A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures

Weiqi Sun, Yiguang Ju

Research output: Contribution to conferencePaper

Abstract

Original languageEnglish (US)
StatePublished - Jan 1 2016
Event2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016 - Princeton, United States
Duration: Mar 13 2016Mar 16 2016

Other

Other2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016
CountryUnited States
CityPrinceton
Period3/13/163/16/16

Fingerprint

Hydrogen
chemistry
air
hydrogen
Air
Reaction kinetics
Reaction rates
Ignition
Stiffness
reaction kinetics
spontaneous combustion
stiffness
transport properties
inversions
matrices

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Chemical Engineering(all)
  • Physical and Theoretical Chemistry

Cite this

Sun, W., & Ju, Y. (2016). A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures. Paper presented at 2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016, Princeton, United States.
Sun, Weiqi ; Ju, Yiguang. / A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures. Paper presented at 2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016, Princeton, United States.
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title = "A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures",
abstract = "A Smart CSP (S-CSP) method is developed and integrated with our previously developed correlated dynamic adaptive chemistry and transport (CO-DACT) method to further accelerate the chemical integration. The S-CSP method resolved a few challenges in the original simple CSP method including the failure of matrix inversion of the fast reaction subspace and the inefficiency in computation of reaction timescales. A pre-generated library is proposed to eliminate the linearly dependent reactions in the matrix associated with the fast reaction subspace. The characteristic time of elementary reactions are obtained analytically. The reaction rates of the selected fast reactions are represented by the combinations of slow reactions. Therefore, the proposed Smart CSP method can automatically remove the stiffness of the ODE system on-the-fly without human experience. The correlated reduced mechanisms and correlated transport coefficients are also updated dynamically by using the CO-DACT method. The homogenous auto-ignition in the H2/air mixture is numerically modeled as the validation of the present S-CSP method. The results not only demonstrate the accuracy of the proposed S-CSP method but also show its potential to accelerate the chemical integration with large detailed chemical kinetics.",
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Sun, W & Ju, Y 2016, 'A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures' Paper presented at 2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016, Princeton, United States, 3/13/16 - 3/16/16, .

A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures. / Sun, Weiqi; Ju, Yiguang.

2016. Paper presented at 2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016, Princeton, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures

AU - Sun, Weiqi

AU - Ju, Yiguang

PY - 2016/1/1

Y1 - 2016/1/1

N2 - A Smart CSP (S-CSP) method is developed and integrated with our previously developed correlated dynamic adaptive chemistry and transport (CO-DACT) method to further accelerate the chemical integration. The S-CSP method resolved a few challenges in the original simple CSP method including the failure of matrix inversion of the fast reaction subspace and the inefficiency in computation of reaction timescales. A pre-generated library is proposed to eliminate the linearly dependent reactions in the matrix associated with the fast reaction subspace. The characteristic time of elementary reactions are obtained analytically. The reaction rates of the selected fast reactions are represented by the combinations of slow reactions. Therefore, the proposed Smart CSP method can automatically remove the stiffness of the ODE system on-the-fly without human experience. The correlated reduced mechanisms and correlated transport coefficients are also updated dynamically by using the CO-DACT method. The homogenous auto-ignition in the H2/air mixture is numerically modeled as the validation of the present S-CSP method. The results not only demonstrate the accuracy of the proposed S-CSP method but also show its potential to accelerate the chemical integration with large detailed chemical kinetics.

AB - A Smart CSP (S-CSP) method is developed and integrated with our previously developed correlated dynamic adaptive chemistry and transport (CO-DACT) method to further accelerate the chemical integration. The S-CSP method resolved a few challenges in the original simple CSP method including the failure of matrix inversion of the fast reaction subspace and the inefficiency in computation of reaction timescales. A pre-generated library is proposed to eliminate the linearly dependent reactions in the matrix associated with the fast reaction subspace. The characteristic time of elementary reactions are obtained analytically. The reaction rates of the selected fast reactions are represented by the combinations of slow reactions. Therefore, the proposed Smart CSP method can automatically remove the stiffness of the ODE system on-the-fly without human experience. The correlated reduced mechanisms and correlated transport coefficients are also updated dynamically by using the CO-DACT method. The homogenous auto-ignition in the H2/air mixture is numerically modeled as the validation of the present S-CSP method. The results not only demonstrate the accuracy of the proposed S-CSP method but also show its potential to accelerate the chemical integration with large detailed chemical kinetics.

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Sun W, Ju Y. A Smart CSP method and correlated dynamic adaptive chemistry and transport modeling with hydrogen/air mixtures. 2016. Paper presented at 2016 Spring Technical Meeting of the Eastern States Section of the Combustion Institute, ESSCI 2016, Princeton, United States.