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Fluid dynamic simulation of a measurement chamber for electronic noses
Sensors and Actuators B 85 (2002) 166±174
Fluid dynamic simulation of a measurement chamber for electronic noses M. Falcitellia, A. Benassib, F. Di Francescob,*, C. Domenicib, L. Maranoc, G. Pioggiac a
Department of Chemical Engineering, University of Pisa, Via Diotisalvi 2, 56126 Pisa, Italy National Research Council, Institute of Clinical Physiology, Via G. Moruzzi 1, 56100 Pisa, Italy c Interdepartmental Research Center ``E. Piaggio'', University of Pisa, Via Diotisalvi 2, 56126 Pisa, Italy b
Received 18 December 2001; received in revised form 5 March 2002; accepted 10 March 2002
Abstract A ¯uid dynamic study of a sensor chamber used in a hit-commercial electronic nose is presented. In order to optimise the sensor signals in terms of stability, repeatability as well as amplitude and response time, the in¯uence of many factors of the sampling device has to be kept under control. Concerning the characteristics of ¯ow, the existence of a time-window where each sensor is exposed to a constant odour concentration has to be assured. This condition can be achieved by the proper dimensioning of the chamber volume and by other modi®cations to the inlet and outlet. The numerical analysis was performed by a CFD code which solves the Navier±Stokes equations for a dilatable ¯uid in 3D enclosures, discretised with ®nite volume elements. Two con®gurations were simulated: a basic case, referring to the conditions existing in the commercial device, and an optimised case. In each case, a static solution was calculated for the ¯ow ®eld and then the dynamic evolution of odour concentration was simulated by solving the transient transport equation of a tracer injected as a square pulse ¯ow. Far from optimum conditions were found for the basic case; the improvement achieved through simple modi®cations in the geometry of the chamber for the optimised case was discussed. # 2002 Published by Elsevier Science B.V. Keywords: Measurement chamber; Cell; Computational ¯uid dynamics; Electronic noses; Sensors
1. Introduction Mimicking biological systems, an electronic nose uses an array of sensors with partial overlapping sensitivities to classify and recognise odours [1]. Generally, it is composed of a sampling system, a sensor array, a data acquisition system and a pattern recognition algorithm. The role of the sampling system is to collect and convey the volatile sample to the sensors, then to restore previous conditions by means of a cleaning procedure. The interaction between sensors and odours is the ®rst fundamental step of the data acquisition process, since its execution in¯uences all successive steps. In order to obtain measurements with an optimum stability and repeatability as well as high amplitude signals and fast sensor responses, the sampling device has to be designed in such a way that all factors capable of in¯uencing sensor responses are optimised and kept under control, so that the only variable left is odour composition. Several papers have been published describing methods to improve or optimise performances of E-nose systems [2±7], *
Corresponding author. Tel.: 39-050-315-2471; fax: 39-050-315-2166. E-mail address: [email protected] (F. Di Francesco). 0925-4005/02/$ ± see front matter # 2002 Published by Elsevier Science B.V. PII: S 0 9 2 5 - 4 0 0 5 ( 0 2 ) 0 0 0 7 1 - 0
but little attention has been paid to the study of the measurement chamber which also plays an important role in the sampling process [8]. Non-adsorbent and inert materials have to be selected with care to avoid memory effects [9]. The choice of the material has remarkable effect on the chamber projecting, as unsuitable mechanical properties can hamper re®ned machining and complex design. Glass or fused silica, for example, are mainly processed as craftwork, while stainless steel is very hard to machine on a small scale by common techniques. PTFE is soft enough to be machined easily but, for this same reason, its mechanical properties make it dif®cult to craft it into small components. Moreover, it is microporous and so acts as a polymer trap, hence attention has to be paid during use to avoid contaminations and memory effects. All these materials can be treated with aggressive solvents and dried in a oven at high temperatures, even if in the case of Te¯on it is suggested not to exceed 200 8C. The volume of the chamber has to be properly dimensioned for any range of gas ¯ow rates, in order to obtain a homogeneous ¯ow with a low speed gradient with no recirculating zones or stagnant regions. It is also important that all the sensors are exposed at the same time to the same odour concentration, so that their performances will not be related to their position inside the chamber.
M. Falcitelli et al. / Sensors and Actuators B 85 (2002) 166±174
167
This computational ¯uid dynamic (CFD) study was carried out to assess the performances of a sensor chamber used in a hit-commercial instrument and to acquire information on the criteria for designing a chamber capable of achieving optimum operating conditions. The study was carried out creating a 3D model of the sensor chamber, discretising the domain with ®nite volume elements and numerically solving the Navier±Stokes equations for the transport of momentum and mass. A basic case and an optimised case were simulated, obtaining 3D ®elds of velocity and speci®c mass ¯ow. The intake of volatiles was simulated as well, calculating the evolution of the tracer concentration within the chamber of both cases. Different from the commercial instrument, which uses metal oxide sensors operating at 400 8C with high power consumption, our instrument uses conducting polymer sensors, hence room temperature (25 8C) was considered. Fig. 2. Schematic representation of one sensor.
2. Sensor chamber The sensor chamber (Fig. 1) is essentially a right parallelepiped with rounded corners and dimensions 33 54 8 mm. Inlet and outlet (internal diameter 2:5 mm) are centred in the smaller faces along the longitudinal axis; airtight conditions are obtained by using suitable seals (PTFE-OR 143 Polypac). Sensors (Fig. 2) are arranged in four rows and four columns on the inner face of the chamber bottom. They are numbered starting from the inlet and following rows and columns (Fig. 1). The longitudinal symmetry of the chamber allows simulating half chamber and extending the results to the whole system.
