An iterative dynamic chemical stiffness removal method for reacting flow simulations

Chao Xu , Tianfeng Lu

Propulsion and Energy ›› 2025, Vol. 1 ›› Issue (1) : 3

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Propulsion and Energy ›› 2025, Vol. 1 ›› Issue (1) : 3 DOI: 10.1007/s44270-024-00006-2
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An iterative dynamic chemical stiffness removal method for reacting flow simulations

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Abstract

An iterative dynamic chemical stiffness removal method (IDCSR) based on quasi-steady-state approximation (QSSA) is proposed. The IDCSR method is built on a previously developed non-iterative method which has proved to work well for small timestep sizes. A novel iterative procedure is designed in IDCSR to enable explicit time integration of stiff chemistry at relatively large timestep sizes relevant to practical reacting flow simulations. The effectiveness of the iterative procedure is first demonstrated with a toy problem and homogeneous auto-ignition with fixed integration step sizes, showing that larger timestep sizes can be allowed for explicit time integration using IDCSR compared with the previous non-iterative method. IDCSR is then compared with existing explicit chemistry solvers for simulations of homogeneous auto-ignition and shows similar or lower computational cost but significantly higher accuracy across a wide range of timestep sizes. IDCSR is further combined with an automatic adaptive time-stepping scheme for simulations of 0-D homogeneous auto-ignition and a 2-D laminar lifted n-dodecane jet flame. For the 0-D auto-ignition simulations, IDCSR is shown to reduce both the error (by 43%–90%) and computational cost (by 6–15 times) compared with existing explicit solvers, while achieving speed-up factors of up to 400 compared with VODE for a wide range of timestep sizes and reaction mechanisms. For the 2-D jet flame simulations, speed-up factors of 15 and 31 for chemistry integration, and 5 and 9 for overall simulation, are achieved by IDCSR compared with CVODE with and without analytic Jacobian, respectively.

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Chao Xu, Tianfeng Lu. An iterative dynamic chemical stiffness removal method for reacting flow simulations. Propulsion and Energy, 2025, 1(1): 3 DOI:10.1007/s44270-024-00006-2

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References

[1]

Lu T, Law CK. Toward accommodating realistic fuel chemistry in large-scale computations Prog Energy Combust Sci, 2009, 35(2): 192-215.

[2]

Lu T, Law CK. A directed relation graph method for mechanism reduction Proc Combust Inst, 2005, 30(1): 1333-1341.

[3]

Lu T, Law CK. On the applicability of directed relation graphs to the reduction of reaction mechanisms Combust Flame, 2006, 146(3): 472-483.

[4]

Sun W, Chen Z, Gou X, Ju Y. A path flux analysis method for the reduction of detailed chemical kinetic mechanisms Combust Flame, 2010, 157(7): 1298-1307.

[5]

Pepiot-Desjardins P, Pitsch H. An efficient error-propagation-based reduction method for large chemical kinetic mechanisms Combust Flame, 2008, 154(1–2): 67-81.

[6]

Zheng XL, Lu TF, Law CK. Experimental counterflow ignition temperatures and reaction mechanisms of 1,3-butadiene Proc Combust Inst, 2007, 31(1): 367-375.

[7]

Tomlin AS, Pilling MJ, Turányi T, Merkin JH, Brindley J. Mechanism reduction for the oscillatory oxidation of hydrogen: sensitivity and quasi-steady-state analyses Combust Flame, 1992, 91(2): 107-130.

[8]

Strang G. On the construction and comparison of difference schemes SIAM J Numer Anal, 1968, 5(3): 506-517.

[9]

Wu H, Ma PC, Ihme M. Efficient time-stepping techniques for simulating turbulent reactive flows with stiff chemistry Comput Phys Commun, 2019, 243: 81-96.

[10]

Ren Z, Xu C, Lu T, Singer MA. Dynamic adaptive chemistry with operator splitting schemes for reactive flow simulations J Comput Phys, 2014, 263: 19-36.

[11]

Brown PN, Byrne GD, Hindmarsh AC. VODE: A Variable-Coefficient ODE Solver SIAM J Sci Stat Comput, 1989, 10(5): 1038-1051.

[12]

Caracotsios M, Stewart WE. Sensitivity analysis of initial value problems with mixed odes and algebraic equations Comput Chem Eng, 1985, 9(4): 359-365.

[13]

Gao Y, Liu Y, Ren Z, Lu T. A dynamic adaptive method for hybrid integration of stiff chemistry Combust Flame, 2015, 162(2): 287-295.

[14]

Xu C, Gao Y, Ren Z, Lu T. A sparse stiff chemistry solver based on dynamic adaptive integration for efficient combustion simulations Combust Flame, 2016, 172: 183-193.

