基于多重分形的离心压缩机出口动态压力非线性特征研究及其在喘振识别中的应用

摘要
图/表
参考文献(19)
相关文章 (6)

全文: PDF (1005 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要以800kW离心压缩机系统的工作状态从平稳到喘振阶段的出口动态压力为分析对象，从多重分形角度研究了多重分形谱参数与出口动态压力之间的关系，着重研究了系统在喘振状态表现出的复杂非线性特征。首先，通过控制系统出口防喘振阀的开度得到离心压缩机在不同工况下的出口动态压力。然后，研究系统出口动态压力的结构函数在不同权重因子下的尺度特征，最后，研究了多重分形谱参数与离心压缩机出口动态压力的关系。研究结果显示，系统稳态时，动态压力结构函数的曲线线性特征较为明显，而随着系统从过渡状态进入喘振时，表现出明显的非线性和多重分形特征。从多重分形谱的形状可以看出，当离心压缩机在稳态时，多重分形谱的宽度Δα最小，接近于零。随着系统进入喘振状态，宽度明显增加，而且在过渡过程时Δα的变化量较大，系统的不稳定性增加。从出口动态压力的多重分形谱参数的变化可以看出，多重分形谱参数在离心压缩机的过渡状态均出现了突变特征，参数Δα、αmax和Δf(α)随着系统从稳态进入失稳时而增大，但参数αmin和f(αmax)则反方向变化，并且参数Δα、αmin、αmax的突变最为明显，这一特征可以作为系统进入过渡过程的判断依据。参数Δα、αmin、αmax在喘振时的波动量远大于稳态时的波动，表明在喘振时动态压力的分布不如稳态时均匀。参数f(αmin)在喘振初期出现了短暂的峰值平台，说明此阶段的动态压力峰值出现的概率最大，而且Δf(α)的峰值也出现在喘振初期，这一特征可以用于初始喘振预测。研究结果能够为离心压缩机初始喘振的识别和预测提供新的依据，并可进一步用于压缩机喘振的主动控制中。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘雁
	高宽
	何浩
	马凯

关键词 ：离心压缩机, 分形动力学, 结构函数, 多重分形谱, 喘振

Abstract：Taking the outlet dynamic pressure of a 800 kW centrifugal compressor system from steady state to surge stage as the analysis object, the relationship between multifractal spectrum parameters and outlet dynamic pressure was studied from the perspective of multifractal. The complex nonlinear characteristics of the system in surge state were emphatically studied. Firstly, the outlet dynamic pressure of centrifugal compressor was obtained by controlling the opening of anti-surge valve at the outlet of the system. Then, the scale characteristics of the structure function of the system outlet dynamic pressure under different weight factors were studied. Finally, the relationship between multifractal spectrum parameters and the outlet dynamic pressure of centrifugal compressor was studied. The study results showedthatlinear feature of dynamic pressure structure function is obvious in steady state, but obvious nonlinear and multifractal characteristics appear when the system enters surge from transition state; when the centrifugal compressor is in steady state, the width Δα of multifractal spectrum is the smallest and close to zero;as the system enters the surge state, the width Δα increases obviously, and the change of Δα is larger during the transition process, and the instability of the system increases; parameters of multifractal spectrum have mutation characteristics in the transition state of centrifugal compressor,parameters Δα, αmax and Δf(α) increase with the system from steady state to instability, but parameters αminand f(αmax) change in the opposite direction, and abrupt changes of parameters Δα, αmin and αmax are the most obvious, this feature can be used as the judging basis for the system to enter the transition process;the fluctuation of parameters Δα, αmin and αmax in surge is much larger than that in steady state, so the distribution of dynamic pressure in surge is less uniform than that in steady state; the short peak value platform of parameter f(αmin) appears in the initial stage of surge, so the probability of dynamic pressure peak appearing at this stage is the largest, and the peak value of Δf(α) also appears in the initial stage of surge, it can be used to predict initial surge; the study results can provide a new basis for recognizing and predicting initial surge of centrifugal compressor, and be further used in the active control of compressor surge.

Key words： centrifugal compressor fractal dynamics structure function； multifractal spectrum surge

收稿日期: 2019-08-27 出版日期: 2021-01-15

引用本文:

刘雁,高宽,何浩,马凯. 基于多重分形的离心压缩机出口动态压力非线性特征研究及其在喘振识别中的应用[J]. 振动与冲击, 2021, 40(1): 205-211.
LIU Yan, GAO Kuan, HE Hao, MA Kai. Nonlinear characteristics of centrifugal compressor outlet dynamic pressure based on multifractal and their application in surge identification. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(1): 205-211.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I1/205

[1] Sorokes J, Kuzdzal M. Centrifugal Compressor Evolution[M]. Texas A&M: College Station, TX, USA, 2010.
[2] Vepa R. Modelling and Quasilinear Control of Compressor Surge and Rotating Stall Vibrations[J]. Mathematical Problems in Engineering. 2010: 314172.
[3] Li ZP, Zhang P, Pan TY, et al. Hysteresis behaviors of compressor rotating stall with cusp catastrophic model[J]. Chinese Journal of Aeronautics. 2018, 31(5): 1075-1084
[4] Jombo G, Pecinka J, Sampath S, et al. Influence of Fouling on Compressor Dynamics: Experimental and Modeling Approach[J]. Journal of Engineering for Gas Turbines and Power-Transactions of the ASME. 2018, 140(3): 032603.
[5] 王聪, 文彬鹤, 司文杰, 等. 轴流压气机旋转失速建模与检测Ⅰ-基于确定学习理论与高阶Moore-Greitzer模型的研究[J]. 自动化学报. 2014,40(7):1265-1277.
Wang C, Wen BH, Si WJ, et al. Modeling and Detection of Rotating Stall in Axial Flow Compressors: Part I Investigation on High-order M-G Models via Deterministic Learning[J]. ACTA
AUTOMATICA SINICA. 2014,40(7):1265-1277.
[6] 苏灵波, 房旭鹏. 压缩机喘振及其预防[J].压缩机技术.2011(01):15-17.
Sun LB, Fang XP. The Compressor Surge and Its Precautions[J].Compressor Technology. 2011(01):15-17.
[7] 刘雁，陈党民，柳黎光，等.离心式压缩机出口动态压力的单重分形特征研究[J]. 西北工业大学学报,2013,31(01):60-66.
Liu Y, Chen DM, Liu LG, Wang H, et al. Exploring monofractal characteristics of dynamic pressure at exit of centrifugal compressor[J]. Journal of Northwestern Polytechnical University.2013,31(01): 60-66.
[8] Xu XG, Liu HX, Wang SL, et al. Real-time stall detection of centrifugal fan based on the analysis of symmetrized dot pattern and wavelet packet transform[J]. Journal of Vibroengineering. 2017, 19(3):1823-1832
[9] Gholamrezaei M, Ghorbanian K. Application of integrated fuzzy logic and neural networks to the performance prediction of axial compressors[J]. Proceedings of the Institution of Mechanical Engineers Part A-Journal of power and Energy. 2015, 229(8): 928-947.
[10] Javadi Moghaddam J, Farahani M H, Amanifard N.A neural network-based sliding-mode control for rotating stall and surge in axial compressors[J].Applied Soft Computing, 2011, 11(1): 1036-1043.
[11] Lin S, Yang CJ, Wu P, et al. Active surge control for variable speed axial compressors [J]. Isa Transactions. 2014, 53(5): 1389-1395.
[12] Oswiecimka P, Kwapien J, Drozdz S. Multifractality in the stock market: price increments versus waiting times[J]. Physica A.2005, 347 626–638
[13] 刘雁，慕德俊. 基于多重分形谱的局域网流量预测[J].系统仿真学报, 2009, 21(12):3743-3747.
Liu, Y, Mu DJ, Zhang JZ. Traffic prediction for LAN based on multifractal spectrums[J].Journal of System Simulation.2009,21(12), 3743–3747.
[14] 孙霞，吴自勤，黄畇．分形原理及应用[M]. 合肥: 中国科学技术大学出版社, 2003.
Sun X, Wu ZQ, Huang Y. Fractal Principle and Its Application[M]. Hefei: China University of Science and Technology Press, 2003.
[15] 张济忠. 分形[M]．北京: 清华大学出版社, 1995.
[16] Kantelhardt JW, Zschiegner SA, Koscielny BE, Havlin S, et al. Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A: Statistical Mechanics and its Applications. 2002, 316(1), 87-114.
[17] 刘雁，丁冬晓. 基于分形的心电信号非线性特征研究[J]. 西北工业大学学报. 2018, 36(2): 287-293.
Liu Y, Ding DX. Nonlinear characteristics of electrocardiograph signals based on fractal[J]. Journal of Northwestern Polytechnical University. 2018, 36(2): 287-293.
[18] Liu Y, Liu LG, Hang. Prediction of Congestion and Bursting Phenomena in Network Traffic Based on Multifractal Spectrums[J].Journal of Dynamic Systems Measurement and Control-
Transactions of the ASME,2013,135(3):31012.
[19] 刘雁，慕德俊. 局域网流量的多重分形谱分析及关联性研究[J].计算机应用研究, 2008 25(10):3153-3155.
Liu Y, Mu DJ. Study on the multi-fractal spectrum of LAN traffic and their correlations[J]. Application Research of Computers.2008, 25(10), 3153–3155.