A Collaboration-based Approach to CFD Model Validation and Uncertainty Quantification (VUQ) Using Data from a Laminar Helium Plume

被引:0
|
作者
Weston M. Eldredge
Pál Tóth
Laurie Centauri
Eric G. Eddings
Kerry E. Kelly
Terry A. Ring
Axel Schönbucher
Jeremy N. Thornock
Philip J. Smith
机构
[1] University of Utah,Institute for Clean and Secure Energy
[2] University of Duisburg-Essen,Institute for Chemical Engineering I
来源
关键词
Collaboration; Model validation; CFD; Data set consistency; Buoyant plumes;
D O I
暂无
中图分类号
学科分类号
摘要
An effective approach to the model VUQ process by means of direct collaboration between computationalist and experimental data analyst is proposed. An analysis of data from a laminar helium plume experiment provides a demonstration of the proposed collaboration process. Consistency analysis serves a central role in the collaboration. It takes the data and uncertainties from both analyst and computationalist and provides an objective and quantifiable measure of agreement between the two. Despite the simplicity of the laminar helium system and the computational model, certain phenomena brought to light in the collaboration process make it difficult to find quantitative agreement in the data. These phenomena include the unsteady behavior of air flow in an open room, and the presence of helium permeation to the region near the plume. Important sources of error in the simulation include uncertainty in the room temperature (295.15 to 305.15 K), uncertainty in the helium inlet velocity (0.1215 ms\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {m}{s}$\end{document} to 0.1415 ms\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {m}{s}$\end{document}), and uncertainty in local helium permeation (0 % to 3 % by mass.) The collaboration process allows for a better understanding of the phenomena affecting the plume and the relative sensitivies of the system to these phenomena.
引用
收藏
页码:427 / 449
页数:22
相关论文
共 45 条
  • [1] A Collaboration-based Approach to CFD Model Validation and Uncertainty Quantification (VUQ) Using Data from a Laminar Helium Plume
    Eldredge, Weston M.
    Toth, Pal
    Centauri, Laurie
    Eddings, Eric G.
    Kelly, Kerry E.
    Ring, Terry A.
    Schoenbucher, Axel
    Thornock, Jeremy N.
    Smith, Philip J.
    FLOW TURBULENCE AND COMBUSTION, 2016, 97 (02) : 427 - 449
  • [2] Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements
    Brown, Andrew M.
    Peck, Jeffrey A.
    Stewart, Eric C.
    MODEL VALIDATION AND UNCERTAINTY QUANTIFICATION, VOL 3, 2019, : 283 - 289
  • [3] Quantification and Evaluation of Uncertainty in the Mathematical Modelling of a Suspension Strut Using Bayesian Model Validation Approach
    Mallapur, Shashidhar
    Platz, Roland
    MODEL VALIDATION AND UNCERTAINTY QUANTIFICATION, VOL 3, 2017, : 113 - 124
  • [4] Application of Bound-to-Bound Data Collaboration approach for development and uncertainty quantification of a reduced char combustion model Salvatore
    Iavarone, Salvatore
    Oreluk, James
    Smith, Sean T.
    Hegde, Arun
    Li, Wenyu
    Packard, Andrew
    Frenklach, Michael
    Smith, Philip J.
    Contino, Francesco
    Parente, Alessandro
    FUEL, 2018, 232 : 769 - 779
  • [5] Parameter uncertainty quantification for a four-equation transition model using a data assimilation approach
    Yang, Muchen
    Xiao, Zhixiang
    RENEWABLE ENERGY, 2020, 158 (158) : 215 - 226
  • [6] Development of new wall functions for RANS model in superhydrophobic surface based on CFD and data-driven uncertainty quantification
    Nguyen, Hoai-Thanh
    Kim, Byeong-Cheon
    Lee, Sang-Wook
    Ryu, Jaiyoung
    Kim, Minjae
    Yoon, Jaemoon
    Chang, Kyoungsik
    COMPUTERS & FLUIDS, 2025, 292
  • [7] A likelihood-based approach of uncertainty quantification using both sparse point data and interval estimates
    Yang, Lechang
    Guo, Yanling
    Kong, Zifan
    Niu, Nanpo
    2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-PARIS), 2019, : 192 - 197
  • [8] Data-driven model-based flow measurement uncertainty quantification for building central cooling systems using a probabilistic approach
    Sun, Shaobo
    Shan, Kui
    Wang, Shengwei
    SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT, 2023, 29 (03) : 297 - 310
  • [9] Analysis of the shear-driven flow in a scale model of a phase separator: Validation of a coupled CFD approach using experimental data from a physical model
    Ortner, Benjamin
    Schmidberger, Christian
    Prieler, Rene
    Mally, Valentin
    Hochenauer, Christoph
    INTERNATIONAL JOURNAL OF MULTIPHASE FLOW, 2024, 177
  • [10] A Framework for In Silico Clinical Trials for Medical Devices Using Concepts From Model Verification, Validation, and Uncertainty Quantification
    Bodner, Jeff
    Kaul, Vikas
    JOURNAL OF VERIFICATION, VALIDATION AND UNCERTAINTY QUANTIFICATION, 2022, 7 (02):