Research Papers: Forced Convection

Modified Collision Energy, a New Chemical Model in the DSMC Algorithm

[+] Author and Article Information
Ramin Zakeri

Department of Mechanical Engineering,
Shahrood University of Technology,
P.O. Box 3619995161,
Shahrood, Iran

Ramin Kamali-Moghadam

Aerospace Research Institute,
Ministry of Science, Research and Technology,
P.O. Box 14665-834,
Tehran, Iran

Mahmoud Mani

Department of Aerospace,
Amirkabir University of Technology and
Center of Excellence for Computational
Aerospace Engineering,
P.O. Box 15875-4413,
Tehran, Iran

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received February 13, 2018; final manuscript received September 11, 2018; published online November 20, 2018. Assoc. Editor: George S. Dulikravich.

J. Heat Transfer 141(1), 011701 (Nov 20, 2018) (11 pages) Paper No: HT-18-1088; doi: 10.1115/1.4041552 History: Received February 13, 2018; Revised September 11, 2018

A new chemical model in the direct simulation Monte Carlo (DSMC) algorithm, entitled modified collision energy (MCE), has been developed for simulation of reactive rarefied flows without some limitations of the conventional macroscopic models. Determination of correct values of the experimental parameters for computing the Arrhenius reaction rate is a serious challenge in some macroscopic chemical reaction models such as total collision energy (TCE) and general collision energy (GCE). A slight variation of these constant parameters in the Arrhenius relation could lead to significant change in the results. On the other hand, these experimental parameters have been extracted empirically only for limit number of gases and so they cannot be used for simulation of chemical reactions in various types of gases. Since some of these constants have been determined experimentally by several studies, they have been reported by different values in different references. The proposed MCE model in the present study is a reliable method to properly determine values of these parameters for all types of gases with the intrinsic properties of the particles and without need of any experimental data. Extraction of the constant parameters has been carried out using the analytical method or numerical quantum kinetics (QK) or modified quantum kinetics (MQK) models. The proposed MCE method has been evaluated in four test cases, including assessment of the reaction rate in equilibrium and nonequilibrium conditions, studying of the rarefied flow along the stagnation line, and investigation of the hypersonic gas flow over the axial symmetry blunt nose. The results show that the proposed method has desirable accuracy without use of any experimental parameters. The MCE method can also be used to calibrate the macroscopic reactive models such as the TCE and GCE.

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Fig. 1

Comparison of the nitrogen reaction rate at equilibrium condition

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Fig. 2

Comparison of the nitrogen species concentration at nonequilibrium condition with different methods

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Fig. 3

Comparison of different types of temperature along the stagnation line

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Fig. 4

Comparison of density and velocity distribution along the stagnation line

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Fig. 5

Comparison of the surface heat flux at the stagnation point for different stream velocity (U)

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Fig. 6

Blunt body configuration [8] and used mesh in the solutions

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Fig. 7

Comparison of different rate of reactions of air species along the temperature: N2 + N → N + N + N, N2 + O → N + N + O; N2 + N2→ N + N + N2, N2 + O2→ N + N + O2; O2 + N2→ O + O + N2, O2 + O2→ O + O + O2; N2 + NO → N + N + NO; NO + O2→ N + O + O2, NO + N2→ N + O + N2; O2 + N → O + O + N, O2 + O → O + O + O2; NO + N → N + O + NO, NO + O → N + O + O; NO + O2→ N + O + O2, NO + N → N + O + N

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Fig. 8

Mach number contours around the blunt body

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Fig. 9

Translational temperature contours around the blunt body

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Fig. 10

Vibrational temperature contours around the blunt body

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Fig. 11

Total temperature contours around the blunt body

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Fig. 12

Pressure contours around the blunt body

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Fig. 13

Knudsen number contours around the blunt body

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Fig. 14

Comparison of the surface heat flux around the blunt body

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Fig. 15

Convergence rate of parameters variations

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Fig. 16

Comparison of computational cost of different methods along the integration number



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