Particle trajectories in model current sheets. 2 - Analytical and numerical applications to auroras using a geomagnetic tail model
Trapped particle trajectories for various mathematical models of geomagnetic field
TRAPPED PARTICLE↗
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Results for “Application Models”
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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Estimating thermal conductivity of cermet fuel materials for nuclear reactor application
Mathematical models for estimating thermal conductivity of cermet fuel materials used in nuclear reactor
MATHEMATICAL MODEL↗
The effects on nonuniform swash plate stiffness on coupled blade control system dynamics and stability. Part 1 - Analysis and application
Mathematical models for calculating flexible swash-plate effects on vibratory and mechanical stability characteristics of helicopter rotor systems
Piarulli, V. J.↗
Simplified jet-A kinetic mechanism for combustor application
Successful modeling of combustion and emissions in gas turbine engine combustors requires an adequate description of the reaction mechanism. For hydrocarbon oxidation, detailed mechanisms are only available for the simplest types of hydrocarbons such as methane, ethane, acetylene, and propane. These detailed mechanisms contain a large number of chemical species participating simultaneously in many elementary kinetic steps. Current computational fluid dynamic (CFD) models must include fuel vaporization, fuel-air mixing, chemical reactions, and complicated boundary geometries. To simulate these conditions a very sophisticated computer model is required, which requires large computer memory capacity and long run times. Therefore, gas turbine combustion modeling has frequently been simplified by using global reaction mechanisms, which can predict only the quantities of interest: heat release rates, flame temperature, and emissions. Jet fuels are wide-boiling-range hydrocarbons with ranges extending through those of gasoline and kerosene. These fuels are chemically complex, often containing more than 300 components. Jet fuel typically can be characterized as containing 70 vol pct paraffin compounds and 25 vol pct aromatic compounds. A five-step Jet-A fuel mechanism which involves pyrolysis and subsequent oxidation of paraffin and aromatic compounds is presented here. This mechanism is verified by comparing with Jet-A fuel ignition delay time experimental data, and species concentrations obtained from flametube experiments. This five-step mechanism appears to be better than the current one- and two-step mechanisms.
Lee, Chi-Ming↗
The axiomatic definition of a linguistic scale fuzziness degree, its major properties, and applications
A model that makes use of fuzzy linguistic scales (FLS) is considered in this report. The definition of FLS fuzziness and its major properties are given in the report. Definitions that are concerned with information loss and noise are also presented.
Ryjov, Alexander P.↗
Simplified jet fuel reaction mechanism for lean burn combustion application
Successful modeling of combustion and emissions in gas turbine engine combustors requires an adequate description of the reaction mechanism. Detailed mechanisms contain a large number of chemical species participating simultaneously in many elementary kinetic steps. Current computational fluid dynamic models must include fuel vaporization, fuel-air mixing, chemical reactions, and complicated boundary geometries. A five-step Jet-A fuel mechanism which involves pyrolysis and subsequent oxidation of paraffin and aromatic compounds is presented. This mechanism is verified by comparing with Jet-A fuel ignition delay time experimental data, and species concentrations obtained from flametube experiments. This five-step mechanism appears to be better than the current one- and two-step mechanisms.
Lee, Chi-Ming↗
Self-Consistent Thermal Accretion Disk Corona Models for Compact Objects: Application to Cygnus X-1 - II
We apply our self-consistent accretion disk corona (ADC) model, with two different geometries, to the broadband X-ray spectrum of the black hole candidate Cygnus X-1. As shown in a companion paper, models in which the Comptonizing medium is a slab surrounding the cold accretion disk cannot have a temperature higher than about 140 keV for optical depths greater than 0.2, resulting in spectra that are much softer than the observed 10-30 keV spectrum of Cyg X-1. In addition, the slab-geometry models predict a substantial "soft excess" at low energies, a feature not observed for Cyg X-1, and Fe K-alpha fluorescence lines that are stronger than observed. Previous Comptonization models in the literature have invoked a slab geometry with optical depth tau(sub T) approx. greater than 0.3 and coronal temperature T(sub c) approx. 150 keV, but they are not self-consistent. Therefore, ADC models with a slab geometry are not appropriate for explaining the X-ray spectrum of Cyg X-1. Models with a spherical corona and an exterior disk, however, predict much higher self-consistent coronal temperatures than the slab-geometry models. The higher coronal temperatures are due to the lower amount of reprocessing of coronal radiation in the accretion disk, giving rise to a lower Compton cooling rate. Therefore, for the sphere-plus-disk geometry, the predicted spectrum can be hard enough to describe the observed X-ray continuum of Cyg X-1 while predicting Fe fluorescence lines having an equivalent width of approx. 40 eV. Our best-fit parameter values for the sphere-plus-disk geometry are tau(sub T) approx. equal to 1.5 and T(sub c) approx. equal to 90 keV.
Dove, James B.↗
A GPU-based Approach for Turbomachinery Application
Accurate modeling of coolant airflows, which form protective films over turbine blades, is essential for designing fuel-efficient and environmentally sustainable gas turbine engines. Excessive coolant reduces thermal efficiency, while insufficient coolant leads to blade overheating and causes damage. Therefore, precise prediction of flow field interactions with cooling air is critical for optimizing turbine performance. This study numerically investigates the cooling effectiveness of purge and film cooling flows within a high-pressure turbine (HPT) rotor using Large Eddy Simulation (LES). The study utilizes NASA Glenn Research Center’s Glenn-HT solver. The simulation models ethe conditions of the Penn State University START rotating rig. A high-fidelity structured mesh comprising up to 800 million cells is employed to resolve high-Reynolds number flow (Re ≈ 350,000) and to capture intricate secondary flow structures, including tip leakage and purge-induced vortices. Film cooling effectiveness computations are highly sensitive to boundary conditions at the cooling holes and to grid resolution. Even with well-resolved grids and included plena, strong mixing challenges traditional eddy viscosity models. To address this, a simplified configuration is simulated: a truncated row of shaped holes on the suction side near the leading edge and a row on the pressure side, both fed from internal plena while the purge slot and tip clearance are also modeled. Two isothermal LES cases are conducted at two distinct wall temperatures, which yield the adiabatic wall temperature and the heat transfer coefficient. The definition and means of computation of the effectiveness is discussed in this paper. The simulations reveal detailed three-dimensional unsteady flow features, including coherent vortical structures and secondary flows originating from the purge cavity. Film cooling effectiveness and Nusselt number distributions are presented for both the blade surface and tip, highlighting regions of elevated heat transfer and complex thermal behavior. These findings underscore the importance of high-resolution LES and realistic boundary conditions in capturing the dynamics of purge and film cooling, offering valuable insights for improving turbine blade design and thermal management strategies.
Gas Turbine↗
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↗
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.↗
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↗
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.↗
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↗
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↗
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↗
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↗
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↗
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↗