Skeletal mechanism generation for surrogate fuels using directed relation graph with error propagation and sensitivity analysis

Abstract

A novel implementation for the skeletal reduction of large detailed reaction mechanisms using the directed relation graph with error propagation and sensitivity analysis (DRGEPSA) is developed and presented with examples for three hydrocarbon components, n-heptane, iso-octane, and n-decane, relevant to surrogate fuel development. DRGEPSA integrates two previously developed methods, directed relation graph-aided sensitivity analysis (DRGASA) and directed relation graph with error propagation (DRGEP), by first applying DRGEP to efficiently remove many unimportant species prior to sensitivity analysis to further remove unimportant species, producing an optimally small skeletal mechanism for a given error limit. It is illustrated that the combination of the DRGEP and DRGASA methods allows the DRGEPSA approach to overcome the weaknesses of each, specifically that DRGEP cannot identify all unimportant species and that DRGASA shields unimportant species from removal. Skeletal mechanisms for n-heptane and iso-octane generated using the DRGEP, DRGASA, and DRGEPSA methods are presented and compared to illustrate the improvement of DRGEPSA. From a detailed reaction mechanism for n-alkanes covering n-octane to n-hexadecane with 2115 species and 8157 reactions, two skeletal mechanisms for n-decane generated using DRGEPSA, one covering a comprehensive range of temperature, pressure, and equivalence ratio conditions for autoignition and the other limited to high temperatures, are presented and validated. The comprehensive skeletal mechanism consists of 202 species and 846 reactions and the high-temperature skeletal mechanism consists of 51 species and 256 reactions. Both mechanisms are further demonstrated to well reproduce the results of the detailed mechanism in perfectly-stirred reactor and laminar flame simulations over a wide range of conditions.

47 pages, 10 figures

Cited by in corpus (18)

• Improving aircraft performance using machine learning: a review
• On the importance of graph search algorithms for DRGEP-based mechanism reduction methods
• pyJac: analytical Jacobian generator for chemical kinetics
• Mechanism reduction for multicomponent surrogates: a case study using toluene reference fuels
• Model reduction in chemical dynamics: slow invariant manifolds, singular perturbations, thermodynamic estimates, and analysis of reaction graph
• On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds
• Reduced chemistry for a gasoline surrogate valid at engine-relevant conditions
• An automated target species selection method for dynamic adaptive chemistry simulations
• Interface-resolved simulation of the evaporation and combustion of a fuel droplet suspended in normal gravity
• Reduced chemistry for butanol isomers at engine-relevant conditions
• A species-clustered ODE solver for large-scale chemical kinetics using detailed mechanisms
• On sparse identification of complex dynamical systems: A study on discovering influential reactions in chemical reaction networks
• Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms
• Accelerating reactive-flow simulations using vectorized chemistry integration
• Skeletal Model Reduction with Forced Optimally Time Dependent Modes
• Efficient Simulations of Propagating Flames Using Adaptive Mesh Refinement
• Requirements Towards Predictive Simulations of Turbulent Reacting Flows
• A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks