Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1747-1754.doi: 10.13229/j.cnki.jdxbgxb20170854

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Effect of control parameters on heat release rate with ANN method

JIANG Tao(),LIN Xue-dong,LI De-gang(),YANG Miao,TANG Xue-lin   

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
  • Received:2017-08-14 Online:2018-11-20 Published:2018-12-11

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Abstract:

A combustion simulation model is built with AVL-Fire, and the dynamic distribution characteristics of the mixture concentration field and temperature field are analyzed by verification of the simulation results on test bench. The start point of combustion, premixed rate, diffusion rate and combustion duration are taken as the evaluation parameters. The effect of the control parameters on Heat Release Rate (HRR) at different stage is analyzed. An Artificial Neural Network (ANN) model is established for quantitative prediction of the evaluation parameters. The structure and of the ANN algorithm is confirmed. The effects of the injection parameters and intake parameters on the evaluation parameters are analyzed and predicted. The results show that, when the structure 5-18-4 ANN with trainlm algorithm are adopted, the robustness, responsiveness and convergence precision are good, indicating the ANN model is a powerful tool to predict HRR.

Key words: power machinery and engineering, artificial neural network(ANN), control parameter, heat release rate (HRR), quantitative evaluation parameter, quantitative prediction

CLC Number: 

  • TK421

Table 1

Selected range of experiment conditions"

参数 变化范围
喷射压力/MPa 120, 140,160
喷射时刻/°CA -7,-9,-11
喷射比例/mg 0:40,2:38,4:36
VNT开度/% 30,40,50
EGR率/% 20,30,40

Table 2

Basic technical parameters of test engine"

项目 单位 技术参数
缸数 缸 4
缸径 mm 83
冲程 mm 92
压缩比 - 17.3
排量 L 1.991
标定功率/转速 kW/(r·min-1) 100/3600
最大转矩/转速 N·m/(r·min-1) 310/2200~2800
共轨系统 - BOSCH二代共轨系统
喷油器 - Bosch CR 12
喷孔数×喷孔直径 孔数×mm 7×0.131

Table 3

Main measurement instruments"

仪器设备 型号
电涡流测功机 CW260
油耗仪 FCMM-2
气缸压力传感器 Kistler6056A31U20
电荷放大器 Kistler5018A0001
燃烧分析仪 DS9100

Fig.1

Flow chart of computational mesh generation"

Table 4

Initial conditions, boundary condition and submodels"

项目 数值
头部温度/K 420
活塞温度/K 473
缸内温度/K 420
初始压力/MPa 0.1
初始温度/K 330
TKE/( m2?s-2) 70.78
TLS/m 0.00307
初次破碎模型 DDM
二次破碎模型 KH-RT
液滴扩散模型 Hug-Gosman
碰壁模型 walljet1
蒸发模型 Dukowicz
燃烧模型 ECFM-3Z

Fig.2

Comparisons between simulation and experiment results of cylinder pressure and rate of heat release curve"

Fig.3

Concentration and temperature distribution characteristic of different crank angle"

Fig.4

Quantitative evaluation parameters of heat release rate"

Table 5

Quantitative evaluation parameters of heat release rate of different injection pressure"

参 数 喷射压力/MPa
120 140 160
燃烧始点/°CA 0.3 0.3 0.3
预混合燃烧速率/[J·(°CA)-1] 2.83 3.19 4.28
扩散燃烧速率/[J·(°CA)-1] 3.19 1.91 1.48
燃烧持续期/°CA 35.75 31.07 25.07

Table 6

Quantitative evaluation parameters of heat release rate of different injection timing"

参 数 喷射时刻/℃A
120 140 160
燃烧始点/°CA 1.9 0.3 -1.1
预混合燃烧速率/[J·(°CA)-1] 4.12 4.28 4.32
扩散燃烧速率/[J·(°CA)-1] 0.75 0.85 0.98
燃烧持续期/°CA 26.41 26.06 26.68

Table 7

Quantitative evaluation parameters of heat release rate of different injection proportion"

参 数 喷射比例
0:40 2:38 4:36
燃烧始点/°CA 0.3 -0.1 -1.3
预混合燃烧速率/[J·(°CA)-1] 4.28 3.93 3.86
扩散燃烧速率/[J·(°CA)-1] 0.85 1.56 1.81
燃烧持续期/°CA 26.06 27.35 28.33

Table 8

Quantitative evaluation parameters of heat release rate of different EGR rate"

参 数 EGR率/%
20 30 40
燃烧始点/°CA 0.3 0.7 1.3
预混合燃烧速率/[J·(°CA)-1] 4.28 4.22 4.18
扩散燃烧速率/[J·(°CA)-1] 1.48 1.58 1.66
燃烧持续期/°CA 26.06 26.07 26.79

Table 9

Quantitative evaluation parameters of heat release rate of different injection VNT level"

参 数 VNT开度/%
30 40 50
燃烧始点/°CA 0.3 0.1 0.1
预混合燃烧速率/[J·(°CA)-1] 4.28 3.96 3.79
扩散燃烧速率/[J·(°CA)-1] 1.3 1.48 1.85
燃烧持续期/°CA 26.06 24.01 23.04

Fig.5

Basic structure of ANN model"

Fig.6

MSE versus number of neurons in hidden layer"

Fig.7

The best ANN training result"

Fig.8

MSE varying with epochs by back-propagation algorithm"

Fig.9

Scatter plots of experiment data versus ANN predicted model for training, validation, and test phases"

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