaging-system collimation. Apodization obtained with spatially dithered distributions of small transparent or opaque pixels improves the measurement accuracy by reducing the spatial-frequency content of the mask holes. Simulations and experiments demonstrate the excellent accuracy of this diagnostic over a wide range of parameters, making it suitable, for example, to characterize laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Modeling the thermal deformation of TATB-based explosives. Part 1: Thermal expansion of “neat-pressed” polycrystalline TATB

We detail a modeling approach to simulate the anisotropic thermal expansion of polycrystalline (1,3,5-triamino-2,4,6-trinitrobenzene) TATB-based explosives that utilizes microstructural information including porosity, crystal aspect ratio, and processing-induced texture. This report, the first in a series, focuses on nonlinear thermal expansion of “neat-pressed” polycrystalline TATB specimens which do not contain any binder; additional complexities related to polymeric binder and irreversible ratcheting behavior are briefly discussed, however detailed investigation of these aspects are deferred to subsequent reports. In this work we have, for the first time, developed a mesoscale continuum model relating the thermal expansion of polycrystal TATB specimens to their microstructural characteristics. A self-consistent homogenization procedure is used to relate macroscopic thermoelastic response to the constitutive behavior of single-crystal TATB. The model includes a representation of grain aspect ratio, porosity, and crystallographic texture attributed to the consolidation process. A quantitative model is proposed to describe the evolution of preferred orientation of graphitic planes in TATB during consolidation and an algorithm constructed to develop a discrete representation of the associated orientation distribution function. Analytical and numerical solutions using this model are shown to produce textures consistent with previous measurements and characterization for isostatic and uniaxial “die-pressed” specimens. Predicted thermal strain versus temperature for textured specimens are shown to be in agreement with corresponding experimental measurements. Using the developed modeling approach, several simulations have been run to investigate the influence of microstructure on macroscopic thermal expansion behavior. Results from these simulations are used to identify qualitative trends. Implications of the identified trends are discussed in the context of thermal deformation of engineered components whose consolidation process is generally more complex than isostatic or die-pressed specimens. Finally, an envisioned application of the modeling approach to simulating thermal expansion of weapon systems and components is outlined along with necessary future work to introduce the effects of binder and ratcheting behavior. Key conclusions from this work include the following. Both porosity and grain aspect ratio have an influence on the thermal expansion of polycrystal TATB considering realistic material variability. Thepreferred orientation of the single crystal TATB [001] poles within a polycrystal gives rise to pronounced anisotropy of the macroscopic thermal expansion. The extent of this preferred orientation depends on the magnitude of deformation, and consequently, is expected to vary spatially throughout manufactured components much like porosity. The modeling approach presented here has utility toward bringing spatially variable microstructural features into macroscale system engineering modelsAbstract Not Provided

36 MATERIALS SCIENCE↗
DOE OSTI2014

Clustering Method and Representative Feeder Selection for the California Solar Initiative

The screening process for DG interconnection procedures needs to be improved in order to increase the PV deployment level on the distribution grid. A significant improvement in the current screening process could be achieved by finding a method to classify the feeders in a utility service territory and determine the sensitivity of particular groups of distribution feeders to the impacts of high PV deployment levels. This report describes the utility distribution feeder characteristics in California for a large dataset of 8,163 feeders and summarizes the California feeder population including the range of characteristics identified and most important to hosting capacity. The report describes the set of feeders that are identified for modeling and analysis as well as feeders identified for the control group. The report presents a method for separating a utility's distribution feeders into unique clusters using the k-means clustering algorithm. An approach for determining the feeder variables of interest for use in a clustering algorithm is also described. The report presents an approach for choosing the feeder variables to be utilized in the clustering process and a method is identified for determining the optimal number of representative clusters.

14 SOLAR ENERGY↗
DOE OSTI2019

Estimation and uncertainty quantification for the output from quantum simulators

The problem of estimating certain distributions over {0, 1} d is considered here. The distribution represents a quantum system of d qubits, where there are non-trivial dependencies between the qubits. A maximum entropy approach is adopted to reconstruct the distribution from exact moments or observed empirical moments. The Robbins Monro algorithm is used to solve the intractable maximum entropy problem, by constructing an unbiased estimator of the un-normalized target with a sequential Monte Carlo sampler at each iteration. In the case of empirical moments, this coincides with a maximum likelihood estimator. A Bayesian formulation is also considered in order to quantify uncertainty a posteriori. Several approaches are proposed in order to tackle this challenging problem, according to recently developed methodologies. In particular, unbiased estimators of the gradient of the log posterior are constructed and used within a provably convergent Langevin-based Markov chain Monte Carlo method. The methods are illustrated on classically simulated output from quantum simulators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Relative entropy minimization over Hilbert spaces via Robbins-Monro

One way of getting insight into non-Gaussian measures is to first obtain good Gaussian approximations. These best fit Gaussians can then provide a sense of the mean and variance of the distribution of interest. They can also be used to accelerate sampling algorithms. This begs the question of how one should measure optimality, and how to then obtain this optimal approximation. Here, we consider the problem of minimizing the distance between a family of Gaussians and the target measure with respect to relative entropy, or Kullback-Leibler divergence. As we are interested in applications in the infinite dimensional setting, it is desirable to have convergent algorithms that are well posed on abstract Hilbert spaces. We examine this minimization problem by seeking roots of the first variation of relative entropy, taken with respect to the mean of the Gaussian, leaving the covariance fixed. We prove the convergence of Robbins-Monro type root finding algorithms in this context, highlighting the assumptions necessary for convergence to relative entropy minimizers. Numerical examples are included to illustrate the algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis: Preprint

High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. This paper will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generatingcoherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one-minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km.

14 SOLAR ENERGY↗