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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Wind Power Plant Short-Circuit Modeling Guide

This report presents a discussion on modeling a wind power plant for short-circuit studies at the transmission level. Due to the longer history of use and experience with synchronous machines in the electric power grid, the short-circuit behavior of power plants using these types of machines is well understood compared to a wind power plant. It is the goal of this guideline to educate utility engineers on short-circuit modeling of wind power plants in order to increase the level of understanding of wind power plant behavior. The methods presented in this paper will assist in determining the maximum short-circuit contribution from a wind power plant for system impact studies (verifying current is within interrupting ability of protection devices), relay coordination, and arc flash hazard studies.

Williams, Joseph R.↗
DOE OSTI2014

Temperature-dependent 780-nm laser absorption by engineering grade aluminum, titanium, and steel alloy surfaces

When modeling laser interaction with metals for various applications it requires a knowledge of absorption coefficients for real, commercially available materials with engineering grade (unpolished, oxidized) surfaces. But, most currently available absorptivity data pertain to pure metals with polished surfaces or vacuum-deposited thin films in controlled atmospheres. A simple laboratory setup is developed for the direct calorimetric absorptivity measurements using a diode-array laser emitting at 780 nm. A scheme eliminating the effect of convective and radiative losses is implemented. Futhermore, the obtained absorptivity results differ considerably from existing data for polished pure metals and are essential for the development of predictive laser-material interaction models.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Library of Advanced Materials for Engineering (LAMÉ) 4.50

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Coupling of CTF and RELAP5-3D Within an Enhanced Fidelity Nuclear Power Plant Simulator

A robust and accurate multiphysics engineering simulator is being developed to model the core behavior and system response of pressurized water reactors. This simulator relies on the NESTLE and CTF computer codes to model the neutronics and thermal hydraulics (TH), respectively, inside the core on a nodal scale and on the Reactor Excursion and Leak Analysis Program—Three Dimensional (RELAP5-3D) to model the entire nuclear steam supply system. The RELAP5-3D model includes highly detailed nodalization and multidimensional flow modeling throughout the vessel. Previously, pin-resolved data generated via the Virtual Environment for Reactor Analysis core simulator were used to improve the accuracy of the NESTLE core predictions. The engineering simulator being developed as part of this work uses the 3KEYMASTER platform to couple the enhanced NESTLE model to a nodal-fidelity CTF model to balance run time with accuracy; NESTLE provides node-dependent powers to CTF, and CTF provides node-dependent coolant densities and fuel temperatures to NESTLE.An overlapping domain approach is used for the core TH in which RELAP5-3D provides core boundary conditions based on the system response and CTF provides a node-dependent coolant heating rate to the RELAP5-3D core solution. In the preliminary TH demonstration discussed in this paper, CTF and RELAP5-3D provided similar steady-state core predictions, indicating the hydraulic compatibility between the codes, as well as reasonable and expected behavior under hypothetical transient conditions. This provides an initial step in ongoing efforts toward a robust, multiscale TH/neutronics engineering simulator capability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2016

SU-F-T-377: Monte Carlo Re-Evaluation of Volumetric-Modulated Arc Plans of Advanced Stage Nasopharygeal Cancers Optimized with Convolution-Superposition Algorithm

Background: Commercial treatment planning system Pinnacle3 (Philips, Fitchburg, WI, USA) employs a convolution-superposition algorithm for volumetric-modulated arc radiotherapy (VMAT) optimization and dose calculation. Study of Monte Carlo (MC) dose recalculation of VMAT plans for advanced-stage nasopharyngeal cancers (NPC) is currently limited. Methods: Twenty-nine VMAT prescribed 70Gy, 60Gy, and 54Gy to the planning target volumes (PTVs) were included. These clinical plans achieved with a CS dose engine on Pinnacle3 v9.0 were recalculated by the Monaco TPS v5.0 (Elekta, Maryland Heights, MO, USA) with a XVMC-based MC dose engine. The MC virtual source model was built using the same measurement beam dataset as for the Pinnacle beam model. All MC recalculation were based on absorbed dose to medium in medium (Dm,m). Differences in dose constraint parameters per our institution protocol (Supplementary Table 1) were analyzed. Results: Only differences in maximum dose to left brachial plexus, left temporal lobe and PTV54Gy were found to be statistically insignificant (p> 0.05). Dosimetric differences of other tumor targets and normal organs are found in supplementary Table 1. Generally, doses outside the PTV in the normal organs are lower with MC than with CS. This is also true in the PTV54-70Gy doses but higher dose in the nasal cavity near the bone interfaces is consistently predicted by MC, possibly due to the increased backscattering of short-range scattered photons and the secondary electrons that is not properly modeled by the CS. The straight shoulders of the PTV dose volume histograms (DVH) initially resulted from the CS optimization are merely preserved after MC recalculation. Conclusion: Significant dosimetric differences in VMAT NPC plans were observed between CS and MC calculations. Adjustments of the planning dose constraints to incorporate the physics differences from conventional CS algorithm should be made when VMAT optimization is carried out directly with MC dose engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Advanced Multiphysics Thermal-Hydraulics Models for the High Flux Isotope Reactor

