Applications of advanced diagnostic and active control for ...
Transcript of Applications of advanced diagnostic and active control for ...
Applications of advanced diagnostic and active control for
lean combustion
The Department focuses on new technologies and is devoted to promote and to disseminate technology innovation; is involved in:
• Renewable energies• Materials science and technology• ICT• Bio-applications of materials and ICT• Nanotechnologies• Manufacturing technologies• Robotics• Design and testing in Mechanical and Civil Engineering
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About 100 of staff people teaching in Engineering faculty and Science faculty.An average of 100 of PhD students and Postdocs.On a regional basis our Department in the last years was capable to win about 30% of all supported research projects, competing against other 3 Universities (two in Bari and one in Foggia). Our facilities are on 1500 square meters of laboratories
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Green Engine Lab @ Department of Engineering for Innovation 1 Full Professor
2 Associate Professors
1 Permanent Assistant Professor
1 Temporal Assistant Professor
5 Assistant Researchers
Ph.D Students
LASER DIAGNOSTIC LAB
COMBUSTION LAB
AEROSPACE PROPULSION
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PARTNERS AND COLLABORATIONS
GREEN ENGINE- FacilitiesLASER DIAGNOSTIC TECHNIQUES:
• Stereo Particle Image Velocimetry• Laser Doppler Velocimetry• Phase Doppler Particle Analyzer• High speed visualization systems (UV- VIS-
NIR)• Hot Wire measurements• Acetone LIF• Smoke Visualizations
PARTICLE IMAGE VELOCIMETRY & FLOW
VISUALIZATIONS
LASER DOPPLER VELOCIMETRY
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GREEN ENGINE- Combustion Facilities
SPRAY CHARACTERIZATIONHIGH-PRESSURE GAS-FUELED COMBUSTOR
LIQUID-FUELEDCOMBUSTOR
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LIQUID-FUELED SWIRLING COMBUSTOR @ Green Engine Lab
1. Flame stability monitoring and characterization through VIS, UV-VIS and NIR Flamedigital imaging
2. Flame stability monitoring by statistical and spectral analysis of flame parameters
3. Chemiluminescence images by ICCD
4. Acetone LIF, Particle Image Velocimetry (PIV), Hotwire measurements and LaserDoppler Velocimetry (LDV) measurements
5. Flame diagnostic technique based on Machine Learning Techniques
LIQUID-FUELED SWIRLING COMBUSTOR @ Green Engine Lab
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LIQUID-FUELED SWIRLING COMBUSTOR
UV-VIS-NIR images INTENSIFIED CAMERA Phantom M320 High speed camera MemrecamIntensified camera LaVisionNIR-FLIR camera
OH*/CH* chemiluminescence emissions PMTSS of Thorlabs® equipped with an interference filter at 307 nm for the OH* signal acquisition with 10 nm FWHM (10 KHz)
Pollutant emissions (NOX, SO2, CO, CO2 and O2) Complete analyzer system PG-350E Horiba equipped with gas sampling, sample conditioning, analyzer and system control unit
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EXPERIMENTAL SETUP
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LIQUID-FUELED SWIRLING COMBUSTOR @ Green Engine Lab
MAIN AIMS Development of diagnostic techniques “early detection”
and monitoring flow unsteadiness
Implementation of supervision and control algorithms toincrease safety and reliability of combustors
Development of active control of instabilities in leancombustors
Investigation of the effect of injection mode on flame leanblowout : comparison of non premixed and partiallypremixed combustion mode
Validate numerical models of combustion instabilities, fora better description of the phenomena
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LBO MARGIN SENSING APPROACHES
Instability precursor sensors for active/reactive control systems safely and more efficiently lean combustors, operational flexibility, reliability and availability (i.e., downtime for inspections, component repairs and unplanned shutdowns).
