A mmWave Measuring Procedure for Mass Flow ... - IEEE Xplore

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Jul 25, 2014 - mass flow determination of pneumatic conveyed particles is presented. ... expanded paper from the IEEE SENSORS 2013 Conference.
IEEE SENSORS JOURNAL, VOL. 14, NO. 9, SEPTEMBER 2014

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A mmWave Measuring Procedure for Mass Flow Monitoring of Pneumatic Conveyed Bulk Materials Christoph Baer, Student Member, IEEE, Timo Jaeschke, Student Member, IEEE, Philipp Mertmann, Member, IEEE, Nils Pohl, Member, IEEE, and Thomas Musch, Member, IEEE

Abstract— In this contribution a novel measuring concept for mass flow determination of pneumatic conveyed particles is presented. Therefore, the so-called pseudo transmission measurement concept was adapted for the simultaneous determination of volume fraction and particle flow velocity within a pneumatic conveying tube. The application of two ultra wideband 80-GHz FMCW radar systems increases the measuring accuracy and is used for measurements on a continuous particle flow. Therefore, an experimental pneumatic conveying system is introduced. Flow simulations of the conveying pipe and 3-D electromagnetic simulations of the measurement method illustrate the flow behavior inside the conveying tube and the RF-measuring performance, respectively. Measurement results concerning the volume fraction determination as well as the velocity determination will be presented and discussed in detail. Index Terms— Pneumatic conveying, mmWave, radar, FMCW, correlation.

I. I NTRODUCTION

P

NEUMATIC conveying is an advantageous and widely used transportation technique for bulked materials, e.g. powder and dust in the coal and steel industry or grain and flour in the food processing industry [1]. In order to perform pneumatic conveyance, bulk material is given portion wise into a gas flow, where it disperses and forms a two-phase flow. Measuring the mass flow inside a conveying tube is challenging because two physical quantities, i.e. the volume fraction ζ of the particles and the flow velocity v of the particles have to be investigated, simultaneously. Additionally, a noncontact measuring method is often compulsory due to licensing requirements and orders. Common measurement concepts rely on optical, acoustical, and electro-statical concepts [2], [3].

Manuscript received March 31, 2014; accepted May 8, 2014. Date of publication May 21, 2014; date of current version July 25, 2014. This is an expanded paper from the IEEE SENSORS 2013 Conference. The associate editor coordinating the review of this paper and approving it for publication was Prof. Elena Gaura. C. Baer, P. Mertmann, and T. Musch are with the Institute of Electronic Circuits, Ruhr-University Bochum, Bochum 44801, Germany (e-mail: [email protected]; [email protected]; [email protected]). T. Jaeschke is with the Institute of Integrated Circuits, Ruhr-University Bochum, Bochum 44801, Germany (e-mail: [email protected]). N. Pohl is with the Department of Millimetre Wave Radar and High Frequency Sensors, Fraunhofer FHR, Wachtberg 53343, Germany (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2326042

Furthermore, ultrasonic based approaches for pure gas flows are commonly known [4]. Microwave based measuring techniques are often based on cut-off frequency measurements [5], backscatter coefficients determination [6], or resonator tuning methods [7]. Although these methods are promising, they exhibit measuring inaccuracies that make them inappropriate for feedback control, which is needed for the optimization of production processes. A novel, microwave based measurement technique for the volume fraction determination was introduced in [8]. By means of a time of flight (TOF) measurement, the authors could reach a measuring accuracy of about 3%. However, in order to guarantee an adequate signal detection, two consecutive measurements on a two-state radar target had to be performed. This slowed down the measuring process drastically, which made the measuring technique unsuitable for the continuous mass flow determination. In [9], an advanced TOF measurement concept was introduced that made use of a polarimetric approach. By means of a polarization disjunction, the volume fraction determination was performed with a single measurement. In [10], first measurements on a quasi static scenario were published that illustrated the advantages of the so-called pseudo transmission method and showed an average error of 2.3% for the volume fraction determination. In this contribution, we optimized the known pseudo transmission measurement concept and added a second measuring port for performing velocity measurements. Furthermore, we increased the operating frequency as well as the operating bandwidth which raised measuring accuracy. In order to verify the presented measuring methods we built up an experimental conveying system. II. F UNDAMENTALS A. Materials The application of the pneumatic conveying of bulk materials is widely spread. It can be used for storage purposes as well as for the transportation of pulverized fuels to combustion chambers. However, nearly all bulk materials are dielectric, which makes the microwave based measuring method universal for most pneumatic conveying systems. Therefore, most solid carbon based materials have dielectric constants between 2 and 5 with a low dispersive behavior. In this work, two materials, i.e. coal dust and wheat flour, were picked

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Fig. 3.