An effective chamber design should assure that the transient time necessary to reach a stationary and uniform volatile concentration is much shorter than the sensor response time, so that all sensors are exposed at the same time to the same concentration. This result can be achieved by a proper dimensioning of the inner volume with respect to the carrier ¯ow rate, by creating the homogeneous ¯ow conditions required to avoid recirculating zones and stagnant regions. Simulations were carried out ®rst on the basic case, which was taken from a hit-commercial instrument, to determine the ¯ow conditions and then on an optimised model to check the effects of the proposed design variations.
Fig. 1. Basic chamber geometry and sensor numbers.
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2.1. Base chamber
3. Calculation methodology
The geometric features of the basic case are shown in Fig. 1. The size of the inlet hole compared to the transversal dimensions of the chamber is the weak point of this design. The gas ¯ow entering the chamber undergoes an abrupt variation of the section, which can lead to a recirculating jet. Moreover, on both sides of the chamber there is a considerable volume not occupied by the sensors, which can be a bypass for the volatiles, causing uneven conditions for both the mass ¯ow and the concentration.
Simulations were performed by using a CFD code, named IPSE (Industrial Process Simulation Environment), which solves iteratively the differential equations describing the ¯ow on a de®nite domain of ®nite volumes [10]. The code allows also the analysis of the temporal evolution of a diffusive tracer introduced into the system at a certain time.
2.2. Optimised chamber The optimised chamber is shown in Fig. 3. The following changes were introduced: The overall volume was reduced by adding a Teflon curb to fill the lateral spaces and modify the profile of inlet and outlet to avoid dead volumes. Two diffusers were introduced at both the inlet and the outlet crosswise to the flow to break up the jet, to increase the uniformity of the flow over the sensors and to minimise the width of possible recirculating zones. They were placed 3 mm from the first and the last row of sensors. The diffusers were two stainless steel grids (8 16 mm, 40 holes arranged in five rows and eight columns, F 1 mm).
3.1. Governing equations of fluid flow The equations implemented into the code and used to describe the system in a Cartesian coordinate system are the following [11]: Mass conservation: @r @ ruj 0 @t @xj
(1)
Momentum equation: @ @ rui ruj ui @t @xj
tij
@p si @xi
(2)
where t is the time, xj the Cartesian coordinate j 1; 2; 3, r the density, uj the component of ¯uid velocity in the xj direction, tij the component of stress tensor, p the pressure
Fig. 3. Optimised chamber geometry and grid.
M. Falcitelli et al. / Sensors and Actuators B 85 (2002) 166±174
and si the component of the source of momentum in the xi direction. In the case of laminar ¯ow (see Section 4.2), the stress tensor is related to the viscosity and to the gradient of velocity by the following relationship: @ui @uj 2 @uk m tij m dij (3) 3 @xk @xj @xi where m is the dynamic molecular viscosity of the ¯uid and dij the Kroenecker delta. Having determined the stationary ¯ow ®eld, the following equation was used to simulate the temporal evolution of a diffusive tracer: @ @ @ @f rf rfuj rD (4) Sf @t @xj @xj @xj where f is the scalar ®eld of the tracer mass fraction, D the tracer diffusivity and Sf the source term of f. 3.2. Discretisation scheme and numerical methods Equations describing the problem are general conservation laws, which apply to any ®nite volume of the domain. The discretisation scheme is the strategy adopted to turn these equations into a system of algebraic equations, which can be solved numerically in an iterative way. The hybrid
169
scheme [12], which is a combination of the central and upwind schemes, was adopted in the present work. The central scheme, a second order approximation, is used when the Peclet number is smaller than 2, the upwind scheme, a ®rst order approximation, is used when the Peclet number is equal or greater than 2. The time discretisation is formulated as explicit for all the transport equations. The solution scheme is transient SIMPLE like [13], with the difference that the use of a direct matrix inversion algorithm yields at each time step the exact solution of the pressure equation. 4. CFD simulation The longitudinal symmetry allowed the modelling of half volume of the chamber and the extension of results to the remainder, to optimise the use of computational resources. Four simulations were carried out, two for each case (basic and modi®ed chamber), calculating ®rst the stationary ¯ow ®eld, then the temporal evolution of the concentration ®eld of a tracer, simulating the diffusion of the volatiles. 4.1. Computational domain A Cartesian coordinate system (x, y, z) was adopted with the Z axis placed along the ¯ow direction and the XY plane
Fig. 4. Velocity field, X±Z section (Y 2:76 mm): (a) base case; (b) optimised case.
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coinciding with the sensor plane (Figs. 1 and 3). The computational domain was divided into 49,896 hexahedral cells for the basic case and 54,288 cells for the modi®ed case. Inlet and outlet zones, the most critical ones, were more ®nely discretised (40 cells were used to represent the semicircumference of the inlet and outlet holes) to increase the accuracy of the velocity pro®les. 4.2. Fluid properties and initial conditions The simulation ¯uid is dry air consisting of 77% (w/w) of nitrogen and 23% (w/w) of oxygen. It has the following physical properties: temperature, T 300:15 K; density, r 1:2 kg/m3; dynamic molecular viscosity, m 1:8 10
5
kg/(m s).
The ¯uid enters the chamber through a hole 2.5 mm internal diameter with an average velocity hvz i 0:2 m/s (¯ow rate, 59 ml/min), so that the value of the Reynolds number Re Dhvz ir=m is 33. In these conditions, the ¯ow is laminar, so that it can be assumed that velocity vx and vy
are null and vz has a parabolic pro®le across the hole section: r 2 vz r 2hvz i 1 (5) R It may be interesting to note that these are typical conditions for electronic noses [8,14] so, in general, it can be said that they are usually operated with laminar ¯ows. The IPSE code requires, for each single boundary source cell, the incoming mass ¯ow rate (kg/s) and the ¯ow rate of the momentum components (kg m/s2) to be speci®ed. The corresponding values were determined by the following relationships: Mass flow rate entering the cell: Z r vz da
(6)
Momentum flow rate entering the cell: Z r v2z da
(7)
A
A
where the integral is carried out on the surface A of the cell side.
