乙醇液滴撞击高温壁面蒸发过程的模拟预测研究

马小晶; 周鑫; 吐松江·卡日; 许瀚文

doi:10.21656/1000-0887.430139

乙醇液滴撞击高温壁面蒸发过程的模拟预测研究

doi: 10.21656/1000-0887.430139

新疆大学电气工程学院，乌鲁木齐 830047

基金项目:

国家自然科学基金项目 12002296

新疆维吾尔自治区自然科学基金项目 2022D01C47

新疆自治区重大科技专项 2022A01002-2

新疆自治区天山英才支持项目 2022TSYCCX0054

详细信息

通讯作者:
马小晶(1983—)，女，教授，博士，博士生导师(通讯作者. E-mail: maxiaojing1983@xju.edu.cn)

中图分类号: O359⁺.1
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- 文章访问数: 250
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- PDF下载量: 58
- 被引次数: 0
出版历程
- 收稿日期: 2022-04-20
- 修回日期: 2022-07-11
- 刊出日期: 2023-05-01

Simulation and Prediction of the Evaporation Process of Ethanol Droplets Impacting High Temperature Wall

School of Electrical Engineering, Xinjiang University, Urumqi 830047, P.R.China

摘要

摘要: 采用CLSVOF方法，引入描述壁面润湿特性的动态接触角，建立了乙醇液滴撞击高温壁面的数值模型，对乙醇液滴撞击高温壁面后的沸腾蒸发过程展开了研究，并与实验数据进行了对比验证. 研究表明：在相同液滴温度下，壁面温度越高，亲水性越强，乙醇液滴的撞击速度越快，液滴的沸腾时间越早，蒸发完成所用时间也越短. 在此研究基础上，基于机器学习算法，建立了液滴蒸发预测模型，对乙醇液滴撞击高温壁面后蒸发剩余量随时间的变化进行了预测研究，并通过将不同机器学习算法的预测结果与模拟结果对比，选出最优预测模型.
- CLSVOF方法 /
- 动态接触角 /
- 沸腾蒸发 /
- 机器学习
Abstract: The coupled level set and volume of fluid (CLSVOF) method was used to establish a numerical model for ethanol droplets impacting high temperature wall through introduction of the dynamic contact angle to describe the wetting characteristics of the wall surface. The boiling and evaporation process of ethanol droplets impacting high temperature wall was studied and compared with the experimental data. The results show that, at a fixed droplet temperature, the higher the wall temperature is, the stronger the hydrophilicity will be, and the faster the impacting velocity of ethanol droplet is, the earlier the droplet will boil and the shorter the evaporation time will be. Based on this, a prediction model for droplet evaporation was established with the machine learning algorithm, to study the change of the evaporation residual with time after the ethanol droplet collision with the high temperature wall. The optimal prediction model was selected through comparison of the prediction results of different machine learning algorithms with the simulation results.
- coupled level set and volume of fluid method /
- dynamic contact angle /
- explosive evaporation /
- machine learning

HTML全文

图 1 不同网格密度下蒸发剩余量S_v随时间的变化曲线

Figure 1. Curves of evaporation residual S_v under different mesh densities varying with time

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图 2 乙醇液滴撞击高温壁面形态变化的模拟结果与实验结果^[14]对比

Figure 2. The simulation results of ethanol droplets impacting high temperature wall compared with the experimental results^[14]

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图 3 乙醇液滴撞击不同温度壁面S_v随时间的变化曲线

Figure 3. S_v curves of ethanol droplets impacting wall surface at different temperatures with time

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图 4 乙醇液滴撞击不同温度壁面蒸发时间对比

Figure 4. Comparison of evaporation time of ethanol droplets impacting walls at different temperatures

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图 5 液滴以不同速度撞击高温壁面后S_v随时间的变化曲线

Figure 5. S_v curves with time after droplet collison with high temperature wall at different velocities

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图 6 乙醇液滴撞击不同润湿性壁面S_v随时间的变化曲线

Figure 6. S_v curves of ethanol after droplet collison with different wettability wall with time

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图 7 不同机器学习算法的预测结果与模拟结果的对比

Figure 7. Comparison between prediction results of different machine learning algorithms and simulation results

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图 8 SSA优化预测曲线

Figure 8. Prediction curves after the SSA optimization

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表 1 乙醇液滴完全蒸发时间与实验结果误差对比

Table 1. Comparison of errors between complete evaporation time of ethanol droplets and experimental results

T_w/K	experimental t_max/s	simulated t_max/s	relative error δ/%
360	5.58	5.19	-6.98
365	4.81	4.75	-1.24
370	4.29	4.49	4.66
378	3.31	3.21	-3.02

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表 2 模拟工况相关参数

Table 2. The relevant parameters of the simulation condition

parameter name	parameter value
wall temperatureT_w/K	370~420
contact angle θ_e/(°)	30~60
impact velocity u_c/(m·s^-1)	0.23~0.8
initial droplet temperature T_d/K	293

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表 3 预测结果与模拟结果的误差对比

Table 3. Error comparison between prediction results and simulation results

algorithm name	mean absolute error	mean squared error	R²
SVR	0.067 5	0.005 7	0.943 6
RF	0.104 0	0.018 3	0.804 9
KNN	0.019 0	0.001 0	0.992 4
MLP	0.016 5	0.000 4	0.996 2

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表 4 采用SSA优化后预测结果与模拟结果误差对比

Table 4. Error comparison between prediction results of the SSA optimization and simulation results

algorithm name	mean absolute error	mean squared error	R²
SSA-KNN	0.015 8	0.000 39	0.995 7
SSA-MLP	0.015 4	0.000 29	0.997 0

下载: 导出CSV

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