SEM image of coal dust with particle diameter of 25μm.

Fig. 1. Complex permittivity of coal within a frequency range between 5 GHz and 40 GHz.

Fig. 4.

SEM image of wheat flour with particle size of 30 μm.

Fig. 2. Complex permittivity of flour within a frequency range between 5 GHz and 40 GHz.

out exemplarily. Fig. 1 and Fig. 2 show the complex permittivities of coal and flour, respectively. We investigated both material permittivities with a SPEAG DAK 2.5 Dielectric Probe Kit over a broad frequency range from 5 GHz to 40 GHz. By mixing dielectric particles with the conveying gas, an effective permittivity occurs, which depends on the volume fraction as well as the permittivity of the transported material. Background for the mixing theory of dielectric materials is the assumption that the transported particles can be described as spherical inclusions within a surrounding material [11]. Solely, the particle size is of interest when choosing an adequate frequency range for the microwave measurements. Particle sizes below a tenth of a wavelength will effect nearly no scattering behavior. With increasing particle to wavelength ratio, the dielectric particles will cause a Rayleigh and Mie scattering, which results in an increased extinction cross-section of the material composition [13]. Fig. 3 and Fig. 4 show SEM images of coal dust particles that are used for combustion processes in generating stations and commercially available wheat flour, respectively. Although, both materials are not perfectly spherical in shape, they will assumed to be spherical in a good approximation. In literature, numerous mixing equations for an effective permittivity can be found [12]. Nevertheless, for low permittivities all mixing equations show the same, linear

behavior. Hence, a mixing equation for pneumatic conveyed materials can be obtained: εr,eff = 1 +

3 · ζ · (εi − 1) εi + 2 − ζ (εi − 1)

(1)

The mixing-equation in (1) is based on the Maxwell-Garnett mixing equation. In terms of the pneumatic conveyance scenario, the equation was simplified because we can assume that the background material is constantly air with a permittivity of εr,air = 1 for most applications. B. Volume Fraction Determination In (1), εi is the permittivity of the particles which is, refering to Fig. 1 and Fig. 2, approximately 2.4 for coal or flour particles. The quantity ζ is the volume fraction of the conveyed particles. Its determination is coercive for the mass flow determination. By means of a TOF measurement, that is carried out perpendicular to the tubes cross section as shown in Fig. 5.a, we can determine the relative permittivity and can calculate the volume fraction. By means of a polarization discrimination and a transpolarizing reflector, the influences of feed side reflections and multiple reflections are decimated which improves the measuring accuracy. This special measurement technique is called pseudo-transmission measurement. It was discussed in detail in [9] and [10].

BAER et al.: mmWAVE MEASURING PROCEDURE FOR MASS FLOW MONITORING

Fig. 5. Schemetical assembly of the pseudo transmission measurement setup.

The TOF of the proposed assembly is given by: √ deff · εr,eff + T0 (2) T OF = c0 In (2), the parameter deff is the effective propagation path of the electromagnetic wave. It differs slightly from the tube’s diameter because of the two antenna arrangement. However, we can determine deff by means of a reference measurement on a well known interior permittivity. The parameter T0 adds additional propagation paths to the TOF, that can be eliminated by a reference measurement on the empty tube. Therefore, the reference measurement TOFref must be substracted from the measured TOF. The resulting time difference T is given by: T = TOFmeas − TOFref

(3)

