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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Optical and electronic properties of mixed Ag-Au tetramer cations

We present experimental and theoretical studies of the optical response of mixed Ag {sub n} Au {sub 4−n}{sup +} (n=1–3) clusters in the photon energy range ℏω = 1.9–3.5 eV. Absorption spectra are recorded by a newly built longitudinal molecular beam depletion spectroscopy apparatus providing lower limits to absolute photodissociation cross sections. The experimental data are compared to optical response calculations in the framework of long-range corrected time-dependent density functional theory with initial cluster geometries obtained by the unbiased Birmingham Cluster Genetic Algorithm coupled with density functional theory. Experiments and excited state calculations shed light on the structural and electronic properties of the mixed Ag-Au tetramer cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

SBbadger: biochemical reaction networks with definable degree distributions

Abstract Motivation An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. Availability and implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Dynamic Programming↗
DOE OSTI2024

Collinear limit of the four-point energy correlator in N = 4 supersymmetric Yang-Mills theory

We present a compact formula, expressed in terms of classical polylogarithms up to weight three, for the leading order four-point energy correlator in maximally supersymmetric Yang-Mills theory, in the limit where the four detectors are collinear. This formula is derived by combining a simplified, manifestly dual conformal invariant form of the 1 → 4 splitting function obtained from the square of the tree-level five-particle form factor of stress-tensor multiplet operators, with a novel integration-by-parts algorithm operating directly on Feynman parameter integrals. Our results provide valuable data for exploring the structure of physical observables in perturbation theory, and for calculations of jet substructure observables in quantum chromodynamics. Published by the American Physical Society 2024

Chicherin, Dmitry (ORCID:000000028985084X)↗
DOE OSTI2015

Determination of crystallographic intensities from sparse data

X-ray serial microcrystallography involves the collection and merging of frames of diffraction data from randomly oriented protein microcrystals. The number of diffracted X-rays in each frame is limited by radiation damage, and this number decreases with crystal size. The data in the frame are said to be sparse if too few X-rays are collected to determine the orientation of the microcrystal. It is commonly assumed that sparse crystal diffraction frames cannot be merged, thereby setting a lower limit to the size of microcrystals that may be merged with a given source fluence. The EMC algorithm [Loh & Elser (2009), Phys. Rev. E , 80 , 026705] has previously been applied to reconstruct structures from sparse noncrystalline data of objects with unknown orientations [Philipp et al. (2012), Opt. Express , 20 , 13129–13137; Ayyer et al. (2014), Opt. Express , 22 , 2403–2413]. Here, it is shown that sparse data which cannot be oriented on a per-frame basis can be used effectively as crystallographic data. As a proof-of-principle, reconstruction of the three-dimensional diffraction intensity using sparse data frames from a 1.35 kDa molecule crystal is demonstrated. The results suggest that serial microcrystallography is, in principle, not limited by the fluence of the X-ray source, and collection of complete data sets should be feasible at, for instance, storage-ring X-ray sources.

Chemistry↗
DOE OSTI2014

24.77 Pflops on a Gravitational Tree-Code to Simulate the Milky Way Galaxy with 18600 GPUs, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

We have simulated, for the first time, the long term evolution of the Milky Way Galaxy using 51 billion particles on the Swiss Piz Daint supercomputer with our N-body gravitational tree-code Bonsai. Herein, we describe the scientific motivation and numerical algorithms. The Milky Way model was simulated for 6 billion years, during which the bar structure and spiral arms were fully formed. This improves upon previous simulations by using 1000 times more particles, and provides a wealth of new data that can be directly compared with observations. We also report the scalability on both the Swiss Piz Daint and the US ORNL Titan. On Piz Daint the parallel efficiency of Bonsai was above 95%. The highest performance was achieved with a 242 billion particle Milky Way model using 18600 GPUs on Titan, thereby reaching a sustained GPU and application performance of 33.49 Pflops and 24.77 Pflops respectively.

