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Title: K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

Abstract

Abstract not provided.

Authors:
 [1];  [2];  [2];  [2];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1429825
Report Number(s):
SAND-2017-8551J
656142
DOE Contract Number:
AC04-94AL85000
Resource Type:
Journal Article
Country of Publication:
United States
Language:
English

Citation Formats

DeChant, Lawrence J., Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution. United States: N. p., 2017. Web. doi:10.2514/6.2017-4167.
DeChant, Lawrence J., Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, & Arunajatesan, Srinivasan. K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution. United States. doi:10.2514/6.2017-4167.
DeChant, Lawrence J., Ray, Jaideep, Lefantzi, Sophia, Ling, Julia, and Arunajatesan, Srinivasan. Fri . "K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution". United States. doi:10.2514/6.2017-4167.
@article{osti_1429825,
title = {K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution},
author = {DeChant, Lawrence J. and Ray, Jaideep and Lefantzi, Sophia and Ling, Julia and Arunajatesan, Srinivasan},
abstractNote = {Abstract not provided.},
doi = {10.2514/6.2017-4167},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jun 02 00:00:00 EDT 2017},
month = {Fri Jun 02 00:00:00 EDT 2017}
}
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  • Abstract not provided.
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  • Plasma liner driven magnetoinertial fusion (PLMIF) is a fusion energy concept that utilizes an imploding plasma liner to shock heat and compress a magnetized target plasma to fusion conditions. The fusion burn fraction is linearly proportional to the confinement (or ''dwell'') time of the liner-target system at peak compression, and therefore it is important to estimate the dwell time accurately in order to assess the fusion energy yield and gain. In this work, the dwell time has been estimated using the exact solution to a self-similar converging shock model. The dwell time was found to be determined by the summore » of the outgoing shock and rarefaction times through the plasma liner at peak compression, and for chosen PLMIF conditions the dwell time was on the order of 1 {mu}s. In addition, we show that the engineering gain, i.e., the total energy extracted as electricity (from fusion plus expanded liner energy) divided by the electrical energy required to implode the liner, exceeds unity for a wide range of liner thicknesses and specific heat ratios.« less
  • We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less