By means of the equations (1), (2), and (3), we can obtain an expression for the measured volume fraction of the particles, which is important for the subsequent signal processing:   2 T ·c0 − 1 (εi + 2) · deff   ζmeas = (4) 2 T ·c0 3 · (εi − 1) + − 1 · − 1) (ε i deff Eq. (4) shows that we need a priori information concerning the tube’s diameter and antenna arrangement as well as information of the transported material for the determination of the volume fraction ζmeas . Refering to the assumption, that the transported bulk material’s permittivity differs from the air permittivity, the edge case for εi = 1 is not defined in (4). In the case that the tranportation air is aspired from the surrounding, the air humidity must be taken into consideration. This can be done by a sensor fusion with a moisture meter. However, in terms of the pulverized fuel conveying, the conveying gas is arid carbon dioxid or nitrogen and we can neglect the humidity influence. C. Flow Velocity Determination Flow inside of tubes and pipes show different flow regimes. We are able to classify the appearing flow regime by obtaining Reynolds number. For flow inside of pipes and tubes it is given by: v·d (5) Re = ν

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Fig. 6. Schematical drawing of the measurement device arrangement showing time steps and the most important parameters for (a) the Full Flow Reconstruction (FFR) and (b) the Reduced Data Rate (RDR) sampling theorems.

In (5), d is the tube’s diameter, v is the flow velocity, and ν is the viscosity of the pipes interior. For Reynolds numbers greater than 2300, a turbulent, non-steady flow will be observed [14]. Due to the extreme low viscosity of gas 2 of about νgas ≈ 150 · 10−5 ms , nearly all gas flows can be described to be turbulent. For turbulent flow a theoretical flow segmentation, called Reynolds Decomposition, can be performed that splits the flow into a mean and a fluctuation part for all its parameters like velocity or pressure [15]. It is given by: (6) u (x, t) = u (x) + u  (x 0 , t) In (6), u (x) expresses the average values while u  (x 0 , t) describes the timely fluctuations on a local spot. While the mean values are taken as predictable variables determined by dynamics laws, we can regard the turbulent fluctuations as stochastic variables. The Reynolds segmentation can be interpreted as inhomogeneities in pressure and particle distribution inside the tube, which are caused by swirls. These inhomogeneities are conveyed through the pipe with the mean transportation velocity. A second assumption that is fundamental for the flow velocity determination is the so called mixinglength hypothesis by Prandtl [16]. Within a short flow distance, the particle concentration and inhomogeneities remain steady for a short time period after they collapse and loose their uniqueness. Within this specific transportation distance, swirls are identifiable which allows for a correlation based velocity measurement. Adding a second measuring port for the volume fraction determination to the conveying tube with a short, but known distance to the first measuring port allows a cross correlation of the conveyed swirls. The arrangement of both measuring ports is shown in Fig. 6.a. The closer the measuring ports are arranged, the higher the correlation coefficient will be. The cross correlation coefficient rxy of the data vector given by the measuring ports x and y can be calculated as follows: n ¯ · (yi − y¯ )) ((x i − x) (7) rxy =  i=1  n ¯ 2 · ni=1 (yi − y¯ )2 i=1 (x i − x) In (7), x¯ and y¯ are the mean values of the data vector for the measuring ports x and y, respectively. For a synchronous

