Influence of Pressure and Mixture Composition on Ignition Kernel Properties in Inert and Reactive Configuration
AIAA SCITECH 2024 FORUM(2024)
Safran Helicopter Engines | Combust Dept
Abstract
The impact of initial pressure and mixture composition was investigated in this study to observe their influence on kernel properties during energy depositing using a sunken fire igniter. Experiments were conducted in a cylindrical combustion chamber using high-speed Schlieren and direct visualizations. Comparison with reference tests performed in pure nitrogen highlighted the influence of composition variation on kernel volume and surface at the end of energy depositing (t = 130 µs). The effect of equivalence ratio was observed to be enhanced by lower pressure conditions. A dominant effect of pressure confirms results from previous studies. Filtered plasma chemiluminescence performed through direct visualization showed a negligible effect of composition and pressure during the first instants of kernel generation (~ 30 µs). Timing of intervening chemical reactions is traced comparing inert and reactive tests. This was done using two approaches. Specifically, the aim was to identify these variations and determine their occurrence in relation to the duration of energy depositing. It was observed that these start appearing already during energy depositing.
MoreTranslated text
Key words
Premixed Combustion
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
Related Papers
2014
被引用10 | 浏览
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