Detection of Stall Regions in a Low-Speed Axial Fan. Part II – Stall Warning by Visualisation of Sound Signals   Alessandr...
Background motivations (i) •  Huge amount of research works have been dedicated in the last decades to the goal of  findin...
Background motivations (ii) Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Ex...
Techniques and concepts for stall warning,  a review (i) Detection of stall regions in a low-speed axial fan by visualisat...
Techniques and concepts for stall warning,  a review (ii) Detection of stall regions in a low-speed axial fan by visualisa...
Techniques and concepts for acoustic based diagnostics,  a review Detection of stall regions in a low-speed axial fan by v...
Part II - Aims Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turb...
Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress an...
Methodology.  Fan geometry & design •  Low-speed axial fans for industrial applications intended for high-temperature  ope...
Methodology.  Fan performance •  Total pressure-to-volume performance map Detection of stall regions in a low-speed axial ...
Methodology.  Fan performance •  Total pressure-to-volume performance map,  conclusions from GT2010-22753 Detection of sta...
Methodology.  Test apparatus Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo E...
SYMMETRIZED DOT PATTERN (SDP) Technique,  introduction Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall...
SYMMETRIZED DOT PATTERN (SDP) Technique,  rationale (i) •   The algorithm that maps a normalised time  waveform into symme...
SYMMETRIZED DOT PATTERN (SDP) Technique,  rationale (ii) •   The frequency content manifests in the dot pattern curvature ...
•  Recent studies, Bianchi et al. ( IMechE J. of Mech. Engrg   Sc. , 2009), have used the SDP as a tool to assess the aero...
SYMMETRIZED DOT PATTERN (SDP) Technique, Sensitivity analyses Turbo Expo 2010, Turbine Congress and Exhibition Detection o...
SYMMETRIZED DOT PATTERN (SDP) Technique, Dot Pattern parameter selection (i) Turbo Expo 2010, Turbine Congress and Exhibit...
SYMMETRIZED DOT PATTERN (SDP) Technique, Dot Pattern parameter selection (i) Turbo Expo 2010, Turbine Congress and Exhibit...
SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling frequency (ii) Detection of stall regions in a low-speed axial fan by vi...
SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and Exh...
rpm SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and...
SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and Exh...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of s...
Conclusions Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine...
Acknowledgement The present research was done in the context of the contract FW-DMA 10-11 , between  Flakt Woods Ltd and  ...
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GT2010-22754

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Development of a stall control tool for axial fans.

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GT2010-22754

  1. 1. Detection of Stall Regions in a Low-Speed Axial Fan. Part II – Stall Warning by Visualisation of Sound Signals Alessandro CORSINI , Stefano BIANCHI FMGroup @ DMA-URLS Anthony G. SHEARD Flakt Woods Ltd Turbo Expo Turbine Congress and Exhibition June 14 – 18, 2010, Glasgow, Scotland UK GT2010-22754
  2. 2. Background motivations (i) • Huge amount of research works have been dedicated in the last decades to the goal of finding reliable methods to monitor the approach of fans or compressors to the stability limits while running literature review spans from ’70s&’80s (Day and Cumpsty, 1978) (Greitzer, 1980) to stall detection, management & control studies (Paduano et al., 2001) (Christensen et al., 2006) • Two questions are still under scrutiny alert methods are proposed for individual test-beds techniques for the detection of stall initiation, based on experimental observation of pre-stall behaviours, have sought to identify such behaviour as early as possible i.e. early detection period lasts less than two rotor revolutions for axial compressors and it could be ten times longer in low-speed single stage industrial fans Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition FMGroup @ DMA-Sapienza
  3. 3. Background motivations (ii) Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • Several studies suggested the presence of tip flow phenomena (in low-speed and high- speed rotors) directly responsible for the generation of those disturbances (also called ‘ spikes’ or ‘pips’) causing the inception of part-span stall cells • This inception mechanism could be correlated also with a modification of the noise signal in the casing region • A number of scholars have utilised azimuthal measurements in an attempt to link the rotating unsteady patterns of flow instabilities to their acoustic signatures to detect in centrifugal turbomachines low frequency noise due to rotating stall in (Okada, 1987) or aerodynamic sound sources (Mongeau et al., 1995) In a similar vein (Kameier and Neise, 1997) established a link between tip-clearance noise and associated blade-tip flow instabilities in axial turbo-machinery by correlating rotating source and vortex mechanisms with rotating stall cells FMGroup @ DMA-Sapienza
