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Title: Computational ESD Study: Physics- Based Simulations

Authors:
 [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1394938
Report Number(s):
LA-UR-17-28492
DOE Contract Number:
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: Computational ESD Study: Physics- Based Simulatio ; 2017-04-25 - 2017-04-25 ; Pantex, Texas, United States
Country of Publication:
United States
Language:
English

Citation Formats

Mace, Jonathan Lee. Computational ESD Study: Physics- Based Simulations. United States: N. p., 2017. Web.
Mace, Jonathan Lee. Computational ESD Study: Physics- Based Simulations. United States.
Mace, Jonathan Lee. 2017. "Computational ESD Study: Physics- Based Simulations". United States. doi:. https://www.osti.gov/servlets/purl/1394938.
@article{osti_1394938,
title = {Computational ESD Study: Physics- Based Simulations},
author = {Mace, Jonathan Lee},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 9
}

Conference:
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  • A computational code system based on coupling the Monte Carlo code MCNP5 and the Computational Fluid Dynamics (CFD) code STAR-CD was developed as an audit tool for lower order nuclear reactor calculations. This paper presents the methodology of the developed computer program 'McSTAR'. McSTAR is written in FORTRAN90 programming language and couples MCNP5 and the commercial CFD code STAR-CD. MCNP uses a continuous energy cross section library produced by the NJOY code system from the raw ENDF/B data. A major part of the work was to develop and implement methods to update the cross section library with the temperature distributionmore » calculated by STARCD for every region. Three different methods were investigated and implemented in McSTAR. The user subroutines in STAR-CD are modified to read the power density data and assign them to the appropriate variables in the program and to write an output data file containing the temperature, density and indexing information to perform the mapping between MCNP and STAR-CD cells. Preliminary testing of the code was performed using a 3x3 PWR pin-cell problem. The preliminary results are compared with those obtained from a STAR-CD coupled calculation with the deterministic transport code DeCART. Good agreement in the k{sub eff} and the power profile was observed. Increased computational capabilities and improvements in computational methods have accelerated interest in high fidelity modeling of nuclear reactor cores during the last several years. High-fidelity has been achieved by utilizing full core neutron transport solutions for the neutronics calculation and computational fluid dynamics solutions for the thermal-hydraulics calculation. Previous researchers have reported the coupling of 3D deterministic neutron transport method to CFD and their application to practical reactor analysis problems. One of the principal motivations of the work here was to utilize Monte Carlo methods to validate the coupled deterministic neutron transport and CFD solutions. Previous researchers have successfully performed Monte Carlo calculations with limited thermal feedback. In fact, much of the validation of the deterministic neutronics transport code DeCART in was performed using the Monte Carlo code McCARD which employs a limited thermal feedback model. However, for a broader range of temperature/fluid applications it was desirable to couple Monte Carlo to a more sophisticated temperature fluid solution such as CFD. This paper focuses on the methods used to couple Monte Carlo to CFD and their application to a series of simple test problems.« less
  • Experimental data often can only be interpreted by means of a computational simulation that approximately models the physical situation. The authors will discuss techniques that facilitate application to complex, large-scale simulations of the standard approach to inversion in which gradient-based optimization is used to find the parameters that bet match the data. The fundamental enabling techniques are adjoint differentiation to efficiently compute the gradient of an objective function with respect to all the variables of a simulation and relatively new gradient-based optimization algorithms. These techniques will be illustrated through the simulation of the time-dependent diffusion of infrared light through tissue,more » which has been used to perform optical tomography. The techniques discussed have a wide range of applicability to modeling including the optimization of models to achieve a desired design goal.« less
  • A standard approach to solving inversion problems that involve many parameters uses gradient-based optimization to find the parameters that best match the data. The authors discuss enabling techniques that facilitate application of this approach to large-scale computational simulations, which are the only way to investigate many complex physical phenomena. Such simulations may not seem to lend themselves to calculation of the gradient with respect to numerous parameters. However, adjoint differentiation allows one to efficiently compute the gradient of an objective function with respect to all the variables of a simulation. When combined with advanced gradient-based optimization algorithms, adjoint differentiation permitsmore » one to solve very large problems of optimization or parameter estimation. These techniques will be illustrated through the simulation of the time-dependent diffusion of infrared light through tissue, which has been used to perform optical tomography. The techniques discussed have a wide range of applicability to modeling including the optimization of models to achieve a desired design goal.« less
  • The shear-driven flow in a cavity is examined using two experimental techniques and computations. Unlike the more commonly studied lid-driven cavity, the flow in the cavity of interest here is driven by a fully developed laminar channel flow passing over the top of the cavity. The experimental techniques applied are laser Doppler velocimetry (LDV) and video-based particle-tracking particle image velocimetry (PIV). The computational simulations are performed using the commercial finite element CFD code FIDAP. The cavity Reynolds number ranged from 100 to 900 in the experiments, and from 0 to 1000 in the simulations. Results of the various techniques aremore » compared, and found to be in fairly good agreement.« less