Sensing methodology identifying LBO precursors
Transition from stable combustion to LBO transient regime with localized flame extinctions and reignitions
Close to LBO large scale flame pulsations
Large scale pulsations noise, changes in radiative emissions and cyclic thermal loads precursors for LBO
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GEOMETRICPARAMETERS
LUMINOUSPARAMETERS
TIME AND FREQUENCY
PARAMETERSFlame shapeVolume integralSurface areaCircularity factorLength
BrightnessNon UniformityChemiluminescenceemissions
Flame oscillationfrequency• SPECTRAL OR WAVELET-
BASED TIME FREQUENCY ANALYSIS
• POD DECOMPOSITION TECHNIQUES
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COMPARING COMBUSTION MODE CO starts at leaner condition (=0.35) in the partially premixed combustion . The maximum value of CO is about the same for the two combustion modes. NOx emissions are significantly higher in non-premixed mode compared to partially premixed
regime.
Pollutant emissions (NOX, CO)
If NOx aim ofthe lean combustion
When instability occurs CO
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HIGH SPEED CCD ACQUISITIONS
VIS
NIR
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Example VIS acquisition, non-premixed regime
High φ: flame looks
stable
If φ flame is
unstable and a relevant
non-uniformities as well
as geometrical and
luminous instabilities of
the flame are evident
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Example VIS acquisition partially-premixed regime
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Normalization
Mean
Variance
TIME SERIES OF IMAGES
TIME SERIES OF PIXEL INTENSITIES
STATISTICAL ANALYSIS
SPECTRAL ANALYSIS
PSD
Wavelet decomposition
Energy content
Wavelet entropy
SHAPE ANALYSIS
Heywood
circularity factor
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SHAPE ANALYSIS: HEYWOOD CIRCULARITY FACTOR
NON-PREMIXED MODE PART. PREM. MODE
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VARIANCE of VIS acquisition, non-premixed
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The instability becomes evident
VARIANCE of VIS acquisition, non-premixed
Flame fluctuations lead to substantial variations in luminosity in the zone further away from the injector where the effects of the mixture fluctuations are more evident
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Spatially averaged values of VIS acquisitions variance
Variance for below 0.25 for the non-premixed combustion regime and 0.20 for the partially-premixed mode
The premixing more promising to reduce emission because it permits to reach leaner condition than in non-premixed strategies
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One-dimensional Discrete Wavelet Transform decomposition (Daubechieswavelet decomposition filters) was applied to the single-pixel signals and to the PMT signals to analyse different spectral ranges
A 1
O rig ina l s ignal f
D 1
A 2 D 2
A 3 D 3
A 4 D 4
A 5 D 5
A 6 D 6
A 7 D 7
A 8 D 8
A 1
O rig ina l s ignal f
D 1
A 2 D 2
A 3 D 3
A 4 D 4
A 5 D 5
A 6 D 6
A 7 D 7
A 8 D 8
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Discrete Wavelet Transform decomposition
It is possible to analyze the energy content of the detail components of the decomposed signal. If Ej is the wavelet energy of the j’s decomposition scale of the signal, the probability distribution of energy for each scale is given by pi:
where Ei is defined as the sum of square of detailed wavelet transform coefficients.
A 1
O rig ina l s ignal f
D 1
A 2 D 2
A 3 D 3
A 4 D 4
A 5 D 5
A 6 D 6
A 7 D 7
A 8 D 8
A 1
O rig ina l s ignal f
D 1
A 2 D 2
A 3 D 3
A 4 D 4
A 5 D 5
A 6 D 6
A 7 D 7
A 8 D 8
m
i
iEE1 E
Ep i
i
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Discrete Wavelet Transform decomposition
Wavelet Energy details contents spatially averaged
values from VIS imagesanalysis
Towards LBO Ei in the ranges 42-84 Hz and 21-41 Hz in both the regimes, but while in non-premixed combustion the range 42-84 Hz is dominant, the frequency range 21-41 Hz is more energetic in the partially premixed combustion.
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Discrete Wavelet Transform decomposition
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Discrete Wavelet Transform decomposition
=0.66 =0.57 =0.40
=0.27 =0.19
WAVELET ENTROPY MAPS of visible images
m
1iii
ppWEE )log(
m
i
iEE1 E
Ep i
i
Variance Non premixed regime
WD Ei components
STATISTICAL AND SPECTRAL ANALYSIS OF THE PMT SIGNAL
If =0.21 Ei mostly distributed in low-frequency spectral ranges.