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data acquisition with the acquisition time T and the distance l between the measuring ports, the flow velocity can be determined to: l v= (8) i rxy ,max · T In (8), the product i rxy ,max · T indicates the time step of the maximum value for rxy . With knowledge of the distance between the measuring ports, we now can obtain the flow velocity. Furthermore, the second measuring port is a redundant port for the volume fraction determination which, e.g. by averaging methods, results in an increased accuracy for the volume fraction determination. The described measurement setup allows for a precise velocity measurement, but has also some restrictions and requirements concerning the data acquisition speed. D. Full Flow Reconstruction Sampling Theorem We will achieve the highest correlation coefficient, if the full flow is reconstructable at both measuring ports. Requirements given by the Full Flow Reconstruction Sampling Theorem (FFR) will enable to reconstruct the flow completely. For a precise and accurate correlation based velocity determination, we must ensure a synchronized measurement for both measuring ports. Further, we have to choose an adequate measurement repetition rate f rep . Because the flow can be regarded as piecewise sampled, an undersized repetition rate will not capture enough information for a significant correlation. In a good approximation, we can assume that the effective beamwidth of the antennas that penetrates the transported material composition has the diameter dbeam . Furthermore, we can assume that the particle distribution inside a swirl is Gaussian. A Gaussian function is reconstructable, when at least three points of the function are gathered. This means that a swirl must be sampled by at least 3 points as shown in Fig. 6.a. This yields the requirement for the repetition rate: 3 · v max (9) frep ≥ dswirl In (9), v max indicates the maximum flow velocity of the conveying system. The quantity dswirl describes the detectable diameter of a swirl or increased particle distribution. Further, eq. (9) sets the requirement, that the penetrating beam diameter of the measurement assembly has to be smaller than the third part of the detectable swirl diameter. A further requirement to the correlation based velocity determination is the distance between the two measuring devices. We may not set the distance l to large, because the flow will loose its uniqueness, due to Prandtl’s mixing length hypothesis. E. Reduced Data Rate Sampling Theorem Setting the measurement repetition rate too high will result in a very complex and expensive setup, because data rates become extremely high. The Reduced Data Rate Sampling Theorem (RDR) describes a possibility to reduce the repetition rate by taking a priori information into account. We can reduce the sampling rate if we roughly know the gas velocity. Additionally, we need to set up multiple measuring ports

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for the correlation as shown in Fig. 6.b. The ports X1 , X2 , X3 are arranged in close proximity, which allows us to penetrate a wider but spatially resolved flow area. Therefore, the slowest repetition rate that guarantees a correlation is reached when a flow region that is penetrated in time step t = t0 by measuring port Y gets penetrated by one of the ports X1 , X2 , X3 . Therefore, the ports X1,2,3 always measure at the same consecutive time step t = t1 . Although we have to calculate three correlation coefficients for the three measuring ports, each coefficient contains a single value for the time step t1 only. Because the penetrated area at the ports X1 , X2 , X3 is spatially resolved, we can easily locate the flow region of interest by means of the correlation value. We can calculate the flow velocity by chosing the right distance l1 , l2 , or l3 . Further we may set set the index in (8) to constantly 1. Additionally, we can calculate an intermediate distance l by weighting the distances with the correlation coefficients. This is advantageous when the spatial resolution of the penetration area is too sparse. The repetition rate will decrease for the increasing distances of the measuring devices l. Because of the unlasting mixinglength of the swirls we must set the maximum distance lmax to less than the coherent mixing-length of the swirls lcoh . In order to ensure a proper gathering of both, slightly faster and slower flow velocities, we set the reduced repetition rate f rep,red to: v max f rep,red ≈ (10) l2 Since the RDR requires a priori information of the flow velocity, we cannot use it as a universal method. However, if the a priori information is gathered, eg. by the FFR, the data processing might be changed in order to reduce the data rate, which will save processing power. III. S ETUP For the verification of the proposed measuring concept as well as to perform further investigations on the pneumatic flow behavior, we built a closed loop conveying system as an experimental plant. The setup consists, except for the measuring tube, of metallic tubes with a diameter of 100 mm. The measuring tube is made of borosilicate glass and has a diameter of 200 mm. It is arranged vertically in order to investigate vertical flow behavior. Fig. 7 shows a photography of the described closed loop conveying system. The gas flow is produced by a radial ventilator (A) which is driven by a 900 W synchronous motor. We can regulate the rotational speed by the exciting frequency of the motor with a frequency converter. In order to charge or to discharge the setup with particles, the system features a charging tube and a cyclone (D), respectively. By means of a bypass lever (C), we are able to switch the cyclone in series for separating the particles. Because the conveyed gas particle mixtures are highly explosive, depending on the volume fraction and particles, the setup has a gas port to flood the whole setup with nitrogen. In order to supervise and to ensure hazard-free operation, the oxygen content exhausted and filtered random sampled air is measured at all times. Numerous measuring

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Fig. 9. Characteristic curve of the close loop conveying system showing the static system pressure for the ventilator revolution of 22 Hz and 47 Hz versus the particle content.

