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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development and Refinement of Bison Models for Higher Burnup Application

Electric Power Research Institute (EPRI) is an international research organization funded by organizations in the energy and government sectors. The company conducts research to tackle the energy challenges of its member organizations. In support of the U.S. nuclear industry, EPRI and Idaho National Laboratory were awarded a U.S. Department of Energy Technology Commercialization Fund to develop a loss of coolant accident (LOCA) analysis tool. This tool was developed to support burnup extension activities by supplementing the existing capabilities of operators of existing large light-water reactor plants. The tool couples INL developed codes Bison and RELAP5-3D for advanced simulation of LOCA behavior. The project consists of seven major tasks, one of which is the development and refinement of Bison models for higher burnup applications. This task is broken down into six targeted areas of development and refinement including: formation of high burnup structure, fuel fracture and pulverization, transient fission gas release, axial gas communication within a fuel rod, cladding ballooning and burst, and axial fuel relocation. This report summarizes the models for both the fuel and cladding in Bison for high burnup application, identifies the areas of development and refinement necessary to enhance the Bison capabilities to be used in the coupled simulation tool with RELAP5-3D. Integration and regression testing to demonstrate proper implementation of the new features in Bison is highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1993

Digraph reliability model processing advances and applications

This paper describes a new algorithm, called SourceDoubls, which efficiently solves for singletons and doubletons of a digraph reliability model. Compared with previous methods, the SourceDoubls algorithm provides up to a two order of magnitude reduction in the amount of time required to solve large digraph models. This significant increase in model solution speed allows complex digraphs containing thousands of nodes to be used as knowledge bases for real time automated monitoring and diagnosis applications. Currently, an application to provide monitoring and diagnosis of the Space Station Freedom Data Management System is under development at NASA/Ames Research Center and NASA/Johnson Space Center. This paper contains an overview of this system and provides details of how it will use digraph models processed by the SourceDoubls algorithm to accomplish its task.

Iverson, D. L.↗
NASA NTRS1994

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

space network↗
NASA NTRS1984

Numerical modeling of D-mappings with applications to chemical kinetics

Numerical modeling of D-mappings was studied and applied to solving nonlinear stiff systems. These mappings were locally linearized for convergence analysis, and some applications were made to chemical kinetics. The technique avoids using multistep implicit codes that require inversion of Jacobian matrices, but depends on the Jacobians for its convergence analysis.

Dey, S. K.↗
NASA NTRS2011

The NASA Lightning Nitrogen Oxides Model (LNOM): Recent Updates and Applications

Improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) and its application to the Community Multiscale Air Quality (CMAQ) modeling system are presented. The LNOM analyzes Lightning Mapping Array (LMA) and National Lightning Detection Network(tm) (NLDN) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NOx (= NO + NO2). Lightning channel length distributions and lightning 10-m segment altitude distributions are also provided. In addition to NOx production from lightning return strokes, the LNOM now includes non-return stroke lightning NOx production due to: hot core stepped and dart leaders, stepped leader corona sheath, K-changes, continuing currents, and M-components. The impact of including LNOM-estimates of lightning NOx for an August 2006 run of CMAQ is discussed.

Koshak, William↗
DOE OSTI2019

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Strategies for Integrating Deep Learning Surrogate Models with HPC Simulation Applications

The emerging trend of the convergence of high performance computing (HPC), machine learning/deep learning (ML/DL), and big data analytics presents a host of challenges for large-scale computing campaigns that seek best practices to interleave traditional scientific simulation-based workloads with ML/DL models. A portfolio of systematic approaches to incorporate deep learning into modeling and simulation serves a vital need when we support AI for science at a computing facility. In this paper, we evaluate several strategies for deploying deep learning surrogate models in a representative physics application on supercomputers at the Oak Ridge Leadership Computing Facility (OLCF). We discuss a set of recommended deployment architectures and implementation approaches. We analyze and evaluate these alternatives and show their performance and scalability up to 1000 GPUs on two mainstream platforms equipped with different deep learning hardware and software stacks.

Yin, Junqi↗
DOE OSTI2018

The Lorenzo–Hartley’s function for fractional calculus and its applications pertaining to fractional order modelling of anomalous relaxation in dielectrics

Considering the applications and a great significance of the study pertaining to generalized functions in applied sciences, the author investigated the Lorenzo–Hartley’s function which is a generalization of classical functions widely used in fractional calculus. The Laplace transform pairs are derived and the requirements for the Lorenzo–Hartley’s function G{sub ν,μ,δ}(a,c,t) to be completely monotonic (for t>0) are investigated. The author has shown the applications of this generalized function to describe the relaxation models, particularly in dielectrics. The Lorenzo–Hartley’s function G{sub ν,μ,δ}(a,c,t) of a real variable t is considered to investigate a computable mathematical framework for standard Debye and non-Debye relaxation processes in dielectric materials. The non-negative spectral distribution function is obtained for the corresponding response function. It is also demonstrated that the classical models like Cole–Cole (C–C), Davidson–Cole (D–C), and Havriliak–Negami (H–N) for non-Debye relaxation and standard Debye relaxation are the particular cases of the Lorenzo–Hartley’s function. Some of the study cases are also worked out to visualize the effects of variations of parameters on the response function and corresponding spectral distribution function. A generalized and unified fractional relaxation differential equation which governs response functions for classical dielectrics models pertaining to non-Debye relaxation (C–C, D–C, and H–N) is also established in the present investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2019

Review of MLI Behavior at Low Temperatures and Application to L'Ralph Thermal Modeling

