1. Signal Processing TSI LDV/PDPA Workshop & Training
Presented by Joseph Shakal Ph.D.
Copyright© 2008 TSI Incorporated

2. Copyright© 2008 TSI Incorporated
Outline
Nature of the Signal
Processor Requirements
FSA Architecture- Front End & Burst Detector
FSA Architecture- Samplers
FSA Architecture- Firmware Processor
Burst Centering
Dynamic Sampling Rate Selection
Other Features
Conclusion
Frequency shifting is most often associated with the need to measure negative flows. It also provides the ability to measure a small component of a large velocity vector, “bring” the Doppler frequency within the range of the processor, etc. In addition, frequency shifting is used to measure flow velocities in highly turbulent flows.
Frequency shifters have also been used to separate the Doppler (velocity) signals in a multichannel LDV system using the frequency separation approach.
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3. Nature of Doppler Signals
Amplitude not constant
Lasts for only a short time, which itself varies
Amplitude varies from burst to burst
Presence of noise
High frequency
Random arrival
Doppler signals are difficult to process because of these reasons.
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4. Signal Processor - Key Requirements
Use multi-bit sampling up to a high maximum frequency
Detect and validate bursts based on SNR and amplitude
Automatically optimize the sampling rate for each burst
Gives the best resolution in processing, even for a wide range of velocities
This will ensure the maximum number of cycles are used
Detect the burst center, before processing
Use the data from the middle portion of the burst first
Use this value as the arrival time of the particle
Detect burst duration separately
Digitize and record additional analog and digital signals, including cyclic markers and the burst amplitude
The ideal signal processor for LDV and PDPA should have the following capabilities
Detect and validate bursts based on SNR and amplitude
Automatically optimize the sampling rate for each burst
This will ensure the best resolution in processing, even while velocity changes
This will ensure the maximum number of cycles are used, for best accuracy
Detect the burst center, before processing. Then use this value to:
Use the data from the middle portion of the burst first, avoiding the beginning and end portions, which have lower SNR.
Use this value as the arrival time of the particle, which in fact is more accurate than the initial or final time value.
Detect burst duration separately, since this value would depend on the initial and final time values.
Digitize and record additional analog and digital signals, including cyclic markers and the burst amplitude
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FSA Signal Processor
Uses 8 bit sampling up to a 800MHz
Detects and validates bursts based on patented real-time SNR measurement, and also amplitude
Automatically selects the optimum sampling rate for each burst, which is also a patented technique
Detects the burst center, since processing is done after sampling process is complete
The FSA EB option digitizes and records additional signals, including cyclic markers
Burst amplitude is measured as part of the patented Intensity Validation technique
The FSA fulfills all the requirements listed on the previous slide.
Uses 8 bit sampling up to a 800MHz
Detects and validates bursts based on patented real-time SNR measurement, and also amplitude
Automatically optimize the sampling rate for each burst, which is also a patented technique
Detects the burst’s beginning, center, and end, since processing is done after sampling process is complete
The FSA EB option digitizes and records additional signals, including cyclic markers
Burst amplitude is measured as part of the patented Intensity Validation technique
The FSA is a firmware based processor, which means these operations are done in programmable logic type circuitry, rather than fixed logic circuitry.
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Nature of Input Signal
Out of PMT Detector
This slide shows an oscilloscope screenshot of a burst signal from an LDV or PDPA system. Due to the d-squared relationship of scattered power with particle size, the intensity drops off rapidly for small particles.
Clearly, SNR based burst detection is required here, to pull out the burst based on signal coherency, not amplitude.
Noisy Signal
Out of PDM
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7. Impact of Type of Burst Detection
Signal
Method of Burst Detection
Source
Amplitude
Amplitude/
Envelope Based
SNR Based
Small Particles
Small
Ignores
Detects
Large Particles
Large
Surface Reflections
Other Noise, Spikes, etc.
Any
Detects some
Shown here is a summary of the various types of signals the FSA processor may see, based on the range of particle size and measurement conditions found in many experiments.
Low amplitude signals will not trigger an amplitude based burst detector, but their coherency will trigger an SNR based detector.
High amplitude signals will trigger an amplitude based burst detector, and also their coherency will trigger an SNR based detector.
High amplitude noise, like surface reflections, spray background, and the like will trigger an amplitude based burst detector, but their lack of coherency will not trigger an SNR based detector.
Other noise, like spikes, PMT noise, and the like may trigger an amplitude based burst detector, but their lack of coherency will not trigger an SNR based detector.
Copyright© 2008 TSI Incorporated

8. The FSA Signal Processor
Shown here is the FSA processor.
In the next few slides we will take a tour of the inner workings of the FSA so that we can see how its component blocks work together to detect, sample, and quantify burst signals.
Copyright© 2008 TSI Incorporated

9. FSA Processor Burst Detector Subsection
Burst Gate
Signal Out
Filters
LUT/DFT
Controller
Ch 1
Downmixer
Amplitude
Threshold
Burst Gate
Frequency Estimate
Shown here is the front end and Burst Detection parts of the FSA.
The front end consists of the downmixer, filter, and amplifier subsections.
The Burst Detection part of the FSA, shown inside the red dashed line, consists of the Look-up Table (LUT) based Discrete Fourier Transform (DFT) unit, which obtains a real-time measure of the signal coherency, or SNR. In parallel is an amplitude threshold, which monitors the signal amplitude. These devices feed information to the controller, which determines if there is a valid burst present or not.
If a burst is present, its beginning, center, and ending timestamp are recorded, as well as a frequency estimate (from the DFT). These values are sent down to the Burst Sampling subsection.
Dynamic Optimum Sampling Rate Selection
Downmix Frequency
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10. FSA Processor Burst Sampling Subsection
Signal Out
Burst Gate
Filters
LUT/DFT
Controller
Ch 1
Burst Gate
Frequency Estimate
Firmware
Processing
Downmix Frequency
The incoming signals are sampled in parallel with burst detection, using high-speed, 8 bit A/D converters. The A/D converters sample the signal at multiple rates, simultaneously with detection of the Doppler burst. The frequency estimate provided by the burst detector determines which sampler’s output is optimum for the actual burst frequency. This patented approach ensures that the sampling rate of the incoming signal is dynamically selected based on its frequency. Sampling at the optimum frequency gives the best resolution. Basing the sampling rate on the burst frequency ensures that the samples are spread over the most possible Doppler cycles, for best accuracy.
As a result, each burst has the optimum resolution and accuracy. The optimally sampled data is then passed on to the Firmware Processing subsection.
Optimally Sampled
Data
Sample
Memory
Multibit
A/D
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11. FSA Processor Firmware Processing
Burst Processing
Burst gate &
Burst center
to PC
Firewire
Interface
The optimally sampled data and burst gate information are sent to the processing subsection, to obtain frequency and phase (velocity and size) information.
Firmware processing gives a fast, flexible method for implementing accurate, robust processing algorithms to extract flow and particle information. Multiple digital signal processor (DSP) chips are used in the FSA, to perform the complex processing operations at a high data rate. The results are sent to the PC via a Firewire interface. This industry standard interface is bi-directional and allows instant communication between the FlowSizer software and the FSA. Many customers use a laptop PC for data portability and security.
Optimally Sampled Data
DSPs
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12. FSA Block Diagram Summary
Signal Out
Burst Gate
Bandpass
filters
LUT/DFT
Controller
Downmixer
Firewire
Interface
to PC
Ch 1
Amplitude
threshold
Burst gate
Frequency estimate
Downmix
frequency
generator
DSPs
Shown here is a schematic overview of the FSA signal processor.
External analog or digital values may be tagged to each burst data packet using the EB and EIC options to the FSA processor. Shaft encoders and OPR signals may also be used for rotating machinery systems.
Ch 2
8 bit
A/D s
Ch 3
Sample Memory
EIC/EB
pressure, temp
OPR or Shaft Encoder
Signals from PDM
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13. Burst Detection and Sampling
Burst Detector
Determines the approximate burst frequency
Determines the beginning, end, and center of the burst
Burst Sampler
Dynamically selects the optimum sampling frequency (using the approximate frequency value) so that each and every burst is sampled at the optimum rate
Obtains the 8 bit digital values, and passes them on to the Firmware Processing subsection
We will review the parts of the FSA processor covered thus far.
The Burst Detector has two functions:
Determines the approximate burst frequency
Determines the beginning, end, and center of the burst
The Burst Sampler also has two functions:
Dynamically selects the optimum sampling frequency (using the approximate frequency value from the Burst Detector) so that each and every burst is sampled at the optimum rate
Obtains the 8 bit digital values, and passes them on to the Firmware Processing subsection
The end result is that we get the best of both worlds- the best resolution and the best accuracy.