Fig. 5. Z component of specific mass flow, X±Z section (Y 2:76 mm): (a) base case; (b) optimised case.
M. Falcitelli et al. / Sensors and Actuators B 85 (2002) 166±174
4.3. Boundary conditions As laminar ¯ow conditions are present, null ¯uid velocity was attributed to the boundary nodes de®ning the chamber surfaces. The boundary conditions used along the plane of symmetry X 0 are: absence of any ¯ow through the plane, null normal velocity, values of any other ¯ow parameter made equal to the value assumed in the nearest cell inside the domain. 4.4. Temporal evolution of the odour concentration The simulation of the odour diffusion within the chamber was carried out by solving the transport equations for the tracer mass fraction after obtaining the stationary solution for the ¯ow ®eld. The tracer was introduced as a square wave signal of unit concentration and 15 s duration. A constant time step of 0:68 10 5 s was used to integrate the transport equation. The simulation reproduces the evolution of the concentration ®eld over a 30 s period. The value D 1:5 10 5 m2/s was adopted for the odour diffusivity, which corresponds to a Schmidt number with unit value. 5. Results Results of 3D simulations are represented by two-dimensional plots of characteristic sections of the model and by
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graphs displaying the odour concentration versus time in the cells placed just above the sensors. The velocity ®eld is represented in Fig. 4 by arrows. It must be noted that a recirculation region exists in the basic case around the jet and that the ¯uid ¯ows faster along the borders of the chamber (Fig. 4a). On the contrary, in the modi®ed chamber the diffuser breaks up the incoming jet so that a uniform velocity ®eld is obtained throughout the chamber volume (Fig. 4b). Plots in Figs. 5 and 6 show the distribution of the ¯ow within the chambers by visualising the Z component of the speci®c mass ¯ow (kg/s m2). In the basic case (Figs. 5a and 6a), the ¯ow is clearly uneven. Near the inlet there is a wide stagnation zone (black colour, indicating negative ¯ow values due to recirculation), while moving through the chamber the ¯uid ¯ows mainly along the borders bypassing the sensors. The modi®cations produced remarkable improvements (Figs. 5b and 6b). Downstream from the diffuser the ¯ow was uniform over the sensors without signi®cant variations in the speci®c mass ¯ow. The results of the simulation of the odour transport through the chamber are shown in Figs. 7 and 8. The tracer was introduced as a square wave signal of unit concentration (mass fraction), constant mass ¯ow rate and 15 s duration. Mass ¯ow values and tracer concentrations plotted in the graphs refer to the averages calculated in the ®rst and last row of cells crossed by the tracer for inlet and outlet and to the averages calculated in the cells right above the sensors for the sensors. In the basic case, Fig. 7a and b shows the
Fig. 6. Z component of specific mass flow, X±Y section (Z 12 mm): (a) base case; (b) optimised case.
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Fig. 7. Basic case: (a) mass flow rate of the tracer through control areas normal to the sensor surfaces; (b) tracer concentration on the sensor surfaces. The recirculation near the inlet withdraws air from the sides of the jet, lowering the tracer concentration. As a consequence, the concentration calculated in the first node in front of the inlet (INLET) is lower than the concentration on the first row of sensors.
effect of the recirculation zone near the inlet. The mass ¯ow signal quickly looses the square wave shape, a considerable fraction of the tracer is still inside the chamber at the end of the simulation (30 s), the concentration value above the sensors never reaches the maximum and assumes values notably different depending on the sensor position. Fig. 8a and b shows the completely different situation of the optimised case. The mass ¯ow signal is quite similar to a square wave at the inlet, at the outlet and above all the sensors. Furthermore, mass fraction values above the sensors soon reach unit (the maximum), with a constant pro®le slightly shifted in time. 6. Conclusions This ¯uid dynamic study of a sensor chamber was carried out to assess performances of a hit-commercial model and to
®nd modi®cations that could improve operating conditions. The study was performed by creating a 3D model of the chamber, discretising the computational domain in ®nite volumes and numerically solving the transport equations of both momentum and mass (Navier±Stokes equations). Simulations were performed for both a basic con®guration and an optimised con®guration, obtaining the 3D ®eld of velocity and speci®c mass ¯ow. The introduction of volatiles was simulated for both con®gurations, calculating the temporal evolution of the concentration ®eld of a tracer. The base chamber does not show a good performance with respect to ¯uid dynamics. A wide stagnation zone exists around the incoming jet, while two channels along the borders collect most of the volatiles, so that a considerable fraction of the sample goes through the chamber without signi®cant interaction with the sensors. Moreover, there is a considerable difference in the concentration values between
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Fig. 8. Optimised case: (a) mass flow rate of the tracer through control areas normal to the sensor surfaces; (b) tracer concentration on the sensor surfaces.