Engineering design studies to determine the feasibility of converting the High Flux Isotope Reactor (HFIR) from using highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel are ongoing at Oak Ridge National Laboratory (ORNL). This work is part of an effort sponsored by the US Department of Energy (DOE) Reactor Conversion Program. HFIR is a very high flux pressurized light-water-cooled and moderated flux-trap type research reactor. HFIR s current missions are to support neutron scattering experiments, isotope production, and materials irradiation, including neutron activation analysis. Advanced three-dimensional multiphysics models of HFIR fuel were developed in COMSOL software for safety basis (worst case) operating conditions. Several types of physics including multilayer heat conduction, conjugate heat transfer, turbulent flows (RANS model) and structural mechanics were combined and solved for HFIR s inner and outer fuel elements. Alternate design features of the new LEU fuel were evaluated using these multiphysics models. This work led to a new, preliminary reference LEU design that combines a permanent absorber in the lower unfueled region of all of the fuel plates, a burnable absorber in the inner element side plates, and a relocated and reshaped (but still radially contoured) fuel zone. Preliminary results of estimated thermal safety margins are presented. Fuel design studies and model enhancement continue.

COMSOL↗
DOE OSTI2022

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-output multilevel best linear unbiased estimators via semidefinite programming

Multifidelity forward uncertainty quantification (UQ) problems often involve multiple quantities of interest and heterogeneous models (e.g., different grids, equations, dimensions, physics, surrogate and reduced-order models). While computational efficiency is key in this context, multi-output strategies in multilevel/multifidelity methods are either sub-optimal or non-existent. In this paper we extend multilevel best linear unbiased estimators (MLBLUE) to multi-output forward UQ problems and we present new semidefinite programming formulations for their optimal setup. Not only do these formulations yield the optimal number of samples required, but also the optimal selection of low-fidelity models to use. While existing MLBLUE approaches are single-output only and require a non-trivial nonlinear optimization procedure, the new multi-output formulations can be solved reliably and efficiently. Here, we demonstrate the efficacy of the new methods and formulations in practical UQ problems with model heterogeneity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Predicting biomass comminution: Physical experiment, population balance model, and deep learning

An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design.

09 BIOMASS FUELS↗
DOE OSTI2018

Building and using dynamic risk-informed diagnosis procedures for complex system accidents

Accidents pose unique challenges for operating crews in complex systems such as nuclear power plants, presenting limitations in plant status information and lack of detailed monitoring, diagnosis, and response planning support. Advances in severe accident simulation and dynamic probabilistic risk assessment provide an opportunity to garner detailed insight into accident scenarios. In this article, we demonstrate how to build and use a framework which leverages dynamic probabilistic risk assessment, simulation, and dynamic Bayesian networks to provide real-time monitoring and diagnostic support for severe accidents in a nuclear power plant. We use general purpose modeling technology, the dynamic Bayesian network, and adapt it for risk management of complex engineering systems. This article presents a prototype model for monitoring and diagnosing system states associated with loss of flow and transient overpower accidents in a generic sodium fast reactor. We discuss using this framework to create a risk-informed accident management framework called Safely Managing Accidental Reactor Transients procedures. This represents a new application of risk assessment, expanding probabilistic risk assessment techniques beyond static decision support into dynamic, real-time models which support accident diagnosis and management.