Copyright© 2008 TSI Incorporated

14. Burst Centering
Burst centering is automatically done by the FSA, and it helps give higher quality data by using only the high-SNR portion of the signal for processing.
The center point of the burst is identified.
From this reference point, samples are used out to a certain noise threshold, until the FSA’s data block is filled.
Gate Time (transit time) is still based on the actual time the particle was in the measurement region, regardless of the portion of the signal used for processing.
Burst centering is automatically done by the FSA, and it helps give higher quality data by using only the high-SNR portion of the signal, ie the central portion, for processing. This is how it is done:
The beginning, center point, and end of the burst are identified.
From this reference point, samples are used outwards, to a certain noise threshold, until the FSA’s data block is filled.
Gate Time (transit time) is still based on the actual time the particle was in the measurement region, regardless of the portion of the signal used for processing. This is indicated by the red line above.
This portion used for processing
End
Beginning
Center of
burst
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15. Dynamic Sampling Rate Selection
Example
Burst gate
Particle 1
velocity = u
sampling rate: F
Particle 2
velocity = 2u
sampling rate: 2F
Burst gate
This slide shows a normalized range of velocities that may be found within a single bandpass filter setting.
As the velocity of the flow changes (from one burst to the next) the sampling rate is also dynamically changed. This is done automatically (burst detector provides the optimum sampling rate information) so that the the processor can collect the same maximum number of samples in all these cases.
Particle 3
velocity = 4u
sampling rate: 4F
Burst gate
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16. Flow and Size Analyzer (FSA)
Other unique features and benefits of the FSA
Built in input buffer, for high data rate situations
Size measurement validation
Patented intensity validation uses an independent measured quantity to validate the diameters. We do not need receiver masks, and we can see what is being rejected and what is being accepted.
Phase validation, uses the degree of agreement between the two independent phase measurements to discriminate between reflection and refraction
Short transit time flows (50ns minimum gate time)
Particle size measurements in dense sprays, where we need very small measuring volumes, resulting in short-transit-time burst signals
Size measurements in (pulsed) high velocity sprays
Velocity measurements in supersonic flows
If the PC or FireWire system are busy, the FSA’s input buffer can store data until FlowSizer is allowed to transfer it again. This is sometimes noted by the user, when their flow stops the FlowSizer screen may continue updating for a few moments.
Intensity Validation eliminates the need for error-prone Aperture Masks. The FSA measures both phases independently, which provides another parameter for validation.
Short transit time capability allows measurement of high speed, high concentration flows. Previous generation signal processors could only reliably measure phases down to about 1us gate time.
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Measured Parameters
Plots
Statistics
Ch. 1
Velocity Mean (m/sec)
Velocity RMS (m/sec)
Turbulence Intensity (%) 7.01
Frequency Mean (MHz)
Frequency RMS (MHz)
Frequency TI (%) 7.01
Gate Time Mean (usec) 2.26
Gate Time RMS (usec) 1.32
Data Rate (Hz)
Valid Count 2663
Invalid Count 0
Elapsed Time (sec)
We obtain a wide variety of graphical and numerical data from the processor. Shown here is a sample of LDV data from a pulsating jet. Only channel 1 data is shown here. Notice the easily observed fluctuation frequency at about 210Hz, and corresponding peak in the power spectrum.
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18. Copyright© 2008 TSI Incorporated
Conclusions
Examined the nature of Doppler signals
Looked at processor requirements
FSA Architecture- Front End & Burst Detector
FSA Architecture- Samplers
FSA Architecture- Firmware Processor
Saw how burst centering works and its benefits
Looked at how dynamic sampling rate selection is done and its benefits
Other benefits of the FSA, like high-speed capabilities for dense sprays and high speed flows, and FireWire connectivity
In this presentation we have:
Examined the nature of Doppler signals
Looked at processor requirements
FSA Architecture- Front End & Burst Detector
FSA Architecture- Samplers
FSA Architecture- Firmware Processor
Saw how burst centering works and its benefits
Looked at how dynamic sampling rate selection is done and its benefits
Other benefits of the FSA, like high-speed capabilities for dense sprays and high speed flows, and FireWire connectivity
Copyright© 2008 TSI Incorporated