Calculation and Analysis of Combustion Noise Transfer Function by Improved Genetic Algorithm
-
摘要: 为改善传统的多元线性拟合传递函数方法所具有的局限性,更准确地进行内燃机燃烧噪声分析与预测,本文采用改进的遗传算法对燃烧噪声传递函数和机械噪声进行寻优计算。首先在相同工况下改变喷油策略计算得到燃烧噪声传递函数,并通过实验验证了燃烧噪声传递函数的不同负荷下的一致性规律;随后根据这一规律,在负荷特性下计算燃烧噪声传递函数,并结合缸压数据预测不同喷油策略下发动机总噪声水平。结果表明:改进遗传算法在计算燃烧噪声传递函数方面更具优势,通过负荷特性数据进行计算能够准确得到燃烧噪声传递函数和机械噪声,并且对总噪声的预测偏差均小于2%。Abstract: In order to improve the limitation of the traditional multiple linear fitting transfer function methods, to analyze and predict combustion noise of internal combustion engines more accurately, the improved genetic algorithm was used to optimize the combustion noise transfer functions and mechanical noise. Firstly, the combustion noise transfer functions were calculated by changing oil injection strategies under the same working condition, and the consistency rule of the combustion noise transfer functions under different loads was verified by experiments. According to this rule, combustion noise transfer functions were calculated under load characteristics, and overall noises were predicted under different oil injection strategies with cylinder pressure data. The results showed that the improved genetic algorithm had more advantages in calculating the combustion noise transfer function; the combustion noise transfer function and mechanical noise can be accurately obtained by calculating the load characteristic data, and predicted deviations of overall noise were less than 2%.
-
表 1 实验柴油机主要参数
项目 参数 发动机型式 电控高压共轨四冲程直喷增压柴油机 气缸数 4 排量/L 1.85 压缩比 18.5∶1 点火顺序 1-3-4-2 进气方式 增压中冷 冷却方式 强制循环水冷式 表 2 实验测试工况
项目 参数 转速(负荷) 2 000 r/min(100%, 200 N·m) 2 000 r/min(80%, 160 N·m) 2 000 r/min(60%, 120 N·m) 2 000 r/min(40%, 80 N·m) 喷射策略 仅主喷喷油策略下调整主喷定时 调整目标值 调整主喷定时标定值
(-4°, -2°, 基础值, +2°, +4°, +5°, +7°, +9°)注: 选取1 800 r/min调整主喷定时(-2°, 基础值, +2°, +4°)作为验证数据。 表 3 2 000 r/min时计算得到总噪声与测试总噪声偏差
% 主喷定时调整 40%负荷 60%负荷 80%负荷 100%负荷 -4° 0.86 0.31 -0.11 0.97 -2° 0.53 -0.21 -0.11 1.19 标定值 -0.31 -0.62 -0.21 -0.32 +2° -0.31 0.52 -0.21 -0.64 +4° -0.92 -1.03 -0.53 -0.75 +5° -1.22 0.31 -1.24 -0.53 +7° -0.81 -0.41 0.00 -0.73 +9° -1.11 -1.60 -1.52 -1.75 表 4 2 000 r/min时机械噪声计算与测试值对比
顶端方向 声压级/dB(A) 偏差百分数/% 倒拖法测试值 84.12 0.02 遗传算法计算值 84.10 - 表 5 1 800 r/min时计算得到总噪声与测试总噪声偏差
% 主喷定时调整 40%负荷 60%负荷 80%负荷 100%负荷 -2° 1.10 0.15 0.02 -0.16 标定值 0.04 -0.02 -0.39 0.06 +2° 0.18 0.01 0.03 -0.23 +4° -0.34 0.06 -0.05 -0.03 表 6 2 000 r/min时机械噪声计算与测试值对比
顶端方向 声压级/dB(A) 偏差百分数/% 倒拖法测试值 84.12 1.2 遗传算法计算值 85.20 - 表 7 2 000 r/min时不同负荷喷油策略总噪声预测偏差
% 主喷定时调整 40%负荷 60%负荷 80%负荷 100%负荷 -4° 0.18 0.30 0.20 -1.21 -2° 0.11 0.35 0.27 -0.80 标定值 -0.46 0.36 -0.32 -0.32 +2° 0.09 -0.12 -0.19 -0.61 +4° 0.16 0.35 0.31 -0.39 +5° -0.77 0.21 -0.15 -0.11 +7° -0.60 0.09 0.47 0.54 +9° -0.76 -0.24 0.19 0.19 -
[1] 张学龙, 王奔, 王亚军, 等. 柴油机发动机噪声测试与分析[J]. 内燃机, 2019(3): 33-37, 40 https://www.cnki.com.cn/Article/CJFDTOTAL-NRJJ201903012.htmZHANG X L, WANG B, WANG Y J, et al. Test and analysis on diesel engine noise[J]. Internal Combustion Engines, 2019(3): 33-37, 40 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRJJ201903012.htm [2] 施雨骁. 内燃机燃烧噪声的传递特性试验研究[D]. 武汉: 武汉理工大学, 2014SHI Y X. Experimental research on the transmission characteristics of combustion noise[D]. Wuhan: Wuhan University of Technology, 2014 (in Chinese) [3] 赵烈剑. 汽油机燃烧噪声分离及预测方法研究[D]. 天津: 天津大学, 2016ZHAO L J. Separation and prediction studies of gasoline engine combustion noise[D]. Tianjin: Tianjin University, 2016 (in Chinese) [4] TORREGROSA A J, BROATCH A, MARTÍN J, et al. Combustion noise level assessment in direct injection Diesel engines by means of in-cylinder pressure components[J]. Measurement Science and Technology, 2007, 18(7): 2131-2142 doi: 10.1088/0957-0233/18/7/045 [5] 王志强, 吴坚, 魏超, 等. 汽油机燃烧噪声的多元回归分析[J]. 机械科学与技术, 2017, 36(8): 1265-1271 doi: 10.13433/j.cnki.1003-8728.2017.0820WANG Z Q, WU J, WEI C, et al. Multiple regression analysis of gasoline engine combustion noise[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(8): 1265-1271 (in Chinese) doi: 10.13433/j.cnki.1003-8728.2017.0820 [6] 郑旭, 郝志勇, 金阳, 等. 