Fig. 7. Closed loop conveying system: (a) ventilator, (b) borosilicate glass measuring tube, (c) bypass lever, and (D) cyclone.

Fig. 8. Characteristic curve of the close loop conveying system showing the pure gas velocity and the static system pressure versus the ventilator revolution.

openings enable pressure and temperature surveillance of the setup. Additional openings are used for pure gas flow velocity measurements with a hot-wire anemometer in the leading as well as in the measuring tube (B). Because of the measuring technique, the hot-wire anemometer is only used for pure gas flows without particle content. Furthermore, an industrial differential pressure measurement device allows a recording of the static system pressure at all times. For a pure gas flow, Fig. 8 shows the characteristic velocity v pipe inside the measurement pipe and the static pressure P versus the ventilator revolution f vent .

The velocity curve in Fig. 8 will be used as a reference for the subsequent velocity determination of the gas-particle composition. Hence, we have to ensure that the bulk material has no influence on the transportation velocity. A good indicator that the velocity is not affected by the particles is the static system pressure for constant ventilator revolution. Therefore, the characteristic curve for the pressure dependence on the particle volume fraction was recorded for two exemplary ventilator revolutions. Fig. 9 shows the result of this investigation. We observe that up to a volume fraction of 0.32% the static pressure can assumed to be constant. For volume fractions greater than 0.32% the static pressure decreases. This implies that the characteristic curve for the revoltution-velocity dependency may only be used as a reference for volume fractions below the critical value of ζ = 0.32%. However, this limitation does not affect the volume fraction determination. In (6), we already discussed the Reynolds decomposition which made us come to the conclusion that swirls and areas of increased particle distributions are transported by the mean velocity through the pipe. In order to illustrate this behavior, we video taped the flow with a sampling rate of 120 fps. Fig. 10 shows four consecutive pictures of the video showing the unsteady flow behavior. We highlighted a big swirl in order to show its movement in flow direction. IV. S IMULATIONS A. Flow Simulation For predicting the flow behavior inside the test setup, different flow simulations with ANSYS FLUENT were performed. Therefore, a small part of the setup, i.e. the ventilator tube, the measuring tube and the exhaust tube, were constructed inside the simulation environment. The ventilator tube was excited with a gas flow containing particles with a size of 100 μm and had a flow velocity of 24 ms . Fig. 11 shows the results of this investigation. The colored particle traces in Fig. 11.a show occurring turbulences inside the measuring tube that were expected due to Reynolds number. Fig. 11.b shows the mass concentration inside the assembly for a single time step, showing likewise the non-steady flow.

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Fig. 10. Four consecutive pictures taken from a backlighted high speed video with 120 fps. The shading of a swirl is highlightened for illustrating the movement of the swirl.

Fig. 12. Result of the flow simulation: Correlation coefficients over flow velocity from data history of the measuring ports within the flow simulation.

by Fig. 11 we observe a mean error of 1%. Concerning the correlation coefficient we observe a descending behavior for an increasing distance of the correlation layers from 60% to 30%. This observation proves the adaptability of the mixinglength hypothesis by Prandtl that was already discussed. Fig. 11. Result of the flow simulation: (a) shows the particle traces with corresponding particle velocities and (b) shows the mass distribution for a single time step with inserted measuring ports.

B. 3D Electromagnetic Simulation

In order to investigate the correlation behavior of the flow, we inserted six equidistant circular measuring layers inside the measuring tube. The distances between the layers were set to 5 cm. For every time step of the simulation, each layer recorded the actual volume fraction at its position. In order to keep the requirements for the Full Flow Reconstruction Theorem in (9), we set the acquisition sampling rate to f rep,sim = 2 kHz. In a post process, the gathered data of the upper five layers were correlated with the reference layer which is the lowest layer. We plotted the results of the correlation over the according flow velocity in Fig. 12. Regarding the flow velocity, we observe values around 8.65 ms with a span of 0.4 ms . Compared to the mean particle velocity of 8.74 ms given

