Global quasi-linearization (GQL) versus QSSA for a hydrogen-air auto-ignition problem

Area of Science:

• Chemical Kinetics
• Combustion Science
• Computational Chemistry

Background:

• Traditional methods like the Quasi-Steady State Assumption (QSSA) for chemical kinetics model reduction have limitations.
• QSSA's sensitivity to parameters and initial conditions, and difficulties in selecting quasi-steady state species, necessitate improved approaches.

Purpose of the Study:

• To implement and evaluate the Global Quasi-Linearization (GQL) method for reducing homogeneous combustion systems.
• To compare the efficacy of GQL against QSSA in analyzing chemical kinetics models.
• To address the drawbacks associated with standard model reduction techniques.

Main Methods:

• Application of the Global Quasi-Linearization (GQL) method to a homogeneous combustion system.
• Comparative analysis of GQL and Quasi-Steady State Assumption (QSSA) results.
• Investigation of the hydrogen-air auto-ignition problem across a wide parameter range.

Main Results:

• The GQL method demonstrates robustness and automatic scaling invariance for timescale hierarchy analysis.
• GQL effectively reduces the dimensionality of chemical kinetics systems.
• The study illustrates GQL's potential to overcome QSSA's limitations in handling system parameters and initial conditions.

Conclusions:

• The Global Quasi-Linearization (GQL) method provides a superior, automated approach for chemical kinetics model reduction compared to QSSA.
• GQL offers a more reliable and less sensitive method for analyzing complex combustion systems like hydrogen-air auto-ignition.
• This work highlights GQL as a powerful tool for advancing combustion modeling and chemical kinetics research.