A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

Yue, Zongyu; Xu, Chao; Som, Sibendu; Sluder, C. Scott; Edwards, K. Dean; Whitesides, Russell A.; McNenly, Matthew J.

doi:10.1115/1.4050583

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

Journal Article · Mon May 31 00:00:00 EDT 2021 · Journal of Engineering for Gas Turbines and Power

DOI:https://doi.org/10.1115/1.4050583· OSTI ID:1811393

Yue, Zongyu ^[1]; Xu, Chao ^[2]; Som, Sibendu ^[3]; ^[4]; ^[4]; Whitesides, Russell A. ^[5]; McNenly, Matthew J. ^[5]

Tianjin Univ. (China). State Key Laboratory of Engines; Argonne National Lab. (ANL), Argonne, IL (United States). Energy Systems Division
Argonne National Lab. (ANL), Argonne, IL (United States). Energy Systems Division,
Argonne National Lab. (ANL), Argonne, IL (United States). Energy Systems Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy and Transportation Science Division
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1811393

Journal Information:: Journal of Engineering for Gas Turbines and Power, Journal Name: Journal of Engineering for Gas Turbines and Power Journal Issue: 9 Vol. 143; ISSN 0742-4795

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

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