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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)

The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

TWO-DIMENSIONAL CELLULAR AUTOMATON MODEL FOR THE EVOLUTION OF ACTIVE REGION CORONAL PLASMAS

We study a two-dimensional cellular automaton (CA) model for the evolution of coronal loop plasmas. The model is based on the idea that coronal loops are made of elementary magnetic strands that are tangled and stressed by the displacement of their footpoints by photospheric motions. The magnetic stress accumulated between neighbor strands is released in sudden reconnection events or nanoflares that heat the plasma. We combine the CA model with the Enthalpy Based Thermal Evolution of Loops model to compute the response of the plasma to the heating events. Using the known response of the X-Ray Telescope on board Hinode, we also obtain synthetic data. The model obeys easy-to-understand scaling laws relating the output (nanoflare energy, temperature, density, intensity) to the input parameters (field strength, strand length, critical misalignment angle). The nanoflares have a power-law distribution with a universal slope of –2.5, independent of the input parameters. The repetition frequency of nanoflares, expressed in terms of the plasma cooling time, increases with strand length. We discuss the implications of our results for the problem of heating and evolution of active region coronal plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-T-491: Importance of Energy Dependent Protons Per MU Calibration Factors in IMPT Dose Calculations Using Monte Carlo Technique

Purpose: In intensity modulated proton therapy (IMPT), energy dependent, protons per monitor unit (MU) calibration factors are important parameters that determine absolute dose values from energy deposition data obtained from Monte Carlo (MC) simulations. Purpose of this study was to assess the sensitivity of MC-computed absolute dose distributions to the protons/MU calibration factors in IMPT. Methods: A “verification plan” (i.e., treatment beams applied individually to water phantom) of a head and neck patient plan was calculated using MC technique. The patient plan had three beams; one posterior-anterior (PA); two anterior oblique. Dose prescription was 66 Gy in 30 fractions. Of the total MUs, 58% was delivered in PA beam, 25% and 17% in other two. Energy deposition data obtained from the MC simulation were converted to Gy using energy dependent protons/MU calibrations factors obtained from two methods. First method is based on experimental measurements and MC simulations. Second is based on hand calculations, based on how many ion pairs were produced per proton in the dose monitor and how many ion pairs is equal to 1 MU (vendor recommended method). Dose distributions obtained from method one was compared with those from method two. Results: Average difference of 8% in protons/MU calibration factors between method one and two converted into 27 % difference in absolute dose values for PA beam; although dose distributions preserved the shape of 3D dose distribution qualitatively, they were different quantitatively. For two oblique beams, significant difference in absolute dose was not observed. Conclusion: Results demonstrate that protons/MU calibration factors can have a significant impact on absolute dose values in IMPT depending on the fraction of MUs delivered. When number of MUs increases the effect due to the calibration factors amplify. In determining protons/MU calibration factors, experimental method should be preferred in MC dose calculations. Research supported by National Cancer Institute grant P01CA021239.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-E-11: Novel Matching Module for Respiration-Gated Motion Tumor of Cone-Beam Computed Tomography (CBCT) to 4DCT

Purpose: Four dimensional computed tomography (4DCT) scans reliably record whole respiratory phase and generate internal target volumes (ITV) for radiotherapy planning. However, image guiding with cone-beam computed tomography (CBCT) cannot acquire all or specific respiratory phases. This study was designed to investigate the correlation between average CT and Maximum Intensity Projection (MIP) from 4DCT and CBCT. Methods: Retrospective respiratory gating were performed by GE Discovery CT590 RT. 4DCT and CBCT data from CRIS Dynamic Thorax Phantom with simulated breathing mode were analyzed. The lung tissue equivalent material encompassed 3 cm sphere tissue equivalent material. Simulated breathing cycle period was set as 4 seconds, 5 seconds and 6 seconds for representing variation of patient breathing cycle time, and the sphere material moved toward inferior and superior direction with 1 cm amplitude simulating lung tumor motion during respiration. Results: Under lung window, the volume ratio of CBCT scans to ITVs derived from 10 phases average scans was 1.00 ± 0.02, and 1.03 ± 0.03 for ratio of CBCT scans to MIP scans. Under abdomen window, the ratio of CBCT scans to ITVs derived from 10 phases average scans was 0.39 ± 0.06, and 0.06 ± 0.00 for ratio of CBCT scans to MIP scans. There was a significant difference between lung window Result and abdomen window Result. For reducing image guiding uncertainty, CBCT window was set with width 500 and level-250. The ratio of CBCT scans to ITVs derived from 4 phases average scans with abdomen window was 1.19 ± 0.02, and 1.06 ± 0.01 for ratio of CBCT to MIP scans. Conclusion: CBCT images with suitable window width and level can efficiently reduce image guiding uncertainty for patient with mobile tumor. By our setting, we can match motion tumor to gating tumor location on planning CT more accurately neglecting other motion artifacts during CBCT scans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Accelerating lattice gauge theory studies with Agentic AI

