Examination of temperature probe setup using computational fluid dynamics simulators

Transcription

1 Ŕ periodica polytechnica Chemical Engineering 56/2 (2012) doi: /pp.ch web: ch c Periodica Polytechnica 2012 Examination of temperature probe setup using computational fluid dynamics simulators RESEARCH ARTICLE Received , accepted Abstract Engineering problem solving such as process design, process optimization, safety analysis, etc.; relies widely on mamatical models of process. One of most important aspects in a chemical plant is safety protocols assuring safety of workers and equipment. In this study Computational Fluid Dynamics (CFD) methods are used to model different temperature probe positions in a pipe elbow. Different models were computed toger in order to solve heat transfer model: heat transfer in fluid and solid substances and momentum balance model. Three probe geometries are defined to obtain different results containing velocity field, and heat transfer. Based on results geometries and positions are compared to each or in order to find out which position is most suitable for control studies, based on time response of probes. COMSOL Multiphysics was used to implement and to couple of physics models. Due to number of geometries and model parameters (position and geometry of probe; inlet velocity) COMSOL model was connected to MATLAB via COMSOL MATLAB LiveLink for solving repeatable steps. Keywords Computational Fluid Dynamics heat transfer temperature probe position Acknowledgement This work was supported by European Union and financed by European Social Fund in frame of TAMOP-4.2.1/B-09/1/KONV project and in part by TAMOP-4.2.2/B-10/ project. This work was presented at Conference of Chemical Engineering, Veszprém, Attila Egedy Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Egyetem Street 10, Hungary egedya@fmt.uni-pannon.hu Tamás Varga Tibor Chovan Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Egyetem Street 10, Hungary Introduction Temperature is one of most common operating parameter in process industries. From refrigeration to high temperatures re is a wide range of operating temperatures in a chemical plant. Hence, control of temperature will be one of most important tasks in order to ensure safe operation of a plant. Mamatical models of real systems are often used as a tool to achieve a better understanding of operation of system. With detailed modelling of fluid dynamics and heat transfer, operating regimes can be determined, and system can be operated with expected efficiency. With analysis of involved processes re is a possibility to optimize products of a technology, with defining adequate model parameters. A detailed mamatical model can be used for testing systems, and for development purposes. With an adequate model of heat transfer, and or physical phenomena in a device even controller parameters can be identified and an application can be developed capable of computing adequate controller parameters with different operational circumstances [1]. The number and position of measuring instruments are important factors for operating device in defined operating regime. Unfortunately in real systems re is only a few temperature probe (resistance probe) can measure temperature. This makes model validation difficult, and makes probe positioning a more crucial problem, because wrong probe positioning makes temperature detection harder [12].There is a practical side of number of probes. For example in a stirred vessel a measuring probe disturbs flow field and cause differences in measured value. In multiphase systems solid or fluid particles can attach to probe and make measurement more difficult. The position of probe sensors is a crucial problem in food industries too. Using Computational Fluid Dynamics (CFD) methods different probe positions can be tested to achieve an optimal experimental setup, by identifying model parameters [14]. Using a mamatical model of temperature sensor behaviour of physical system (such as bioreactor) can be monitored, and probe model can be validated and tested Examination of temperature probe setup