Groth, Katrina M.↗
DOE OSTI2020

Numerical methods for nonlocal and fractional models

Partial differential equations (PDEs) are used, with huge success, to model phenomena arising across all scientific and engineering disciplines. However, across an equally wide swath, there exist situations in which PDE models fail to adequately model observed phenomena or are not the best available model for that purpose. On the other hand, in many situations, nonlocal models that account for interaction occurring at a distance have been shown to more faithfully and effectively model observed phenomena that involve possible singularities and other anomalies. In this article, we consider a generic nonlocal model, beginning with a short review of its definition, the properties of its solution, its mathematical analysis, and specific concrete examples. We then provide extensive discussions about numerical methods, including finite element, finite difference, and spectral methods, for determining approximate solutions of the nonlocal models considered. In that discussion, we pay particular attention to a special class of nonlocal models that are the most widely studied in the literature, namely those involving fractional derivatives. The article ends with brief considerations of several modeling and algorithmic extensions which serve to show the wide applicability of nonlocal modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of Saturated Zone Three-Dimensional Initial Condition Plumes for the Composite Analysis and Cumulative Impacts Evaluation Modeling

The purpose of this environmental calculation file (ECF) is to document and present the methodology, input data, and results of extending two-dimensional (2-D) plume depictions documented in DOE/RL- 2017-66, Hanford Site Groundwater Monitoring for 2017, into three dimensions (3-D) for select contaminants of interest (COIs) in the 200-BP-5, 200-UP-1, 200-ZP-1, and 200-PO-1 Groundwater Operable Units at the Hanford Site. Plumes with sufficient available vertical profile data were interpolated in 3-D using the Leapfrog Geo®1 geologic modeling platform, which includes an interpolation engine for analyzing and visualizing 3-D data. Results of these interpolations are intended for use in developing inputs for transport modeling to support forecasts of Central Plateau contaminant plume fate and transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Advances in the Co-Simulation of Detailed Electrical and Whole-Building Energy Performance

This article describes recent co-simulation advances for the simultaneous modeling of detailed building electrical distribution systems and whole-building energy performance. The co-simulation architecture combines the EnergyPlus® engine for whole-building energy modeling with a new Modelica library for building an electrical distribution system model that is based on harmonic power flow. This new library allows for a higher-fidelity modeling of electrical power flows and losses within buildings than is available with current building electrical modeling software. We demonstrate the feasibility of the architecture by modeling a simple, two-zone thermal chamber with internal power electronics converters and resistive loads, and we validate the model using experimental data. The proposed co-simulation capability significantly expands the capabilities of building electrical distribution system models in the context of whole-building energy modeling, thus enabling more complex analyses than would have been possible with individual building performance simulation tools that are used to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Investigation and Optimization of Biodiesel Chemistry for HCCI Combustion

Over the past 5 years, ORNL has run 95 diesel range fuels in homogene-ous charge compression ignition (HCCI), including 40 bio-diesels and associated diesel fuels in their blending. The bio-diesel blends varied in oxygen content, iodine number, cetane, boiling point distribution, chemical composition, and some contained nitrogen. All fuels were run in an HCCI engine at 1800 rpm, in the power range of 2.5 to 4.5 bar IMEP, using intake air heating for combustion phasing control, and at a compression ratio of 10.6. The engine response to fuel variables has been analyzed statistically. Generally, the engine responded well to fuels with lower nitrogen and oxygen, lower cetane, and lower aromatics. Because of the wide range of fuels combined in the model, it provides only a broad overview of the engine response. It is recommended that data be truncated and re-modeled to obtain finer resolution of engine response to particular fuel variables.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Review of multi-fidelity models

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources. These models are especially beneficial when acquiring high-accuracy data is costly or computationally intensive. This review offers a comprehensive analysis of multi-fidelity models, focusing on their applications in scientific and engineering fields, particularly in optimization and uncertainty quantification. It classifies publications on multi-fidelity modeling according to several criteria, including application area, surrogate model selection, types of fidelity, combination methods and year of publication. The study investigates techniques for combining different fidelity levels, with an emphasis on multi-fidelity surrogate models. Here this work discusses reproducibility, open-sourcing methodologies and benchmarking procedures to promote transparency. The manuscript also includes educational toy problems to enhance understanding. Additionally, this paper outlines best practices for presenting multi-fidelity-related savings in a standardized, succinct and yet thorough manner. The review concludes by examining current trends in multi-fidelity modeling, including emerging techniques, recent advancements, and promising research directions.

42 ENGINEERING↗