the ®rst and the last row of sensors, thus making a sensor response dependent on sensor position inside the chamber. Some modi®cations of this con®guration (reduction of the volume and adoption of two diffusing grids) were proposed. The numerical simulation showed that these modi®cations produce a clear improvement in terms of both homogeneity of ¯ow rate and maximisation of the odour concentration. In the modi®ed case, differences due to the sensor position are minimised. Although these conclusions were drawn in a case study of a tracer with a Schmidt number with unit value, they can be extended to most volatiles. What happens if volatiles are more than one and one of them is humidity? The phenomena considered in this paper have a physical nature and any interaction among different volatiles can be neglected with a very good approximation. This means that humidity behaves more or less like the tracer without a signi®cant in¯uence on its concentration. This is valid only when chemical reactions are absent. These
reactions were not taken into account, but they could be studied in a similar way by adding appropriate modules to the CFD code. Absorption of volatiles on the chamber surfaces was neglected too, as well as the interaction of volatiles with the sensitive layer of sensors. Our hypothesis is that the occurrence of chemical reactions among the sample components and with humidity is limited to particular cases, since we expect the sample to be stable during the measurement, and that absorption does not affect volatile concentration signi®cantly. These aspects may be dealt with in future development together with the extension of simulation to the overall E-nose system design. Acknowledgements The authors wish to thank ENEL S.P.A. Produzione Ricerca for granting the use of its CFD code and Laura Barsanti for carefully revising the manuscript. The study
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was ®nancially supported by the ESPRIT Project No. EP25254 ``INTESA'' and CNR Project MADESS II ``Materials and devices for solid state electronics''. References [1] J.W. Gardner, P.N. Bartlett, A brief history of electronic noses, Sens. Actuators B 18±19 (1994) 211±220. [2] P. Mielle, F. Marquis, Gas sensor arrays (`electronic noses'): a study about the speed/accuracy ratio, Sens. Actuators B 68 (2000) 9±16. [3] T. Maekawa, K. Cai, Suzuki, N. Dougami, T. Takada, M. Egashira, Compensatory methods for the odor concentration in an electronic nose system using software and hardware, Sens. Actuators B 76 (2001) 430±435. [4] S. Roussel, G. Forsberg, P. Grenier, V. Bellon-Maurel, Optimisation of electronic nose measurements. Part I: Methodology of output feature selection, J. Food Eng. 37 (1998) 207±222. [5] S. Roussel, G. Forsberg, P. Grenier, V. Bellon-Maurel, Optimisation of electronic nose measurements. Part II: Influence of experimental parameters, J. Food Eng. 39 (1999) 9±15. [6] W. Muenchmeyer, A. Walte, G. Matz, Improving electronic noses using a trap and a thermal desorption unit, Sens. Actuators B 69 (2000) 379±383. [7] P. Mielle, F. Marquis, An alternative way to improve the sensitivity of electronic olfactometers, Sens. Actuators B 58 (1999) 526±535. [8] A.M. Lezzi, G.P. Beretta, E. Comini, G. Faglia, G. Galli, G. Sberveglieri, Influence of gaseous species transport on the response of solid state gas sensors within enclosures, Sens. Actuators B 78 (2001) 144±150. [9] Olfactometry: odour threshold determination, Sampling, VDI 3881, Part 2. [10] D. Benedetto, M. Falcitelli, S. Pasini, C. La Marca, L. Tognotti, Predicting pollutant emissions from combustion systems by a novel integrated methodology, Prog. Comput. Fluid Dyn. 1 (1±3) (2001) 50±61. [11] R.B. Bird, W.E. Stewart, E.N. Lighfoot, Transport Phenomena, Wiley, New York, 1960. [12] D.B. Spalding, A novel finite-difference formulation for differential expressions involving both first and second derivatives, Int. J. Numer. Meth. Eng. 4 (1972) 551. [13] H.K. Versteg, W. Malalasekera, An Introduction to Computational Fluid Dynamics, The Finite Volume Method, Wiley, New York, 1995. [14] B. Kondratowicz, R. Narayanaswamy, K.C. Persaud, An investigation into the use of electrochromic polymers in optical fibre gas sensors, Sens. Actuators B 74 (2001) 138±144.
Biographies M. Falcitelli graduated from University of Pisa in 1997, in physics, with a thesis on crystal lasers. Since 1997, he has been working at ENEL
Research Centre on the development of numerical models and CAE software for Process Engineering. At present, he has a research grant at the Department of Chemical Engineering, University of Pisa. His research activity includes numerical modelling of combustion systems and advanced combustion technologies for the reduction of pollutants emissions. He works also as consultant, with a special interest in computational fluid dynamics. A. Benassi received the Doctoral degree (Laurea) in Informatics and then the PhD from the University of Pisa, Italy. Since 1969 he is researcher at the Institute of Clinical Physiology, Pisa, Italy, where he is at present responsible of the Electronic Bioengineering and Clinic Engineering Department. F. Di Francesco graduated as a physicist in 1994 from University of Pisa. He worked for 2 years to the development of mercury pollution detection methods at the Institute of Biophysics (National Research Council, CNR), then he joined Interdepartmental Research Center ``E. Piaggio'', where he started working on electronic noses with two distinct application areas, evaluation of olfactory annoyance of industrial emissions and detection of olive oils defects. At present, he is a research scientist at the Institute of Clinical Physiology (CNR), where within the MAST group, he is responsible for the development and use of electronic noses. C. Domenici received the Doctoral degree (Laurea) in physics from the University of Pisa, Italy, in 1977. In 1980 he received the D.E.A. degree from the University of Grenoble, France. He also holds an Italian Research Doctorate degree (PhD) in material engineering, awarded in 1987. From 1977 to 1979 he worked at the Institute of Physics, University of Pisa. From 1979 to 1981 he was a Research Fellow at L.E.T.I.-C.E.A., Grenoble, France. Since 1981 he has been involved in the research activities of Centro ``E. Piaggio'', University of Pisa, and at the Institute of Clinical Physiology, Pisa, Italy, where he is currently a staff member of the Italian National Research Council. His research interest and scientific activity include electrical and mechanical properties of polymeric materials and their interaction with biological systems. He is also engaged in research on sensors and transducers in the biomedical and environmental fields. L. Marano graduated from University of Pisa in 2001 in mechanical engineering with a thesis on the optimisation of electronic nose components. He continued working at the Interdepartmental Research Center ``E. Piaggio'' on sensor production processes. He then joined Pierburg S.P.A., where he is currently employed at the R&D division. His main field of research is concerned with the simulation and design of variable displacement oil pumps used in internal combustion engines. He is also involved in the testing of prototypes and in the analysis of relevant data. G. Pioggia graduated in electronic engineering from University of Pisa in 1997 with a thesis focused on the development of an electronic nose. He continued working on the same subject at the Interdepartmental Research Center ``E. Piaggio'' for his PhD activity. Involved in European research and development projects, at present he is working at the MAST group of the Institute of Clinical Physiology.
