A tangent linear approximation of the ignition delay time. I: Sensitivity to rate parameters

Abstract A tangent linear approximation is developed to estimate the sensitivity of the ignition delay time with respect to individual rate parameters in a detailed chemical mechanism. Attention is focused on a gas mixture reacting under adiabatic, constant-volume conditions. The uncertainty in the rates of elementary reactions is described in terms of uncertainty factors, and are parameterized using independent canonical random variables. The approach is based on integrating the linearized system of equations governing the evolution of the partial derivatives of the state vector with respect to individual random variables, and a linearized approximation is developed to relate the ignition delay sensitivity to the scaled partial derivatives of temperature. The efficiency of the approach is demonstrated through applications to chemical mechanisms of different sizes. In particular, the computations indicate that for detailed reaction mechanisms the TLA leads to robust local sensitivity predictions at a computational cost that is order-of-magnitude smaller than that incurred by finite-difference approaches based on one-at-a-time rate perturbations.

[1]  H. Pitsch,et al.  Adjoint-based sensitivity analysis of quantities of interest of complex combustion models , 2018, Combustion Theory and Modelling.

[2]  S. M. Sarathy,et al.  Alcohol combustion chemistry , 2014 .

[3]  T. Turányi Sensitivity analysis of complex kinetic systems. Tools and applications , 1990 .

[4]  C. Law,et al.  Evolution of sensitivity directions during autoignition , 2019, Proceedings of the Combustion Institute.

[5]  C. Westbrook,et al.  Chemical kinetic modeling of component mixtures relevant to gasoline , 2009 .

[6]  R. Ghanem,et al.  Quantifying uncertainty in chemical systems modeling , 2004 .

[7]  C. Druzgalski,et al.  Numerical study of a micro flow reactor at engine pressures: Flames with repetitive extinction and ignition and simulations with a reduced chemical model , 2018, Combustion and Flame.

[8]  B. Weber,et al.  Autoignition of n-butanol at elevated pressure and low-to-intermediate temperature , 2011, 1706.00867.

[9]  Jingping Liu,et al.  Numerical study on auto-ignition characteristics of hydrogen-enriched methane under engine-relevant conditions , 2019, Energy Conversion and Management.

[10]  Tamás Turányi,et al.  Applications of sensitivity analysis to combustion chemistry , 1997 .

[11]  Serge Prudhomme,et al.  Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic Nitrogen , 2011, J. Comput. Phys..

[12]  N. Peters,et al.  Shock tube investigations of ignition delays of n-butanol at elevated pressures between 770 and 1250 K , 2011 .

[13]  Richard A. Yetter,et al.  A Comprehensive Reaction Mechanism For Carbon Monoxide/Hydrogen/Oxygen Kinetics , 1991 .

[14]  Tamás Turányi,et al.  Effect of the uncertainty of kinetic and thermodynamic data on methane flame simulation results , 2002 .

[15]  O. Knio,et al.  A hierarchical method for Bayesian inference of rate parameters from shock tube data: Application to the study of the reaction of hydroxyl with 2-methylfuran , 2017 .

[16]  O. Knio,et al.  Uncertainty quantification of ion chemistry in lean and stoichiometric homogenous mixtures of methane, oxygen, and argon , 2015 .

[17]  David A. Sheen,et al.  The method of uncertainty quantification and minimization using polynomial chaos expansions , 2011 .

[18]  Habib N. Najm,et al.  Uncertainty quantification in the ab initio rate-coefficient calculation for the reaction , 2013 .

[19]  Zhao Yang,et al.  Sensitivity analysis and chemical reaction mechanism simplification of blast furnace gas in gas turbine combustor environment , 2017 .

[20]  Ronald K. Hanson,et al.  Shock tube measurements of ignition delay times for the butanol isomers , 2012 .

[21]  R. D. Berry,et al.  DATA-FREE INFERENCE OF UNCERTAIN PARAMETERS IN CHEMICAL MODELS , 2014 .

[22]  R. Tempone,et al.  Optimal Bayesian Experimental Design for Priors of Compact Support with Application to Shock‐Tube Experiments for Combustion Kinetics , 2016 .

[23]  Xin He,et al.  An experimental and modeling study of iso-octane ignition delay times under homogeneous charge compression ignition conditions , 2005 .

[24]  Alison S. Tomlin,et al.  The role of sensitivity and uncertainty analysis in combustion modelling , 2013 .

[25]  Paul G. Constantine,et al.  Global sensitivity metrics from active subspaces , 2015, Reliab. Eng. Syst. Saf..

[26]  William J. Pitz,et al.  Detailed Kinetic Modeling of Low-Temperature Heat Release for PRF Fuels in an HCCI Engine , 2009 .

[27]  Paul Roth,et al.  Autoignition of gasoline surrogates mixtures at intermediate temperatures and high pressures , 2008 .

[28]  Tamás Turányi,et al.  Determination of the uncertainty domain of the Arrhenius parameters needed for the investigation of combustion kinetic models , 2012, Reliab. Eng. Syst. Saf..

[29]  Omar M. Knio,et al.  Global sensitivity analysis of n-butanol reaction kinetics using rate rules , 2018, Combustion and Flame.

[30]  M. Vohra,et al.  Subspace-based dimension reduction for chemical kinetics applications with epistemic uncertainty , 2018 .

[31]  Soonho Song,et al.  A rapid compression machine study of hydrogen effects on the ignition delay times of n-butane at low-to-intermediate temperatures , 2020 .

[32]  S. Scott Goldsborough,et al.  A chemical kinetically based ignition delay correlation for iso-octane covering a wide range of conditions including the NTC region , 2009 .

[33]  S. Karimkashi,et al.  Numerical study on tri-fuel combustion: Ignition properties of hydrogen-enriched methane-diesel and methanol-diesel mixtures , 2020, International Journal of Hydrogen Energy.

[34]  F. Egolfopoulos,et al.  Direct sensitivity analysis for ignition delay times , 2019, Combustion and Flame.

[35]  Sankaran Mahadevan,et al.  Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates , 2018, Journal of Scientific Computing.

[36]  O P Le Maître,et al.  Spectral stochastic uncertainty quantification in chemical systems , 2004 .

[37]  Tamás Turányi,et al.  Uncertainty analysis of updated hydrogen and carbon monoxide oxidation mechanisms , 2004 .

[38]  Taotao Zhou,et al.  Numerical simulation of the effects of evaporation on the n-heptane/air auto-ignition process under different initial air temperatures , 2019, Fuel.

[39]  A. Hindmarsh,et al.  CVODE, a stiff/nonstiff ODE solver in C , 1996 .

[40]  Cosmin Safta,et al.  Chemical model reduction under uncertainty , 2017 .

[41]  Martin A. Reno,et al.  Coefficients for calculating thermodynamic and transport properties of individual species , 1993 .

[42]  Cosmin Safta,et al.  Uncertainty quantification of reaction mechanisms accounting for correlations introduced by rate rules and fitted Arrhenius parameters , 2013 .

[43]  G. D. Byrne,et al.  VODE: a variable-coefficient ODE solver , 1989 .