个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:能源与动力学院
学科:动力机械及工程
办公地点:能源与动力学院416
电子邮箱:tianjp@dlut.edu.cn
Development of an improved hybrid multi-component vaporization model for realistic multi-component fuels
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论文类型:期刊论文
发表时间:2014-10-01
发表刊物:INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
收录刊物:SCIE、EI
卷号:77
页面范围:173-184
ISSN号:0017-9310
关键字:Vaporization; Multi-component droplet; Multi-diffusion; Computational efficiency
摘要:An improved hybrid multi-component (HMC) vaporization model was developed and applied to predict the vaporization characteristics of a realistic multi-component fuel droplet under various operating conditions in this study. Firstly, the realistic multi-component fuel is modeled as a mixture with a finite number of discrete hydrocarbon classes, and each hydrocarbon class is presented by a probability density function (PDF). Then, an HMC model was constructed based on the recent progress including an improved multi-diffusion sub-model, a corrected formulation for the calculation of Stefan flow velocity, and the temporal variation of thermal physical properties of fuels with temperature and compositions. The predictions of the improved HMC model were validated with the experimental data from literatures for the vaporization of realistic multi-component fuels and satisfactory agreements between the predictions and measurements are achieved. Finally, extensive comparisons of the hybrid multi-component (HMC), continuous multi-component (CMC), and discrete multi-component (DMC) models in the aspects of computational accuracy and efficiency were performed. It is found that the CMC model shows the highest computational efficiency and the lowest accuracy. The DMC model with a large amount of fuel components has the highest accuracy but the lowest efficiency. The HMC model not only could improve the computational efficiency compared with the full DMC model considering all fuel components, but also illustrates significantly better accuracy than the CMC model under the conditions tested in this study. (C) 2014 Elsevier Ltd. All rights reserved.