Fluid dynamic simulation of a measurement chamber for electronic noses M. Falcitellia, A. Benassib, F. Di Francescob,*, C. Domenicib, L. Maranoc, G. Pioggiac a
Department of Chemical Engineering, University of Pisa, Via Diotisalvi 2, 56126 Pisa, Italy National Research Council, Institute of Clinical Physiology, Via G. Moruzzi 1, 56100 Pisa, Italy c Interdepartmental Research Center ``E. Piaggio'', University of Pisa, Via Diotisalvi 2, 56126 Pisa, Italy b
Received 18 December 2001; received in revised form 5 March 2002; accepted 10 March 2002
Abstract A ¯uid dynamic study of a sensor chamber used in a hit-commercial electronic nose is presented. In order to optimise the sensor signals in terms of stability, repeatability as well as amplitude and response time, the in¯uence of many factors of the sampling device has to be kept under control. Concerning the characteristics of ¯ow, the existence of a time-window where each sensor is exposed to a constant odour concentration has to be assured. This condition can be achieved by the proper dimensioning of the chamber volume and by other modi®cations to the inlet and outlet. The numerical analysis was performed by a CFD code which solves the Navier±Stokes equations for a dilatable ¯uid in 3D enclosures, discretised with ®nite volume elements. Two con®gurations were simulated: a basic case, referring to the conditions existing in the commercial device, and an optimised case. In each case, a static solution was calculated for the ¯ow ®eld and then the dynamic evolution of odour concentration was simulated by solving the transient transport equation of a tracer injected as a square pulse ¯ow. Far from optimum conditions were found for the basic case; the improvement achieved through simple modi®cations in the geometry of the chamber for the optimised case was discussed. # 2002 Published by Elsevier Science B.V. Keywords: Measurement chamber; Cell; Computational ¯uid dynamics; Electronic noses; Sensors
1. Introduction Mimicking biological systems, an electronic nose uses an array of sensors with partial overlapping sensitivities to classify and recognise odours [1]. Generally, it is composed of a sampling system, a sensor array, a data acquisition system and a pattern recognition algorithm. The role of the sampling system is to collect and convey the volatile sample to the sensors, then to restore previous conditions by means of a cleaning procedure. The interaction between sensors and odours is the ®rst fundamental step of the data acquisition process, since its execution in¯uences all successive steps. In order to obtain measurements with an optimum stability and repeatability as well as high amplitude signals and fast sensor responses, the sampling device has to be designed in such a way that all factors capable of in¯uencing sensor responses are optimised and kept under control, so that the only variable left is odour composition. Several papers have been published describing methods to improve or optimise performances of E-nose systems [2±7], *
Corresponding author. Tel.: 39-050-315-2471; fax: 39-050-315-2166. E-mail address: [email protected] (F. Di Francesco). 0925-4005/02/$ ± see front matter # 2002 Published by Elsevier Science B.V. PII: S 0 9 2 5 - 4 0 0 5 ( 0 2 ) 0 0 0 7 1 - 0
but little attention has been paid to the study of the measurement chamber which also plays an important role in the sampling process [8]. Non-adsorbent and inert materials have to be selected with care to avoid memory effects [9]. The choice of the material has remarkable effect on the chamber projecting, as unsuitable mechanical properties can hamper re®ned machining and complex design. Glass or fused silica, for example, are mainly processed as craftwork, while stainless steel is very hard to machine on a small scale by common techniques. PTFE is soft enough to be machined easily but, for this same reason, its mechanical properties make it dif®cult to craft it into small components. Moreover, it is microporous and so acts as a polymer trap, hence attention has to be paid during use to avoid contaminations and memory effects. All these materials can be treated with aggressive solvents and dried in a oven at high temperatures, even if in the case of Te¯on it is suggested not to exceed 200 8C. The volume of the chamber has to be properly dimensioned for any range of gas ¯ow rates, in order to obtain a homogeneous ¯ow with a low speed gradient with no recirculating zones or stagnant regions. It is also important that all the sensors are exposed at the same time to the same odour concentration, so that their performances will not be related to their position inside the chamber.
M. Falcitelli et al. / Sensors and Actuators B 85 (2002) 166±174
167
This computational ¯uid dynamic (CFD) study was carried out to assess the performances of a sensor chamber used in a hit-commercial instrument and to acquire information on the criteria for designing a chamber capable of achieving optimum operating conditions. The study was carried out creating a 3D model of the sensor chamber, discretising the domain with ®nite volume elements and numerically solving the Navier±Stokes equations for the transport of momentum and mass. A basic case and an optimised case were simulated, obtaining 3D ®elds of velocity and speci®c mass ¯ow. The intake of volatiles was simulated as well, calculating the evolution of the tracer concentration within the chamber of both cases. Different from the commercial instrument, which uses metal oxide sensors operating at 400 8C with high power consumption, our instrument uses conducting polymer sensors, hence room temperature (25 8C) was considered. Fig. 2. Schematic representation of one sensor.
2. Sensor chamber The sensor chamber (Fig. 1) is essentially a right parallelepiped with rounded corners and dimensions 33 54 8 mm. Inlet and outlet (internal diameter 2:5 mm) are centred in the smaller faces along the longitudinal axis; airtight conditions are obtained by using suitable seals (PTFE-OR 143 Polypac). Sensors (Fig. 2) are arranged in four rows and four columns on the inner face of the chamber bottom. They are numbered starting from the inlet and following rows and columns (Fig. 1). The longitudinal symmetry of the chamber allows simulating half chamber and extending the results to the whole system.
