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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 289 records · Page 16

A Comparison of the Predictive Capabilities of Several Turbulence Models using Upwind and Central-Difference Computer Codes

Four turbulence models are described and evaluated for transonic flows using the upwind code CFL3D and the central-difference code TLNS3D. In particular, the effects of recent modifications to the half-equation model of Johnson-King are explored in detail, and different versions of the model are compared. This model can obtain good results for both two-dimensional (2D) and three-dimensional (3D) separated flows. The one-equation models of Baldwin-Barth and Spalart-Allmaras perform well for separated airfoil flows, but can predict the shock too far forward at the outboard stations of a separated wing. The equilibrium model of Baldwin-Lomax predicts the shock location too far aft for both 2D and 3D separated flows, as expected. In general, all models perform well for attached or mildly separated flows.

Rumsey, Christopher L.↗

FIDDLE: A Computer Code for Finite Difference Development of Linear Elasticity in Generalized Curvilinear Coordinates

A three-dimensional numerical solver based on finite-difference solution of three-dimensional elastodynamic equations in generalized curvilinear coordinates has been developed and used to generate data such as radial and tangential stresses over various gear component geometries under rotation. The geometries considered are an annulus, a thin annular disk, and a thin solid disk. The solution is based on first principles and does not involve lumped parameter or distributed parameter systems approach. The elastodynamic equations in the velocity-stress formulation that are considered here have been used in the solution of problems of geophysics where non-rotating Cartesian grids are considered. For arbitrary geometries, these equations along with the appropriate boundary conditions have been cast in generalized curvilinear coordinates in the present study.

Kaul, Upender K.↗

Computer Code for Nanostructure Simulation

Due to their small size, nanostructures can have stress and thermal gradients that are larger than any macroscopic analogue. These gradients can lead to specific regions that are susceptible to failure via processes such as plastic deformation by dislocation emission, chemical debonding, and interfacial alloying. A program has been developed that rigorously simulates and predicts optoelectronic properties of nanostructures of virtually any geometrical complexity and material composition. It can be used in simulations of energy level structure, wave functions, density of states of spatially configured phonon-coupled electrons, excitons in quantum dots, quantum rings, quantum ring complexes, and more. The code can be used to calculate stress distributions and thermal transport properties for a variety of nanostructures and interfaces, transport and scattering at nanoscale interfaces and surfaces under various stress states, and alloy compositional gradients. The code allows users to perform modeling of charge transport processes through quantum-dot (QD) arrays as functions of inter-dot distance, array order versus disorder, QD orientation, shape, size, and chemical composition for applications in photovoltaics and physical properties of QD-based biochemical sensors. The code can be used to study the hot exciton formation/relation dynamics in arrays of QDs of different shapes and sizes at different temperatures. It also can be used to understand the relation among the deposition parameters and inherent stresses, strain deformation, heat flow, and failure of nanostructures.

Filikhin, Igor↗

Integration of Multifidelity Multidisciplinary Computer Codes for Design and Analysis of Supersonic Aircraft

This paper documents the development of a conceptual level integrated process for design and analysis of efficient and environmentally acceptable supersonic aircraft. To overcome the technical challenges to achieve this goal, a conceptual design capability which provides users with the ability to examine the integrated solution between all disciplines and facilitates the application of multidiscipline design, analysis, and optimization on a scale greater than previously achieved, is needed. The described capability is both an interactive design environment as well as a high powered optimization system with a unique blend of low, mixed and high-fidelity engineering tools combined together in the software integration framework, ModelCenter. The various modules are described and capabilities of the system are demonstrated. The current limitations and proposed future enhancements are also discussed.