Towards LBO ( =0.13), the contribution of the lowest frequency range decreases, while the frequencies in the range between 39 and 78 Hz become more relevant
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COMBUSTIONPMT acquisition CH*/OH* emissions
CaseOH*
Mean Value
OH* Variance
CH*MeanValue
CH* Variance
CH*/OH*
A 4.903 0.050 23.300 0.065 4.752
B 5.008 0.049 14.810 0.161 2.957
C 4.463 0.062 12.175 0.256 2.728
D 5.429 0.042 20.344 0.090 3.747
E 4.143 0.071 16.184 0.153 3.906
Mean CH* intensity and the variance decrease and increase respectively as the blowout is approached. This trend is not so remarkable in the case of OH* emissions.
It may be observed a high variability of the CH* signals.
CH*/OH* ratio, related to the flame’s global heat release, decreases in particular for the non-premixed cases. At the same fuel/air ratio, the CH*/OH* ratio assumes different values from those in the case of partially premixed flames, where it remains almost constant lowering the fuel/air ratio.
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COMBUSTIONSTATISTICAL PARAMETER of UV acquisition, CH* emissions
Variance when blowout is approached.
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COMBUSTIONDOMINANT FREQUENCY of UV acquisition, CH* emissions
)()/(= a
a
aa
a
DOM fPSDfPSDff PSD (fa)is the component of the power spectral density of the acquired signal for the single frequency
fDOM when blowout is approached. This trend is evident both in the non-premixed and in partially premixed cases.
fDOM lower for the partially premixed cases.
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IDENTIFICATION OF FLAME STRUCTURES AND INSTABILITIES BY DECOMPOSITION TECHNIQUES
Proper Orthogonal Decomposition (POD)
• POD is a statistical method that reduces a set of original data into a set of Eigen bases that contain all of the spatial information and constants that contain all of the temporal information.
• POD is based on energy considerations. If the flame has energetic and periodic structures, they will be captured in the first couple of POD modes.
• Therefore, POD can be used to identify the dominant flow structures.
• Each spatial POD mode represents the fluctuations in the high speed flame images, while eigenvalues represent the respective energies of each mode
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IDENTIFICATION OF FLAME STRUCTURES AND INSTABILITIES BY DECOMPOSITION TECHNIQUES
Proper Orthogonal Decomposition (POD)It extracts an orthogonal basis of eigenvalues from a snapshots of the flow data field u(x; t).Spatial and temporal contributions decoupled obtaining two different eigenfunctions
where,• Φi(x) → i-th mode eigenface, able to capture the scales and shapes of its modal coherent
structure• ai(t) → i-th mode temporal eigenfunction, it collects information about the dynamics of each
modal coherent structureFast Fourier Transform (FFT) analysis of ai(t) → frequency characterization of the dynamics associated to the corresponding modal structures
STRENGTHRobust techniques to detect the most
energetic coherent structures!
WEAKNESSLoss of accuracy in characterizing the
dynamics of modal structures!Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
Dynamic Mode Decomposition (DMD)– based on the Singular Value Decomposition (SVD) –
It decomposes the flow data field u(x; t) into spatial coherent structures, or wave patterns, and oscillatory modes (frequency and decay/growth rate)
where,• Φi
DMD → i-th spatial DMD mode (aka dynamic mode)• λi
DMD ϵ ₵ → i-th oscillatory mode with– arg(λi
DMD) is the i-th mode frequency– || λi
DMD || is the i-th mode decay rate
STRENGTHOne single modal frequency and
growth/decay rate!
WEAKNESSNo ranking criterion of DMD modes!
Modes are not orthogonal!
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POD MODE DECOMPOSITION of VIS acquisition
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=0.37
POD MODE DECOMPOSITION of VIS acquisition
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=0.37
First modes shows the regions of high and low intensities around the axis of the fuel nozzle. This indicates the rotation of the flame, which is an inherent nature of the swirl-stabilized flame.