  4. 4. Techniques and concepts for stall warning, a review (i) Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • Some non-model based early-warning techniques have tested the suitability of real- time control applications using a variety of diagnostic methods most based on time-axis signals or Fourier analyses of pressure as measured on the casing of the machines • HPC and full-scale multi-stage configurations Tryfonidis et al. (1995) studied the pre-stall behaviour of high-speed compressors using a technique based on spectral analysis of Fourier harmonics for measured pressure on the casing Christensen et al. (2006) developed a full-scale fan/compressor aerodynamic stability- management system by computing a correlation measure with signal multiplication and integration Tong et al. (2009) developed an on-line control system that combined pressure correlation measures in the form of a distribution function FMGroup @ DMA-Sapienza
  5. 5. Techniques and concepts for stall warning, a review (ii) Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • Scholars have attempted to develop new metrics to identify early stall events in compressors Bright et al. (1998) investigated rotating ‘pip’ detection prior to stall and proposed a method called ‘ temporal structure function ’ which was inspired by a statistical approach linked to chaos dynamics Tahara et al. (2007) proposed a stall-warning index (utilising pressure signals with high- response transducers on the casing wall at the rotor leading edge) based on the normalised inner product of pressure fluctuations over two successive rotor revolutions FMGroup @ DMA-Sapienza
  6. 6. Techniques and concepts for acoustic based diagnostics, a review Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition <ul><li>• Noise-related detection techniques have been considered only recently as possible </li></ul><ul><li>candidate solutions to the problem of general fault detection </li></ul><ul><li> (Shibata et al., 2001) (Wu et al. 2005) </li></ul><ul><li>• Techniques based on acoustic/vibration signals visualisation have been successfully </li></ul><ul><li>proposed to fault diagnosis in rotating machinery and ICE </li></ul><ul><ul><ul><li>e.g. in the case of bearing failures or faults due obstruction of cooling fan </li></ul></ul></ul><ul><li>• These approaches were found to be suitable to work under operating conditions when the </li></ul><ul><li>signal-to-noise ratio is low </li></ul><ul><li>i.e. the instability signature-to-background noise ratio is low as is the case with regard to rotor </li></ul><ul><li>stall-inception at partial speed </li></ul><ul><ul><ul><li>in the case of pressure signals dominated by discrete narrowband frequency components related to the speed of the scaling phenomenon </li></ul></ul></ul>FMGroup @ DMA-Sapienza
  7. 7. Part II - Aims Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • To develop an alternative approach to stall warning based on a method able to discriminate over the sound quality, mimiking the human perception of the noise emitted The present study presents a stall-detection technique based on symmetrised dot pattern (SDP) images • To test the proposed diagnostic tool, the study presents an analysis of acoustic data across nine aerodynamic operating conditions (i) three speeds (full-, half-, and quarter-speed) and (ii) three operational states (stable aerodynamic operation, incipient stall and rotating stall) FMGroup @ DMA-Sapienza
  8. 8. Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition Part II - Outline • Methodology Test fan and apparatus • Sound visualization technique Rationale and background information Sensitivity analysis • Discussion of the stall warning @ full and part-speed Sound visualization of the stall Warning system concept • Conclusions FMGroup @ DMA-Sapienza
  9. 9. Methodology. Fan geometry & design • Low-speed axial fans for industrial applications intended for high-temperature operations in tunnel emergency ventilation systems blade profiles modified ARA-D geometry type arbitrary vortex radial work distribution Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition FMGroup @ DMA-Sapienza
  10. 10. Methodology. Fan performance • Total pressure-to-volume performance map Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition FMGroup @ DMA-Sapienza
