Instability Inception Research Articles

Detection of Rotating Stall Inception of Axial Compressors Based on Deep Dilated Causal Convolutional Neural Networks

The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as stall inception in axial compressors is observed in aero-engine. Based on deep learning theory, this paper conducts the accurate and rapid detection of stall precursors based on deep neural network. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. The LR-WaveNet model can implement fast anomaly detection and stall prediction in long-time term series data. Furthermore, a single LR-WaveNet can be trained to capture and learn the time-domain statistical characteristics of many different stall inception training data with equal fidelity. The trained LR-WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis and the wavelet analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of the stall detection approach based on LR-WaveNet. Especially, the LR-WaveNet can detect the irregular and imperfect stall inception and capture the small fault features when the characteristics of stall inception sigals or data are not very unobvious, irregular and imperfect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Note to Practitioners–This paper was motivated by the problem of detecting the stall and surge of axial compressor for aeroengine, but it also applies to other small fault diagnosis with imperfect data or time series prediction problem. The stall Inception is very difficult to detect for the reason of imperfect data with small fault or features. These existing detection methods of stall inception can only be applied under certain conditions, because they can only extract the pre-stall features partly. Meanwhile, the excessive number of compressor sensors is demanded in this method, so the application of these traditional methods are limited in practical situations in the stall inception detection. This paper suggests a new approach using Deep Dilated Causal Convolutional Neural Networks. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. In this paper, the new approach is proved to be suitable and feasible to all the types of stall inception including the irregular stall inception with imperfect data. We then show how the deep learning method can be efficiently applied in the imperfect data situation with small fault and time series prediction. The experiments suggest that this approach is feasible. In future research, we will create the novel approach and theory for the detection of stall inception and small fault diagnostics with the imperfect data.

Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors.

The detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability inception are extracted by generally combining multiscale entropy and statistical features. First, nonlinear features are extracted by refined composite multiscale entropy to avoid the inaccurate estimation or undefined entropy of multiscale entropy for short time series. Second, the time-domain-based statistical features are chosen to capture more information on instability inception, and the dominant statistical features are determined by random forests implemented with the mean decrease accuracy algorithm at each time scale. The obtained refined composite dominant statistical features are regarded as weighting factors and integrated with the refined composite multiscale entropy to generate a combined feature. Finally, numerical simulation results on two synthetic noise datasets and a compressor instability model dataset are presented to demonstrate the effectiveness, efficiency, and robustness of the combined features under different conditions.

Numerical Investigation of Rotating Instability Development in a Wide Tip Gap Centrifugal Compressor

In the current study, full-stage unsteady simulations were performed to investigate rotating instability inception mechanisms in a particularly large tip clearance centrifugal compressor with a vaneless diffuser and a volute. Four operating points along a speed line were analysed to understand the influence of the mass flow reduction on flow structures. Close to the peak efficiency, an unsteady interaction between the tip clearance vortices and splitter blades was observed. Considering other studies, the influence of the tip gap size was analysed. Then, a large-scale vortex shedding from the leading edges of the main blades was detected when the stage operated near the maximum pressure ratio. It was demonstrated that shed vortices were caused by the combination of the radial gradient of the tangential velocity under the tip vortex and the reverse backflow near the casing. Previous studies on axial compressors refer to these vortical structures as backflow vortices. These vortices cause a significant increase in the incidence angle in the tip region.

Experimental investigation of instability inception on a transonic compressor under various radial distributions of loading

The radial distribution of blade loading could be various due to the inlet distortion, the change of rotor speed or mismatching between stages, which is a key factor affecting the flow stability. In this paper, the instability inceptions under the various radial distribution of loading are experimentally investigated on a transonic compressor. The research subject is chosen as a 1.5-stage transonic axial flow compressor in which three types of instability inceptions were previously detected, including spike-type inception initiated at the rotor tip under the low speed, partial surge initiated at the stator hub under the high speed, and hub instability when the slot casing treatment was mounted. In order to facilitate the conversion of the compressor instability inception, the rotating speed is selected (17,160 r/min in this study) at which the blade loading at both the stator hub and rotor tip reach the critical value near the rotating stall. Furthermore, the variation of radial distribution of loading is induced by the radial non-uniformity of total pressure. Meanwhile, the high-response dynamic test points are mounted to measure static pressures at rotor tips and total pressures at stator trailing edges during the experiments. To detect the system response, an additional high response sensor is installed on the plenum inner wall. As demonstrated by the WFT and filtering methods, the results indicate that the instability evolution varies gradually with the radial distribution of loading at the inlet. The partial surge instability converts into the spike with the increase of tip loading intensity. With the increase of hub loading intensity, the partial surge instability converts into the hub instability. For the hub instability, a special process is that a sudden decrease of total pressure occurs at the stator hub. Furthermore, the short-length scale disturbance occurs at the rotor tip, and leads to the final compressor stall.

Modal analysis of propeller wake dynamics under different inflow conditions

The evolution mechanisms from near to far field of propeller wakes under different inflow conditions were analyzed. Modal decomposition was performed on the propeller wakes calculated by previous numerical simulations [Wang et al., “Propeller wake instabilities under turbulent-inflow conditions,” Phys. Fluids 34, 085108 (2022)]. In particular, the Sparsity-Promoting Dynamic Mode Decomposition (SPDMD) was employed to identify the flow modes that play a dominant role in the inception mechanisms of the propeller wake destabilization. The results showed that the turbulent inflow contributes to the destabilization process of the tip vortex system under the low-turbulence inflow condition, and as a perturbation, it accelerates the interference between adjacent tip-vortex structures, intensifies the instability motion of the tip vortex, and then accelerates the breakdown of the tip vortex system. SPDMD succeeds in selecting a subset of dynamic mode decomposition modes that have the most substantial influence on the quality of approximation of the snapshot sequence. The present study further extends knowledge of propeller wake instability inception mechanisms under different inflow conditions, which can support the development and improvement of next-generation propellers.

Two-Regime Surge Mechanism in Centrifugal Compressors

Instability inception and evolution leading to surge have been key limiting factors to the stable operation of centrifugal compressors. The compressor generally experiences flow oscillation, which results from a deep surge, either or not preceded by a mild surge, depending on the operating condition. Research works over the years have identified a so-called two-regime surge phenomenon, which features a nonsurge region between the mild and deep surges. In this work, the flow mechanisms responsible for this phenomenon were investigated both experimentally and analytically using a dynamic compression system model. Results show that the slope alternation in the pressure ratio characteristic from positive to negative was the necessary condition for triggering this phenomenon. Correlation between the impeller inlet recirculation and the slope alternation has been established to elucidate the flow mechanisms responsible for this phenomenon. The growth rates of the work input and the flow loss were altered through the interloping of the impeller inlet recirculation such that a subtle balance between the two was achieved to formulate the slope alternation. Finally, both computational fluid dynamics and experiments were conducted to justify the correlation.

Modal analysis of propeller wakes under different loading conditions

Propeller wakes under different loading conditions obtained by the improved delayed detached eddy simulation method were studied based on the flow decomposition technique. The sparsity-promoting dynamic mode decomposition was used to study the flow physics in the wake of a propeller, with particular emphasis placed on identifying the underlying temporal and spatial scales that play important roles in the onset of propeller wake instabilities. The morphology of flow structures of different modes selected by the sparsity-promoting algorithm at different frequencies characterizes the instability process of the wake system. It shows that the circumferential diffusion of tip vortex structures promotes the approaching of adjacent tip vortices, enhancing the interaction of the vortex pairs, which plays an important role in the instability triggering mechanism of the propeller wake, especially the mutual inductance between neighboring tip vortices. The present study further extends knowledge of propeller wake instability inception mechanisms under different loading conditions.

Modal analysis of the propeller wake under the heavy loading condition

Knowledge of the dynamics of propeller wakes is fundamental to design and optimize the next-generation propellers. This work aims at investigating the wake instability of a propeller operating under the heavy loading condition. Modal decomposition techniques are used to analyze the evolution characteristics of the propeller wake achieved by previous numerical simulations using different turbulence models [Wang et al., “Numerical simulation of the wake instabilities of a propeller,” Phys. Fluids 33, 125125 (2021)]. Modal analysis is performed on snapshots extracted from improved delay-detached eddy simulations and large eddy simulations of the propeller wake topologies under the high loading condition. In particular, proper orthogonal decomposition and dynamic mode decomposition are employed to identify the modes that play dominant roles in the destabilization physics of the propeller wake. The present study further extends knowledge of propeller wake instability inception mechanisms under heavy loading conditions.

Instability Inception of a Single Rotor Embedded in a Transonic Stage with Partial Surge Inception

Instability Inception of a Single Rotor Embedded in a Transonic Stage with Partial Surge Inception

Numerical simulation of the wake instabilities of a propeller

Propeller wake instability mechanisms are investigated using the improved detached-eddy and large-eddy simulation approaches under heavy loading condition. Simulations involving a rotating propeller are performed using a dynamic overset technique in order to compare experimental propeller loads and phase-averaged flow fields and validate the modeling approach and grid system. Analysis of the three-dimensional vortical structures, out-of-plane vorticity, velocity components, Reynolds stresses, and kinetic energy power spectral density reveal the flow physics that drive the tip vortex instability process. The present study extends knowledge of propeller wake instability inception mechanisms under heavy loading conditions. It also helps to promote further understanding of propeller wake dynamics and provides a reference for the assessment of turbulence modeling approaches used to study propeller wake instability.

Effect of casing treatment to switch the type of instability inception in a high-speed axial compressor

Flow instability phenomena, such as surge and rotating stall, limit the operating range of compressors. Casing treatment is a commonly used technique to extend the operating range, and some corresponding physical explanations have been proposed for the effects of casing treatment on the flow field in compressors. While there is also a situation that a casing treatment that is effective for one compressor may be useless for another, as a result, which limits the practical application of casing treatments. Based on the knowledge that the radial distribution of blade loading has a great influence on the type of instability inception, a hypothesis is put forward: casing treatment could change the type of instability inception by altering the loading distribution, and the resulting influence on the occurrence of instability may be one of the reasons to explain some unsuccessful tests of casing treatment. This hypothesis is then verified experimentally and numerically in a transonic axial flow compressor operating at a specific speed where the loading of both rotor tip and stator hub is close to each critical value. The results present that casing treatment can alter the type of instability inception by redistributing the blade loading, and thus leads to an earlier part-span stall in the hub region for this compressor. The main concluding remarks in this paper could be practically beneficial for the design of casing treatment in compressors.

Numerical simulation of vortex instabilities in the wake of a preswirl pumpjet propulsor

A numerical analysis based on detached eddy simulations is conducted to investigate vortex instabilities in the wake of a preswirl pumpjet propulsor. Three models are established to separate the roles that the rotor, stator, and duct play in the vortex structure of the pumpjet propulsor. In this paper, only the vortex structure of the rotor is considered. The results show that the vortex system of the rotor is mainly composed of the tip vortices, a hub vortex, the trailing tip vortices, and the trailing root vortices. The trailing tip vortices are generated by the premature shedding of the tip vortices in the rotor model compared with a normal single propeller. The existence of trailing root vortices increases the stability of the hub vortex. Furthermore, a unique multi-inductance instability mode of the tip vortex, called the “overlap–forward” phenomenon, is found for low values of the advance coefficient J. It is found that the instability of the tip vortex depends not only on the spiral-to-spiral distance but also on the highest-efficiency point of the propeller. The instability inception point of the tip vortex moves farther downstream with increasing J, whereas when J is greater than the highest-efficiency point of the propeller, the stable length of the tip vortices drops sharply. The energy transfer process from blade harmonics to shaft harmonics of the tip vortices depends on J and is related to the spatial evolution of the tip vortices.

Effect of system response on partial surge initiated instability in a transonic axial flow compressor

Partial surge is a type of instability inception in transonic compressors and occurs in the form of axisymmetric low-frequency disturbances localized in the hub region. Previous studies illustrate that the frequency of partial surge is set by the Helmholtz frequency of the entire system, which motivates to propose a hypothesis that the system response performs an important role in the formation of partial surge. For further verification, a series of experiments are conducted to explore the link between the propagating of the partial surge and the system feedback in this study. In the first case, an additional test point is set on the wall of the plenum to detect the system response. Combining the flow behaviors inside the plenum with the disturbances in the rotor tip and stator hub/tip regions, the effects of the system feedback on the occurrence of the continuous disturbances and the rotating stall cells are illustrated. In the second case, a screen is mounted at the compressor outlet to prevent positive feedback from the plenum. The experimental results demonstrate that in the absence of system feedback, it is the occurrence of spike-type stall inception that leads to the flow instability instead of that of partial surge. In addition, three flow phenomena in the second case are discussed, including the occurrence of the single pulse, the unstable process during the stall evolution and the switch of instability inception.

Detection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural Networks

An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as spike-type stall inception in axial compressors is observed in aero-engine. In this paper, a deep dilated causal convolutional neural network(CNN) named as WaveNet is applied to spike-type stall inception detection and prediction in time-series data of axial compressors. WaveNet can implement fast anomaly detection and spike-type stall prediction in long-time term series data. Furthermore, a single WaveNet can be trained to capture and learn the time-domain statistical characteristics of different spike-type stall inception training data with equal fidelity. The trained WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of spike-type stall detection approach based on WaveNet.

Nonlinear response of swirling premixed flames to helical flow disturbances

This paper considers the relationship between nonlinearly interacting helical flow disturbances and flame area response in a swirling premixed flame. The present study was performed to determine whether there are nonlinear mechanisms through which helical modes ( ) can lead to non-zero unsteady heat release rate oscillations. The results show that for single frequency content (at ), helical modes excite unsteady heat release rate response of and that two-frequency excitation (e.g. at and ), leads to a response of at . There are two mechanisms through which this can occur: First, helical flow disturbances can distort the time-averaged flame shape to have an azimuthal component that matches that of the incident disturbance, . Second, multiple helical modes can nonlinearly interact to cause axisymmetric unsteady flame wrinkling. The paper derives the various modal contributions in the incident velocity disturbance that satisfy these criteria. These results suggest that it is only the mode which controls the linear dynamics (e.g. instability inception conditions) of these flames (where ), but that their nonlinear dynamics is also controlled by the helical modes.

Validation and Analysis of Nonlinear System of Centrifugal Compressor using Hot Wire Flow Measurement and Jacobian Matrix

ABSTRACTA mathematical method is developed to predict surge and instability inception in a centrifugal compressor map based on Greitzer and Moore model. The method uses phase plane and Jacobian matrix to determine stable and unstable zone of centrifugal compressor and to extract limit cycle in unstable zone. In order to evaluate accuracy of analytical method, a compressor test rig facility is designed and tested in order to be compared with theoretical results. Accurate Honeywell piezoelectric pressure transducers are used to measure time averaged pressure at inlet and outlet ducts of the compressor. In order to record the flow fluctuation precisely, hot wire sensors are installed in entrance and exit of impeller. The results represent instability inception near true stall point but different from measured data. The discrepancy mostly originates from inlet and outlet ducts which causes measure stall point of compressor deviates from true one. An analytical method which is able to predict surge and instability in a compressor is highly significant for instrumentation and controlling of system.

