Method and apparatus for measuring parameters of a fluid flow using an array of sensors

An apparatus for measuring velocity of a fluid passing through a pipe is provided. The apparatus includes a spatial array of sensors having at least two sensors disposed at different axial locations along the pipe, wherein the sensors provide at least one signal indicative of a stochastic parameter associated with a characteristic of the fluid, wherein the characteristic includes at least one of unsteady temperature, density, consistency, transparency, conductivity, capacitance, resistivity, and inductance. A signal processor is also provided, wherein the signal processor is configured to receive the at least one signal and determine the velocity of the fluid using the at least one signal.

FIELD OF THE INVENTION

The present disclosure relates generally to a method and apparatus for measuring parameters of a fluid flow, and more particularly to a method and apparatus for measuring parameters of a fluid flow using an array of sensors.

BACKGROUND OF THE INVENTION

A fluid flow process, or flow process, typically includes any process that involves the flow of a fluid through pipes, ducts, or other conduits, as well as through fluid control devices such as pumps, valves, orifices, heat exchangers, and the like. Flow processes are found in many different types of industries such as the oil and gas industry, refining, food and beverage industry, chemical and petrochemical industry, pulp and paper industry, power generation, pharmaceutical industry, and water and wastewater treatment industry. Additionally, the flow process may involve many different types of fluids, such as single phase fluids (e.g., gas, liquid or liquid/liquid mixture) and/or multi-phase mixtures (e.g. paper and pulp slurries or other solid/liquid mixtures), wherein the multi-phase mixture may be a two-phase liquid/gas mixture, a solid/gas mixture or a solid/liquid mixture, gas entrained liquid or a three-phase mixture. Currently, a variety of sensing technologies exist for measuring various physical parameters of the fluids in an industrial flow process, wherein the physical parameters may include, for example, volumetric flow rate, composition, consistency, density, and mass flow rate.

One such sensing technology is described in commonly-owned U.S. Pat. No. 6,609,069 to Gysling, entitled “Method and Apparatus for Determining the Flow Velocity Within a Pipe” (hereinafter “'069 patent”), which is incorporated herein by reference in its entirety. The '069 patent describes a method and corresponding apparatus for measuring the flow velocity of a fluid flowing within an elongated body, such as a pipe, by sensing vortical disturbances convecting with the fluid. The method as disclosed in the '069 patent includes providing an array of at least two sensors disposed at predetermined locations along the elongated body, wherein each sensor is for sampling the pressure of the fluid at the position of the sensor at a predetermined sampling rate. The method also includes accumulating the sampled data from each sensor at each of a number of instants of time spanning a predetermined sampling duration and constructing from the accumulated sampled data at least a portion of a so called k-ω plot, where the k-ω plot is indicative of a dispersion relation for the propagation of acoustic pressures emanating from the vortical disturbances. Furthermore, the method includes identifying a convective ridge in the k-ω plot, determining the orientation of the convective ridge in the k-ω plot and determining the flow velocity based on a predetermined correlation of the flow velocity with the slope of the convective ridge of the k-ω plot.

Another such sensing technology is described in commonly-owned U.S. Pat. No. 6,354,147 (hereinafter “'147 patent”) and U.S. Pat. No. 6,732,575 (hereinafter “'575 patent”) to Gysling et. al, both of which are incorporated by reference herein in their entireties. Both the '167 patent and the '575 patent describe a spatial array of acoustic pressure sensors placed at predetermined axial locations along a pipe. The pressure sensors provide acoustic pressure signals to signal processing logic which determines the speed of sound of the fluid (or mixture) in the pipe using any of a number of acoustic spatial array signal processing techniques with the direction of propagation of the acoustic signals along the longitudinal axis of the pipe. The speed of sound is provided to logic, which calculates the percent composition of the mixture, e.g., water fraction, or any other parameter of the mixture, or fluid, that is related to the sound speed, wherein the logic may also determine the Mach number of the fluid.

SUMMARY OF THE INVENTION

An apparatus for measuring velocity of a fluid passing through a pipe is provided. The apparatus includes a spatial array of sensors having at least two sensors disposed at different axial locations along the pipe, wherein the sensors provide at least one signal indicative of a stochastic parameter associated with a characteristic of the fluid, wherein the characteristic includes at least one of unsteady temperature, density, consistency, transparency, conductivity, capacitance, resistivity, and inductance. A signal processor is also provided, wherein the signal processor is configured to receive the at least one signal and determine the velocity of the fluid using the at least one signal.

Furthermore, a method for measuring velocity of a fluid passing through a pipe is provided and includes generating at least one signal indicative of a stochastic parameter associated with the fluid, wherein the stochastic parameter includes at least one of unsteady temperature, density, consistency, transparency, conductivity, capacitance, resistivity, and inductance. The method also includes determining a velocity of the fluid responsive to the at least one signal.

Furthermore, an apparatus for measuring velocity of a fluid passing through a pipe is provided, wherein the apparatus includes at least two sensors disposed at different axial locations along the pipe, wherein the at least two sensors provide at least one signal indicative of a stochastic parameter associated with a characteristic of the fluid and include at least one of a magmeter sensor and a consistency meter sensor, the at least two sensors being configurable for operation in at least one of a first mode and a second mode. The apparatus also includes a signal processor, wherein the signal processor is configured to receive the at least one signal and determine the velocity of the fluid using the at least one signal.

