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

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. Copyright© 2008 TSI Incorporated

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. Copyright© 2008 TSI Incorporated

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 Copyright© 2008 TSI Incorporated

Copyright© 2008 TSI Incorporated 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. Copyright© 2008 TSI Incorporated

Copyright© 2008 TSI Incorporated 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 Copyright© 2008 TSI Incorporated

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

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

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 Copyright© 2008 TSI Incorporated

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 Copyright© 2008 TSI Incorporated

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 Copyright© 2008 TSI Incorporated

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 Copyright© 2008 TSI Incorporated

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

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 Copyright© 2008 TSI Incorporated

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 Copyright© 2008 TSI Incorporated

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. Copyright© 2008 TSI Incorporated

Copyright© 2008 TSI Incorporated Measured Parameters Plots Statistics Ch. 1 Velocity Mean (m/sec) 20.390 Velocity RMS (m/sec) 1.4284 Turbulence Intensity (%) 7.01 Frequency Mean (MHz) 6.3207 Frequency RMS (MHz) 0.4428 Frequency TI (%) 7.01 Gate Time Mean (usec) 2.26 Gate Time RMS (usec) 1.32 Data Rate (Hz) 43245 Valid Count 2663 Invalid Count 0 Elapsed Time (sec) 0.0616 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. Copyright© 2008 TSI Incorporated

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