An effective chamber design should assure that the transient time necessary to reach a stationary and uniform volatile concentration is much shorter than the sensor response time, so that all sensors are exposed at the same time to the same concentration. This result can be achieved by a proper dimensioning of the inner volume with respect to the carrier ¯ow rate, by creating the homogeneous ¯ow conditions required to avoid recirculating zones and stagnant regions. Simulations were carried out ®rst on the basic case, which was taken from a hit-commercial instrument, to determine the ¯ow conditions and then on an optimised model to check the effects of the proposed design variations.
Fig. 1. Basic chamber geometry and sensor numbers.
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2.1. Base chamber
3. Calculation methodology
The geometric features of the basic case are shown in Fig. 1. The size of the inlet hole compared to the transversal dimensions of the chamber is the weak point of this design. The gas ¯ow entering the chamber undergoes an abrupt variation of the section, which can lead to a recirculating jet. Moreover, on both sides of the chamber there is a considerable volume not occupied by the sensors, which can be a bypass for the volatiles, causing uneven conditions for both the mass ¯ow and the concentration.
Simulations were performed by using a CFD code, named IPSE (Industrial Process Simulation Environment), which solves iteratively the differential equations describing the ¯ow on a de®nite domain of ®nite volumes [10]. The code allows also the analysis of the temporal evolution of a diffusive tracer introduced into the system at a certain time.
2.2. Optimised chamber The optimised chamber is shown in Fig. 3. The following changes were introduced: The overall volume was reduced by adding a Teflon curb to fill the lateral spaces and modify the profile of inlet and outlet to avoid dead volumes. Two diffusers were introduced at both the inlet and the outlet crosswise to the flow to break up the jet, to increase the uniformity of the flow over the sensors and to minimise the width of possible recirculating zones. They were placed 3 mm from the first and the last row of sensors. The diffusers were two stainless steel grids (8 16 mm, 40 holes arranged in five rows and eight columns, F 1 mm).
3.1. Governing equations of fluid flow The equations implemented into the code and used to describe the system in a Cartesian coordinate system are the following [11]: Mass conservation: @r @ ruj 0 @t @xj
(1)
Momentum equation: @ @ rui ruj ui @t @xj
tij
@p si @xi
(2)
where t is the time, xj the Cartesian coordinate j 1; 2; 3, r the density, uj the component of ¯uid velocity in the xj direction, tij the component of stress tensor, p the pressure
Fig. 3. Optimised chamber geometry and grid.
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and si the component of the source of momentum in the xi direction. In the case of laminar ¯ow (see Section 4.2), the stress tensor is related to the viscosity and to the gradient of velocity by the following relationship: @ui @uj 2 @uk m tij m dij (3) 3 @xk @xj @xi where m is the dynamic molecular viscosity of the ¯uid and dij the Kroenecker delta. Having determined the stationary ¯ow ®eld, the following equation was used to simulate the temporal evolution of a diffusive tracer: @ @ @ @f rf rfuj rD (4) Sf @t @xj @xj @xj where f is the scalar ®eld of the tracer mass fraction, D the tracer diffusivity and Sf the source term of f. 3.2. Discretisation scheme and numerical methods Equations describing the problem are general conservation laws, which apply to any ®nite volume of the domain. The discretisation scheme is the strategy adopted to turn these equations into a system of algebraic equations, which can be solved numerically in an iterative way. The hybrid
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scheme [12], which is a combination of the central and upwind schemes, was adopted in the present work. The central scheme, a second order approximation, is used when the Peclet number is smaller than 2, the upwind scheme, a ®rst order approximation, is used when the Peclet number is equal or greater than 2. The time discretisation is formulated as explicit for all the transport equations. The solution scheme is transient SIMPLE like [13], with the difference that the use of a direct matrix inversion algorithm yields at each time step the exact solution of the pressure equation. 4. CFD simulation The longitudinal symmetry allowed the modelling of half volume of the chamber and the extension of results to the remainder, to optimise the use of computational resources. Four simulations were carried out, two for each case (basic and modi®ed chamber), calculating ®rst the stationary ¯ow ®eld, then the temporal evolution of the concentration ®eld of a tracer, simulating the diffusion of the volatiles. 4.1. Computational domain A Cartesian coordinate system (x, y, z) was adopted with the Z axis placed along the ¯ow direction and the XY plane
Fig. 4. Velocity field, X±Z section (Y 2:76 mm): (a) base case; (b) optimised case.
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coinciding with the sensor plane (Figs. 1 and 3). The computational domain was divided into 49,896 hexahedral cells for the basic case and 54,288 cells for the modi®ed case. Inlet and outlet zones, the most critical ones, were more ®nely discretised (40 cells were used to represent the semicircumference of the inlet and outlet holes) to increase the accuracy of the velocity pro®les. 4.2. Fluid properties and initial conditions The simulation ¯uid is dry air consisting of 77% (w/w) of nitrogen and 23% (w/w) of oxygen. It has the following physical properties: temperature, T 300:15 K; density, r 1:2 kg/m3; dynamic molecular viscosity, m 1:8 10
5
kg/(m s).
The ¯uid enters the chamber through a hole 2.5 mm internal diameter with an average velocity hvz i 0:2 m/s (¯ow rate, 59 ml/min), so that the value of the Reynolds number Re Dhvz ir=m is 33. In these conditions, the ¯ow is laminar, so that it can be assumed that velocity vx and vy
are null and vz has a parabolic pro®le across the hole section: r 2 vz r 2hvz i 1 (5) R It may be interesting to note that these are typical conditions for electronic noses [8,14] so, in general, it can be said that they are usually operated with laminar ¯ows. The IPSE code requires, for each single boundary source cell, the incoming mass ¯ow rate (kg/s) and the ¯ow rate of the momentum components (kg m/s2) to be speci®ed. The corresponding values were determined by the following relationships: Mass flow rate entering the cell: Z r vz da
(6)
Momentum flow rate entering the cell: Z r v2z da
(7)
A
A
where the integral is carried out on the surface A of the cell side.