3D electromagnetic simulations are an indispensable and powerful tool for the investigation of microwave based measuring methods. Within the development of the pseudo transmission measurement, numerous simulations and optimizations were performed. Exemplary, the positive influence on the TOF determination of the pseudo transmission method is discussed. Within the simulation environment, a circular horn antenna, the measuring tube and a transpolarizing reflector were arranged next to each other. A standard radar as well as the pseudo transmission simulation were performed. Fig. 13 shows the resulting time signals. While the dotted, blue line shows a standard, monostatic radar measurement, the solid green line indicates the pseudo transmission signal. For the radar simulation, we are not able to identify the reflector of the other side of the tube. This is caused by feed side and

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Fig. 13. Simulative comparison between a standard monostatic (· · · ) radar and the pseudo transmission method (-). Fig. 14. Results of the volume fraction measurement. Due to comparison reasons, effective mass was calculated from the the measured volume fractions.

multiple reflections caused by the close antenna arrangement and the tube’s wall. In contrast, we observe that feed side reflections are suppressed by more than 40 dB within the pseudo transmission simulation. Apparently, we can clearly recognize the reflector at a TOF of 4.3 ns. Furthermore, multiple reflections are also suppressed, which leads to a robust signal processing of the transmission phase. V. M EASUREMENTS The measuring device, used for the following investigations is an 80 GHz FMCW radar system. It offers a bandwidth of 20 GHz with a frequency ramp duration of 2 ms. The transmit and receive path of the radar device were divided in order to connect two rectangular horn antennas. Because we require the polarization disjunction the antennas are twisted by 90 degrees to each other. A more detailed description of this device can be found in [17]. The used transpolarizing reflector exhibits metallic gratings, which are approximately a quarter wavelength in width and depth. The orientation of the reflector gratings is 45 degrees compared to the direction of polarization. Furthermore, all measurements were performed on flour, because of its good usability and known properties. A. Volume Fraction Determination In order to verify the accuracy of the volume fraction determination, we inserted increasing amounts of flour into the gas flow. By means of a high precision scale, the inserted flour mass was known with a preciseness better than 1 g. The inserted amounts of flour were for each step approximately 50 g. The ventilator revolution was set to be constantly 30 Hz resulting in a pure gas flow velocity in the measuring tube of 8 ms . After the inserted flour was mainly dispersed, numerous pseudo transmission measurement were carried out. Because of the non-steady flow regime, 200 measurements were gathered within one minute and the mean value was taken. With the aid of known setup parameters, i.e. the flour permittivity and mass density, the conveying tube’s diameter, and the total setup volume, we calculated an equivalent mass. Fig. 14 shows the result of this investigation. With an average error of 1.3% the volume fraction determination shows a high accuracy.

Fig. 15. Measured correlation coefficients for different flow velocities indicating a measured flow velocity of 6 ms .

B. Flow Velocity Determination For the verification of the correlation based velocity measurement we added a second radar device to the measuring tube. The two 80 GHz FMCW radar systems were arranged at the measuring tube with a distance of 22 cm to each other. Simultaneous measurements of both radar systems were performed with a duty cycle of 37 ms. In order to compare the measured data to an adequate reference value, we ensured that the volume fraction of the conveyed flour was set to a maximum of ζ = 0.3%. The ventilator was driven at 23 revolutions per seconds that leads, according to the setup characteristic for pure gas flow shown in Fig. 8, to a flow velocity of 6.2 ms in the measuring tube. Due to the required fast data collection, the signal post processing was executed after the pseudo transmission measurement, which collected a total of 500 flow samples with a time interval of 37 ms between each sample. After the data collection, a signal post processing that includes the correlation algorithm was performed. Fig. 15 shows the result of this investigation. With a maximum correlation coefficient of 15.8% a velocity of 6 ms can be identified. The resulting error of the velocity measurement compared to the setup characteristic is about 3.3%. Because the maximum correlation point is easy to indentify and the determination error is small, we can assume that the correlation based velocity measurement is quite robust.

