journal article·2026

EXPERIMENTAL STUDY ON THE EFFECT OF DWELL TIMING ON FUEL ADHESION CHARACTERISTICS FROM TRIPLE-INJECTION SPRAY IMPINGEMENT UNDER COLD-START CONDITIONS

Hongliang Luo, Chao Lu, Chang Zhai, Long Liu, Keyan Liu, Meiqi Yu, Qing Wu, Liqiu Wang

Atomization and Sprays· Volume 36· Issue 4
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Abstract

The issue of fuel adhesion resulting from spray-wall impingement during the cold-start phase of direct injection (DI) engines was addressed experimentally. This study investigated the effects of different dwell-timing (DT) settings in a triple-injection spray on the dynamic behavior of the wall fuel adhesion. Experiments were conducted using the refractive index matching method within a constant-volume chamber simulating cold-start conditions. A comparative analysis was performed on the mass, thickness, area, distribution uniformity, and spread characteristics of fuel adhesion under three dwelltiming strategies: DT2-5, DT5-2, and DT5-5. The results indicate that the DT5-5 strategy, with the longest total DT, yields the smallest total adhesion mass. The DT2-5 strategy demonstrates the strongest capability for both lateral dispersion and downstream spread of the fuel adhesion, resulting in the largest adhesion area and the smallest thickness. The distribution pattern of the DT has a more pronounced influence on adhesion spread behavior than the total duration alone. Placing the shorter interval in the initial segment is more conducive to enhancing spread. Furthermore, this study proposed and compared three definitions of the fuel adhesion angle. The half mass angle is considered to provide a more comprehensive reflection of the adhesion distribution characteristics. This research offers experimental evidence and theoretical support for optimizing split-injection strategies to reduce unburned emissions from DI engines during cold start.
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Research context

OpenAlex topics: Advanced Combustion Engine Technologies · Fluid Dynamics and Heat Transfer · Combustion and flame dynamics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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