Lattice gauge theory research, with its computationally intensive simulations and complex multi‑stage workflows, is well positioned to benefit from agentic AI systems. We demonstrate how such tools can support key components of lattice gauge theory research, including novel simulation code development using standard LQCD frameworks, HPC job orchestration, simulation data analysis, and expert‑guided tuning of algorithmic parameters such as Hasenbusch mass preconditioning and multigrid solvers. Our results show that agentic AI can reduce manual effort, improve productivity, and accelerate the research cycle while maintaining essential human oversight.

Ayyar, Venkitesh [Fermilab]↗
DOE OSTI2022

Modern Trends in Neutron Scattering Instrument Technologies

This article reviews some current trends that can be observed in the development of neutron scattering instrument technologies. While the number of neutron scattering facilities worldwide and the number of beam days they offer are largely stable, their scientific impact is increasing through improving instrumental capabilities, new and more versatile instruments, and more efficient data collection protocols. Neutron beams are becoming smaller but more intense, and instruments are being designed to utilize more ‘useful’ neutrons in unit time. This article picks and discusses a few recent developments in the areas of integrated source and instrument design, use of computational tools, new detectors, and experiment automation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

BAYESIAN CALIBRATION OF SAFETY CODES USING DATA FROM SEPARATE-AND INTEGRAL EFFECTS TESTS

Large-scale system codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. In order to be able to use the results of these simulation codes with confidence, it is important to learn how the uncertainty on the values of these parameters affects the output of the codes. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the safety code, and thereby improves its support for decision-making. Modern analysis capabilities afford very significant improvements on classical ways of doing calibration, and the work reported here implements some of those improvements. The key innovation has come from development of safety code surrogate model (code emulator) construction and prediction algorithms. A surrogate is needed for calibration of plant-scale simulation codes because the multivariate nature of the problem (i.e., the need to adjust multiple uncertain parameters at once to fit multiple pieces of new information) calls for multiple evaluations of performance, which, for a computation-intensive model, makes calibration very computation-intensive. Use of a fast surrogate makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. Moreover, most traditional surrogates do not provide uncertainty information along with their predictions, but the Gaussian Process (GP) based code surrogates used here do. This improves the soundness of the code calibration process. Results are demonstrated on a simplified scenario with data from Separate and Integral Effect Tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Mathematical modelling the pathway of genomic instability in lung cancer

Genomic instability plays a significant role in lung cancer. Although substantial research has been conducted using both clinical and theoretical studies, it is still a hotly debated issue to whether genomic instability is necessary or whether genomic instability precedes oncogenes activation and tumor suppressor genes inactivation for lung cancer. In response to this issue, we come up with a mathematical model incorporating effects of genomic instability to investigate the genomic instability pathway of human lung cancer. The presented model are applied to match the incidence rate data of lung cancer from the Life Span Study cohort of the atomic bomb survivors in Nagasaki and Hiroshima and the Surveillance Epidemiology and End Results registry in the United States. Model results suggest that genomic instability is necessary in the tumorigenesis of lung cancer, and genomic instability has no significant impact on the net proliferation rate of cells by statistical criteria. By comparing the results of the LSS data to those of the SEER data, we conclude that the genomic instability pathway exhibits a sensitivity to radiation exposure, more intensive in male patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Rapid retrieval of first-order spatiotemporal distortions for ultrashort laser pulses

High-intensity, ultrashort laser sources are a foundational pillar for High-Energy-Density (HED) physics, and as the repetition-rates of these systems increase, diagnostics must be developed to match their data collection speed. Characterization of any spatiotemporal distortions in the laser pulse is necessary in order to standardize results across facilities. Here, we have previously developed a laser diagnostic called Spatially and Temporally Resolved Intensity and Phase Evaluation Device: Full Information from a Single Hologram, or STRIPED FISH, to measure the full spatiotemporal laser electric field on a single shot. In order to provide rapid feedback, we here adapt the STRIPED FISH retrieval algorithm, which typically has computation times of up to 30 minutes due to the high spatial resolution of the device and its retrieval of the complete four-dimensional pulse field, to distill the key electric field quantities into a handful of scalars for rapid assessment of the pulse's first order distortions. Here, our new, rapid non-iterative retrieval algorithm is validated with simulated and experimental data, and it provides, within seconds, the same first-order information as given by the full analysis. This algorithm is suitable for on-shot assessment of spatiotemporal distortions and can be adapted to assess high repetition-rate laser quality in HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Energy-Efficient and Resilient Infrastructure: Simulation, Validation, and Installation

Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.