  11. 11. Methodology. Fan performance • Total pressure-to-volume performance map, conclusions from GT2010-22753 Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition FMGroup @ DMA-Sapienza
  12. 12. Methodology. Test apparatus Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • One microphones were mounted flush with the inner wall of the casing • Microphone mounted above the blade vane FMGroup @ DMA-Sapienza
  13. 13. SYMMETRIZED DOT PATTERN (SDP) Technique, introduction Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals • Technique first developed for the visual characterisation of speech waveforms in automatic human-voice recognition algorithms @ IBM (Pickover, 1986) SDP of the steady-state phoneme “AH” and nasalized after Pickover (1986) Able to determine noise peculiarities and to perceive otherwise ‘unquantifiable’ differences in sound signals Experiments on psychological perception (Sottek and Genuit, 2007), demonstrated that noise ‘annoyance’ is influenced by the sound power level (produced by tonal components) but also by ‘howling’ sounds and modulated signals FMGroup @ DMA-Sapienza
  14. 14. SYMMETRIZED DOT PATTERN (SDP) Technique, rationale (i) • The algorithm that maps a normalised time waveform into symmetrised dot patterns on a polar graph produces SDPs A point in the time waveform is mapped onto a radial component and the adjacent point is mapped to an angular component. The polar transformation R(i) from waveform to SDP can be formulated as Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals FMGroup @ DMA-Sapienza
  15. 15. SYMMETRIZED DOT PATTERN (SDP) Technique, rationale (ii) • The frequency content manifests in the dot pattern curvature while the signal variation results in the dot fuzziness which increase the SDP footprint Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals f ~ 1/2 fan rotor BPF f ~ fan rotor BPF white noise FMGroup @ DMA-Sapienza
  16. 16. • Recent studies, Bianchi et al. ( IMechE J. of Mech. Engrg Sc. , 2009), have used the SDP as a tool to assess the aeroacoustic performance associated with tip end-plate for low noise emission in fan rotors Detection of stall regions in a low-speed axial fan by visualisation of sound signals SYMMETRIZED DOT PATTERN (SDP) Technique, background studies Corsini et al., ASME J. of Turbomachinery , 2010
  17. 17. SYMMETRIZED DOT PATTERN (SDP) Technique, Sensitivity analyses Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals A first sub-set influences the shape of the dotted patterns time lag L and angular gain  The transformation of time waveforms of the pressure signal into a set of dots, six-fold mirror symmetry snow-flake shaped, in a view to detect the occurrence of aerodynamic instabilities heavily depends on the accurate choice of a set of parameters A second sub-set is mostly related to the peculiar application developed i.e. stall-warning sampling frequency and sampling time FMGroup @ DMA-Sapienza
  18. 18. SYMMETRIZED DOT PATTERN (SDP) Technique, Dot Pattern parameter selection (i) Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals The discrimination of the aerodynamic phenomena depends on the following parameters time lag L and angular gain  FMGroup @ DMA-Sapienza
  19. 19. SYMMETRIZED DOT PATTERN (SDP) Technique, Dot Pattern parameter selection (i) Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals The discrimination of the aerodynamic phenomena depends on the following parameters time lag L and angular gain  FMGroup @ DMA-Sapienza
  20. 20. SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling frequency (ii) Detection of stall regions in a low-speed axial fan by visualisation of sound signals The sampling rate (determined by the data acquisition response time) and the sampling time determine the number of points in a data series to detect aerodynamic instability as stall precursor, thus smaller sampling frequency is also a key factor L = 30,  = 20° at 50 kHz dot density is concentrated on the pattern attractor with an anomaly angle sets at about Θ = 12°, slightly less than the baseline anomaly ξ = 20° The effect of the sample rate reduction likely produced a down-scaling of the SDP diagram at 1 kHz dot density reduced such that no indication of the pattern anomaly is possible FMGroup @ DMA-Sapienza
  21. 21. SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals L = 30,  = 20°, f = 50 kHz FMGroup @ DMA-Sapienza
  22. 22. rpm SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals L = 30,  = 20°, f = 50 kHz FMGroup @ DMA-Sapienza
  23. 23. SYMMETRIZED DOT PATTERN (SDP) Technique, Sampling time vs rotor operations (iii) Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals L = 30,  = 20°, f = 50 kHz rpm In terms of absolute sampling window durations 0.1 sec (3 revolutions) 0.004 sec (0.1 revolution) FMGroup @ DMA-Sapienza