Fig. 5. Z component of specific mass flow, X±Z section (Y 2:76 mm): (a) base case; (b) optimised case.
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4.3. Boundary conditions As laminar ¯ow conditions are present, null ¯uid velocity was attributed to the boundary nodes de®ning the chamber surfaces. The boundary conditions used along the plane of symmetry X 0 are: absence of any ¯ow through the plane, null normal velocity, values of any other ¯ow parameter made equal to the value assumed in the nearest cell inside the domain. 4.4. Temporal evolution of the odour concentration The simulation of the odour diffusion within the chamber was carried out by solving the transport equations for the tracer mass fraction after obtaining the stationary solution for the ¯ow ®eld. The tracer was introduced as a square wave signal of unit concentration and 15 s duration. A constant time step of 0:68 10 5 s was used to integrate the transport equation. The simulation reproduces the evolution of the concentration ®eld over a 30 s period. The value D 1:5 10 5 m2/s was adopted for the odour diffusivity, which corresponds to a Schmidt number with unit value. 5. Results Results of 3D simulations are represented by two-dimensional plots of characteristic sections of the model and by
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graphs displaying the odour concentration versus time in the cells placed just above the sensors. The velocity ®eld is represented in Fig. 4 by arrows. It must be noted that a recirculation region exists in the basic case around the jet and that the ¯uid ¯ows faster along the borders of the chamber (Fig. 4a). On the contrary, in the modi®ed chamber the diffuser breaks up the incoming jet so that a uniform velocity ®eld is obtained throughout the chamber volume (Fig. 4b). Plots in Figs. 5 and 6 show the distribution of the ¯ow within the chambers by visualising the Z component of the speci®c mass ¯ow (kg/s m2). In the basic case (Figs. 5a and 6a), the ¯ow is clearly uneven. Near the inlet there is a wide stagnation zone (black colour, indicating negative ¯ow values due to recirculation), while moving through the chamber the ¯uid ¯ows mainly along the borders bypassing the sensors. The modi®cations produced remarkable improvements (Figs. 5b and 6b). Downstream from the diffuser the ¯ow was uniform over the sensors without signi®cant variations in the speci®c mass ¯ow. The results of the simulation of the odour transport through the chamber are shown in Figs. 7 and 8. The tracer was introduced as a square wave signal of unit concentration (mass fraction), constant mass ¯ow rate and 15 s duration. Mass ¯ow values and tracer concentrations plotted in the graphs refer to the averages calculated in the ®rst and last row of cells crossed by the tracer for inlet and outlet and to the averages calculated in the cells right above the sensors for the sensors. In the basic case, Fig. 7a and b shows the
Fig. 6. Z component of specific mass flow, X±Y section (Z 12 mm): (a) base case; (b) optimised case.
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Fig. 7. Basic case: (a) mass flow rate of the tracer through control areas normal to the sensor surfaces; (b) tracer concentration on the sensor surfaces. The recirculation near the inlet withdraws air from the sides of the jet, lowering the tracer concentration. As a consequence, the concentration calculated in the first node in front of the inlet (INLET) is lower than the concentration on the first row of sensors.
effect of the recirculation zone near the inlet. The mass ¯ow signal quickly looses the square wave shape, a considerable fraction of the tracer is still inside the chamber at the end of the simulation (30 s), the concentration value above the sensors never reaches the maximum and assumes values notably different depending on the sensor position. Fig. 8a and b shows the completely different situation of the optimised case. The mass ¯ow signal is quite similar to a square wave at the inlet, at the outlet and above all the sensors. Furthermore, mass fraction values above the sensors soon reach unit (the maximum), with a constant pro®le slightly shifted in time. 6. Conclusions This ¯uid dynamic study of a sensor chamber was carried out to assess performances of a hit-commercial model and to
®nd modi®cations that could improve operating conditions. The study was performed by creating a 3D model of the chamber, discretising the computational domain in ®nite volumes and numerically solving the transport equations of both momentum and mass (Navier±Stokes equations). Simulations were performed for both a basic con®guration and an optimised con®guration, obtaining the 3D ®eld of velocity and speci®c mass ¯ow. The introduction of volatiles was simulated for both con®gurations, calculating the temporal evolution of the concentration ®eld of a tracer. The base chamber does not show a good performance with respect to ¯uid dynamics. A wide stagnation zone exists around the incoming jet, while two channels along the borders collect most of the volatiles, so that a considerable fraction of the sample goes through the chamber without signi®cant interaction with the sensors. Moreover, there is a considerable difference in the concentration values between
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Fig. 8. Optimised case: (a) mass flow rate of the tracer through control areas normal to the sensor surfaces; (b) tracer concentration on the sensor surfaces.