In this presentation, the theory and application of multi-layer insulation (MLI) behavior, with a specific focus on lower temperature applications (<180K), is discussed. Many parameters can affect the performance of MLI (i.e. construction method, size, materials, grounding, penetrations, etc.) and these factors can make the prediction of MLI performance a challenge. Often, MLI performance is measured in terms of estar, and analysts commonly apply bias between a high and a low estar value. However, this approach can be dangerous when a mission goes through a wide range of temperatures during its lifetime (such as our mission, L'Ralph) due to temperature dependence of estar, with estar values increasing exponential as temperatures get colder. Many research papers and correlations have been published about MLI behavior, showing how estar values can rapidly rise at low temperatures. These correlations also show how the different parameters of MLI can affect and amplify this growth. Various correlations are presented as well as how L'Ralph is approaching the MLI problem. L'Ralph thermal model is built with Thermal Desktop (TD), and a discussion of how to apply the temperature dependent MLI behavior within TD is included. The presentation also includes reviews of different methods of mitigating heat leaks through MLI, touching briefly on topics such as integrated-MLI (IMLI), Dacron vs silk netting, and using multi-layered meshes to improve estar performance.

MLI↗
DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗
NASA NTRS1974

Performance of a model cascade thrust reverser for short-haul applications

Aerodynamic and acoustic characteristics are presented for a cowlmounted, model cascade thrust reverser suitable for short-haul aircraft. Thrust reverser efficiency and the influence on fan performance were determined from isolated fan-driven models under static and forward velocity conditions. Cascade reverser noise characteristics were determined statically in an isolated pipe-flow test, while aerodynamic installation effects were determined with a wind-tunnel, fan-powered airplane model. Application of test results to short-haul aircraft calculations demonstrated that such a cascade thrust reverser may be able to meet both the performance and noise requirements for short-haul aircraft operation. However, aircraft installation effects can be quite significant.

Dietrich, D. A.↗
NASA NTRS2018

Application of Machine-Learning Algorithms for On-Board Asteroid Shape Model Determination

The Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.

Machine learning algorithms↗
NASA NTRS1988

Application of triggered lightning numerical models to the F106B and extension to other aircraft

The goal of the F106B Thunderstorm Research Program is to characterize the lightning environment for aircraft in flight. This report describes the application of numerical electromagnetic models to this problem. Topics include: (1) Extensive application of linear triggered lightning to F106B data; (2) Electrostatic analysis of F106B field mill data; (3) Application of subgrid modeling to F106B nose region, including both static and nonlinear models; (4) Extension of F106B results to other aircraft of varying sizes and shapes; and (5) Application of nonlinear model to interaction of F106B with lightning leader-return stroke event.

Ng, Poh H.↗
NASA NTRS1971

Mathematical models of the open magnetosphere - Application to dayside auroras.

Two static mathematical models of the open or Dungey model of the magnetosphere are constructed. The process of construction is similar to that for early closed magnetosphere models, such as the Taylor-Hones model. The first model in fact is simply an addition of an interplanetary field in arbitrary direction to a Taylor-Hones image dipole model. In order to preserve the shape of the magnetosphere at high latitudes, and to partially exclude the exterior field, another model is constructed with the magnetopause approximated by a diamagnetic sphere. We find that there are some interplanetary field lines connected to the earth for all orientations of the interplanetary field other than strictly northward, and that the maximum number of connected field lines occurs with a due southward field. For an average spiral hose angle of the interplanetary field, the dayside neutral point occurs on the magnetopause at about 10 o'clock local time. Dayside auroras, convection patterns, and other phenomena may exhibit symmetry about this local time. For a positive (negative) interplanetary field sector, energetic, anisotropic particle fluxes should have direct access to the northern (southern) polar caps, as is supported by many recent observations.

Forbes, T. G.↗
NASA NTRS1982

An application of tensor ideas to nonlinear modeling of a turbofan jet engine

An application of tensor modelling to a digital simulation of NASA's Quiet, Clean, Shorthaul Experimental (QCSE) gas turbine engine is presented. The results show that the tensor algebra offers a universal parametrization which is helpful in conceptualization and identification for plant modelling prior to feedback or for representing scheduled controllers over an operating line.

Klingler, T. A.↗
NASA NTRS2008

Earth Global Reference Atmospheric Model 2007 (Earth-GRAM07) Applications for the NASA Constellation Program

Engineering models of the atmosphere are used extensively by the aerospace community for design issues related to vehicle ascent and descent. The Earth Global Reference Atmosphere Model version 2007 (Earth-GRAM07) is the latest in this series and includes a number of new features. Like previous versions, Earth-GRAM07 provides both mean values and perturbations for density, temperature, pressure, and winds, as well as monthly- and geographically-varying trace constituent concentrations. From 0 km to 27 km, thermodynamics and winds are based on the National Oceanic and Atmospheric Administration Global Upper Air Climatic Atlas (GUACA) climatology. For altitudes between 20 km and 120 km, the model uses data from the Middle Atmosphere Program (MAP). Above 120 km, EarthGRAM07 now provides users with a choice of three thermosphere models: the Marshall Engineering Thermosphere (MET-2007) model; the Jacchia-Bowman 2006 thermosphere model (JB2006); and the Naval Research Labs Mass Spectrometer, Incoherent Scatter Radar Extended Model (NRL MSIS E-OO) with the associated Harmonic Wind Model (HWM-93). In place of these datasets, Earth-GRAM07 has the option of using the new 2006 revised Range Reference Atmosphere (RRA) data, the earlier (1983) RRA data, or the user may also provide their own data as an auxiliary profile. Refinements of the perturbation model are also discussed which include wind shears more similar to those observed at the Kennedy Space Center than the previous version Earth-GRAM99.

Leslie, Fred W.↗