  24. 24. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals SYMMETRIZED DOT PATTERN (SDP), Stall diagnosis L = 30,  = 20°, f = 50 kHz, 1 rev throttle FMGroup @ DMA-Sapienza
  25. 25. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals L = 30,  = 20°, f = 50 kHz, 1 rev SYMMETRIZED DOT PATTERN (SDP), Stall diagnosis throttle FMGroup @ DMA-Sapienza
  26. 26. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals SYMMETRIZED DOT PATTERN (SDP), Stall diagnosis L = 30,  = 20°, f = 50 kHz, 1 rev FMGroup @ DMA-Sapienza
  27. 27. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals L = 30,  = 20°, f = 50 kHz, 1 rev, @ 100% rpm SYMMETRIZED DOT PATTERN (SDP), Interpretation at full-speed Superposition of normal operation and stall incipience SDPs @ 100% rotor speed FMGroup @ DMA-Sapienza Normal operation SDP (white) Stall incipience SDP (black)
  28. 28. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals pattern arms are gradually losing their curvature signifies the predominance of low frequency components in the rotating stall operation footprint in the SDP map extends with the appearance of increasing instability in the pressure time history. SYMMETRIZED DOT PATTERN (SDP), Interpretation at full-speed Superposition of normal operation and stall incipience SDPs @ 100% rotor speed L = 30,  = 20°, f = 50 kHz, 1 rev, @ 100% rpm FMGroup @ DMA-Sapienza
  29. 29. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals SYMMETRIZED DOT PATTERN (SDP), Interpretation at part-speed L = 30,  = 20°, 50 kHz, 50% rpm Different figures apply @ half and quarter-speed SDPs that indicate reduced speed influences in the low frequency. The dominance of low frequency content is signified by the tendency to lose curvature The reduction of the dot patterns radial area extension. Similar findings, giving rise to the shifting inward of the SDP geometrical centre, were correlated to white noise-like signals SDP behavioural modifications, under part-speed conditions, could be interpreted as a further evidence of progressive stall Normal operation Stall incipience FMGroup @ DMA-Sapienza
  30. 30. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals <ul><li>Template-matching system </li></ul><ul><li>signal patterns are converted into </li></ul><ul><li>standard picture files and stored into a data bank </li></ul><ul><li>ii. the signal under measurement is transformed into pattern and used as input into the template-matching system </li></ul><ul><li>iii. according to the rotor rpm , the system will find the best matching pattern from the databank </li></ul><ul><li>iv. the ‘best match’ pattern is the operative condition pattern for the experiment, thus enabling the control system to act appropriately </li></ul>SDP-based stall warning method, template matching FMGroup @ DMA-Sapienza
  31. 31. Turbo Expo 2010, Turbine Congress and Exhibition Detection of stall regions in a low-speed axial fan by visualisation of sound signals SDP-based stall warning method, set-up <ul><li>Legenda </li></ul><ul><li>Microphone </li></ul><ul><li>SDP processor </li></ul><ul><li>Template matching processor </li></ul><ul><li>Feedback signal to fan rotational frequency controller </li></ul><ul><li>Fan motor </li></ul>Sheard, A.G., Corsini, A. & Bianchi, S. , Forthcoming , “ Method of detecting stall in an axial fan”, British Patent Application No. P 10896 GB, Application date 1 March 2010 FMGroup @ DMA-Sapienza
  32. 32. Conclusions Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition • The diagnostic approach reported in this paper detects early aerodynamic instabilities in an axial fan and in so doing facilitates prevention of the rotor’s stall onset • The stall detection method based on an analysis of SDP’s that visually expresses the changes in amplitude and frequency of pressure signals The analysis associated with the generation of the SDP has been shown to be possible within one rotor revolution, and therefore is fast enough to be used in an active stall control system • SDP’s are significantly differentiated at each of the operating condition and speed combinations studied SDP’s may therefore be used to differentiate between stall conditions that will lead to mechanical failure of the fan, and those that will give rise to progressive stall FMGroup @ DMA-Sapienza
  33. 33. Acknowledgement The present research was done in the context of the contract FW-DMA 10-11 , between Flakt Woods Ltd and Fluid Machinery Research Team @ Dipartimento di Meccanica e Aeronautica “Sapienza” University of Rome Colchester – The Castle park view Roma – Foro Romano view Detection of stall regions in a low-speed axial fan by visualisation of sound signals Turbo Expo 2010, Turbine Congress and Exhibition FMGroup @ DMA-Sapienza

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