the ®rst and the last row of sensors, thus making a sensor response dependent on sensor position inside the chamber. Some modi®cations of this con®guration (reduction of the volume and adoption of two diffusing grids) were proposed. The numerical simulation showed that these modi®cations produce a clear improvement in terms of both homogeneity of ¯ow rate and maximisation of the odour concentration. In the modi®ed case, differences due to the sensor position are minimised. Although these conclusions were drawn in a case study of a tracer with a Schmidt number with unit value, they can be extended to most volatiles. What happens if volatiles are more than one and one of them is humidity? The phenomena considered in this paper have a physical nature and any interaction among different volatiles can be neglected with a very good approximation. This means that humidity behaves more or less like the tracer without a signi®cant in¯uence on its concentration. This is valid only when chemical reactions are absent. These
reactions were not taken into account, but they could be studied in a similar way by adding appropriate modules to the CFD code. Absorption of volatiles on the chamber surfaces was neglected too, as well as the interaction of volatiles with the sensitive layer of sensors. Our hypothesis is that the occurrence of chemical reactions among the sample components and with humidity is limited to particular cases, since we expect the sample to be stable during the measurement, and that absorption does not affect volatile concentration signi®cantly. These aspects may be dealt with in future development together with the extension of simulation to the overall E-nose system design. Acknowledgements The authors wish to thank ENEL S.P.A. Produzione Ricerca for granting the use of its CFD code and Laura Barsanti for carefully revising the manuscript. The study
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was ®nancially supported by the ESPRIT Project No. EP25254 ``INTESA'' and CNR Project MADESS II ``Materials and devices for solid state electronics''. References [1] J.W. Gardner, P.N. Bartlett, A brief history of electronic noses, Sens. Actuators B 18±19 (1994) 211±220. [2] P. Mielle, F. Marquis, Gas sensor arrays (`electronic noses'): a study about the speed/accuracy ratio, Sens. Actuators B 68 (2000) 9±16. [3] T. Maekawa, K. Cai, Suzuki, N. Dougami, T. Takada, M. Egashira, Compensatory methods for the odor concentration in an electronic nose system using software and hardware, Sens. Actuators B 76 (2001) 430±435. [4] S. Roussel, G. Forsberg, P. Grenier, V. Bellon-Maurel, Optimisation of electronic nose measurements. Part I: Methodology of output feature selection, J. Food Eng. 37 (1998) 207±222. [5] S. Roussel, G. Forsberg, P. Grenier, V. Bellon-Maurel, Optimisation of electronic nose measurements. Part II: Influence of experimental parameters, J. Food Eng. 39 (1999) 9±15. [6] W. Muenchmeyer, A. Walte, G. Matz, Improving electronic noses using a trap and a thermal desorption unit, Sens. Actuators B 69 (2000) 379±383. [7] P. Mielle, F. Marquis, An alternative way to improve the sensitivity of electronic olfactometers, Sens. Actuators B 58 (1999) 526±535. [8] A.M. Lezzi, G.P. Beretta, E. Comini, G. Faglia, G. Galli, G. Sberveglieri, Influence of gaseous species transport on the response of solid state gas sensors within enclosures, Sens. Actuators B 78 (2001) 144±150. [9] Olfactometry: odour threshold determination, Sampling, VDI 3881, Part 2. [10] D. Benedetto, M. Falcitelli, S. Pasini, C. La Marca, L. Tognotti, Predicting pollutant emissions from combustion systems by a novel integrated methodology, Prog. Comput. Fluid Dyn. 1 (1±3) (2001) 50±61. [11] R.B. Bird, W.E. Stewart, E.N. Lighfoot, Transport Phenomena, Wiley, New York, 1960. [12] D.B. Spalding, A novel finite-difference formulation for differential expressions involving both first and second derivatives, Int. J. Numer. Meth. Eng. 4 (1972) 551. [13] H.K. Versteg, W. Malalasekera, An Introduction to Computational Fluid Dynamics, The Finite Volume Method, Wiley, New York, 1995. [14] B. Kondratowicz, R. Narayanaswamy, K.C. Persaud, An investigation into the use of electrochromic polymers in optical fibre gas sensors, Sens. Actuators B 74 (2001) 138±144.
Biographies M. Falcitelli graduated from University of Pisa in 1997, in physics, with a thesis on crystal lasers. Since 1997, he has been working at ENEL
Research Centre on the development of numerical models and CAE software for Process Engineering. At present, he has a research grant at the Department of Chemical Engineering, University of Pisa. His research activity includes numerical modelling of combustion systems and advanced combustion technologies for the reduction of pollutants emissions. He works also as consultant, with a special interest in computational fluid dynamics. A. Benassi received the Doctoral degree (Laurea) in Informatics and then the PhD from the University of Pisa, Italy. Since 1969 he is researcher at the Institute of Clinical Physiology, Pisa, Italy, where he is at present responsible of the Electronic Bioengineering and Clinic Engineering Department. F. Di Francesco graduated as a physicist in 1994 from University of Pisa. He worked for 2 years to the development of mercury pollution detection methods at the Institute of Biophysics (National Research Council, CNR), then he joined Interdepartmental Research Center ``E. Piaggio'', where he started working on electronic noses with two distinct application areas, evaluation of olfactory annoyance of industrial emissions and detection of olive oils defects. At present, he is a research scientist at the Institute of Clinical Physiology (CNR), where within the MAST group, he is responsible for the development and use of electronic noses. C. Domenici received the Doctoral degree (Laurea) in physics from the University of Pisa, Italy, in 1977. In 1980 he received the D.E.A. degree from the University of Grenoble, France. He also holds an Italian Research Doctorate degree (PhD) in material engineering, awarded in 1987. From 1977 to 1979 he worked at the Institute of Physics, University of Pisa. From 1979 to 1981 he was a Research Fellow at L.E.T.I.-C.E.A., Grenoble, France. Since 1981 he has been involved in the research activities of Centro ``E. Piaggio'', University of Pisa, and at the Institute of Clinical Physiology, Pisa, Italy, where he is currently a staff member of the Italian National Research Council. His research interest and scientific activity include electrical and mechanical properties of polymeric materials and their interaction with biological systems. He is also engaged in research on sensors and transducers in the biomedical and environmental fields. L. Marano graduated from University of Pisa in 2001 in mechanical engineering with a thesis on the optimisation of electronic nose components. He continued working at the Interdepartmental Research Center ``E. Piaggio'' on sensor production processes. He then joined Pierburg S.P.A., where he is currently employed at the R&D division. His main field of research is concerned with the simulation and design of variable displacement oil pumps used in internal combustion engines. He is also involved in the testing of prototypes and in the analysis of relevant data. G. Pioggia graduated in electronic engineering from University of Pisa in 1997 with a thesis focused on the development of an electronic nose. He continued working on the same subject at the Interdepartmental Research Center ``E. Piaggio'' for his PhD activity. Involved in European research and development projects, at present he is working at the MAST group of the Institute of Clinical Physiology.