Computer Science↗
DOE OSTI2019

Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This work develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Furthermore, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗
DOE OSTI2013

Joint surface reconstruction and 4D deformation estimation from sparse data and prior knowledge for marker-less Respiratory motion tracking

Purpose: The intraprocedural tracking of respiratory motion has the potential to substantially improve image-guided diagnosis and interventions. The authors have developed a sparse-to-dense registration approach that is capable of recovering the patient's external 3D body surface and estimating a 4D (3D + time) surface motion field from sparse sampling data and patient-specific prior shape knowledge.Methods: The system utilizes an emerging marker-less and laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is recovered, which describes the spatio-temporal 4D deformation of the complete patient body surface, depending on the type and state of respiration. It yields both a reconstruction of the instantaneous patient shape and a high-dimensional respiratory surrogate for respiratory motion tracking. The method is validated on a 4D CT respiration phantom and evaluated on both real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured-light scanner.Results: In the experiments, the authors estimated surface motion fields with the proposed algorithm on 256 datasets from 16 subjects and in different respiration states, achieving a mean surface reconstruction accuracy of ±0.23 mm with respect to ground truth data—down from a mean initial surface mismatch of 5.66 mm. The 95th percentile of the local residual mesh-to-mesh distance after registration did not exceed 1.17 mm for any subject. On average, the total runtime of our proof of concept CPU implementation is 2.3 s per frame, outperforming related work substantially.Conclusions: In external beam radiation therapy, the approach holds potential for patient monitoring during treatment using the reconstructed surface, and for motion-compensated dose delivery using the estimated 4D surface motion field in combination with external-internal correlation models.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

MO-F-CAMPUS-T-02: Optimizing Orientations of Hundreds of Intensity-Modulated Beams to Treat Multiple Brain Targets

Purpose: To investigate a new modulated beam orientation optimization (MBOO) approach maximizing treatment planning quality for the state-of-the-art flattening filter free (FFF) beam that has enabled rapid treatments of multiple brain targets. Methods: MBOO selects and optimizes a large number of intensity-modulated beams (400 or more) from all accessible beam angles surrounding a patient’s skull. The optimization algorithm was implemented on a standalone system that interfaced with the 3D Dicom images and structure sets. A standard published data set that consisted of 1 to 12 metastatic brain tumor combinations was selected for MBOO planning. The planning results from various coplanar and non-coplanar configurations via MBOO were then compared with the results obtained from a clinical volume modulated arc therapy (VMAT) delivery system (Truebeam RapidArc, Varian Oncology). Results: When planning a few number of targets (n<4), MBOO produced results equivalent to non-coplanar multi-arc VMAT planning in terms of target volume coverage and normal tissue sparing. For example, the 12-Gy and 4-Gy normal brain volumes for the 3-target plans differed by less than 1 mL ( 3.0 mLvs 3.8 mL; and 35.2 mL vs 36.3 mL, respectively) for MBOO versus VMAT. However, when planning a larger number of targets (n≥4), MBOO significantly reduced the dose to the normal brain as compared to VMAT, though the target volume coverage was equivalent. For example, the 12-Gy and 4-Gy normal brain volumes for the 12-target plans were 10.8 mL vs. 18.0 mL and 217.9 mL vs. 390.0 mL, respectively for the non-coplanar MBOO versus the non-coplanar VMAT treatment plans, yielding a reduction in volume of more than 60% for the case. Conclusion: MBOO is a unique approach for maximizing normal tissue sparing when treating a large number (n≥4) of brain tumors with FFF linear accelerators. Dr Ma and Dr Sahgal are currently on the board of international society of stereotactic radiosurgery. Dr Sahgal has received support for educational presentations from Elekta company.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-TeP1-05: Fast Processed 3D Printing-Aided Urethane Resin (PUR) Bolus in Radiation Therapy

