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

Literature Information

Publication Date 2018-01-15
DOI 10.1039/C7CP07213A
Impact Factor 3.676
Authors

Chunkan Yu, Viatcheslav Bykov, Ulrich Maas


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Abstract

A recently developed automatic reduction method for systems of chemical kinetics, the so-called Global Quasi-Linearization (GQL) method, has been implemented to study and reduce the dimensions of a homogeneous combustion system. The results of application of the GQL and the Quasi-Steady State Assumption (QSSA) are compared. A number of drawbacks of the QSSA are discussed, i.e. the selection criteria of QSS-species and its sensitivity to system parameters, initial conditions, etc. To overcome these drawbacks, the GQL approach has been developed as a robust, automatic and scaling invariant method for a global analysis of the system timescale hierarchy and subsequent model reduction. In this work the auto-ignition problem of the hydrogen–air system is considered in a wide range of system parameters and initial conditions. The potential of the suggested approach to overcome most of the drawbacks of the standard approaches is illustrated.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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