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VI. C ONCLUSION In this contribution, we presented a milimeter wave based measuring method for the mass flow determination of pneumatic conveyed materials. Fundamentals concerning permittivity behavior of gas particles mixture were introduced. The volume fraction dependence of the gas particle mixture’s effective permittivity lead us to a fast and effective time of flight measuring concept. The so called advanced pseudo transmission measurement enabled a continuous and fast determination of the volume fraction. Concerning the velocity measurement, fundamentals about the occurring flow regimes were discussed. Based on the explained Reynolds decomposition, we set up a correlation based flow velocity measurement. In order to maximize the correlation effect, a sampling theorem for turbulent flows was introduced. Flow simulations as well as 3D electromagnetic simulations proved the velocity measurement concept and the effectiveness of the pseudo transmission measurement, respectively. In order to verify the described theory, we built up a experimental conveying system to perform test measurements. Essential conveying system characteristics were presented, which were important for subsequent measurement limitations. Measurements on a continuous mass flow were carried out and discussed. With an error of 1.3% for the volume fraction determination and an error of 3.3% for the velocity measurement, the proposed measuring concept shows a high accuracy.

[13] L. Tsang and J. A. Kong, “Scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow,” J. Electromagn. Waves Appl., vol. 6, nos. 1–4, pp. 265–286, 1992. [14] J. H. Spurk and N. Aksel, Fluid Mechanics. Berlin, Germany: Springer-Verlag, 2008, pp. 206–207. [15] R. G. Deissler, Turbulent Fluid Motion. New York, NY, USA: Taylor & Francis, 1998, p. 49. [16] H. Oertl, Prandtl Essentials of Fluid Mechanics. New York, NY, USA: Springer Science and Business, 2010, p. 133. [17] N. Pohl, T. Jaeschke, and K. Aufinger, “An ultra-wideband 80 GHz FMCW radar system using a SiGe bipolar transceiver chip stabilized by a fractional-N PLL synthesizer,” IEEE Trans. Microw. Theory Tech., vol. 60, no. 3, pp. 757–765, Mar. 2012.

Christoph Baer was born in Bochum, Germany, in 1985. He received the Dipl.Ing. degree in electrical engineering from Ruhr-University Bochum, Bochum, in 2009. Since 2009, he has been a Research Assistant with the Institute of Electronic Circuits, Ruhr-University Bochum. His current fields of research concern RADAR systems, antenna design, RF-circuit design, and material characterization. He has authored and co-authored more than 30 scientific papers, and holds several patents. He is a member of VDE.

R EFERENCES [1] G. E. Klinzing, F. Rizk, R. Marcus, and L. S. Leung, Pneumatic Conveying of Solids. Berlin, Germany: Springer-Verlag, 2010, pp. 2–10. [2] J. Shao, J. Krabicka, and Y. Yan, “Velocity measurement of pneumatically conveyed particles using intrusive electrostatic sensors,” IEEE Trans. Instrum. Meas., vol. 59, no. 5, pp. 1477–1484, May 2010. [3] D. Miller, P. Baimbridge, and D. Eyre, “Technology status review of PF flow measurement and control methods for utility boilers,” Crown Copyright, Didcot, U.K., Tech. Rep. COAL R201 DTI/PUB 00/1445, 2000. [4] J. Reyes and A. Acevedo, “Simulation and experimental validation of a transit time in an ultrasonic gas flow meter using air,” in Proc. IEEE ANDESCON, Sep. 2010, pp. 1–6. [5] H. D. Conrads, “Method and device for contact-free measurement of the mass flow rate in a two-phase pneumatic transport using microwaves,” Eur EP Patent 07 176 269A2, Jan. 29, 1997. [6] J. Happel, “Method and device for measuring a mass flow,” U.S. Patent 7 102 133, Jan. 6, 2005. [7] A. Penirschke, A. Angelovski, and R. Jakoby, “Helix-shaped CRLH-mass flow detector for the cross-sectional detection of inhomogeneous distributed pneumatic conveyed pulverized solids,” in Proc. IEEE Instrum. Meas. Technol. Conf., May 2011, pp. 1–5. [8] C. Baer, M. Gerding, N. Pohl, T. Musch, and M. Vogt, “Evaluation of a double transmission measurement concept for the characterization of dielectric material compositions with microwaves,” in Proc. 9th ISEMA, 2011, pp. 15–21. [9] C. Baer, M. Vogt, and T. Musch, “Volume fraction determination in pneumatic conveying systems by means of the pseudo transmission measurement concept using a transpolarizing reflector,” in Proc. 42nd EuMC, Oct./Nov. 2012, pp. 651–654. [10] C. Baer, M. Vogt, and T. Musch, “Pseudo transmission measurement concept for the volume fraction determination of rice in a pneumatic conveying system,” in Proc. ICEAA, Sep. 2012, pp. 744–747. [11] A. H. Sihvola, Electromagnetic Mixing Formulas and Applications (IEE Electromagnetic Waves Series 47). Padstow, U.K.: IET, 1999, pp. 39–42. [12] L. Jylha and A. H. Sihvola, “Numerical modeling of disordered mixture using pseudorandom simulations,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 59–64, Jan. 2005.

Timo Jaeschke (S’07) was born in Hattingen, Germany, in 1984. He received the Dipl.Ing. degree in electrical engineering from Ruhr-University Bochum, Bochum, Germany, in 2011. Since 2011, he has been a Research Assistant with the Institute of Integrated Systems, Ruhr-University Bochum. His current fields of research are concerned with frequency synthesis, integrated ultrawideband FMCW radar systems up to 240 GHz, high-resolution radar imaging, and the highest precision distance and vibration measurements for various applications. He was a recipient of the IEEE MTT-S Graduate Fellowship Award in 2013, the Airbus Group Cassidian ARGUS Award in 2010, and was a co-recipient of the EuMIC Prize in 2012. He is a member of VDE, ITG, EuMA, and DGON.

Philipp Mertmann was born in Bochum, Germany, in 1982. He received the Dipl.Ing in electrical engineering from Ruhr-University Bochum, Bochum, in 2007. From 2007 to 2011, he was a Research Assistant with the Institute for Plasma Technology, Ruhr-University Bochum, where he received the Ph.D. degree in 2010. From 2011 to 2013, he was with the Institute of Electronic Circuits, Ruhr-University Bochum. Since 2011, he has been with the Krohne Corporate Research, Krohne Messtechnik GmbH, Duisburg, Germany. His current fields of research are numerical simulations of all kind, such as particle and electric field simulations and computational fluid dynamics.

BAER et al.: mmWAVE MEASURING PROCEDURE FOR MASS FLOW MONITORING

Nils Pohl received the Dipl.Ing. and Dr. Ing. degrees in electrical engineering from Ruhr-University Bochum, Bochum, Germany, in 2005 and 2010, respectively. From 2006 to 2011, he was a Research Assistant with the Institute of Integrated Systems, Ruhr-University Bochum, where he was involved in integrated circuits for millimeter-wave radar applications. In 2011, he became an Assistant Professor of Integrated Systems at Ruhr-University Bochum. Since 2013, he has been the Head of the Department of Millimeter-Wave Radar and High Frequency Sensors, Fraunhofer Institute FHR, Wachtberg, Germany. His main fields of research are concerned with the design and optimization of millimeter-wave integrated SiGe circuits and system concepts with frequencies up to 100 GHz and above (in particular, for wideband radar applications), frequency synthesis, and antennas. He has authored and co-authored more than 50 scientific papers, and holds several patents.

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Thomas Musch received the Dipl.Ing. and Dr. Ing. degrees in electrical engineering from Ruhr-University Bochum, Bochum, Germany, in 1994 and 1999, respectively. From 1994 to 2002, he was a Research Assistant with the Institute of High Frequency Engineering, Ruhr-University Bochum, where he was involved in system concepts and electronic components at microwave frequencies mainly in the fields of fractional-N frequency synthesis and high-precision radar. From 2003 to 2008, he was with Krohne Messtechnik GmbH, Duisburg, Germany. As Head of the Corporate Research Department, he was responsible for the research activities of the Krohne Group. In 2008, he became a Full Professor heading the Institute of Electronic Circuits at Ruhr-University Bochum. His current fields of research are concerned with fractional-N frequency synthesis, radar systems and antennas for microwave range finding, industrial applications of microwaves, and automotive electronics.