POD MODE DECOMPOSITION of VIS acquisition
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=0.18
POD MODE DECOMPOSITION of VIS acquisition
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=0.18
Mode 2 and Mode 4 give an indication of the blowout and reignitionMode 2 indicates longitudinal oscillations in the flame
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POD MODE DECOMPOSITION of VIS acquisition
POD MODE DECOMPOSITION of VIS acquisition
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=0.37Mode amplitude coefficient represents the time evolution of the
deconstructed flame features in each POD mode
POD MODE DECOMPOSITION of VIS acquisition
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=0.18
POD MODE DECOMPOSITION of VIS acquisition
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=0.18
Knowing, the frequency of the problem, the corresponding dynamic modes and growth rate have been identified using DMD decomposition technique….
DINAMIC MODE DECOMPOSITION (DMD) of VIS acquisition
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=0.18
DINAMIC MODE DECOMPOSITION (DMD) of VIS acquisition
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=0.18
DINAMIC MODE DECOMPOSITION (DMD) of VIS acquisition
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=0.18
MONITORING, DIAGNOSTIC AND PROGNOSTIC TECHNIQUES
1. Real-time combustion instability detection by signal
processing techniques suitable for both non-linear and non-
stationary phenomena
2. Real-time Health Monitoring of Gas Turbine Combustor Using
Online Learning and High Dimensional Data
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TOOL TRAINING
(ANN, SVM,...)
OPERATING
CONDITION
CONTROLLER
HEALTHY COMBUSTORRESIDUAL
GENERATION
TRAINED TOOL(ANN, LS-SVM,..)
OPERATING CONDITION
CONTROLLER
COMBUSTOR
RESIDUAL THRESHOLD
HEALTHY COMBUSTOR
FAULTY COMBUSTOR
>
NO
YES
TOOL TRAINING
?
TOOL ACTIVE
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The technical approach is based on
Experimental testing to gain knowledge of the physical processes associated with instability combustion.
Data-driven modeling and machine learning for development of analytics algorithms.
• Dimensionality Reduction Methods (Principal Component Analysis)
• Data-driven classifier: Logistic Regression, Artificial Neural Networks, Support Vector Machines
• Combination of data-driven and physics-based models (hybrid modeling)
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Machine Learning based and statistical tools
• CUSUM CONTROL CHART
• BAYESIAN TOOL
• ARTIFICIAL NEURAL NETWORK (ANN)
• SUPPORT VECTOR MACHINE (SVM)
• GENETIC PROGRAMMING TOOL
Different tool can be applied to the acquired data from the combustor.The aim could be the detection of a change point in the signal trend and/or the monitoring ofthe signal to build a forecasting tool.The main implemented tools are:
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Machine Learning based and statistical tools
Statistical parameter
Frequency data
Mode decomposition
data
INSTABILITY REGIME
RECOGNITION
INSTABILITY REGIME
RECOGNITION
SUPPORT VECTOR MACHINE
ARTIFICIAL NEURAL
NETWORK (ANN)
GENETIC PROGRAM
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PLASMA ACTUATION TO ENHANCE METHANE-AIR LEAN FLAMEIn collaboration with
CNR Nanotec Bari, Italy
Two different burner configurationsMethane activated (central air jet) or air
activated (central fuel jet)
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PLASMA TO ENHANCE THE FLAME STABILITY
12/30
CH4AIRCH4
CH4AIR AIR
IDF NDF
Steel tube connected to the grounded electrode and a copper
tube (80 mm long, thickness 0.6 mm), on the outer surface of
the quartz tube connected to the HV electrode
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They operate at wide range ofdriving frequenciesDirect current (dc), alternatingcurrent (ac), radio frequency (RF) and microwave
INPUT VOLTAGE WAVEFORM• kV peak-to peak sinusoidal high voltage• Pulsed power supply with a short nanosecond rise
time and pulse duration of tens and hundreds of nanoseconds (Nanosecond Repetitively Pulsed Discharges)
Dielectric Barrier Discharge (DBD)Two electrodes with at least one dielectric in between