DETAILED DESCRIPTION

As is known, U.S. patent application Ser. Nos. 10/007,749, 10/349,716and 10/376,427, all of which are incorporated by reference herein in their entireties, describe how various parameters of a fluid (e.g., velocity, volumetric flow rate, speed of sound, and composition) can be determined by applying array processing techniques to measurements of unsteady pressures within the fluid flow. These unsteady pressures may be caused by one or both of acoustic waves propagating through the fluid within the pipe and/or pressure disturbances that convect with the fluid flowing within the pipe (e.g., turbulent eddies and vortical disturbances). This methodology has been demonstrated using arrays of various transducers, including a variety of pressure and strain based measurement devices. For example, these sensors may include piezoelectric sensors, piezoresistive sensors, strain gauges, PVDF sensors, optical sensors, ported ac pressure sensors, accelerometers, velocity sensors and displacement sensors, among others. While these sensors tend to work well, it is contemplated that other types of sensor may be used as well. For example, as will be discussed further hereinafter, it is contemplated that these unsteady pressures may be sensed using microphones. It is further contemplated that stochastic parameters other than unsteady pressures may be sensed by the array of sensors and used to determine the parameters of the fluid. For example, an array of sensors may sense unsteady temperature, density, consistency, transparency, conductivity, resistivity, capacitance, inductance, and the like. Accordingly, each sensor may include any type of sensor capable of measuring a stochastic parameter of the fluid.

Referring toFIG. 1, an apparatus100for measuring at least one parameter associated with a fluid102flowing within a pipe104is shown, wherein the parameter associated with the fluid102may include, but is not limited to, for example, at least one of: velocity of the fluid102, density of the fluid102, volumetric flow rate of the fluid102, mass flow rate of the fluid102, composition of the fluid102, entrained air in the fluid102, consistency of the fluid102, size of particles in the fluid102, and the health of a device106in fluid communication with the pipe104. Furthermore, it should be appreciated that the fluid102may be a single or multiphase fluid102flowing through a duct, conduit or other form of pipe104.

The apparatus100may include a spatial array108of at least two sensors110disposed at different axial locations x1. . . xNalong the pipe104. Each of the sensors110may provide a signal P(t) indicative of a stochastic parameter of the fluid102within the pipe104at a corresponding axial location x1. . . xNof the pipe104. The stochastic parameter may include, but not be limited to, one or more of: unsteady temperature, pressure, density, consistency, transparency, conductivity, capacitance, resistivity, inductance, and the like. In one embodiment, each sensor110may include a microphone, while in other embodiments each sensor110may include sensors commonly associated with magnetic flow meters (magmeters), temperature sensors, densitometers, consistency meters, light meters, conductivity meters, capacitance meters, inductance meters, or the like. A signal processor114may receive the signals P1(t) . . . PN(t) from the sensors110in the array108, determine the parameter of the fluid102using signals from the sensors110and output the parameter as a signal112.

It should be appreciated that while the apparatus100is shown as including four sensors110, it is contemplated that the array108of sensors110may include two or more sensors110, each providing a signal P(t) indicative of a characteristic associated with the fluid102within the pipe104at a corresponding axial location X of the pipe104. For example, the apparatus100may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 sensors110. Generally, the accuracy of the measurement improves as the number of sensors110in the array108increases. It should be further appreciated that the degree of accuracy provided by the greater number of sensors110is offset by the increase in complexity and time for computing the desired output parameter of the flow102. Therefore, the number of sensors110used is dependent at least on the degree of accuracy desired and the desired update rate of the output parameter provided by the apparatus100.

The signals P1(t) . . . PN(t) provided by the sensors110in the array108are processed by the signal processor114, which may be part of a larger processing unit116. For example, the signal processor114may be a microprocessor and the processing unit116may be a personal computer or other general purpose computer. It should also be appreciated that the signal processor114may be any one or more signal processing devices for executing programmed instructions, such as one or more microprocessors or application specific integrated circuits (ASICS), and may include memory for storing programmed instructions, set points, parameters, and for buffering or otherwise storing data.

The signal processor114may determine the one or more parameters112of the fluid102by applying the data from the sensors110to flow logic118that may be executed by signal processor114, wherein the flow logic118is described in further detail hereinafter. The one or more parameters112may include, but not be limited to, such parameters as velocity, volumetric flow rate, mass flow rate, density, composition, entrained air, consistency, particle size, velocity, mach number, speed of sound propagating through the fluid102, and/or other parameters of the fluid102.

The signal processor114may also apply one or more of the signals from sensors110and/or one or more parameters112from the flow logic118to diagnostic logic120, wherein diagnostic logic120is described in further detail hereinafter. The diagnostic logic120may be executed by the signal processor114to diagnose the health of any device106in the process flow that may cause changes in the characteristic sensed by the sensors110. Referring back toFIG. 1, although device106is depicted as a valve, it is contemplated that device106may be any machinery, component, or equipment, e.g., motor, fan, pump, generator, engine, gearbox, belt, drive, pulley, hanger, clamp, actuator, valve, meter, or the like suitable to the desired end purpose. It should be appreciated that the signal processor114may output one or more parameters112indicative of the health of the device106. Furthermore, the signal processor114may also output a control signal122to control the device106in response to parameter112.

The signal processor114may output the one or more parameters112to a display124or another input/output (I/O) device126, wherein the I/O device126may also accept user input parameters128as may be necessary for the flow logic118and/or diagnostic logic120. Additionally, the I/O device126, display124, and signal processor114unit may be mounted in a common housing, which may be attached to the array108by a flexible cable, wireless connection, or the like, wherein the flexible cable may also be used to provide operating power from the processing unit116to the array108if necessary.

As previously noted, U.S. patent application Ser. Nos. 10/007,749 , 10/349,716, and 10/376,427 describe that various parameters of a fluid (e.g., velocity and volumetric flow rate) can be determined using the measurement of unsteady pressure fluctuations in a fluid flow using array processing techniques. This flow methodology has been demonstrated using a variety of transducers, including various pressure and strain measurement devices. Referring toFIG. 2, it is contemplated that rather than using strain measurement devices as the sensors110, each of the sensors110may include a microphone disposed proximate an outer surface of the pipe104. In this embodiment, each microphone110senses unsteady pressures within the pipe104, as may be caused by one or both of acoustic waves130propagating through the fluid102within the pipe104and/or pressure disturbances132that convect with the fluid102flowing in the pipe104(e.g., turbulent eddies and vortical disturbances), at a corresponding axial position X by sensing the acoustics (e.g., sound pressure level, acoustic signature, etc.) generated in a fluid111disposed between the microphone110and an outer surface of the pipe104. The sensors (microphones)110output a signal P1(t) . . . PN(t) indicative of the acoustics, and provides the signal to the signal processor114, which applies these signals to the flow logic118and/or diagnostic logic120.

The fluid111(e.g., air) may be disposed in a cavity113formed between the microphones110and the pipe104wall, and the microphones110may be clamped to the outside of the pipe104. Because signal energy is transferred by the fluid111between the microphone110and the wall of the pipe104, the sensors110should be relatively immune to pipe vibration in comparison to a sensor110in direct contact with the pipe wall. Testing of this design suggests that there is a good correlation between the internal unsteady pressures in the pipe104and the microphone110signal. One suitable microphone110may be commercially available as model 377A25 from PCB Piezotronics, Inc. of Depew, N.Y. and another suitable microphone may be model number 130D21 from PCB Piezotronics, Inc.

In another aspect, it is contemplated that stochastic parameters other than unsteady pressures in the fluid102may be sensed by the array108. Stochastic parameters of a moving fluid vary over time and move (convect) either at the same velocity as the fluid or at a velocity that can be correlated to the velocity of the fluid102. As will be described in further detail hereinafter, as the stochastic parameter convects with the fluid102past the array108, array processing can be performed by exploiting what is sometimes called the dispersion relationship associated with convecting stochastic parameters.

Referring toFIG. 3andFIG. 4, one example of a stochastic parameter that may be sensed by the array108is temperature. More specifically, many fluids102flowing through the pipe104will exhibit small temperature variations along the fluid102. These temperature variations tend to convect or “ride” along with the fluid flow and therefore represent a characteristic that is directly tied to the flow rate of the fluid102. Any tracking of the temperature propagation can then be applied by the signal processor114(FIG. 1) to the flow logic118to measure the fluid102velocity using array processing algorithms, as described in further detail hereinafter under the heading “velocity measurement”.

In the embodiment ofFIG. 3, each of the sensors110comprises a temperature sensor located within the pipe104. The sensors110are used to map the temperature fluctuations and provide data to enable the measurement. The sensors110output a signal P1(t) . . . PN(t) indicative of the temperature at a corresponding axial location X. The signal processor19(FIG. 1) applies these signals to the flow logic118(FIG. 1) to determine the velocity and other parameters of the fluid102. It should be appreciated that where the fluid102flowing through the pipe104does not inherently have enough temperature fluctuations to produce a measurable signal, a heat source or heat sink134can be used to generate the temperature variations. In the embodiment ofFIG. 4, the effect of the temperature variations on the pipe104is measured. For example, an array of strain-based sensors110may be used to measure the surface strain induced on the pipe104surface by the temperature fluctuations. Each sensor110outputs a signal indicative of the pipe surface strain, which is indicative of temperature variation, at a corresponding axial location X. Alternatively, the temperature on the outer pipe wall may be measured directly. The signal processor114(FIG. 1) applies these signals to the flow logic118to determine the velocity and other parameters of the fluid102. It should be appreciated that for a given temperature change Δt, the strain induced, ε, will be given by:
ε=α*Δt,(Eqn. 1)
where α is the coefficient of linear expansion. For example, consider typical steel which has a coefficient of linear expansion α of 12×10−6/° C. Therefore, a temperature change of 0.1° C. will induce 1.2 microstrains onto the steel.

As described in U.S. patent application Ser. Nos. 10/349,716 and 10/376,427, strain fluctuations on the pipe104surface are to be measured, wherein each of the sensors110may include a piezoelectric film sensor. The piezoelectric film sensors may include a piezoelectric material or film to generate an electrical signal proportional to the degree that the pipe104material is mechanically deformed or stressed. The piezoelectric sensing element is typically conformed to allow complete or nearly complete circumferential measurement of induced strain to provide a circumferential-averaged signal. The sensors110can be formed from PVDF films, co-polymer films, or flexible PZT sensors, similar to that described in “Piezo Film Sensors Technical Manual” provided by Measurement Specialties, Inc., which is incorporated herein by reference. A piezoelectric film sensor that may be used for the present invention is part number 1-1002405-0, LDT4-028K, manufactured by Measurement Specialties, Inc.

Piezoelectric film (“piezofilm”), like piezoelectric material, is a dynamic material that develops an electrical charge proportional to a change in mechanical stress. Consequently, the piezoelectric material measures the strain induced within the pipe104due to temperature variations within the fluid102. Strain within the pipe104is transduced to an output voltage or current by the attached piezoelectric sensor. The piezoelectrical material or film may be formed of a polymer, such as polarized fluoropolymer, polyvinylidene fluoride (PVDF). The piezoelectric film sensors may be similar to that as described in U.S. patent application Ser. Nos. 10/712,818, 10/712,833, and 10/795,111, each of which are incorporated herein by reference in their entireties.

With the aforementioned PVDF sensors, for example, a strain of 10 picostrain can be seen, giving a corresponding temperature change resolution of 8×10−7° C. With this type of resolution, very small changes in temperature can be seen in the fluid102flowing through the pipe104. This indicates that many fluids will have enough fluctuations that can be measured using this technique. In situations where the fluid102flowing through the pipe104does not inherently have enough temperature fluctuations to produce a measurable signal, a heat source or heat sink134can be used to generate the temperature variations.

Referring toFIG. 5,FIG. 6, andFIG. 7, other examples of stochastic parameters that may be sensed by the array108include those which may affect various electrical and magnetic parameters. For example, it is known that flow velocity may be obtained by measuring the changes in voltage induced in a conductive fluid passing across a controlled magnetic field. Commercially available magnetic flow meters (magmeters, electromagnetic flowmeters, or induction meters) use this principle measure the flow rate of the fluid.