Geiselhart, Karl A.↗

Optimization of KINETICS Chemical Computation Code

NASA JPL has been creating a code in FORTRAN called KINETICS to model the chemistry of planetary atmospheres. Recently there has been an effort to introduce Message Passing Interface (MPI) into the code so as to cut down the run time of the program. There has been some implementation of MPI into KINETICS; however, the code could still be more efficient than it currently is. One way to increase efficiency is to send only certain variables to all the processes when an MPI subroutine is called and to gather only certain variables when the subroutine is finished. Therefore, all the variables that are used in three of the main subroutines needed to be investigated. Because of the sheer amount of code that there is to comb through this task was given as a ten-week project. I have been able to create flowcharts outlining the subroutines, common blocks, and functions used within the three main subroutines. From these flowcharts I created tables outlining the variables used in each block and important information about each. All this information will be used to determine how to run MPI in KINETICS in the most efficient way possible.

planetary atmospheres↗

Axial-Flow Turbine Rotor Discharge-Flow Overexpansion and Limit-Loading Condition: Method of Analysis and Performance Evaluation of Computer Code AXOD2 - Part 2

The supercritical flow model-treatment implemented in AXOD2 is unique of its kind, and unique to axial-flow turbines. It is a rather simple, one-dimensional modeling concept which utilizes and captures two supercritical flow characteristics: 1. The mass flow rate remains to be a constant in supercritical flow conditions. 2. The flow angle at the choked blade-row-discharge deflects against the blade angle.

axial-flow↗

PROBLEM DEPENDENT DOPPLER BROADENING OF CONTINUOUS ENERGY CROSS SECTIONS IN THE KENO MONTE CARLO COMPUTER CODE

For many Monte Carlo codes cross sections are generally only created at a set of predetermined temperatures. This causes an increase in error as one moves further and further away from these temperatures in the Monte Carlo model. This paper discusses recent progress in the Scale Monte Carlo module KENO to create problem dependent, Doppler broadened, cross sections. Currently only broadening the 1D cross sections and probability tables is addressed. The approach uses a finite difference method to calculate the temperature dependent cross-sections for the 1D data, and a simple linear-logarithmic interpolation in the square root of temperature for the probability tables. Work is also ongoing to address broadening theS (alpha , beta) tables. With the current approach the temperature dependent cross sections are Doppler broadened before transport starts, and, for all but a few isotopes, the impact on cross section loading is negligible. Results can be compared with those obtained by using multigroup libraries, as KENO currently does interpolation on the multigroup cross sections to determine temperature dependent cross-sections. Current results compare favorably with these expected results.

Monte Carlo↗

Modernization of ORIGEN Library Creation in the SCALE Computer Code

The modernization of SCALE features has been a target for recent SCALE releases. ORIGEN, the activation and depletion code in SCALE, is one such legacy code that has been targeted for modernization. A part of these modernization efforts has been to develop capabilities within ORIGEN to directly read ENDF-formatted data files. In addition, the input format for the COUPLE library creation utility was rewritten to a format more accessible to newer SCALE users. Finally, the ability of COUPLE to create ORIGEN libraries without sublibrary data has been added.KEYWORDS: ORIGEN, depletion, SCALE, API

Hart, Shane↗

React-compact: a computer code for modeling graphite corrosion and fuel hydrolysis - 18526

REACT-COMPACT is used to estimate localized graphite corrosion, fuel compact corrosion, and fuel hydrolysis by water vapor for a unit cell geometry within a High Temperature Gas Cooled Reactor (HTGR) fuel block. REACT-COMPACT may also be used to model corrosion in regions with graphite only, including core support posts. REACT-COMPACT solves the species-conservation equation for water vapor in a porous carbon medium for cylindrical geometry. Boundary-layer mass transfer from a helium coolant is modeled at either the inner or outer radius. Within the porous carbon medium, both diffusive and convective transport mechanisms are modeled. The reaction of water vapor with carbonaceous materials is modeled according to a Langmuir-Hinshelwood mechanism. The fuel hydrolysis reaction rate is based on a semi-empirical model derived from experimental data. Model parameters, including densities, void fractions, burn off factors for the intrinsic reaction rates, and diffusivity of water vapor in porous carbon, are evaluated as functions of local burn off. The effects of void-fraction gradients on transport processes are also modeled. REACT-COMPACT also solves the mass conservation equations for reacting fuel and carbonaceous materials, which are coupled in a nonlinear manner to the water-vapor conservation equation because of burn off effects. For this reason, a numerical finite-difference solution is used. This paper describes the theoretical basis for REACT-COMPACT and results for sample problems under representative HTGR conditions. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NAC - Neutron activation code

Computer coding program for neutron activation analysis of gamma ray radioactivity for composite materials

Weinstein, S. T.↗

Transonic airfoil codes

Computer codes for the design and analysis of transonic airfoils are considered. The design code relies on the method of complex characteristics in the hodograph plane to construct shockless airfoil. The analysis code uses artificial viscosity to calculate flows with weak shock waves at off-design conditions. Comparisons with experiments show that an excellent simulation of two dimensional wind tunnel tests is obtained. The codes have been widely adopted by the aircraft industry as a tool for the development of supercritical wing technology.

Garabedian, P. R.↗

Electrostatic Particle-In-Cell Code For Hypercube Computer

Code simulates two-dimensional motions of plasma particles in self-consistent electrostatic field and externally applied magnetic field developed for execution on Mark IIIfp hypercube computer. Based on generalization of one-dimensional particle-in-cell algorithm applicable to many different parallel computing architectures. Intermediate product of continuing effort to speed particle-in-cell computations by taking advantage of distributed-memory parallel computers like those of hypercube class.

Ferraro, Robert D.↗

Prediction of compliant wall drag reduction, part 1

Computer codes developed to test Bushnell's compliant wall drag reduction model are discussed. One code computes the evolution of mean velocity profiles during the period between bursts as forced by an imposed large-scale pressure pulse due to earlier bursts. Another code computes the local stability characteristics of these computed profiles. The programs use Chebyshev polynomials to resolve the normal boundary layer (y) direction and a staggered grid of mesh points to resolve the x direction. Typically, 257 grid points and 33 Chebyshev polynomials are used in the computations.