2 using real measurements [7]. With and adequate mamatical model temperature of bioreactor can be predicted. Industrial reactors are almost always important parts of a working technology, refore in some cases it is difficult to obtain experimental information, and it takes a long time to collect enough data to build a model. One of solutions can be pilot plant experiments, or using CFD models. The main advantage of a CFD model is capability of examining real system in three dimensions. A validated CFD model can support design, research and development, optimization, scale up or or complex engineering tasks, such as polymerization, crystallization operations; multiphase reactors and oil industry problems [3, 10, 11] Besides chemical engineering applications re are studies showing CFD can be used for example in field of aerodynamics [5, 13] and human sciences too [15]. In viewpoint of temperature and heat exchange CFD tools are used to design heat exchangers and choose most suitable unit for a process based on CFD models. With help of CFD expensive measurements can be avoided and heat exchanger choosing period can be shorter than with conventional methods [4]. Nowadays re are an increasing number of applications of model based controllers. The detailed model of physical devices and processes is used for control algorithm development, and controller tuning. An adequate model can be basis of for example model based control, model predictive control, or hybrid control systems [8, 9]. Model based approaches can be used in field of temperature control using HVAC systems [2] A CFD model can be used to understand operating conditions, and temperature profiles in a device, and n obtained information can be used to support controller design, and tuning tasks [6]. Difficult processes can be coupled, and solved toger, and re is a possibility to do a sensitivity analysis of model parameters. With proper validation of CFD model will be an excellent substitute of real system for development and control studies. In this study we used CFD models to examine heat transfer in a pipe elbow with a temperature measurement point. The primary goal of this study is to identify a good probe geometry and position by eliminating worse solutions. In this case CFD model can support design or re-design purposes. With a proper probe position and geometry system is easier to control. Using connection between CFD model parameter (for example inlet velocity) and controller parameters an adaptive control algorithm can be applied to physical system. COMSOL Multiphysics 4.2a used as a commercial CFD software, and MATLAB 2010b for solving repeatable steps of analysis of CFD models. Modelling and method Fig. 1 shows probe positions different probe types and measurement points we used to compute different simulations, and models. After elbow geometry was imported one of probe types was chosen from three different types (sphere, cone, and flat). Then probe position was set from three different angles (radial, 45 and axial). Each finalized geometry was tested with five different inlet velocities from m/s up to 0.01 m/s. The temperature probes were in same position in every case, and temperature was detected in solid material. Three different physics models were solved. Firstly we implemented laminar flow model based on a laminar model of moving fluid containing Navier-Stokes equation Eq. 1 and continuity equation Eq. (2). In Eq. (1) and Eq. (2) ρ, u, p, I, µ, F refers to density, velocity, pressure, identity matrix, viscosity and or body forces respectively. The moving fluid was water for this study and an inlet and outlet boundary were defined to model continuous system. ρ(u )u = [ pi + µ ( u + ( u) ) T 23 ] µ( u)i + F (1) (ρu) = 0 (2) A stationary momentum balance model was calculated, because after a short time period, fluid flow in this geometry becomes stationary. Two heat transfer equations were applied to describe heat balances in this system, heat transfer in fluid Eq 3 and heat transfer in solid substances ( temperature was defined as a bulk copper material) Eq. (4). In Eq. (3) and Eq. (4) ρ, c p, T, t, u, k, Q, Q vh, W p refers to density, heat capacity, temperature, time, velocity, heat conductivity, heat source, heat loss and pressure work respectively. A temperature inlet boundary an outlet boundary ( boundaries were same, as in momentum balance model). ρc p T t + ρc pu T = (k T) + Q + Q vh + W p (3) ρc p T t + ρ kc p u trans T = (k T) + Q (4) Based on velocity field obtained from solution of stationary momentum balance model, heat transfer model in moving fluid was calculated. A transient study was applied to examine dynamical behaviour of system. A temperature step function was applied in inlet boundary to examine response for set point change. Then different solutions were compared to each or in order to find optimal solution for temperature probe position. The solution with lower time delay is considered optimal. The model material parameters were obtained by using COMSOL Multiphysics built in material library (water for fluid, and copper for solid material). 72 Per. Pol. Chem. Eng.

3 Figure 1 shows probe positions different probe types and measurement points we used to compute different simulations, and models. c.) Fig. 1. Sphere probe type with axial position (, Cone probe type with radial probe position (, and Flat probe type with 45 position (c.) Fig. 2. Figure 2 Velocity field and temperature in elbows with 45 probe position After simulations were completed we compared results to find out which probe position has lesser time delay. Figure 3 shows differences between dynamic of probes in two different flow rates (0.002 m/s and 0.01 m/s). The probe type was spherical in this examination Fig. 3. The dynamical behaviour of temperature probe in two different flow rates (0.002 m/s, 0.01 m/s) m/s) Results Fig. 2a. shows results of momentum balance model Figure 2 Velocity field and temperature in elbows with 45 probe position After simulations were completed we compared results to find out which probe position has lesser time delay. Figure 3 shows differences between dynamic of probes in two different flow rates (0.002 m/s and 0.01 m/s). The probe type was spherical in this examination. Velocity field and temperature in elbows with 45 probe position in The case of radial spherical position probe in is three always different worse, positions. than The axial but as inlet velocity became higher this gap probe became disturbs smaller. flowin in everycase case, of so 45 velocities position near temperature detection is slower than in case of Figure 3 The dynamical behaviour of temperature probe in two different flow rates (0.002 m/s, 0.01 probe axial areone, higher, however than it or takes part of state elbow. almost The inlet m/s) same time to reach stationary state in both cases. The Sphere_90 velocity difference was of m/s. time delays is present only at lower inlet speeds. The effect of different velocities was Fig. The 2b. radial showsposition temperature is always field worse, time 15 than s 5 s after Cone_90 eliminated by correcting data sets to avoid axial misinformation. but as inlet The velocity next examination became higher was this analysis gap of temperature step. Fig. 2b. shows that every case became smaller. In case of 45 position temperature detection is slower than in case Flat_90 effect of probe type of response function. Three different probe types were analysed. of Figure 4 temperature of fluid is higher than copper, because some axial one, however it takes state almost same time to reach stationary state in both cases. The time shows is needed results to temperature at tom/s. reachthe measuring results show, point that response function is almost independent of difference of time delays is present only at lower inlet speeds. 0 The effect 10 of 20 different 30 velocities 40 was 50 via probe heat conduction. type. There is a slight difference, but it is too small to consider. eliminated The final by correcting examination was data sets effect to avoid of misinformation. inlet velocity. The Five next different examination After simulations were completed we compared results effect of probe type of response function. Three different probe types were analysed. Figure 4 velocities was were analysis applied of from to find m/s outup which to 0.01 probem/s. position Figure has 5 lesser shows time delay. Fig. 4. Analysis of different probe types results of Figure this examination. 4 Analysis Figure of different shows probe sphere types probe shows results at m/s. The results show, that response function is almost independent of in radial position. Examination probe type. of temperature There is probe a slight setup difference, but it is too small to consider Figure 4 shows that inlet velocity changes have major effect on dynamical behaviour of The final examination was effect of inlet velocity. Five different velocities were applied from heat transfer process m/s up to 0.01 m/s. Figure 5 shows results of this examination. Figure shows sphere type probe Figure 3 The dynamical behaviour of temperature probe in two different flow rates (0.002 m/s, 0.01

4 Fig. 3 shows differences between dynamic of probes in two different flow rates (0.002 m/s and 0.01 m/s). The probe type was spherical in this examination. The radial position is always worse, than axial but as inlet velocity became higher this gap became smaller. In case of 45 position temperature detection is slower than in case of axial one, however it takes state almost same time to reach stationary state in both cases. The difference of time delays is present only at lower inlet speeds. The effect of different velocities was eliminated by correcting data sets to avoid misinformation. The next examination was analysis of effect of probe type of response function. Three different probe types were analysed. Fig. 4 shows Sphere_90 results at m/s. The results show, that response function is almost Cone_90 independent of probe type. There is a slight difference, but it is too small to consider. Flat_90 The final examination was effect of inlet velocity. Five different 0 velocities 10 were 20applied 30 from m/s up to m/s. Fig. 5 shows results of this examination. Figure shows sphere type probe in radial position. Fig. Figure 5 shows 4 Analysis that inlet of different velocity changes probe have types major effect on dynamical behaviour of heat transfer process Fig. 5. Analysis of different inlet velocities Sphere_0.002 Sphere_0.004 Sphere_0.006 Sphere_0.008 Sphere_0.01 Figure 5 Analysis of different inlet velocities At lower velocities temperature reaches stationary temperature state much slower, reaches that at higher stationary velocities. state However, much slower, differences between curves curves are are getting lower as as velocity velocity is is getting higher. Hence, that at higher velocities. ces between getting higher. Hence, over a level ( m/s) re is not /s) re is not much effect of excess velocity. much effect of excess velocity. Conclusion CFD methods were used to analyse dynamical behaviour used to analyse dynamical behaviour of temperature probes. Different types of temperature probes. Different types and positions of probes s were examined with different fluid velocities in order to find out which probe were examined with different fluid velocities in order to find out e optimal which probe for temperature positions and types probe are setup. optimal for Different temperature models were applied to create pipe elbow. probe setup. The Different obtained models models were applied were to compared create a detailed to each or. The axial position igher velocities, model of but pipeat elbow. higher The velocities obtained models were difference comparedis much smaller compared to to each or. The axial position is better at lower and higher velocities, but at higher velocities difference is much smaller compared to or positions. 50 References 1 Abaoui M A, Vernieres-Hassimi L, Sequin D, Abdelghani-Idrissi M.A, Counter-current tubular heat exchanger: Modelling and adaptive predictive functional control, Applied Thermal Engineering 27 (2007), , DOI /j.applrmaleng Balan R, Cooper J, Chao K-M, Stan S, Donca R, Parameter identification and model based predictive control of temperature inside a house, Energy and Buildings /j.enbuild (2011), , DOI 3 Behjat Y, Shahhosseini S, Marvast M.A, CFD analysis of hydrodynamics, heat transfer and reaction of three phase riser reactor, Chemical Engineering Research and Design 89 (2011), , DOI /j.cherd Bhutta M.M.A, Hayat N, Bashir M.H, Khan A. 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5 Appendix 1. Notation Variable Description Unit ρ density [kg/m 3 ] u velocity vector [m/s] µ viscosity [Pas] t time [s] F force vector [N] P k stress tensor c p Heat capacity [J/(mol K)] T k Heat conductivity [W/(m K)] Q Heat source/sink [W] Q vh Heat loss [W] W p Pressure work [J] ρ k Solid density [kg/m 3 ] u trans Translational velocity [m/s] Examination of temperature probe setup