Opposed-Piston Two-Stroke Diesel Engine Scavenging System Structural Optimization Based on GA-SVM
- DOI
- 10.2991/icsd-17.2017.24How to use a DOI?
- Keywords
- OP2S engine, scavenging system, optimization, GA-SVM
- Abstract
Opposed-Piston Two-Stroke (OP2S) diesel engines have been successfully used in many applications because of its high power efficiency. Uniform scavenging is used on OP2S diesel engines. Different from conventional diesel engine exchanging systems, intake ports and exhaust ports are located at each sides of the cylinder as OP2S scavenging system. A scavenging system optimization method, Genetic Algorithm-Support Vector Machine (GA-SVM), which employs Indicated Mean Effective Pressure (IMEP) as optimization goal is presented. The combinations of five key parameters including the width and length of intake and exhaust ports and the crank asymmetric angle are designed. All these combinations are calculated by GT-Power. The predicting model of OP2S diesel engine`s IMEP is built based on the 1D results. After verifying the accuracy of the predicting model, the optimized parameters are found. The results illustrate: using 1-D simulation coupled GA-SVM to optimize scavenging parameters in OP2S diesel engines is a rapid and effective method. The optimized IMEP is 12.86 bar. This paper presents some guidelines in the further OP2S diesel engines research.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yu-Hang Liu AU - Fu-Jun Zhang AU - Zhen-Feng Zhao AU - Shuan-Lu Zhang PY - 2017/07 DA - 2017/07 TI - Opposed-Piston Two-Stroke Diesel Engine Scavenging System Structural Optimization Based on GA-SVM BT - Proceedings of the 3rd 2017 International Conference on Sustainable Development (ICSD 2017) PB - Atlantis Press SP - 154 EP - 163 SN - 2352-5401 UR - https://doi.org/10.2991/icsd-17.2017.24 DO - 10.2991/icsd-17.2017.24 ID - Liu2017/07 ER -