基于EEMD与广义S变换的内燃机噪声源识别研究[J]. 内燃机工程, 2011, 32(5): 68-73 https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG201105014.htmZHENG X, HAO Z Y, JIN Y, et al. Application of EEMD and GST method in noise characteristics analysis of internal combustion engine[J]. Chinese Internal Combustion Engine Engineering, 2011, 32(5): 68-73 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG201105014.htm [7] 徐红梅, 郝志勇, 郭磊, 等. 基于独立成分小波分析的内燃机噪声源识别[J]. 内燃机工程, 2007, 28(6): 61-65 https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG200706014.htmXU H M, HAO Z Y, GUO J, et al. Noise source identification of internal combustion engine based on independent component and wavelet analysis[J]. Chinese Internal Combustion Engine Engineering, 2007, 28(6): 61-65 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRJG200706014.htm [8] 赵烈剑, 吴坚, 高文志, 等. 汽油机燃烧噪声分离与预测研究[J]. 机械科学与技术, 2017, 36(6): 933-937 doi: 10.13433/j.cnki.1003-8728.2017.0618ZHAO L J, WU J, GAO W Z, et al. Study on gasoline engine combustion noise separation and prediction[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 933-937 (in Chinese) doi: 10.13433/j.cnki.1003-8728.2017.0618 [9] 朱会霞, 李微微, 李彤煜, 等. 区间自适应遗传算法优化无约束非线性规划问题[J]. 数学的实践与认识, 2019, 49(4): 110-116 https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201904014.htmZHU H X, LI W W, LI T Y, et al. Optimization of unconstrained nonlinear programming problems with interval adaptive genetic algorithms[J]. Mathematics in Practice and Theory, 2019, 49(4): 110-116 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201904014.htm [10] 刘生礼, 唐敏, 董金祥. 遗传模拟退火算法在约束求解中的应用[J]. 中国图象图形学报, 2003, 8(8): 938-945 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200308016.htmLIU S L, TANG M, DONG J X, et al. Geometric constraint satisfaction using genetic simulated annealing algorithm[J]. Journal of Image and Graphics, 2003, 8(8): 938-945 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200308016.htm [11] 张奎, 占刚, 毛卫秀, 等. BP神经网络和遗传算法在车用发动机上的运用[J]. 汽车文摘, 2019(9): 57-62 https://www.cnki.com.cn/Article/CJFDTOTAL-QCWZ201909014.htmZHANG K, ZHAN G, MAO W X, et al. Application of BP neural network and genetic algorithm in vehicle engine[J]. Automotive Digest, 2019(9): 57-62 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCWZ201909014.htm [12] 邓友生, 段邦政, 叶万军, 等. 基于遗传算法与边界元理论的声屏障优化[J]. 铁道学报, 2019, 41(6): 115-123 https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201906016.htmDENG Y S, DUAN B Z, YE W J, et al. Optimization of noise barrier based on genetic algorithm and boundary element theory[J]. Journal of the China Railway Society, 2019, 41(6): 115-123 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201906016.htm [13] 陈广洲, 解华明, 鲁祥友. Matlab遗传算法工具箱在非线性优化中的应用[J]. 计算机技术与发展, 2008, 18(3): 246-248, 252 https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ200803070.htmCHEN G Z, XIE H M, LU X Y. Nonlinear optimization based on genetic algorithm toolbox of Matlab[J]. Computer Technology and Development, 2008, 18(3): 246-248, 252 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ200803070.htm [14] 杜理平. 内燃机燃烧噪声与机械噪声的测试方法研究[J]. 内燃机与配件, 2016(5): 39-40 https://www.cnki.com.cn/Article/CJFDTOTAL-NRPJ201605011.htmDU L P. Study on measurement method of combustion noise and mechanical noise in internal combustion engine[J]. Internal Combustion Engine & Parts, 2016(5): 39-40 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NRPJ201605011.htm [15] 周强. 内燃机燃烧噪声的试验研究[D]. 武汉: 武汉理工大学, 2014 (in Chinese)ZHOU Q. Experimental study on the combustion noise of the internal combustion engine[D]. Wuhan: Wuhan University of Technology, 2014 (in Chinese) [16] 马大猷. 现代声学理论基础[M]. 北京: 科学出版社部, 2004MA D Y. Theoretical basis of modern acoustics[M]. Beijing: Science Press, 2004 (in Chinese) [17] 王志强. 汽油机振动和噪声信号时频分析方法研究[D]. 天津: 天津大学, 2017WANG Z Q. Studies of vibration and noise signal time-frequency analysis method in gasoline engine[D]. Tianjin: Tianjin University, 2017 (in Chinese)