A typical magnetic flowmeter (i.e., magmeter) includes electric coils disposed around or near the pipe and a pair of electrodes arranged diametrically across the pipe or at the tip of a probe inserted into the pipe. If the fluid is electrically conductive, its passing through the pipe is equivalent to a conductor passing through the magnetic field, which induces changes in voltage across the electrodes. The higher the flow speed, the higher the voltage. A signal processor within the magnetic flowmeter uses the voltage signal from the pair of electrodes to determine the fluid flow rate based on the cross sectional area of the pipe104. Problematically, however, turbulent flows and multi-phase flows can cause instabilities and problems with magnetic flowmeters such as increased fluctuations in the signal output by the electrodes. These fluctuations are seen as noise, which tend to result in degraded performance of the magnetic flowmeter.

In the embodiment ofFIG. 5, each sensor110in the array108comprises a pair of electrodes136which detects voltage across a fluid102flowing through a magnetic field. The magnetic field may be generated by coils138positioned proximate the pipe104at different axial locations X along the length of the pipe104. For single phase fluids, signals from each pair of electrodes136in the array108can be averaged and used as input to a standard magnetic flowmeter processor140, which determines a flow rate of the fluid102using the averaged signals and provides an output signal142indicative of the flow rate. For multi-phase flow, turbulent flow, and/or when the noise on the individual electrode pairs exceed a certain level, the voltage signals P1(t) . . . PN(t) from each electrode pair is provided to the signal processor114, which applies these signals to the flow logic118to determine the velocity and other parameters of the fluid102. In this mode, the array108detects disturbances due to turbulence, density changes, or other coherent features convecting with the fluid102flow past the array108. In other words, what would be considered “noise” for a typical magnetic flowmeter is tracked across the array108of electrodes136using phased array processing, such as sonar processing to determine the velocity of the fluid flow.

The present invention contemplates a standard magmeter having two or more magnetic sensors disposed a different locations X along the length of the pipe104, as described hereinbefore. The magmeter would include additional processing to perform the array processing of the sensor data as described herein to provide a velocity and volumetric flow of the fluid102flowing within the pipe104. It is further contemplated that this embodiment may function in two modes. For example, the invention may operate in a first mode that functions as a standard magmeter and/or the magmeter may then be switched to a second mode that functions as the array-based meter (described herein) when the noise on the electrodes of a sensor110exceeds a certain level.FIG. 6depicts an embodiment which may be useful for situations where the flow is stratified and may include multiple electrodes136placed around the circumference of the pipe104. In this embodiment, the electrodes136can be used to detect different velocity components due to the turbulence and/or density changes in the fluid flowing through different parts of the pipe104.

FIG. 7depicts an embodiment in which each sensor110in the array108includes a pair of electrodes144positioned across the fluid102, wherein each pair of electrodes144provides a signal indicative of the capacitance of the fluid102. The signals from each electrode pair is provided as signals P1(t) . . . PN(t) to the signal processor114, which applies these signals to the flow logic36to determine the velocity and other parameters of the fluid102. Variations in the capacitance of the fluid102will convect or “ride” along with the fluid flow and therefore represent a characteristic that is directly tied to the flow rate of the fluid102. It should be appreciated that instead of capacitance, or in addition to capacitance, other electrical characteristics of the fluid may be sensed by the array108. For example, conductance, resistance, impedance, and the like may similarly be sensed by the array108.

Other examples of stochastic parameters that may be sensed by the array108include those that affect one or more of: absorption, attenuation, time delay, and phase delay of energy applied to the mixture102.FIG. 8andFIG. 9depict embodiments in which each sensor110includes a transmitter that applies energy to the fluid102in the form of electromagnetic or particulate radiation and a sensor that detects the absorption, attenuation, time delay, or phase delay of the energy as it propagates through the fluid102. In the embodiment ofFIG. 8, each transmitter146applies energy to the fluid in the form of a light (e.g., laser light) signal, and the sensors148(e.g., photodetectors) sense the absorption, attenuation, time delay, or phase delay of the light signal as it passes through the fluid102. The voltage signals from the sensors148are provided as signals P1(T) . . . PN(T) to the signal processor114, which applies these signals to the flow logic118to determine the velocity and other parameters of the fluid102. Stochastic parameters of the fluid102that affect the absorption, attenuation, time delay, and/or phase delay of the light by the fluid will convect or “ride” along with the fluid flow and therefore represent a characteristic that is directly tied to the flow rate of the fluid102.

In the embodiment ofFIG. 9, each transmitter150may apply energy in the form of microwave signals, and the sensors152may sense the absorption, attenuation, time delay, and/or phase delay of the microwave signals. The transmitters150and sensors152may be substantially similar to those found in microwave consistency meters, which typically use only one transmitter/sensor pair. An example of a microwave consistency meter that measures the speed or velocity at which a microwave signal propagates through the fluid is manufactured by Toshiba International Corporation of Japan. An example of a microwave consistency meter that measures the time of flight of a microwave signal through the fluid is manufactured by Metso Automation of Finland and sold under the trade name kajaaniMCA™. Another type of consistency meter employs a small gamma source as the transmitter, which is attenuated as it passes through the fluid, wherein the attenuation, which is detected by a scintillation detector (the receiver), is proportional to the changes in consistency. This type of consistency meter is commercially available from Berthold Industrial Systems of Australia.

Referring again to the embodiment ofFIG. 9, signals from each sensor110in the array108can be averaged and used as input to a processor154which may be associated with a standard consistency meter and which may determine a consistency of the fluid102using the averaged signals. The voltage signals from each sensor110may also be provided as signals P1(T) . . . PN(T) to the signal processor114, which applies these signals to the flow logic118to determine the velocity and other parameters of the fluid102. In this mode, the array108detects disturbances due to coherent features that affect the consistency of the fluid102as these coherent features convect with the flow past the array108.

It should be appreciated that each transmitter150may apply energy in the form of gamma radiation, and the sensors152may sense the absorption of the radiation by the fluid102. The transmitters150and sensors152may be substantially similar to those found in gamma (radiation) densitometers, which typically use only one transmitter/sensor pair, but which may also use multiple transmitter/sensor pairs. In this embodiment, signals from each sensor110in the array108can be averaged and used as input to a processor154which may be associated with a standard gamma densitometer and which may determine a consistency of the fluid using the averaged signals. The voltage signals from each sensor152may also be provided as signals P1(T) . . . PN(T) to the signal processor114, which applies these signals to the flow logic118to determine the velocity and other parameters of the fluid102. In this mode, the array detects disturbances due to coherent features that affect the density of the fluid102as these coherent features convect with the flow past the array108.

Diagnostic Logic

Referring toFIG. 10, a block diagram illustrating the diagnostic logic120is shown, wherein the diagnostic logic120measures the sensor input signals (or evaluation input signals), which may include one or more of the signals P1(t), P2(t), P3(t), . . . PN(t) and the parameters112, as shown in operational block300. The diagnostic logic120compares the evaluation input signals to a diagnostic evaluation criteria, as shown in operational block302, as discussed in further detail hereinafter. The diagnostic logic120checks whether there is a match, as shown in operational block304, and if so, provides a diagnostic signal indicative of the diagnostic condition that has been detected, as shown in operational block306, wherein the diagnostic logic120may also provide information identifying the diagnosed device. Furthermore, the diagnostic signal may also be output as a parameter112. It should be appreciated that where the evaluation input signal is a parameter112, as may be output from the flow logic118, the diagnostic evaluation criteria may be based on a threshold value of the flow signal. For example, the threshold value may be indicative of a maximum or minimum sound speed, mach number, consistency, composition, entrained air, density, mass flow rate, volumetric flow rate, and/or the like. If there is not a criteria match in operational block304, the diagnostic logic120exits.

It should also be appreciated that where the evaluation input signal includes one or more signals P1(t), P2(t), P3(t), . . . PN(t), the diagnostic evaluation criteria may be a threshold (maximum or minimum) signal. Alternatively, the diagnostic evaluation criteria may be based on an acoustic signature, or a convective property (i.e., a property that propagates or convects with the flow). For example, the diagnostic logic120may monitor the acoustic signature of any upstream or downstream device (e.g., motor, fan, pump, generator, engine, gear box, belt drive, pulley, hanger, clamp, actuator, valve, meter, or other machinery, equipment or component). Furthermore, it is contemplated that the data from the array108may be processed in any domain, including the frequency/spatial domain, the temporal/spatial domain, the temporal/wave-number domain, or the wave-number/frequency (k-ω) domain or other domain, or any of the above. As such, any known array processing technique in any of these or other related domains may be used if desired.

For example, for three signals, the equations in the frequency/spatial domain equation may be given by:
P(x,ω)=Ae−ikrx+Be+iklx,  (Eqn. 2)
the temporal/spatial domain may be given by:
P(x,t)=(Ae−ikrx+Be+iklx)eiωt,  (Eqn. 3)
and, the k-ω domain (taking the spatial Fourier transform) may be given by:

P⁡(k,ω)=12⁢π⁢∫-∞+∞⁢P⁢(x,ω)⁢ⅇⅈ⁢⁢kx⁢ⅆx=A⁡(ω)⁢δ⁡(k-ωa)+B⁡(ω)⁢δ⁡(k+ωa),(Eqn.⁢4)
where k is the wave number, a is the speed of sound of the material, x is the location along the pipe, ω is frequency (in rad/sec, where ω=2πf), and δ is the Dirac delta function, which shows a spatial/temporal mapping of the acoustic field in the k-ω plane.

Moreover, any technique known in the art for using a spatial (or phased) array of sensors to determine the acoustic or convective fields, beam forming, or other signal processing techniques, may be used to provide an input evaluation signal to be compared to the diagnostic evaluation criteria.

Flow Logic

Velocity Processing

Referring toFIG. 11, a block diagram illustrating an example of flow logic118is shown. As previously described, the array108of at least two sensors110located at two locations x1, x2axially along the pipe104sense respective stochastic parameter propagating between the sensors110within the pipe104at their respective locations x1. . . xN. Each sensor110provides a signal P1(t),P2(t),P3(t) . . . PN(t) indicative of the characteristic at each instant in a series of sampling instants. One will appreciate that the array108may include more than two sensors110distributed at locations x1. . . xN. The sensors110provide the analog time-varying signals P1(t),P2(t),P3(t) . . . PN(t) to the signal processor114, which in turn applies these signals P1(t),P2(t),P3(t), . . . PN(t) to the flow logic118, wherein the flow logic118processes the signals P1(t),P2(t),P3(t), . . . PN(t) to provide output signals (parameters)112. The signal processor114includes a data acquisition unit156(e.g., A/D converter) that converts the analog signals P1(t) . . . PN(t) to respective digital signals and provides the digital signals P1(t) . . . PN(t) to an FFT logic158. The FFT logic158calculates the Fourier transform of the digitized time-based input signals P1(t) . . . PN(t) and provides complex frequency domain (or frequency based) signals P1(ω),P2(ω),P3(ω), . . . PN(ω) indicative of the frequency content of the input signals to a data accumulator160. It should be appreciated that instead of FFT's, any other technique for obtaining the frequency domain characteristics of the signals P1(t)-PN(t), may be used. For example, the cross-spectral density and the power spectral density may be used to form a frequency domain transfer functions (or frequency response or ratios) discussed hereinafter. One technique of determining the convection velocity of the stochastic parameter associated with the process flow102is by characterizing a convective ridge of the resulting unsteady characteristics associated with the fluid using an array of sensors or other beam forming techniques, similar to that described in U.S. patent application Ser. No. 10/007,736 and U.S. patent application Ser. No. 09/729,994, filed Dec. 4, 2000, now issued into U.S. Pat. No. 6,609,069, all of which are incorporated herein by reference in their entireties.