Estimation of voltage instability inception time by employing k‐nearest neighbour learning algorithm

In this study, the proposed algorithm is a help for utilities to find out when voltage instability will happen, as soon as they realise that a disturbance may/will result in voltage instability. In this regard, the data gathered, on-line, by a wide area monitoring system (WAMS) is supposed to be processed in an energy management system, where, finally, voltage instability inception time will be determined. The proposed algorithm employs k-nearest neighbour algorithm as an instance-based learning algorithm. This estimation uses only pre-disturbance information and exact disturbance data, so it is fast enough for real-time applications. A comprehensive investigation of the input features has resulted in introducing the most appropriate feature set and the most effective search procedure in estimating the instability time. The simulation results show an acceptable accuracy in estimation of voltage instability time.

Stability enhancement using a new hybrid casing treatment in an axial flow compressor

A hybrid slot–groove casing treatment (CT) composed of front axial slots and rear circumferential grooves is experimentally studied in a low-speed axial compressor in consideration of stability and efficiency. Results show that the hybrid slot–groove CT can improve the stall margin by 19.79% with an efficiency penalty of 1.5%. Compared to full-groove and full-slot CTs, the hybrid slot–groove CT's stall margin improvement and minimization of efficiency loss are excellent. Detailed measurements indicate that, unlike smooth casing (SC), the hybrid slot–groove CT can unload the blade tip, weaken the tip leakage vortex, and improve the disturbance observed downstream to postpone stall. Given the influence of serious passage blockage derived from the hub region after improving tip flow capability under the hybrid slot–groove CT, the stall route and inception captured in the casing wall and different downstream radial positions demonstrate that, unlike the SC with a typical spike-type inception (short length-scale with 2–3 blade passages), the hybrid slot–groove CT creates a long length-scale stall inception (6–8 blade passages). In addition, a new type of instability inception with a different frequency band from the stall cell is found in the hub region under the hybrid slot–groove CT. This inception appears more than hundreds of revolutions before the fast trigger of spike-type inception and can be used as an early stall warning signal under high hub loading.

Multi-resolution Delta-plus-SPH with tensile instability control: Towards high Reynolds number flows

It is well known that the use of SPH models in simulating flow at high Reynolds numbers is limited because of the tensile instability inception in the fluid region characterized by high vorticity and negative pressure. In order to overcome this issue, the δ+-SPH scheme is modified by implementing a Tensile Instability Control (TIC). The latter consists of switching the momentum equation to a non-conservative formulation in the unstable flow regions. The loss of conservation properties is shown to induce small errors, provided that the particle distribution is regular. The latter condition can be ensured thanks to the implementation of a Particle Shifting Technique (PST). The novel variant of the δ+-SPH is proved to be effective in preventing the onset of tensile instability. Several challenging benchmark tests involving flows past bodies at large Reynolds numbers have been used. Within this a simulation characterized by a deforming foil that resembles a fish-like swimming body is used as a practical application of the δ+-SPH model in biological fluid mechanics.

Comparison of Different Stall Conditions in Axial Flow Compressor Using Analytic Wavelet Transform

Abstract The rotating stall inception data analysis using Analytic Wavelet Transform (AWT) in a low-speed axial compressor was presented in the authors’ previous studies [1], [2]. These studies focused on the detection of instability inception in an axial flow compressor when it enters into the instability regime due to the modal type of stall perturbation. In this paper, the effectiveness of AWT is further studied by applying it under different testing conditions. In order to examine the results of AWT on highly sampled data, at first, the stall data were acquired at a high sampling frequency and the results were compared with the conventional filtered signals. Secondly, the AWT analysis of stall data was carried out for the condition when compressor experienced a spike type rotating stall disturbance. The stall inception information obtained from the AWT analysis was then compared with the commonly used stall detection techniques. The results show that AWT is equally beneficial for the diagnostic of compressor instability regardless of the data sampling rate and represents an outstanding ability to detect stall disturbance irrespective of the type of stall precursor, i.e. the modal wave or spike.