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HIGH VOLTAGE GENERATOR1) SINUSOIDAL DBD“PVM500 Plasma Resonant and Dielectric Barrier Corona Driver” (0-40 kV peak to peak; 20-70 kHz)
2) NANOSECOND REPETITIVELY PULSED DISCHARGES NPG-18/3500 of MegaImpulse Ltd® (0-80 kV peak to peak; pulse repetition at 3.5 kHz, energy at 30mJ/pulse)
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PLASMA TO ENHANCE THE FLAME STABILITY
12/30
A high voltage probe (Tektronix P6015A), a current probe (Bergoz Current Transformer CT-D1.0-B) and an oscilloscope (Tektronix TDS2024C) were used to retrieve the electrical power dissipation
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Flow Characterization by LDV, PIV and hotwireTemperature2D chemiluminescence (CH*, OH*, CO2)High speed visualizationElectrical characterization
COMBUSTION ACTIVE CONTROL
12/30
GE Lab., Lecce FLAME STABILIZATION OF METHANE/AIR LEAN FLAME
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EXPERIMENTAL SETUP
COLD EXPERIMENTS
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Impact of induced jet velocity
Flow Characterization by LDV, PIV and hot wire
Impact of thermal effectsTemperature measurements
12/30
Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
3 0 0.6
minner moter
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-I-
Temperature profile
Velocity profiles
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
Temperature profile
Velocity profiles
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II- EXPERIMENTS
72
Power spectral density at y / D = 0.5 and different radial positions
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
73
• Actuation OFF:
• Spectra of the baseline flow are dominated by a
frequency peak at about 200 Hz ( shear layer
vortex shedding frequency, fs, Re≈ 1750, Str ≈ 0.7)
• Actuation ON:
• the energy contained at the dominant (shedding)
frequency.
• A broad range of high frequencies (>103 Hz)
presented an increased energy content.
• Such energy variation can be associated to the
decrease of the formation of the coherent
structures at the vortex shedding frequency and to
the occurrence of an energy transfer from the large
to the small scales.
Power spectral density at y / D = 3 and different radial positions
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
74
Power spectral density at y / D = 0.5 and different radial positions
Energy is not concentrated in a specific
frequency band , not a dominant frequency
peak, most probably due to the forced
laminar/turbulent transition in the mixing
length
Spectra are reasonably well fitted by a -5/3
power law as predicted by the Kolmogorov
theory
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
75
Power spectral density at y / D = 3 and different radial positions
ACTUATION OFF:PSD magnitude generally increases
moving along y/D.
ACTUATION ONAt the jet centerline location (x/D =0) the
PSD magnitude of the frequencies
lower than 100 Hz decreases when
moving from y/D=0.5 to y/D=3.
Energy content of the frequencies
higher than 100 Hz increases
There is an energy transfer to the
smaller scales along the jet centerline.
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
76
Power spectral density at y / D = 3 and different radial positions
Increase in magnitude together with the
relatively constant slope between the
baseline and the actuated flow
suggested that the vrms was enhanced
by the actuation. This vrms modification
could imply an increase of the jet
mixing..
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
77
Increase in magnitude together with the
relatively constant slope between the
baseline and the actuated flow
suggested that the vrms was enhanced
by the actuation. This vrms modification
could imply an increase of the jet
mixing..
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Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
minner moter
RESULTS-II-
78
Increase in magnitude together with the
relatively constant slope between the
baseline and the actuated flow
suggested that the vrms was enhanced
by the actuation. This vrms modification
could imply an increase of the jet
mixing..
