A Review of Low Temperature Combustion Techniques and the Evolution of Combustion Strategies
Lecture notes in mechanical engineering(2023)
Abstract
The challenges offered by the conventional gasoline engines and diesel engines, the energy security, and the rigorous emission rules have led the research into the direction of advanced combustion techniques. The low temperature combustion (LTC) technique is an advanced combustion strategy that enables combustion at low temperature and offers a way to realize ultra-low NOx and PM emissions with higher thermal efficiency. Homogeneous charge compression ignition (HCCI), premixed charge compression ignition (PCCI), partially premixed compression ignition (PPCI), reactivity controlled compression ignition (RCCI), and dual direct injection (DDI) are the different forms of LTC. It is noted that all forms of LTC have the potential to control emissions and improve thermal efficiency. However, in the absence of a combustion controlling mechanism unlike conventional engines, control over combustion is highly challenging. The increased level of CO and UHC is also the problem with LTC. It is also reported that as compared to HCCI, the PPCI, RCCI, and DDI provide better control over the combustion by offering stratifications. This paper covers the detailed concepts, positive aspects, and limitations of different forms of LTC.
MoreTranslated text
Key words
low temperature combustion techniques,low temperature
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