Athena↗
DOE OSTI2020

Temperature-dependent phonon lifetimes and thermal conductivity of silicon by inelastic neutron scattering and ab initio calculations

In this paper, inelastic neutron scattering on a single crystal of silicon was performed at temperatures from 100 to 1500 K. These experimental data were reduced to obtain phonon spectral intensity at all wave vectors $\overset{→}{Q}$ and frequencies ω in the first Brillouin zone. Thermal broadenings of the phonon peaks were obtained by fitting and by calculating with an iterative ab initio method that uses thermal atom displacements on an ensemble of superlattices. Agreement between the calculated and experimental broadenings was good, with possible discrepancies at the highest temperatures. Distributions of phonon widths versus phonon energy had similar shapes for computation and experiment. These distributions grew with temperature but maintained similar shapes. Parameters from the ab initio calculations were used to obtain the thermal conductivity from the Boltzmann transport equation, which was in good agreement with experimental data. Despite the high group velocities of longitudinal acoustic phonons, their shorter lifetimes reduced their contribution to the thermal conductivity, which was dominated by transverse acoustic modes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required. To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program Title:tapir CPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1 Developer's repository link:https://gitlab.com/tapir-devs/tapir Licensing provisions: GPLv3 Programming language:python 3, C++ Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions. Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated. Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible. [1]B. Nickel, D. Meiron, G.A.J. Baker, Compilation of 2-pt and 4-pt graphs for continuous spin model, Report, University of Guelph, 1977.[2]A. Pak, J. Phys. Conf. Ser. 368 (2012) 012049, https://doi.org/10.1088/1742-6596/368/1/012049, arXiv:1111.0868.[3]B. Ruijl, T. Ueda, J. Vermaseren, FORM version 4.2, arXiv:1707.06453, 7 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

SU-E-T-572: Normal Lung Tissue Sparing in Radiation Therapy for Locally Advanced Non-Small Cell Lung Cancer

Purpose: To compare normal lung-sparing capabilities of three advanced radiation therapy techniques for locally advanced non-small cell lung cancer (LA-NSCLC). Methods: Four-dimensional computed tomography (4DCT) was performed in 10 patients with stage IIIb LA-NSCLC. The internal target volume (ITV); planning target volume (PTV); and organs at risks (OARs) such as spinal cord, total normal lung, heart, and esophagus were delineated for each CT data set. Intensity-modulated radiation therapy (IMRT), Tomohelical-IMRT (TH-IMRT), and TomoDirect-IMRT (TD-IMRT) plans were generated (total prescribed dose, 66 Gy in 33 fractions to the PTV) for each patient. To reduce the normal lung dose, complete and directional block function was applied outside the normal lung far from the target for both TH-IMRT and TD-IMRT, while pseudo- OAR was set in the same region for IMRT. Dosimetric characteristics of the three plans were compared in terms of target coverage, the sparing capability for the OAR, and the normal tissue complication probability (NTCP). Beam delivery efficiency was also compared. Results: TH-IMRT and TD-IMRT provided better target coverage than IMRT plans. Lung volume receiving ≥–30 Gy, mean dose, and NTCP were significant with TH-IMRT than with IMRT (p=0.006), and volume receiving ≥20–30 Gy was lower in TD-IMRT than in IMRT (p<0.05). Compared with IMRT, TH-IMRT had better sparing effect on the spinal cord (Dmax, NTCP) and heart (V45) (p<0.05). NTCP for the spinal cord, V45 and V60 for the heart, and Dmax for the esophagus were significantly lower in TD-IMRT than in IMRT. The monitor units per fraction were clearly smaller for IMRT than for TH-IMRT and TD-IMRT (p=0.006). Conclusion: In LA-NSCLC, TH-IMRT gave superior PTV coverage and OAR sparing compared to IMRT. TH-IMRT provided better control of the lung volume receiving ≥5–30 Gy. The delivery time and monitor units were lower in TD-IMRT than in TH-IMRT.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