[15]

Perini F, Galligani E, Reitz RD. An analytical Jacobian approach to sparse reaction kinetics for computationally efficient combustion modeling with large reaction mechanisms Energy Fuels, 2012, 26(8): 4804-4822.

[16]

McNenly MJ, Whitesides RA, Flowers DL. Faster solvers for large kinetic mechanisms using adaptive preconditioners Proc Combust Inst, 2015, 35(1): 581-587.

[17]

Wang JH, Pan S, Hu XY. A Species-clustered splitting scheme for the integration of large-scale chemical kinetics using detailed mechanisms Combust Flame, 2019, 205: 41-54.

[18]

Aissa M, Verstraete T, Vuik C. Toward a GPU-aware comparison of explicit and implicit CFD simulations on structured meshes Comput Math Appl, 2017, 74(1): 201-217.

[19]

Lam SH. Model reductions with special CSP data Combust Flame, 2013, 160(12): 2707-2711.

[20]

Lam SH. Using CSP to understand complex chemical kinetics Combust Sci Technol, 1993, 89(5–6): 375-404.

[21]

Lam SH, Goussis DA. The CSP method for simplifying kinetics Int J Chem Kinet, 1994, 26(4): 461-486.

[22]

Maas U, Pope SB. Simplifying chemical kinetics: intrinsic low-dimensional manifolds in composition space Combust Flame, 1992, 88(3–4): 239-264.

[23]

Bodenstein M. Eine Theorie Der Photochemischen Reaktionsgeschwindigkeiten Z Phys Chem, 1913, 85(1): 329-397.

[24]

Chapman DL, Underhill LK. LV.—The interaction of chlorine and hydrogen. The influence of mass J Chem Soc Trans, 1913, 103: 496-508.

[25]

Gou X, Sun W, Chen Z, Ju Y. A dynamic multi-timescale method for combustion modeling with detailed and reduced chemical kinetic mechanisms Combust Flame, 2010, 157(6): 1111-1121.

[26]

Lu TF, Law CK, Yoo CS, Chen JH. Dynamic stiffness removal for direct numerical simulations Combust Flame, 2009, 156(8): 1542-1551.

[27]

Morii Y, Terashima H, Koshi M, Shimizu T, Shima E. ERENA: a fast and Robust Jacobian-free integration method for ordinary differential equations of chemical kinetics J Comput Phys, 2016, 322: 547-558.

[28]

Mott DR, Oran ES, van Leer B. A quasi-steady-state solver for the stiff ordinary differential equations of reaction kinetics J Comput Phys, 2000, 164(2): 407-428.

[29]

Morii Y, Shima E. Optimization of one-parameter family of integration formulae for solving stiff chemical-kinetic ODEs Sci Rep, 2020, 10(1): 1-13.

[30]

Owoyele O, Pal P. ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers. Energy AI. 2021;7. https://doi.org/10.1016/j.egyai.2021.100118.

[31]

Ji W, Qiu W, Shi Z, Pan S, Deng S. Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics J Phys Chem A, 2021, 125(36): 8098-8106.

[32]

Williams FA Combustion theory, 1985 2 Menlo Park Benjamin-Cummings Publishing Company

[33]

Lu T, Law CK. A criterion based on computational singular perturbation for the identification of quasi steady state species: a reduced mechanism for methane oxidation with NO chemistry Combust Flame, 2008, 154(4): 761-774.

[34]

Li J, Zhao Z, Kazakov A, Dryer FL, Dryer FL. An updated comprehensive kinetic model of hydrogen combustion Int J Chem Kinet, 2004, 36(10): 566-575.

[35]

Yao T, Pei Y, Zhong BJJ, Som S, Lu T, Luo KH. A compact skeletal mechanism for n-dodecane with optimized semi-global low-temperature chemistry for diesel engine simulations Fuel, 2017, 191(March): 339-349.

[36]

Richards KJ, Senecal PK, Pomraning E CONVERGE Manual (Version 2.3), 2016 Madison Convergent Science Inc.

[37]

Senecal PK, Richards KJ, Pomraning E, Yang T, Dai MZ, McDavid RM, Patterson MA, Hou S, Shethaji T. A new parallel cut-cell Cartesian CFD code for rapid grid generation applied to in-cylinder diesel engine simulations SAE Technical Papers, 2007.

[38]

Cohen SD, Hindmarsh AC, Dubois PF. CVODE, A Stiff/Nonstiff ODE Solver in C Comput Phys, 1996, 10(2): 138.

[39]

Krisman A, Hawkes ER, Talei M, Bhagatwala A, Chen JH. Polybrachial structures in dimethyl ether edge-flames at negative temperature coefficient conditions Proc Combust Inst, 2015, 35(1): 999-1006.

Funding

Vehicle Technologies Program(DE-EE0008875)

National Science Foundation(CBET-1258646)

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UChicago Argonne, LLC, Operator of Argonne National Laboratory 2024

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