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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 OSTI2015

A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap scheme is introduced to numerically approximate the model’s flow equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗
DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Analysis of steady compaction waves in polyurea aerogel

In this work, steady compaction waves in an inert porous material are investigated using a p - α model. In a steady traveling wave reference frame, the one-dimensional Euler equations are reduced to a set of ordinary differential equations. A Mie-Grüneisen equation of state (EOS) is used with parameters calibrated for polyurea aerogel (PUA). Analytic solutions for non-equilibrium compaction are developed which compliment numerical simulations and are able to predict the complete wave structure, including the compaction wave speed, zone length, and final compacted solid volume fraction. The dynamic compaction of PUA is studied for a range of piston velocities. Three regions of behavior are identified: supersonic, subsonic-complete, and subsonic-partial compaction. Below a critical piston velocity, a subsonic compaction wave is produced without a leading shock. Finally, at even lower piston velocities, there is partial compaction and a greater dependence on the dynamic compaction relation. Some features and limitations of the current model are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multiphysics Modeling of Hydrokinetic Turbine Energy Conversion System

Hydrokinetic turbines (HKTs) hold great promise as a renewable energy source, but high maintenance costs and limited energy output hinder their widespread adoption. The lack of comprehensive research on HKT drivetrain designs creates a knowledge gap in enhancing generation efficiency and cost reduction. A model of the HKT system with a focus on electric drivetrains and power converters is required to address this knowledge gap. This paper first introduces a MATLAB-averaged model integrating electrical-mechanical-thermal domains and aging behaviors within multi-time frames. Then a PLECS model, which incorporates maximum power point tracking and dq reference framed control for AC-DC-AC power converters, is enriched by a dynamic thermal model to predict fast transients accurately. These models optimize the design at the component level, resulting in improved integrated system performance. Furthermore, the validation of the models is carried out through hardware experiments for the averaged model and hardware-in-the-loop testing for the dynamic model.

ADVANCED PROPULSION SYSTEMS,HYDRO ENERGY↗
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Two-body potential model based on cosine series expansion for ionic materials

There is a method to construct a two-body potential model for ionic materials with a Fourier series basis and we examine it. For this method, the coefficients of cosine basis functions are uniquely determined by solving simultaneous linear equations to minimize the sum of weighted mean square errors in energy, force and stress, where first-principles calculation results are used as the reference data. As a validation test of the method, potential models for magnesium oxide are constructed. The mean square errors appropriately converge with respect to the truncation of the cosine series. This result mathematically indicates that the constructed potential model is sufficiently close to the one that is achieved with the non-truncated Fourier series and demonstrates that this potential virtually provides minimum error from the reference data within the two-body representation. The constructed potential models work appropriately in both molecular statics and dynamics simulations, especially if a two-step correction to revise errors expected in the reference data is performed, and the models clearly outperform two existing Buckingham potential models that were tested. Moreover, the good agreement over a broad range of energies and forces with first-principles calculations should enable the prediction of materials behavior away from equilibrium conditions, such as a system under irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

TU-G-BRD-03: IMRT Dosimetry Differences in An Institution with Community and Academic Model

Purpose: Radiation outcome among institutions can be interpreted meaningfully if the dose delivery and prescription to the target volume is documented accurately and consistently. ICRU-83 recommended specific guidelines in IMRT for target volume definitions and dose reporting. This retrospective study evaluates the pattern of IMRT dose prescription and recording in an academic institution (AI) and a community hospital (CH) models in a single institution with reference to ICRU-83 recommendation. Materials & Methods: Dosimetric information of 625 (500 from academic and 125 from community) patients treated with IMRT was collected retrospectively from the AI and a CH. The dose-volume histogram (DVH) for the target volume of each patient was extracted. Standard dose parameters such as D2, D50, D95, D98, D100, as well as the homogeneity index (HI) defined as (D2-D98)/D50 and monitor units (MUs) were collected. Results: Significant dosimetric variations were observed in disease sites and between AI and CH. The variation in the mean value of D95 for AI is 98.48±4.12 and for CH is 96.41±4.13. A similar pattern was noticed for D50 (104.18±6.04 for AI and 101.05±3.49 for CH). Thus, nearly 95% of patients received dosage higher than 100% to the site viewed by D50 and varied between AI and CH models. The average variation of HI is found to be 0.12±0.08 and 0.11±0.08 for AI and CH model, showing better IMRT treatment plans for academic model compared to community. Conclusion: Even with the implementation of ICRU-83 guidelines, there is a large variation in dose prescription and delivery in IMRT. The variation is institution and site specific. For any meaningful comparison of the IMRT outcome, strict guidelines for dose reporting should be maintained in every institution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Performance and Safety Parameters for the High Flux Isotope Reactor

A Monte Carlo depletion model for the High Flux Isotope Reactor (HFIR) Cycle 400 and its use in calculating parameters of relevance to the reactor performance and safety during the reactor cycle are presented in this paper. This depletion model was developed to serve as a reference for the design of a low-enriched uranium (LEU) fuel for an ongoing study to convert HFIR from high-enriched uranium (HEU) to LEU fuel; both HEU and LEU depletion models use the same methodology and ENDV/B-VII nuclear data as discussed in this paper. The calculated HFIR Cycle 400 parameters, which are compared when available with measurement data from critical experiments performed at HFIR, data included in the HFIR Safety Analysis Report (SAR), or data reported by previous calculations, provide a basis for verification or updating of the corresponding SAR data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

