What does this research mean for the field?

Mathematical modeling of the self-ignition mechanism of the unburned charge in spark-ignition piston engines can be accurately performed using the same approach applied to continuous compression in free-flying piston engines. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the feasibility of mathematically modeling engine detonation by analyzing thermodynamic cycle dynamics and the pre-flame self-ignition kinetics of unburned fuel-air mixtures.

September 2, 2026Technical Physics

Mathematical Modeling of Detonation in Spark Ignition Engines

View Full Paper

Ask AI

Bookmark

Authors

AKA. V. Kapustin VCV. L. Chumakov DSDevyanin S.N.

Discussion

Member takes

Overview

Simulation study demonstrates accurate prediction of detonation onset across varying fuel mixtures in spark-ignition engines, indicating reliable modeling via pre-flame chemical kinetics.

Key Points

To evaluate the feasibility of mathematically modeling engine detonation by analyzing thermodynamic cycle dynamics and the pre-flame self-ignition kinetics of unburned fuel-air mixtures.
Constructed a thermodynamic cycle model accounting for time-dependent cylinder pressure, unburned mixture temperature, and heat exchange with combustion chamber walls.
Modeled detonation onset via the chemical kinetics of pre-flame reactions in the unburned charge, mirroring continuous compression mechanisms from free-piston systems.
Verified calculation accuracy experimentally across varied engine speeds, excess-air ratios, filling ratios, and intake manifold temperatures using isooctane ON100, an ON60 blend, and commercial gasolines.
The thermodynamic cycle model captured time-dependent pressure and unburned charge temperature with sufficient predictive accuracy to estimate detonation conditions.
Experimental verification confirmed that self-ignition calculations based on pre-flame chemical kinetics reliably track the onset of detonation across diverse fuels and operating regimes.

Cite This Study

Kapustin et al. (2026) studied this question.

synapsesocial.com/papers/6a97e237c562ede874ec63fb https://doi.org/10.1134/s1063784226700520

View Full Paper

Ask AI

Bookmark

September 2, 2026Technical Physics

Mathematical Modeling of Detonation in Spark Ignition Engines

View Full Paper

Ask AI

Bookmark

Authors

AKA. V. Kapustin VCV. L. Chumakov DSDevyanin S.N.

Discussion

Member takes

Overview

Simulation study demonstrates accurate prediction of detonation onset across varying fuel mixtures in spark-ignition engines, indicating reliable modeling via pre-flame chemical kinetics.

Key Points

To evaluate the feasibility of mathematically modeling engine detonation by analyzing thermodynamic cycle dynamics and the pre-flame self-ignition kinetics of unburned fuel-air mixtures.
Constructed a thermodynamic cycle model accounting for time-dependent cylinder pressure, unburned mixture temperature, and heat exchange with combustion chamber walls.
Modeled detonation onset via the chemical kinetics of pre-flame reactions in the unburned charge, mirroring continuous compression mechanisms from free-piston systems.
Verified calculation accuracy experimentally across varied engine speeds, excess-air ratios, filling ratios, and intake manifold temperatures using isooctane ON100, an ON60 blend, and commercial gasolines.
The thermodynamic cycle model captured time-dependent pressure and unburned charge temperature with sufficient predictive accuracy to estimate detonation conditions.
Experimental verification confirmed that self-ignition calculations based on pre-flame chemical kinetics reliably track the onset of detonation across diverse fuels and operating regimes.

Cite This Study

Kapustin et al. (2026) studied this question.

synapsesocial.com/papers/6a97e237c562ede874ec63fb https://doi.org/10.1134/s1063784226700520

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark