Experimental Study of Spatial Distribution of Knock Events in a Turbocharged Spark-Ignition Engine
INTERNATIONAL JOURNAL OF ENGINE RESEARCH（2024）
Tongji Univ | SAIC Motor R&D Innovat Headquarters
Abstract
An experimental investigation on the spatial distribution of knock events in a turbocharged spark-ignition engine for hybrid vehicle applications was conducted by using a multichannel fiber optic method. The knock positions were detected under different conditions to investigate the influence of crucial engine design and operating parameters on the knock characteristics including the spatial distribution in the combustion chamber and its relationship to knock intensity. The measured data reveal that the spatial distribution in the engine with a port fuel injection (PFI) system is mainly located on the exhaust side with insignificant influence of engine speed and load, which is attributed to the elevated thermal load around the exhaust valves. However, the knock events under gasoline direct-injection (DI) conditions were found to occur in more scattered locations with more occurring on the engine front-end and rear-end sides. These results indicate that the in-cylinder fuel-air mixing process may have a significant impact on the knock occurrence spots under DI conditions. The knock positions of the engine with different excessive air ratios, injection timings, and intake-valve timings were also detected, indicating that engine operating parameters have complex influences on the knock-region distribution in a DI engine. In addition, experiments were also carried out in two different cylinders to verify the cylinder-to-cylinder variations in knock regions which may be caused by the engine cooling design. Furthermore, no apparent correlations were observed between the knock position and the knock intensity by analysis of the experimental data.
MoreTranslated text
Key words
Spark-ignition engine,knock measurement,knock spatial distribution,knock intensity,fiber optic method
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined