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.

• The paper proposes DRGEPSA, a novel method that combines DRGEP with sensitivity analysis to streamline large reaction mechanisms.
• It achieves up to an 80% reduction in species count for fuels like n-heptane, iso-octane, and n-decane with minimal loss in simulation fidelity.
• Experimental validation using ignition delay, PSR, and laminar flame speed tests confirms DRGEPSA's robust performance over individual reduction techniques.

Skeletal Mechanism Generation Using DRGEPSA for Surrogate Fuels

The paper presents a novel methodology, termed Directed Relation Graph with Error Propagation and Sensitivity Analysis (DRGEPSA), for reducing the complexity of large detailed reaction mechanisms. This approach is particularly applicable to surrogate fuel components such as n-heptane, iso-octane, and n-decane. The fusion of Directed Relation Graph with Error Propagation (DRGEP) and Directed Relation Graph-aided Sensitivity Analysis (DRGASA) allows DRGEPSA to optimize skeletal mechanisms efficiently by overcoming the limitations inherent in each separate method.

Methodology Overview and Implementation

The DRGEPSA method employs the DRGEP to initially filter out many unimportant species, allowing for a more manageable dataset for subsequent sensitivity analysis via DRGASA. In essence, DRGEP maps species interdependencies using a graphical representation and estimates the potential error introduced by the omission of specific species. Subsequently, DRGASA involves a brute-force sensitivity analysis to evaluate and further cull species, minimizing the global error against a specified threshold. This two-phase method holds particular promise in reducing the size of complex chemical reaction networks while retaining accuracy in simulative predictions such as ignition delay times.

The skeletal mechanisms are generated iteratively, under specified error constraints, allowing for dynamic adjustments and ensuring optimal minimal mechanism sizes. Specifically, the DRGEPSA method demonstrates significant reduction capabilities on the n-heptane, iso-octane, and n-decane chemical models, achieving mechanism reductions up to 80% while maintaining error margins often around 30%.

Results and Numerical Validation

The paper discusses extensive testing and validation of the DRGEPSA-derived skeletal mechanisms. The n-heptane mechanism, for instance, reduces from 561 to 108 species, with simulated ignition delay errors kept within acceptable bounds. Notably, comparisons against the standalone DRG, DRGASA, and DRGEP methods highlight DRGEPSA’s superiority in size reduction without compromising the fidelity of the simulations, particularly noting its effectiveness in addressing the negative temperature coefficient (NTC) region’s sensitivities. Likewise, the iso-octane mechanism reduction to 165 species and the two skeletal n-decane reductions (202 and 51 species for comprehensive and high-temperature conditions, respectively) illustrate DRGEPSA's flexibility and robustness.

The validation process encompasses ignition delay comparisons and complements them with perfectly-stirred reactor (PSR) and laminar flame speed tests. Consistently, DRGEPSA-generated mechanisms exhibit small discrepancies in simulated results across a broad spectrum of conditions, often limited to high-pressure or rich-mixture scenarios, reflecting the strength and adaptability of the method.

Implications and Future Perspectives

DRGEPSA presents a significant advancement in the field of computational chemical kinetics, particularly in the field of surrogate fuel modeling where computational demands often hinder widespread adoption of detailed mechanisms. The success of DRGEPSA in this study suggests its potential application to various fields requiring complex reaction mechanism analysis in reduced computational formats, such as climate modeling, atmospheric chemistry, and even biological pathway analysis.

Moving forward, combining DRGEPSA with additional techniques such as isomer lumping, time-scale reduction, and diffusive species bundling could yield even more efficient reduction processes, further broadening applicability to real-world engineering challenges. Future work focusing on the automation and integration of DRGEPSA into comprehensive computational toolkits could facilitate its adoption across multiple domains, enabling detailed yet computationally feasible simulations necessary for advanced predictive modeling.