Reconstructing Compressor Non-Uniform Circumferential Flow Field From Spatially Undersampled Data—Part 1: Methodology and Sensitivity Analysis

Lou, Fangyuan; Key, Nicole L.

doi:10.1115/1.4050433

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Details of the method can be found in Ref. [15]. The fundamental concept of the method is to approximate the circumferential variations of the flow field in turbomachines using a few (N) dominant wavelets instead of an infinite number of wavelets:…”

Section: Reconstruction Of the Circumferential Nonuniformmentioning

confidence: 99%

Accounting for Circumferential Flow Nonuniformity in a Multistage Axial Compressor

Lou

Matthews²,

Kormanik³

et al. 2023

Journal of Turbomachinery

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The flow field in a compressor is circumferentially non-uniform due to geometric imperfections, inlet flow nonuniformities, and blade row interactions. Therefore, the flow field, as represented by measurements from discrete stationary instrumentation, can be skewed and contribute to uncertainties in both calculated one-dimensional performance parameters and aerodynamic forcing functions needed for aeromechanics analyses. Considering this challenge, this paper documents a continued effort to account for compressor circumferential flow nonuniformities based on discrete, under-sampled measurements. First, the total pressure field downstream of the first two stators in a three-stage axial compressor was measured across half of the annulus. The circumferential nonuniformities in the stator exit flow, including vane wake variability, were characterized. In addition, the influence of wake variation on stage performance calculations and aerodynamic forcing functions were investigated. In the present study for the compressor with an approximate pressure ratio of 1.3 at design point, the circumferential nonuniformity in total pressure yields an approximate 2.4-point variation in isentropic efficiency and 54% variation in spectral magnitudes of the fundamental forcing frequency for the embedded stage. Furthermore, the stator exit circumferential flow nonuniformity is accounted for by reconstructing the full-annulus flow using a novel multi-wavelet approximation method. The features associated with wake-wake interactions accounting for passage-to-passage variations are resolved in the reconstructed total pressure profile, yielding representative mean flow properties and aerodynamic forcing functions.

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Section: Reconstruction Of the Circumferential Nonuniformmentioning

confidence: 99%

Accounting for Circumferential Flow Nonuniformity in a Multistage Axial Compressor

Lou

Matthews²,

Kormanik³

et al. 2023

Journal of Turbomachinery

Self Cite

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“…Their data-driven model represents the spatial flow-field in the circumferential direction via a Fourier series expansion, while capturing flow in the radial direction using a high-degree polynomial. Although an improvement in the state of the art (Lou and Key, 2021), their model does have limitations. For instance, the placement of probes may lead to Runge's phenomenon (see Chapter 13 in Trefethen, 2013) in the radial direction, while the harmonic content is set by the Nyquist condition (see Chapter 4 in Strang, 2012) in the circumferential direction.…”

Section: State Of the Artmentioning

confidence: 99%

Bayesian assessments of aeroengine performance with transfer learning

Seshadri

Duncan²,

Thorne³

et al. 2022

DCE

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Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements, we require a statistically principled approach for inferring these profiles. In this paper we detail a Bayesian methodology for interpolating the spatial temperature or pressure profile at axial stations within an aeroengine. The profile at any given axial station is represented as a spatial Gaussian random field on an annulus, with circumferential variations modelled using a Fourier basis and radial variations modelled with a squared exponential kernel. This Gaussian random field is extended to ingest data from multiple axial measurement planes, with the aim of transferring information across the planes. To facilitate this type of transfer learning, a novel planar covariance kernel is proposed. In the scenario where frequencies comprising the temperature field are unknown, we utilise a sparsity-promoting prior on the frequencies to encourage sparse representations. This easily extends to cases with multiple engine planes whilst accommodating frequency variations between the planes. The main quantity of interest, the spatial area average is readily obtained in closed form. We term this the Bayesian area average and demonstrate how this metric offers far more representative averages than a sector area average---a widely used area averaging approach. Furthermore, the Bayesian area average naturally decomposes the posterior uncertainty into terms characterising insufficient sampling and sensor measurement error respectively. This too provides a significant improvement over prior standard deviation based uncertainty breakdowns.

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“…This paper proposes fault classification in the air compressor based on the real-time empirical acoustic sensor data. However, the challenging point in designing such an intelligent fault detection system (IFDS) is to identify the sensitive points from where the signals are acquired, and that is performed by the mechanism of the sensitive point or position analysis (SPA) [18][19]. The raw data collected from all the www.ijacsa.thesai.org sensitive positions are exposed to various noises requiring appropriate denoising treatments [20].…”

Section: Introductionmentioning

confidence: 99%

Modeling a Fault Detection Predictor in Compressor using Machine Learning Approach based on Acoustic Sensor Data

N¹,

K²,

L³

2021

IJACSA

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Proper functioning of the air compressor ensures stability for many critical systems. The ill-effect of the breakdown caused by the wear and tear in the system can be mitigated if there exists an effective automated fault classification system. Traditionally, the simulation-based methods help to extend to identify the faults; however, those systems are not so effective enough to build real-time adaptive methods for the fault detection and its type. This paper proposes an effective model for the fault classification in the air compressor based on the realtime empirical acoustic sensor time-series data were taken on a sampling frequency of 50Khz. In the proposed work, the timeseries datais transformedinto the frequency domain using fast Fourier transforms,where half of the signals are considered due to its symmetric representation. Afterward, a masking operation is carried out to extract significant feature vectors fed to the multilayer perception neural network. The uniqueness of the proposed system is that it requires less trainable parameters, thus reduces the training time and imposes lower memory overhead. The model is benchmarked with performance metric accuracy, and it is found that the proposed masked feature setbased MLP-ANN exhibits an accuracy of 91.32%. In contrast, the LSTM based fault classification model gives only 83.12% accuracy, takes more training time, and consumes more memory. Thus, the proposed model is realistic enough to be considered a real-time monitoring system of the fault and control. However, other performance metrics like precision, recall, and F1-Score are also promising with the LSTM based fault classifier.

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Reconstructing Compressor Non-Uniform Circumferential Flow Field From Spatially Undersampled Data—Part 1: Methodology and Sensitivity Analysis

Cited by 9 publications

References 11 publications

Accounting for Circumferential Flow Nonuniformity in a Multistage Axial Compressor

Accounting for Circumferential Flow Nonuniformity in a Multistage Axial Compressor

Bayesian assessments of aeroengine performance with transfer learning

Modeling a Fault Detection Predictor in Compressor using Machine Learning Approach based on Acoustic Sensor Data

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