Orszag, S. A.↗

Q-value (MeV/fission) Determination for the Advanced Test Reactor

This report documents a step-by-step procedure that can be used to calculate a Q-value for the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The Q-value calculation here is an estimate of the total energy released per fission event in the ATR driver fuel in units of MeV/fission and is recognized as a reactor-specific Q-value for the ATR. Such a Q-value is needed INL ATR reactor physicists to normalize calculated fluxes and reaction rates to measured ATR total core and lobe powers. The calculated ATR Q-value and the procedure used to calculate this value are presented in a detailed manner so as to establish a credible basis, such that this single Q-value can be used consistently among all ATR reactor physicists. A Q-value is essentially the summation of all the kinetic energy from all radiation components released from a single fission event that ends up as heat in the reactor coolant. Estimation of a Q-value for a given reactor can theoretically be very complex because it is dependent on several variables, which include (1) the materials in the reactor core, (2) the neutron energy spectrum, and (3) the mix of actinide isotopes that are being simultaneously fissioned. Since these three variables can potentially change over time or with reactor burnup, this potentially makes the Q-value a time-dependent quantity. However, for ATR, we will invoke some simplifying assumptions, based on the unique features of the ATR core and supported by neutronic calculations, which will reduce the complexity of the problem and lead to a single time-independent Q-value. Of the three variables, the materials in the core have the greatest impact on the ATR Q-value. Since the materials in the core are most important, an array of components (materials) have been carefully selected to compose a typical ATR core configuration. It should be noted that the step-by-step procedure here for calculating a reactor-specific Q-value can be applied to any nuclear reactor. In a more general procedure, if one needed to include time-dependent spectral and actinide composition changes as a function of burnup for a specific reactor, this procedure can still be used, but at discrete time steps throughout the burnup. The result will be a set of discrete Q-values as a function of burnup. However, here we will apply the procedure to the ATR, as an example, and obtain a single Q-value that is valid for the ATR reactor as a function of burnup. In the evaluated nuclear data file (ENDF), Q-values for specific actinides are known for a reasonably high degree of accuracy, especially the major uranium and plutonium isotopes. The isotopic Q-values are composed of the kinetic energy of the fission fragments and the energies of the various radiation components emitted from the fission event. One complication is the fact that some of these energy components are dependent on the incident neutron energy (EINC) that induces the fission event (neutron energy spectrum dependence). For ATR, we will estimate an average neutron energy based on the neutron energy flux weighted by the fission cross sections and use this average neutron energy value in the ENDF formulas. This neutron energy dependence is one of two dependencies that make the final ATR Q-value a reactor-specific value. The second dependency, and the most important variable for ATR, involves the materials in the core and the de-excitation radiations produced from neutron radiative capture radiation; radiation energy that becomes heat in the reactor coolant. This important dependency is discussed in detail below. The third dependency, or the mix of fissioning actinides as a function of ATR burnup, is ignored because the bulk of the fissions over a cycle are always due to U-235 in the high-enriched ATR core. In a fission event, the fission energy is partitioned between the fission fragments and the radiation components that include neutrons, gamma rays, and beta particles. The main variable that makes the calculated Q-value reactor-specific is the amount of radiative capture energy produced in the core due to prompt and delayed neutron capture in the reactor core materials and the subsequent release of nuclear binding energy in the form of de-excitation gamma radiation energy in the reactor core. This energy component, neutron radiative capture energy, is not in the ENDF, because it depends on the reactor and its core materials. The radiative capture gamma energy release can be significant, or approximately 3–12 MeV/fission depending on the materials in the reactor core (fuel materials, cladding, structural components, coolant, targets, reflector, etc.). For the ATR, the energy released from radiative capture gamma radiation is calculated to be near the middle of the 3–12 MeV range. This makes the radiative capture radiation component an important and significant contribution to the total ATR Q-value. The ATR has unique reactor features that will allow us to simplify the complex procedure for determining an ATR reactor-specific Q-value. For example, the ATR is a thermal reactor and contains high-enriched fuel (93.15 wt% U-235). Consequently, the bulk of the fissions in the ATR core from beginning-of-cycle (BOC) to end-of-cycle are from U-235. Even though U-238 makes up approximately 7% of the uranium atoms in the fuel, the number of fissions relative to U-235 is insignificant, since U-238 is fertile and ATR is a thermal reactor. The same is true for U-234 and U-236 (both fertile). The ATR’s high-enriched uranium fuel also inhibits the buildup of large quantities of plutonium isotopes. So, even though there is some buildup of U-236, Np-237, and Pu-239 in the ATR driver fuel, the impact or contribution of these isotopes fissions on the ATR Q-value is minor compared to U-235. Nonetheless, we will include fission contributions from all the major uranium isotopes (U-234, U-235, U-236, and U-238) in the determination of an average isotopic Q-value that is used in the ATR Q-value determination. Although the ATR Q-value calculated here is based on BOC conditions (fresh fuel with no higher order actinide buildup), it is believed that the ATR Q-value is relatively constant with burnup or during any given ATR power cycle (14–60 days). The reason is that the vast majority of the core driver fuel fissions are always from U-235 during the cycle. Also, since we effectively assume instantaneous energy release for the fission product gammas and beta particles and delayed neutrons from a fission event, this corresponds to equilibrium conditions for all fission product concentrations in the ATR driver fuel too. The goal of this effort was to determine a specific numerical and core-average Q-value for the recoverable energy released by a single fission event (MeV/fission) for the ATR. This calculated value has the potential to help standardize and add credibility to not only the Monte Carlo tally normalization process used by many ATR core physics analysts, but also to the normalization process for all reactor physics calculation methods involving ATR analyses. Currently, the ATR core physics analysts use slightly different Q-values. Some values are known to be conservative for specific applications. However, sometimes a more accurate Q-value is needed for the calculation in which ATR test specimens require non-conservative and as-accurate-as-possible Q-values to estimate burnups, heat rates, neutron fluxes, nuclear reaction rates, etc., where corresponding measured post-irradiation examination data provides the ultimate validation for the calculated results. A detailed Q-value procedure was developed and is outlined in this document along with numerical values calculated specifically for the determination of the ATR Q-value. This report attempts to document in some detail the calculation methodology, nuclear data, computer code, computer model, code calculated results, hand calculations, assumptions, and logic that went into the determination of the ATR Q-value.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