Purpose: 3D printed custom bolus is regularly used in radiation therapy clinic as a compensator. However, usual method of bolus printing with 100% filling is very time-consuming. The purpose of this study is to evaluate the feasibility and benefit of 3D printed bolus filled with UR. Methods: Two boluses were designed on nose (9e electrons) and ear (6× photons) for a head phantom in treatment planning system (TPS) to achieve dose coverage to the skin. The bolus structures (56–167cc) were converted to STereoLithographic (STL) model using an in-house developed algorithm and sent to a commercial fused deposition modeling (FDM) printer. Only shells were printed with polylactic acid (PLA) material. Liquid UR was then placed in a vacuum pump and slowly poured into the hollow bolus from its top opening. Liquid UR hardened in around half an hour. The phantom was rescanned with custom boluses attached and the dosimetry was compared with original design in TPS. Basic CT and dose properties were investigated. GaF films were irradiated to measure dose profile and output of several open photon and electron beams under solid water and UR slabs of same thicknesses. Results: CT number was 11.2±7.3 and 65.4±7.8, respectively for solid water(∼1.04g/cc) and UR(∼1.08g/cc). The output measurement at dmax for 6× was within 2% for the two materials. The relative dose profiles of the two materials above dmax show 94–99% Gamma analysis passing rates for both photons and electrons. Dose distributions with 3D PUR boluses maintained great coverage on the intended skin regions and resembled that with computer generated boluses. Manufacturing 3D PUR boluses was 3–4 times faster than 100% printed boluses. The efficiency significantly improves for larger boluses. Conclusion: The study suggests UR has similar dose responses as solid water. Making custom bolus with UR greatly increases clinical workflow efficiency.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Accelerating GNNs on GPU Sparse Tensor Cores through N:M Sparsity-Oriented Graph Reordering

Recent advancements in GPU hardware support have introduced the capability to leverage N:M sparse patterns for substantial performance gains. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is often irregular, not conforming to such sparse patterns. In this paper, we propose a novel graph reordering algorithm, the first of its kind, to reshape irregular graph data into the N:M structured sparse pattern at the tile level, allowing linear-algebra-based graph operations in GNNs to benefit from the N:M sparse hardware. The optimization is lossless, maintaining the accuracy of GNN. It can remove 98-100\% violations of the N:M sparse patterns at the vector level, and increase the proportion of conforming graphs in SuiteSparse collection from 5-9\% to 88.7-93.5\%. On A100 GPUs, the optimization accelerates Sparse Matrix Matrix (SpMM) by up to 43X (2.3X -- 7.5X on average) and speeds up the key graph operations in GNNs on real graphs by as much as 8.6X (3.5X on average).

artificial intelligence, graph neural networks↗
DOE OSTI2022

Graph-Env

The Graph-Env library provides a framework for adapting graph search problems into OpenAI Gym environments for reinforcement learning. In other words, Graph-Env enables reinforcement learning algorithms to be applied to graph search problems. Graph search problems include molecule and crystal structure design problems; route planning problems, including traveling salesperson problem; puzzles and games, such as chess and go; shortest path problems; minimum spanning tree; vehicle path generation; and others.

Tripp, Charles↗
DOE OSTI2024

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗
DOE OSTI2013

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models

Background: Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. Methods: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. Results: We applied our method to ID 50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis. Conclusions: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

PeleLMeX: an AMR Low Mach Number Reactive Flow Simulation Code without level sub-cycling

PeleLMeX simulates chemically reacting low Mach number flows with block-structured adaptive mesh refinement (AMR). The code is built upon the AMReX library, which provides the underlying data structures and tools to manage and operate on them across massively parallel computing architectures. PeleLMeX algorithmic features are inherited from its predecessor PeleLM but key improvements allow representation of more complex physical processes. Together with its compressible flow counterpart PeleC, the thermo-chemistry library PelePhysics and the multi-physics library PeleMP, it forms the Pele suite of open-source reactive flow simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MCNPs Easy Sources for (α,n) (MESA):Verification with Sources4c

MESA is intended to be a direct re-implementation of the algorithms documented in the Sources manual in C++. However, some fundamental changes to the computational structure and logic were made to avoid the use of goto, require consistency in input definition, and reduce computational approximations in later steps of three-layer problems. In addition, MESA uses α-decay information from ISC libraries (based on ENDF). In most cases, differences between Sources4c and MESA are dominated by differences in decay energy spectra and intensities. To verify this we have reproduced 6 problems that are documented examples or samples in Sources4c. These problems cover the three types of problems MESA currently supports: homogeneous, interface, and three-layer.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