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OF STALL REGIONS IN A LOW-SPEED AXIAL FAN. PART II - STALL WARNING BY VISUALISATION OF SOUND SIGNALS

机译:速轴流风机中失速区域的变化。第二部分-通过声音信号可视化进行失速警告

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This two-part study describes the development of a novel stall-detection methodology for low-speed axial-flow fans. Because aerodynamic stall is a major potential cause of mechanical failure in axial fans, effective stall-detection techniques have had wide application for many years. However, aerodynamic stall does not always result in mechanical failure. A sub-sonic fan can sometimes operate at low speeds in an aerodynamically stalled condition without incurring mechanical failure. To differentiate between aerodynamic stall conditions that constitute a mechanical risk and those that do not, the stall-detection methodology in the present study utilises a symmetrised dot pattern (SDP) technique that is capable of differentiating between critical and non-critical conditions. The SDP for a stall condition is different from that for a non-stall condition providing, a basis for differentiation of the two.Part I of this study presented the azimuthal experimental data which established the stall characteristics of a variable-speed fan. Part II describes a stall-warning criterion based on an SDP visual waveform analysis and developed stall-detection methodology based on that analysis. The study presents an analysis of the acoustic and structural data across the nine aerodynamic operating conditions represented in a 3 x 3 matrix combination of: (i) three speeds (full-, half-, and quarter-speed) and (ii) three operational states (stable operation, incipient stall and rotating stall). This differentiates critical stall conditions (those that will lead to mechanical failure of the fan) from non-critical ones (those that will not result in mechanical failure), thus providing a basis for an intelligent stall-detection methodology.
机译:这项由两部分组成的研究描述了针对低速轴流风扇的新型失速检测方法的开发。由于空气动力学失速是导致轴流风扇机械故障的主要潜在原因,因此有效的失速检测技术已经广泛应用了很多年。但是,空气动力学失速并不总是会导致机械故障。亚音速风扇有时可以在空气动力学失速的情况下以低速运行,而不会引起机械故障。为了区分构成机械危险的空气动力学失速条件与不构成机械危险的空气动力学失速条件,本研究中的失速检测方法利用了能够区分关键条件和非关键条件的对称点阵图(SDP)技术。失速条件下的SDP与非失速条件下的SDP不同,这是区分两者的基础。 本研究的第一部分介绍了方位角实验数据,该数据确定了变速风扇的失速特性。第二部分介绍了基于SDP可视波形分析的失速警告标准以及基于该分析而开发的失速检测方法。这项研究以3 x 3矩阵组合表示的九种空气动力学运行条件下对声学和结构数据进行了分析:(i)三种速度(全速,半速和四分之一速度)和(ii)三种速度状态(稳定运行,初期失速和旋转失速)。这将关键的失速条件(将导致风扇的机械故障)与非关键的失速条件(不会导致机械故障)区分开,从而为智能失速检测方法提供了基础。

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