吉林大学学报(工学版) ›› 2001, Vol. ›› Issue (2): 65-70.

Previous Articles     Next Articles

Features of Turbocharged Hydrogen Engine Working Processes and Its Variation

MA Jie, LU Jing-fang, SU Yong-kang, CHOU Yu-cheng   

  1. College of Power and Energy Engineering, Shanghai Jiaotong University, Shanghai 200030, China
  • Received:2000-08-18 Online:2001-04-25

PDF (PC)

278

Abstract: Based on mathematics patterns,engine working processes have been simulated so that the performance can be estimated and predicted under several parameter change and variation.The calculation of the parameters during boosting process is done based on the analysis of the combustion processes and load variation.The turbocharger characteristic coefficient can be defined by iteration and interpolation procedures.The proper compression ratio and ignition advance are proved to be of important role to its performance.And the excessive air coefficient is related to the engine boost pressure and exhaust temperature.

Key words: turbocharged hydrogen engine, working process, feature, variation

CLC Number: 

  • TK43
[1] Das L M. On-board hydrogen storage systems for automotive application［J］. Int. J. Hydrogen Energy, 1996, 21 (1):789～800.
[2] Das L M. Hydrogen-oxygen reaction mechanism and its implication to hydrogen engine combustion［J］. Int. J. Hydrogen Energy, 1996, 21(8):1379～1389.
[3] Al-Garni M. A simple and reliable approach for the direct injection of hydrogen in internal combustion pressures［J］. Int.J.Hydrogen Energy, 1994, 20(9):723～726.
[4] Rosen M A, Scott D S.Comparative efficiency assessments for a range of hydrogen production processes［J］. Int J. Hydro gen Energy, 1998, 23(8):653～659.
[5] Specht M, Staiss F, Bandi A, et al. Comparison of the renewable transportation fuels liquid hydrogen and methanol with gasoline energetic and economic aspects［J］. Int. J. Hydrogen Energy, 1998, 23(5):387～396.
[1] LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[2] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[3] LU Zhi-jun,ZHONG Chao,WU Jing-yu. Small feature segmentation method for Spaceborne SAR images [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1925-1930.
[4] CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[5] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[6] YANG Dong-sheng, ZHANG Zhan, LIAN Meng-jia, WANG Li-na. Matching binary feature search algorithm of bitmap locality sensitive hashing [J]. 吉林大学学报(工学版), 2018, 48(3): 893-902.
[7] GENG Qing-tian, YU Fan-hua, WANG Yu-ting, GAO Qi-kun. New algorithm for vehicle type detection based on feature fusion [J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.
[8] LIN Jin-hua, WANG Yan-jie, SUN Hong-hai. Improved feature-adaptive subdivision for Catmull-Clark surface model [J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[9] LUO Yang-xia, GUO Ye. Software recognition based on features of data dependency [J]. 吉林大学学报(工学版), 2017, 47(6): 1894-1902.
[10] FAN Min, HAN Qi, WANG Fen, SU Xiao-lan, XU Hao, WU Song-lin. Scene image categorization algorithm based on multi-level features representation [J]. 吉林大学学报(工学版), 2017, 47(6): 1909-1917.
[11] LIU Wei-na, ZHOU Xiao-long, JIANG Zhen-hai, MA Feng-lei. Improved empirical mode decomposition method based on optimal feature [J]. 吉林大学学报(工学版), 2017, 47(6): 1957-1963.
[12] DONG Qiang, LIU Jing-hong, ZHOU Qian-fei. Improved SURF algorithm used in image mosaic [J]. 吉林大学学报(工学版), 2017, 47(5): 1644-1652.
[13] ZHOU Bao-yu, ZHAO Hong-wei, XIAO Yang, ZANG Xue-bai. Image feature description method based on local entropy [J]. 吉林大学学报(工学版), 2017, 47(2): 601-608.
[14] YUAN Zhe-ming, ZHANG Hong-yang, CHEN Yuan. HIV-1 protease cleavage site prediction based on feature selection and support vector machine [J]. 吉林大学学报(工学版), 2017, 47(2): 639-646.
[15] SHI Xiao-hu, FENG Guo-xiang, LI Mu, LI Ying, WU Chun-guo. Overlapping community detection method based on density peaks [J]. 吉林大学学报(工学版), 2017, 47(1): 242-248.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd