Bifurcation Predication in Axial Compressors with Nonuniform Inflow via Deterministic Learning
Bifurcation Predication in Axial Compressors with Nonuniform Inflow via Deterministic Learning
复制标题
通过确定性学习进行非均匀流入轴流压缩机的分岔预测
DOI：
10.1142/s0218127417501590
复制
发表时间：
2017-09-01
影响因子：
2.2
通讯作者：
Wang, Min
中科院分区：
文献类型：
--
作者：
Lin, Peng;Wang, Cong;Wang, Min
轴流压气机中的旋转失速和喘振等流体不稳定性的非线性动力学过程通常被建模为具有滞后的亚临界Hopf分岔。分叉预测为避免压气机失稳提供了一条有效途径。本文基于新近发展的流体动力学模型，提出了一种基于确定性学习（DL）的轴流压气机失速先兆检测方法，用于非均匀来流条件下压气机的分岔预测。当节流面积参数接近其临界值（或分叉点）时，可以得到分叉点附近的失速先兆。首先，系统动力学下的正常和失速前兆的局部精确近似通过DL。所获得的动态知识存储在常数径向基函数（RBF）网络。其次，建立了一个银行的估计器使用存储常数RBF网络来表示学习正常和失速前兆模式。通过比较每个…
The nonlinear dynamics of fluid instabilities such as rotating stall and surge in axial compressors are typically modeled as subcritical Hopf bifurcations with hysteresis. The bifurcation prediction provides an effective approach to avoid the occurrence of compressor’s instability. In this paper, based on a fluid dynamic model developed recently, a stall precursor detection approach employing deterministic learning (DL) is proposed for bifurcation predication in axial compressors with nonuniform inflow. The stall precursor near the bifurcation can be obtained as the throttle area parameter approaches its critical (or bifurcation) value. Firstly, the system dynamics underlying normal and stall precursor are locally approximated accurately through DL. The obtained knowledge of dynamics is stored in constant radial basis function (RBF) networks. Secondly, a bank of estimators is built up using the stored constant RBF networks to represent the learning normal and stall precursor patterns. By comparing each es...