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RESULTS: TEMPERATURE PROFILES
Temperature profiles taken at y=5 (mm): (a) test case 1, (b) Test case 2, (c) test case 3
)TT(cmQ inoutp
AVm
Test Case (l/min) (l/min)
1 7.5 0.6
2 0.6 7.5
3 0 0.6
minner moter
79Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
COMBUSTIONMETHANE ACTIVATED - BLOWOUT LIMIT
Sinusoidal HV (20 kHz) NRPP HV (1.75 kHz)
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
METHANE ACTIVATED - BLOWOUT LIMITNANOSECOND REPETITIVELY PULSED DISCHARGES
BROADBAND CHEMILUMINESCENCE
Effect of plasma on the flame shape fuel flow rate = 0,2 l/minBaseline air flow rate = 5,2 l/min
Variation of applied voltage fixing the repetition frequency to 1750 Hz
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
Baseline case IDF: OH* emissions mainly occur in an annular ring around the centerline
Plasma power : Increase of the emission intensity in proximity of the quartz exit. Axial movement of the area with maximum OH* intensity upstream towards the quartz exit
COMPARISONS BETWEEN IDF AND NDF FOR TWO DIFFERENT STANDOFF DISTANCE
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82Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
METHANE ACTIVATED SINUSOIDAL PULSED
DISCHARGES
CO2
CHEMILUMINESCENCE EMISSIONS
𝑽 𝒂𝒊𝒓 = 𝟑.𝟓 𝒍/𝒎𝒊𝒏, 𝑽 𝑪𝑯𝟒= 𝟎.𝟔 𝒍/𝒎𝒊𝒏
0.0 W (21.50.9) W (27.21.1) W (19.00.8) W (23.61.0) W s=0 mm s=0 mm s=6 mm s=6 mm
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𝑽 𝒂𝒊𝒓 = 𝟕.𝟓 𝒍/𝒎𝒊𝒏, 𝑽 𝑪𝑯𝟒= 𝟎.𝟔 𝒍/𝒎𝒊𝒏
0.0 W (22.00.9) W (27.41.2) W (19.80.8) W (25.71.1) W
s=0 mm s=0 mm s=6 mm s=6 mm
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𝑽 𝒂𝒊𝒓 = 𝟐.𝟑𝟓 𝒍/𝒎𝒊𝒏, 𝑽 𝑪𝑯𝟒
= 𝟎.𝟒 𝒍/𝒎𝒊𝒏
0.0 W (21.20.9) W (26.61.1) W (20.10.9) W (25.61.1) W s=0 mm s=0 mm s=6 mm s=6 mm
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Horizontal dimension [mm]
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
PROPER ORTHOGONAL DECOMPOSITION (POD) ANALYSIS
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84
The addition of plasma discharge results in decreased heat release fluctuations, as
shown by the overall reduction in the energy content of the other dominant modes
i.e., Modes 1 - 10.
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
GREEN ENGINE
85
HIGH PRESSURE CHAMBER (up to 30 bar)
Chamber dimensions 200x120x120, 575 cm3.Optical windows: 70 mm and 90 mm
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EXPERIMENTAL SETUP AND ELECTRICAL CONNECTIONS
86
HIGH PRESSURE CHAMBER EXPERIMENTS200 x 120 x 120 mm, internal volume 573 cm3
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HIGH PRESSURE CHAMBER EXPERIMENTS: TEST CASES
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elP Actuator dissipated power (W).af Actuation frequency (kHz). Vpp Peak-to-peak applied voltage (kV). Q Flame’s thermal power (W)
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PRESSURE CHAMBER EXPERIMENTS: RESULTS
89
MEAN (a) AND RMS (b) INTENSITY MAPS
elP
Different plasma levels have been compared for the test case at 3 bar absolute and =0.1 (test cases 9-11).
For the highest dissipated power, 0.7 W (test case 11), less than 0.5% of the flame’s thermal power, the plasma effect is quite evident, implying a better anchoring of the flame compared with the not actuated test case (test case 9), even if the RMS intensity increases consistently.
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
FUTURE INVESTIGATIONS
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Preliminary literature studies underlined that nanosecond Repetitively Pulsed (NRP) discharges can be used as an actuator for active control of combustion instabilities, without the drawbacks of traditional actuators such as loudspeakers or fuel valves.
NRP discharges provide unsteady heating and species dissociation on the nanosecond timescale. This generates pressure waves propagating from each discharge
Low-frequency sound generation by modulated repetitively pulsed nanosecond plasma discharges Olaf Bölke et al 2018 J. Phys. D: Appl. Phys. 51 305203
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia
91
Thank You for
Attention
Giornata di Studio sui Combustori di Turbina a Gas, 5 Novembre 2018, Firenze De Giorgi Maria Grazia