發(fā)布時(shí)間：2018-05-06 11:42

  本文選題：軸流壓氣機(jī) + 旋轉(zhuǎn)失速　； 參考：《華南理工大學(xué)》2015年博士論文

【摘要】：旋轉(zhuǎn)失速和喘振是壓氣機(jī)常見(jiàn)的氣動(dòng)失穩(wěn)現(xiàn)象,會(huì)造成壓氣機(jī)中流動(dòng)情況惡化,壓比和效率下降,甚至?xí)䦟?dǎo)致葉片斷裂,結(jié)構(gòu)損壞和空中停車,嚴(yán)重危及飛行安全。如果能及時(shí)可靠地避免旋轉(zhuǎn)失速/喘振的發(fā)生,對(duì)于提高航空發(fā)動(dòng)機(jī)壽命及其性能和保障人身安全具有重要意義。旋轉(zhuǎn)失速一般被認(rèn)為是喘振的先兆,因此,捕捉旋轉(zhuǎn)失速信號(hào)顯得更為重要。本文基于確定學(xué)習(xí)理論,研究軸流壓氣機(jī)內(nèi)部不穩(wěn)定流動(dòng)的建模,提前檢測(cè)旋轉(zhuǎn)失速和喘振的發(fā)生,以擴(kuò)大壓氣機(jī)穩(wěn)定運(yùn)行范圍,達(dá)到改善壓氣機(jī)性能的目的。主要成果和創(chuàng)新點(diǎn)概述如下:1、本文開(kāi)展了低速軸流壓氣機(jī)模態(tài)波型失速的在線試驗(yàn)研究,以北京航空航天大學(xué)航空發(fā)動(dòng)機(jī)重點(diǎn)實(shí)驗(yàn)室的低速軸流壓氣機(jī)實(shí)驗(yàn)臺(tái)為研究對(duì)象,基于確定學(xué)習(xí)理論及動(dòng)態(tài)模式識(shí)別方法,實(shí)現(xiàn)模態(tài)波型失速的在線提前檢測(cè)。首先,在壓氣機(jī)機(jī)匣壁面周向布置多個(gè)動(dòng)態(tài)壓力傳感器,獲取壓氣機(jī)失速前和失速先兆的動(dòng)態(tài)壓力信號(hào),進(jìn)行離線數(shù)據(jù)處理,對(duì)模態(tài)波型旋轉(zhuǎn)失速初始擾動(dòng)的內(nèi)在系統(tǒng)動(dòng)態(tài)近似準(zhǔn)確建模,并把結(jié)果存儲(chǔ)在常值徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)(NN)中。其次,研究在線試驗(yàn)的傳感器布局、數(shù)據(jù)處理和實(shí)時(shí)性計(jì)算等,實(shí)現(xiàn)基于Lab VIEW的旋轉(zhuǎn)失速檢測(cè)系統(tǒng),利用微小振動(dòng)故障檢測(cè)方法,在不同轉(zhuǎn)速情況下,提前0.3-1秒實(shí)現(xiàn)對(duì)旋轉(zhuǎn)失速的實(shí)時(shí)在線提前檢測(cè)。2、本文研究了低速軸流壓氣機(jī)進(jìn)口畸變下的失速檢測(cè)。進(jìn)口畸變是航空發(fā)動(dòng)機(jī)穩(wěn)定邊界縮小和穩(wěn)定性下降的重要因素之一,會(huì)加劇壓氣機(jī)內(nèi)部流場(chǎng)的不穩(wěn)定現(xiàn)象,甚至?xí)饓簹鈾C(jī)喘振的發(fā)生。因此,對(duì)進(jìn)口畸變的非定常流動(dòng)的捕捉為進(jìn)一步提高葉輪機(jī)械的性能和穩(wěn)定性有著非常重要的意義。本論文基于確定學(xué)習(xí)理論實(shí)現(xiàn)在進(jìn)口畸變情況下預(yù)測(cè)流動(dòng)失穩(wěn)的發(fā)生。實(shí)驗(yàn)在北航航空發(fā)動(dòng)機(jī)重點(diǎn)實(shí)驗(yàn)室的一臺(tái)低速軸流壓氣機(jī)實(shí)驗(yàn)臺(tái)上進(jìn)行,利用插板擾流器模擬進(jìn)口畸變的發(fā)生。進(jìn)口畸變會(huì)增加不穩(wěn)定流動(dòng)干擾,使微弱的失速先兆信號(hào)更難捕捉。首先,研究故障估計(jì)器參數(shù)設(shè)置對(duì)故障殘差的影響,尋找最優(yōu)故障估計(jì)器參數(shù),以準(zhǔn)確預(yù)測(cè)出微小振動(dòng)故障的發(fā)生。其次,利用機(jī)匣壁周向布置高頻響應(yīng)傳感器獲得進(jìn)口畸變條件下動(dòng)態(tài)壓力數(shù)據(jù),根據(jù)提出的基于確定學(xué)習(xí)的失速檢測(cè)方法實(shí)現(xiàn)對(duì)畸變條件下失速先兆的檢測(cè)。實(shí)驗(yàn)結(jié)果表明提出的方法可以完成對(duì)進(jìn)口畸變下失速的提前檢測(cè)。3、本文針對(duì)具有傳播速度較快的小尺度擾動(dòng)-突尖型失速開(kāi)展建模與檢測(cè)研究。突尖型失速是小尺度局部擾動(dòng),比模態(tài)波型發(fā)展速度更快,是在軸流壓氣機(jī)中更常見(jiàn)的流動(dòng)崩潰現(xiàn)象。由于突尖型失速先兆的局部特性和流量的急劇衰減,所以很難對(duì)其進(jìn)行失速前的檢測(cè)。因此捕捉旋轉(zhuǎn)失速或者喘振發(fā)生前的突尖型失速對(duì)主動(dòng)控制更有意義。本文分析高階Moore-Greitzer模型(Mansoux模型),開(kāi)展了突尖型失速的建模和快速檢測(cè)研究。首先,基于MIT的Mansoux-C3模型仿真研究,分析其失速初始擾動(dòng)類型;其次,研究通過(guò)改變RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)、尋找最優(yōu)RBF神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)等方法提高微小振動(dòng)信號(hào)的持續(xù)激勵(lì)水平,并進(jìn)而提高確定學(xué)習(xí)性能,實(shí)現(xiàn)對(duì)突尖型旋轉(zhuǎn)失速進(jìn)行近似準(zhǔn)確動(dòng)力學(xué)建模的方法。再次,利用確定學(xué)習(xí)理論對(duì)突尖型失速的未知系統(tǒng)內(nèi)部動(dòng)態(tài)進(jìn)行局部準(zhǔn)確建模;最后,在主要系統(tǒng)動(dòng)態(tài)近似準(zhǔn)確建模的基礎(chǔ)上,實(shí)現(xiàn)對(duì)突尖型失速的快速檢測(cè)。本文分析和研究了模態(tài)波型失速、進(jìn)口擾動(dòng)以及突尖型失速,并進(jìn)行了在線實(shí)驗(yàn)。提出的失速檢測(cè)方法在低速軸流壓氣機(jī)旋轉(zhuǎn)失速檢測(cè)的仿真和試驗(yàn)研究中得到驗(yàn)證。
[Abstract]:Rotating stall and surge are the common aerodynamic instability of the compressor, which will cause the deterioration of the flow in the compressor, the pressure ratio and the decrease of efficiency, even the blade fracture, structural damage and air parking, which seriously endangers the flight safety. If the rotating speed / surge is avoided in time and reliably, the life of the aero engine can be improved and the life of the aero engine can be improved. Its performance and safety are of great significance. Rotating stall is generally considered to be the precursor of surge. Therefore, it is more important to capture the rotating stall signal. Based on the theory of learning, this paper studies the modeling of unsteady flow inside the axial compressor and detects the occurrence of rotating stall and surge ahead of time in order to increase the stability of the compressor. The main achievements and innovation points are summarized as follows: 1. In this paper, the on-line test of modal wave velocity of low speed axial compressor is carried out, and the research object of the low speed axial compressor test platform in the Key Laboratory of the Beihang University is to determine the learning theory and the motion. The state pattern recognition method is used to realize the on-line early detection of modal wave type stall. First, a number of dynamic pressure sensors are arranged in the circumferential direction of the compressor casing wall to obtain the dynamic pressure signal of the compressor stall and the stall precursors, and the off-line data processing is carried out. The internal system dynamics of the initial disturbance of the modal wave type rotating stall is approximately accurate. The results are stored in the constant value radial basis function (RBF) neural network (NN). Secondly, the sensor layout, data processing and real time calculation of the on-line test are studied. The rotating stall detection system based on Lab VIEW is realized, and the micro vibration fault detection method is used to realize the rotational stall at 0.3- 1 second in advance at different speeds. In real-time online early detection.2, this paper studies the stall detection under the inlet distortion of the low-speed axial compressor. The inlet distortion is one of the important factors for the reduction of the stability boundary of the aeroengine and the decline of the stability of the aeroengine. It will aggravate the instability of the internal flow field of the compressor, and even cause the compressor surge. The capture of unsteady flow is of great significance to further improve the performance and stability of turbomachinery. This paper is based on the determination of learning theory to predict the occurrence of flow instability in the case of imported distortion. The plate spoiler simulated the occurrence of the inlet distortion. The inlet distortion will increase the unstable flow interference and make the weak stall signal more difficult to capture. First, the effect of the parameter setting of the fault estimator on the fault residuals is studied, and the parameters of the optimal fault estimator are found to accurately predict the occurrence of small and small vibration faults. Secondly, the circumference of the casing wall is used. A high frequency response sensor is arranged to obtain dynamic pressure data under the condition of imported distortion, and the detection of stall precursors under distortion conditions is realized based on the proposed method based on Determination of learning based stall detection. The experimental results show that the proposed method can complete the early detection.3 for the imported distortion stall, and this paper is aimed at the fast propagation speed. The modeling and detection of small scale disturbance - apex stall is carried out. The sudden tip type stall is a small scale local disturbance, which is faster than the modal wave type. It is a more common flow collapse in axial compressor. It is difficult to detect the stall before stall due to the sharp decline of local characteristics and flow of the sudden tip type stall. Therefore, it is more meaningful to capture the prop type stall before the rotating stall or the surge occurred. In this paper, the high order Moore-Greitzer model (Mansoux model) is used to develop the modeling and rapid detection of the sudden tip type stall. First, the Mansoux-C3 model simulation based on MIT is used to analyze the initial type of the stall initial disturbance. Secondly, the research is done. By changing the structure parameters of the RBF neural network and searching for the optimal RBF neural network structure, the continuous excitation level of the small vibration signals is improved, and the learning performance is improved, and the approximate accurate dynamic modeling method for the sudden sharp rotating stall is realized. Finally, on the basis of the approximate accurate modeling of the main system dynamic and approximate accurate modeling, the rapid detection of the sudden tip type stall is realized. In this paper, the modal wave type stall, the inlet disturbance and the sudden tip type stall are analyzed and studied, and the on-line experiment is carried out. The proposed method of stall detection is used in the rotating stall of the low speed axial compressor. The test is verified in the simulation and experimental research.

【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：V233

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本文編號(hào)：1852179