Performance and safety parameters for the high flux isotope reactor

A Monte Carlo depletion model for the High Flux Isotope Reactor (HFIR) Cycle 400 and its use in calculating parameters of relevance to the reactor performance and safety during the reactor cycle are presented in this paper. This depletion model was developed to serve as a reference for the design of a low-enriched uranium (LEU) fuel for an ongoing study to convert HFIR from high-enriched uranium (HEU) to LEU fuel; both HEU and LEU depletion models use the same methodology and ENDF/B-VII nuclear data as discussed in this paper. The calculated HFIR Cycle 400 parameters, which are compared with measurement data from critical experiments performed at HFIR, data included in the HFIR Safety Analysis Report (SAR), or data reported by previous calculations, provide a basis for verification or updating of the corresponding SAR data. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

TU-G-204-03: Dynamic CT Myocardial Perfusion Measurement Using First Pass Analysis and Maximum Slope Models

Purpose: To evaluate the accuracy of dynamic CT myocardial perfusion measurement using first pass analysis (FPA) and maximum slope models. Methods: A swine animal model was prepared by percutaneous advancement of an angioplasty balloon into the proximal left anterior descending (LAD) coronary artery to induce varying degrees of stenosis. Maximal hyperaemia was achieved in the LAD with an intracoronary adenosine drip (240 µg/min). Serial microsphere and contrast (370 mg/mL iodine, 30 mL, 5mL/s) injections were made over a range of induced stenoses, and dynamic imaging was performed using a 320-row CT scanner at 100 kVp and 200 mA. The FPA CT perfusion technique was used to make vessel-specific myocardial perfusion measurements. CT perfusion measurements using the FPA and maximum slope models were validated using colored microspheres as the reference gold standard. Results: Perfusion measurements using the FPA technique (P-FPA) showed good correlation with minimal offset when compared to perfusion measurements using microspheres (P- Micro) as the reference standard (P -FPA = 0.96 P-Micro + 0.05, R{sup 2} = 0.97, RMSE = 0.19 mL/min/g). In contrast, the maximum slope model technique (P-MS) was shown to underestimate perfusion when compared to microsphere perfusion measurements (P-MS = 0.42 P -Micro −0.48, R{sup 2} = 0.94, RMSE = 3.3 mL/min/g). Conclusion: The results indicate the potential for significant improvements in accuracy of dynamic CT myocardial perfusion measurement using the first pass analysis technique as compared with the standard maximum slope model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Simulation of CO 2 Storage

This report is a compilation of Lawrence Livermore National Laboratory’s (LLNL) accomplishments on CO 2 storage simulation and modeling research, performed for the US-­China Clean Energy Research Center (CERC). Within the CERC project management structure, this work is referred to as Subtask 6.4.a Simulation and Modeling. The task falls under CERC’s Advanced Coal Technology Consortium (ACTC) Research Theme 6—CO 2 Sequestration Capacity and Near-­Term Opportunities. The goals of the task were to develop new CO 2 sequestration simulation approaches and tools, then apply them to CO 2 storage projects in the U.S. and China. Work on this task paused when funding was redirected to CERC’s other efforts. Two sections of this report provide valuable snapshot of LLNL’s progress when funding was curtailed: 1) Section 5.2.2 is a 14-­page presentation written January 8, 2013; and 2) Section 5.1.3 is a progress report from the first quarter of Fiscal year 2013.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2011

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks that are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

A numerical method for the simulation of low Mach number liquid-gas flows

This work is devoted to the numerical simulation of liquid-gas flows. The liquid phase is considered as incompressible, while the gas phase is treated as compressible in the low Mach number approximation. A single fluid two pressure model is developed and the front-tracking method is used to track the interface. Navier-Stokes equations coupled with that of temperature are solved in the whole computational domain. Velocity, pressure and temperature fields are computed yielding a complete description of the dynamics for both phases. We show that our method is much more efficient than the so-called all-Mach methods involving a single pressure, since large time steps can be used while retaining time accuracy. The model is first validated on a reference test problem solved using an accurate ALE technique to track the interface. Numerical examples in two space dimensions are next presented. They consist of air bubbles immersed in a closed cavity filled up with liquid water. The forced oscillations of the system consisting of the air bubbles and the liquid water are investigated. They are driven by a heat supply or a thermodynamic pressure difference between the bubbles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

MELCOR Computer Code Manuals Volume 3: MELCOR Assessment Problems [Draft]

MELCOR is a fully integrated, engineering-level computer code that models the progression of severe accidents in light-water reactor nuclear power plants. MELCOR is being developed at Sandia National Laboratories (SNL) for the U.S. Nuclear Regulatory Commission (NRC) as a second-generation plant risk assessment tool and the successor to the Source Term Code package. A broad spectrum of severe accident phenomena in both boiling and pressurized water reactors is treated in MELCOR in a unified framework. These include thermal-hydraulic response in the reactor coolant system (RCS), reactor cavity, containment, and confinement buildings; core heatup, degradation, and relocation; core-concrete attack; hydrogen production, transport, and combustion; and fission product release and transport behavior. Current uses of MELCOR include estimation of severe accident source terms and their sensitivities and uncertainties in a variety of applications. This publication of the MELCOR computer code manuals corresponds to MELCOR 2.0, released to users in September 2008. Volume 1 contains a primer that describes MELCOR's phenomenological scope, organization (by package), and documentation. The remainder of Volume 1 contains the MELCOR User's Guides, which provide the input instructions and guidelines for each package. Volume 2 contains the MELCOR Reference Manuals, which describe the phenomenological models that have been implemented in each package. Volume 3, MELCOR Assessment Problems, presents a portfolio of test and sample problems consisting of both analyses of experiments and of full plant problems. These analyses will be repeated with future releases of MELCOR in order to provide a metric on code predictions as new versions are released.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