The data accumulator160accumulates the frequency signals P1(ω)-PN(ω) over a sampling interval, and provides the data to an array processor162, which performs a spatial-temporal (two-dimensional) transform of the sensor data, from the x-t domain to the k-ω domain, and then calculates the power in the k-ω plane, as represented by a k-ω plot. The array processor162uses standard so-called beam forming, array processing, or adaptive array-processing algorithms, i.e. algorithms for processing the sensor signals using various delays and weighting to create suitable phase relationships between the signals provided by the different sensors, thereby creating phased antenna array functionality. In other words, the beam forming or array processing algorithms transform the time domain signals from the sensor array into their spatial and temporal frequency components, i.e. into a set of wave numbers given by k=2π/λ where λ is the wavelength of a spectral component, and corresponding angular frequencies given by ω=2πν.

The prior art teaches many algorithms of use in spatially and temporally decomposing a signal from a phased array of sensors, and the present invention is not restricted to any particular algorithm. One particular adaptive array processing algorithm is the Capon method/algorithm. While the Capon method is described as one method, the present invention contemplates the use of other adaptive array processing algorithms, such as MUSIC algorithm. The present invention recognizes that such techniques can be used to determine flow rate, i.e. that the signals caused by a stochastic parameter convecting with a flow are time stationary and have a coherence length long enough that it is practical to locate sensor units apart from each other and yet still be within the coherence length.

It should be appreciated that convective characteristics or parameters have a dispersion relationship that can be approximated by the straight-line equation given by:
k=ω/u,(Eqn. 5)
where u is the convection velocity (flow velocity). Referring toFIG. 13, a plot of k-ω pairs obtained from a spectral analysis of sensor samples associated with convective parameters portrayed so that the energy of the disturbance spectrally corresponding to pairings that might be described as a substantially straight ridge is shown, wherein, in turbulent boundary layer theory, this ridge is called a convective ridge. What is being sensed are not discrete events of the characteristic, but rather a continuum of possibly overlapping events forming a temporally stationary, essentially white process over the frequency range of interest. In other words, the characteristic is distributed over a range of length scales and hence temporal frequencies. To calculate the power in the k-ω plane, as represented by a k-ω plot (seeFIG. 13) of either of the signals, the array processor162determines the wavelength and so the (spatial) wavenumber k, and also the (temporal) frequency and so the angular frequency ω, of various spectral components of the stochastic parameter. There are numerous algorithms available in the public domain to perform the spatial/temporal decomposition of arrays of sensors110and the present invention is not limited to the use of any one of them.

Additionally, the present invention may use temporal and spatial filtering to precondition the signals to effectively filter out the common mode characteristics, Pcommon mode, and other long wavelength (compared to the sensor spacing) characteristics in the pipe104by differencing adjacent sensors110and retain a substantial portion of the stochastic parameter associated with the flow field and any other short wavelength (compared to the sensor spacing) low frequency stochastic parameters.

In the case of suitable turbulent eddies200(seeFIG. 12) being present, the power in the k-ω plane, as shown in the k-ω plot ofFIG. 13, shows a convective ridge202. The convective ridge202represents the concentration of a stochastic parameter that convects with the flow and is a mathematical manifestation of the relationship between the spatial variations and temporal variations described hereinbefore. Such a plot will indicate a tendency for k-ω pairs to appear more or less along a line202with some slope, wherein the slope indicates the flow velocity. Once the power in the k-ω plane is determined, a convective ridge identifier164uses one or another feature extraction method to determine the location and orientation (slope) of any convective ridge202present in the k-ω plane.

In one embodiment, a so-called slant stacking method is used, wherein the slant stacking method is a method in which the accumulated frequency of k-ω pairs in the k-ω plot along different rays emanating from the origin are compared, wherein each different ray is associated with a different trial convection velocity (in that the slope of a ray is assumed to be the flow velocity or correlated to the flow velocity in a known way). The convective ridge identifier134provides information about the different trial convection velocities, information referred to generally as convective ridge information, to an analyzer166, wherein the analyzer166examines the convective ridge information including the convective ridge orientation (slope). Assuming the straight-line dispersion relation given by k=ω/u, the analyzer166determines the flow velocity, Mach number and/or volumetric flow, which are output as parameters112. The volumetric flow is determined by multiplying the cross-sectional area of the inside of the pipe with the velocity of the process flow. It should be appreciated that some or all of the functions within the flow logic118may be implemented in software (using a microprocessor or computer) and/or firmware, and/or may be implemented using analog and/or digital hardware, having sufficient memory, interfaces, and/or capacity to perform the functions described herein.

Speed of Sound (SOS) Processing

Referring toFIG. 14, another example of flow logic118is shown. It should be appreciated that while the examples ofFIG. 11andFIG. 14are shown separately, it is contemplated that the flow logic118may perform all of the functions described with reference toFIG. 11andFIG. 14. As previously described, the array108of at least two sensors110located in at least two locations x1, x2axially along the pipe104sense respective stochastic signals propagating between the sensors110within the pipe104at their respective locations. Each sensor110provides a signal indicating a characteristic associated with the fluid102at the location of each sensor110, at each instant in a series of sampling instants. One will appreciate that the sensor array108may include more than two sensors110distributed at locations x1. . . xN. The sensors110provide analog time-varying signals P1(t),P2(t),P3(t), . . . PN(t) to the flow logic118, wherein the flow logic118processes the signals P1(t),P2(t),P3(t), . . . PN(t) from the sensors110to first provide output signals indicative of the speed of sound propagating through the fluid (process flow)102, and subsequently, provide output signals such as velocity, Mach number and volumetric flow rate of the process flow102.

The signal processor114receives the signals from the array108of sensors110and a data acquisition unit168digitizes the signals P1(t) . . . PN(t) associated with the acoustic waves204propagating through the pipe104. Similarly to the FFT logic158ofFIG. 11, an FFT logic170calculates the Fourier transform of the selected digitized time-based input signals P1(t) . . . PN(t) and provides complex frequency domain (or frequency based) signals P1(ω),P2(ω),P3(ω), . . . PN(ω) indicative of the frequency content of the input signals to a data accumulator172. The data accumulator172accumulates the frequency signals P1(ω) . . . PN(ω) over a sampling interval, and provides the data to an array processor174, which performs a spatial-temporal (two-dimensional) transform of the sensor data, from the x-t domain to the k-ω domain, and then calculates the power in the k-ω plane, as represented by a k-ω plot. To calculate the power in the k-ω plane, as represented by a k-ω plot (seeFIG. 15) of either the signals or the differenced signals, the array processor174determines the wavelength and so the (spatial) wavenumber k, and also the (temporal) frequency and so the angular frequency ω, of various of the spectral components of the stochastic parameter. It should be appreciated that there are numerous algorithms available in the public domain to perform the spatial/temporal decomposition of arrays of sensor units110and the present invention is not limited to the use of any one in particular.

In the case of suitable acoustic waves204being present in both axial directions, the power in the k-ω plane, shown in the k-ω plot ofFIG. 15, so determined will exhibit a structure that is called an acoustic ridge206,208in both the left and right planes of the plot, wherein one of the acoustic ridges206is indicative of the speed of sound traveling in one axial direction and the other acoustic ridge208being indicative of the speed of sound traveling in the other axial direction. The acoustic ridges206,208represent the concentration of a stochastic parameter that propagates through the flow and is a mathematical manifestation of the relationship between the spatial variations and temporal variations described above. Such a plot will indicate a tendency for k-ω pairs to appear more or less along a line206,208with some slope, wherein the slope is indicative of the speed of sound. The power in the k-ω plane so determined is then provided to an acoustic ridge identifier176, which uses one or another feature extraction method to determine the location and orientation (slope) of any acoustic ridge present in the left and right k-ω plane. The velocity may be determined by using the slope of one of the two acoustic ridges206,208or by averaging the slopes of the acoustic ridges206,208. Finally, information including the acoustic ridge orientation (slope) is used by an analyzer178to determine the flow parameters relating to measured speed of sound, such as the consistency or composition of the flow, the density of the flow, the average size of particles in the flow, the air/mass ratio of the flow, gas volume fraction of the flow, the speed of sound propagating through the flow, and/or the percentage of entrained air within the flow.

Similar to the array processor162ofFIG. 11, the array processor174uses standard so-called beam forming, array processing, or adaptive array-processing algorithms, i.e. algorithms for processing the sensor signals using various delays and weighting to create suitable phase relationships between the signals provided by the different sensors, thereby creating phased antenna array functionality. In other words, the beam forming or array processing algorithms transform the time domain signals from the sensor array into their spatial and temporal frequency components, i.e. into a set of wave numbers given by k=2π/λ, where λ is the wavelength of a spectral component, and corresponding angular frequencies are given by ω=2πν. One such technique of determining the speed of sound propagating through the process flow102involves using array processing techniques to define an acoustic ridge in the k-ω plane, as shown inFIG. 15. The slope of the acoustic ridge206,208is indicative of the speed of sound propagating through the process flow102, wherein the speed of sound (SOS) may be determined by applying sonar arraying processing techniques to determine the speed at which the one dimensional acoustic waves propagate past the axial array of sensors110distributed along the pipe104.

The flow logic118of the present embodiment measures the speed of sound (SOS) of one-dimensional sound waves propagating through the process flow102to determine the gas volume fraction of the process flow102. It is known that sound propagates through various mediums at various speeds in such fields as SONAR and RADAR fields. Thus, the speed of sound propagating through the pipe104and process flow102may be determined using any number of known techniques, such as those set forth in U.S. patent application Ser. No. 09/344,094, filed Jun. 25, 1999, now U.S. Pat. No. 6,354,147, U.S. patent application Ser. No. 10/795,111, filed Mar. 4, 2004, U.S. patent application Ser. No. 09/997,221, filed Nov. 28, 2001, now U.S. Pat. No. 6,587,798, U.S. patent application Ser. No. 10/007,749, filed Nov. 7, 2001, and U.S. patent application Ser. No. 10/762,410, filed Jan. 21, 2004, each of which are incorporated herein by reference in their entireties. While a sonar-based flow meter is described herein as using an array of sensors108to measure the speed of sound of an acoustic wave propagating through the mixture, one should appreciate that any means for measuring the speed of sound of the acoustic wave may be used to determine the entrained gas volume fraction of the mixture/fluid or other characteristics of the flow described hereinbefore.

The analyzer178of the flow logic118provides output parameters112indicative of characteristics of the process flow102that are related to the measured speed of sound (SOS) propagating through the process flow102. For example, to determine the gas volume fraction (or phase fraction), the analyzer178assumes a nearly isothermal condition for the process flow102. As such the gas volume fraction or the void fraction is related to the speed of sound by the following quadratic equation:
Ax2+Bx+C=0,  (Eqn. 6)
wherein x is the speed of sound, A=1+rg/rl*(Keff/P−1)−Keff/P, B=Keff/P−2+rg/rl; C=1−Keff/rl*ameas^2); Rg=gas density, rl=liquid density, Keff=effective K (modulus of the liquid and pipewall), P=pressure, and ameas=measured speed of sound. Effectively,
Gas Volume Fraction (GVF)=(−B+sqrt(B^2−4*A*C))/(2*A),  (Eqn. 7)

Alternatively, the sound speed of a mixture can be related to volumetric phase fraction (φi) of the components and the sound speed (a) and densities (ρ) of the component through the Wood equation, as given by,

1ρmix⁢amix∞2=∑i=1N⁢ϕiρi⁢ai2,⁢where,(Eqn.⁢8)ρmix=∑i=1N⁢ρi⁢ϕi.(Eqn.⁢9)
As such, the relationship among the infinite domain speed of sound and density of a mixture, the elastic modulus (E), thickness (t), and radius (R) of a vacuum-backed cylindrical conduit and the effective propagation velocity (aeff) for one dimensional compression may be given by the following expression:

The mixing rule essentially states that the compressibility of a process flow is the volumetrically-weighted average of the compressibilities of the components. For a process flow102consisting of a gas/liquid mixture at pressure and temperatures typical of the paper and pulp industry, the compressibility of gas phase is orders of magnitudes greater than that of the liquid. Thus, the compressibility of the gas phase and the density of the liquid phase primarily determine mixture sound speed, and as such, it is necessary to have a good estimate of process pressure to interpret mixture sound speed in terms of the volumetric fraction of entrained gas. The effect of process pressure on the relationship between sound speed and entrained air volume fraction is shown inFIG. 16.

As described hereinbefore, the flow logic118of the present embodiment includes the ability to accurately determine the average particle size of a particle/air and/or droplet/air mixture within the pipe104and/or the air to particle ratio. Provided there is no appreciable slip between the air and the solid coal particle, the propagation of a one dimensional sound wave through multiphase mixtures is influenced by the effective mass and the effective compressibility of the mixture. For an air transport system, the degree to which the no-slip assumption applies is a strong function of particle size and frequency. In the limit of small particles and low frequency, the no-slip assumption is valid. However, as the size of the particles increases and the frequency of the sound waves increase, the non-slip assumption becomes increasing less valid. For a given average particle size, the increase in slip with frequency causes dispersion, or, in other words, the tendency of the sound speed of the mixture to change with frequency. With appropriate calibration the dispersive characteristic of a process flow102will provide a measurement of the average particle size, as well as, the air to particle ratio (particle/fluid ratio) of the process flow102.

In accordance with the present invention the dispersive nature of the system utilizes a first principles model of the interaction between the air and particles. This model may be viewed as being representative of a class of models that seek to account for dispersive effects, although other models could be used to account for dispersive effects without altering the intent of this disclosure (for example, see the paper titled “Viscous Attenuation of Acoustic Waves in Suspensions” by R. L. Gibson, Jr. and M. N. Toksöz), which is incorporated herein by reference in its entirety. The model allows for slip between the local velocity of the continuous fluid phase and that of the particles.

The following relation can be derived for the dispersive behavior of an idealized fluid particle mixture.

amix⁡(ω)=af⁢11+φp⁢ρpρf⁡(1+ω2⁢ρp2⁢vp2K2),(Eqn.⁢11)
wherein, in the above relation, the fluid SOS, density (ρ) and viscosity (φ) are those of the pure phase fluid, vpis the volume of individual particles and ρpis the volumetric phase fraction of the particles in the mixture.

Two parameters of particular interest in steam processes and air-conveyed particles processes are particle size and air-to-fuel mass ratio or steam quality. To this end, it is of interest to examine the dispersive characteristics of the mixture as a function of these two variables.FIG. 17andFIG. 18show the dispersive behavior in relations to the speed of sound for coal/air mixtures with parameters typical of those used in pulverized coal deliver systems. In particularFIG. 17shows the predicted behavior for nominally 50 micrometer size coal in air for a range of air-to-fuel ratios. As shown, the effect of air-to-fuel ratio is well defined in the low frequency limit. However, the effect of the air-to-fuel ratio becomes indistinguishable at higher frequencies, approaching the sound speed of the pure air at high frequencies (above ˜100 Hz). Similarly,FIG. 18shows the predicted behavior for a coal/air mixture with an air-to-fuel ratio of 1.8 with varying particle size. This figure illustrates that particle size has no influence on either the low frequency limit (quasi-steady) sound speed, or on the high frequency limit of the sound speed. However, particle size does appear to have a pronounced effect in the transition region.

It should be appreciated thatFIG. 17andFIG. 18illustrate an important aspect of the present invention. Namely, that the dispersive properties of dilute mixtures of particles suspended in a continuous liquid, can be broadly classified into three frequency regimes: low frequency range, high frequency range and a transitional frequency range. Although the effect of particle size and air-to-fuel ratio are interrelated, the predominant effect of air-to-fuel ratio is to determine the low frequency limit of the sound speed to be measured and the predominate effect of particle size is to determine the frequency range of the transitional regions. For example, as particle size increases the frequency at which the dispersive properties appear decreases. For typical pulverized coal applications, this transitional region begins at fairly low frequencies, ˜2 Hz for 50 micrometer size particles. Thus, given the difficulties measuring sufficiently low frequencies to apply the quasi-steady model and recognizing that the high frequency sound speed contains no direct information on either particle size or air-to-fuel ratio, it becomes apparent that the dispersive characteristics of the coal/air mixture should be utilized to determine particle size and air-to-fuel ratio based on speed of sound measurements.

It should be appreciated that some or all of the functions within the flow logic118may be implemented in software (using a microprocessor or computer) and/or firmware, and/or may be implemented using analog and/or digital hardware, having sufficient memory, interfaces, and capacity to perform the functions described herein. Moreover, whileFIG. 11andFIG. 14depict two different embodiments of the flow logic118to measure various parameters of the flow process, the present invention contemplates that the functions of these two embodiments may be performed by a single flow logic118. It should be further understood that any of the features, characteristics, alternatives or modifications described regarding a particular embodiment herein may also be applied, used, or incorporated with any other embodiment described herein.

It should also be appreciated that the invention may be embodied in the form of a computer or controller implemented processes. The invention may also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, and/or any other computer-readable medium, wherein when the computer program code is loaded into and executed by a computer or controller, the computer or controller becomes an apparatus for practicing the invention. The invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein when the computer program code is loaded into and executed by a computer or a controller, the computer or controller becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor the computer program code segments may configure the microprocessor to create specific logic circuits.