Integrated data-model analysis facilitated by an Instrumental Model

被引:14
|
作者
Yeates, Devin R. [1 ]
Li, Wenjun [2 ]
Westmoreland, Phillip R. [2 ]
Speight, William [1 ]
Russi, Trent [1 ]
Packard, Andrew [1 ]
Frenklach, Michael [1 ,3 ]
机构
[1] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA
[2] N Carolina State Univ, Dept Chem & Biomol Engn, Raleigh, NC 27695 USA
[3] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Environm Energy Technol Div, Berkeley, CA 94720 USA
关键词
Uncertainty quantification; Model validation; Premixed flame; Data Collaboration; Instrumental Model; PHOTOIONIZATION CROSS-SECTIONS; COMBUSTION CHEMISTRY; MASS-SPECTROMETRY; CYCLOHEXANE FLAME;
D O I
10.1016/j.proci.2014.05.090
中图分类号
O414.1 [热力学];
学科分类号
摘要
A paradigm is described and demonstrated for rigorously evaluating model-versus-data agreement while extracting new insights for improving the model and experiment. "Bound-to-Bound Data Collaboration" (B2B-DC) is augmented with an Instrumental Model, integrating uncertainty quantification of the reactor model, chemical model, and data analysis. The subject of analysis is a fuel-lean C2H2/O-2/Ar premixed laminar flat flame, mapped with VUV-photoionization molecular-beam mass spectrometry at the Advanced Light Source of Lawrence Berkeley National Laboratory. Experimental signals were modeled with a CHEMKIN flame code augmented with an Instrumental Model. Consistency of the model and raw experimental data are determined as a quantitative measure of their agreement. Features of the mole-fraction profiles are predicted for O, OH, C2H3, and background contributions to H2O measurements. Also computed are posterior distributions of the initial targets and model parameters, as well as their correlations. This approach to model-versus-data assessment promises to advance the science and practical utility of modeling, establishing validity rigorously while identifying and ranking the impacts of specific model and data uncertainties for model and data improvements. Published by Elsevier Inc. on behalf of The Combustion Institute.
引用
收藏
页码:597 / 605
页数:9
相关论文
共 50 条
  • [41] Prognosis for stochastic degrading systems with massive data: A data-model interactive perspective
    Li, Tianmei
    Pei, Hong
    Si, Xiaosheng
    Lei, Yaguo
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 237
  • [42] Data-model relationship in text-independent speaker recognition
    Mason, JSD
    Evans, NWD
    Stapert, R
    Auckenthaler, R
    EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2005, 2005 (04) : 471 - 481
  • [43] Mid-Piacenzian mean annual sea surface temperature analysis for data-model comparisons
    Dowsett, H. J.
    Robinson, M. M.
    Stoll, D. K.
    Foley, K. M.
    STRATIGRAPHY, 2010, 7 (2-3): : 189 - 198
  • [44] Exceptional carbon uptake in European forests during the warm spring of 2007: a data-model analysis
    Delpierre, N.
    Soudani, K.
    Francois, C.
    Koestner, B.
    Pontailler, J. -Y.
    Nikinmaa, E.
    Misson, L.
    Aubinet, M.
    Bernhofer, C.
    Granier, A.
    Gruenwald, T.
    Heinesch, B.
    Longdoz, B.
    Ourcival, J. -M.
    Rambal, S.
    Vesala, T.
    Dufrene, E.
    GLOBAL CHANGE BIOLOGY, 2009, 15 (06) : 1455 - 1474
  • [45] Preface: Advances in paleoclimate data synthesis and analysis of associated uncertainty: towards data-model integration to understand the climate
    Jonkers, Lukas
    Bothe, Oliver
    Kucera, Michal
    CLIMATE OF THE PAST, 2021, 17 (06) : 2577 - 2581
  • [46] A Data-Model Fusion Strategy to Improve Detection Performance in the Presence of Target Signal Model Mismatch
    Xu, Xiao
    Li, Yang
    Yeh, Chunmao
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2024, 60 (03) : 3282 - 3293
  • [47] Data-model driven probability model of branch power flow for distribution networks and its application to analysis of overload and line loss
    Zhang, Ren
    Liu, Haoming
    Wang, Jian
    Cai, Haiqing
    Gu, Haohan
    Chen, Wei
    Chen, Zhihao
    ELECTRIC POWER SYSTEMS RESEARCH, 2025, 238
  • [48] Mid-Holocene climate change in Europe: a data-model comparison
    Brewer, S.
    Guiot, J.
    Torre, F.
    CLIMATE OF THE PAST, 2007, 3 (03) : 499 - 512
  • [49] RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics
    Liemohn, Michael W.
    Shane, Alexander D.
    Azari, Abigail R.
    Petersen, Alicia K.
    Swiger, Brian M.
    Mukhopadhyay, Agnit
    JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2021, 218
  • [50] A new global biome reconstruction and data-model comparison for the Middle Pliocene
    Salzmann, U.
    Haywood, A. M.
    Lunt, D. J.
    Valdes, P. J.
    Hill, D. J.
    GLOBAL ECOLOGY AND BIOGEOGRAPHY, 2008, 17 (03): : 432 - 447