Simulation to Analyze Two Models of Agitation System in Quench Process

Transcription

1 20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. Simulation to Analyze Two Models of Agitation System in Quench Process Aulus R. Romão Bineli a, Maria Ingrid Rocha Barbosa a, André Luiz Jardini a, Rubens Maciel Filho a a School of Chemical Engineering, State University of Campinas, Av. Albert Einstein, 500, Campinas, , Brazil, aulus@feq.unicamp.br Abstract In this work, two models of agitation for an industrial quench system carried out in a rectangular in shape tank were simulated. To analyze the system behavior it was considered a submerged agitation system in bottom of the tank and the other adding a lateral arrangement in an attempt to standardize the agitation in the quench zone. In this simulation process the cooling profile and heat transfer coefficient were evaluated through a calculation procedure implemented in the ANSYS CFX software and they were analyzed to understand how far from the uniformity the cooling is. The results shown that the cooling of material was not uniform for the case one (agitation in the bottom of the tank), because the temperature profile of one side was very different from the others, showing that in this case it is possible to occur different formation of microstructures in the same material. For the case were a lateral arrangement is used a better uniformity was achieved. The cooling uniformity is necessary to avoid the formation of different microstructures such as foreseen in case one. The analysis provides information of how CFD can improve control of quench process by studies of optimal quench uniformity. Keywords: Computational fluid dynamics, quenching, heat transfer coefficient, cooling profile, quench uniformity. 1. Introduction The solution of real engineering problems through numerical process simulation with a relatively detailed representation is now a reality in academic and industrial plants. A growing in exponential scale of modern computers especially in terms of high calculation speed, memory availability and graphical facilities is enabling increasingly complex problems to be solved through numerical techniques. Another factor that also contributed to this trend is related to the project cost, which makes it possible that hours of testing laboratories at high costs are replaced by simulations on computers, reducing the costs and left the testing laboratory only for the refinements of the project (Versteeg, 1995; Maliska, 2004). In metallurgic industries, one the most important processing is the metal quench, which consists in to raise steel temperature until austenitizing temperature and cooling rapidly to avoid undesirable internal microstructure as well as to ensure uniform mechanical properties. Hot metal parts are quenched using air, water, oil, or liquid polymers to obtain certain hardness and mechanical properties requirements (Totten, 2007). To achieve optimal properties the cooling rate should be as uniform as possible. Therefore, magnitude and turbulence of fluid flow around a part in the quench zone is critically important relative to the uniformity of heat transfer throughout the quenching

2 A.R.R. Bineli et al. process. The impact of non-uniform flow is increased distortion and cracking. So it is critically important to optimize the uniformity of fluid flow in the quench zone to provide optimum control of distortion and to minimize cracking (Canale, 2005). A uniform cooling can be obtained by changing the layout of the tank or geometry of hot metal parts used in the simulation (Totten, 2007). Thus, one of the most important factors affecting quench uniformity is the design of the quench system. Therefore, there is no extensive compilation of state-of-the-art design criteria to assist the engineer in the design and construction of a quenching system that will provide optimal heat transfer and quench uniformity (Canale, 2005) Bearing these in mind, the objective of this paper is analyze, with ANSYS CFX, two models of agitation system in a quench tank and heat transfer coefficient to evaluate how they impact on the metal cooling uniformity Use of computational fluid dynamics (CFD) In order to produce a computer simulation involving flow requires an analysis of data and parameters involved in the process. The quality of these data in terms of adequacy and accuracy will determine the attribute of the final results. Because of this, users of CFD software should be very familiar with the problems which they wish to simulate (Shaw, 1992), especially concerning details that may affect flow distribution locally. Computational fluid dynamics (CFD) modeling is increasingly used to examine the uniformity of fluid flow in a quench tank. Canale (2005) and Totten (2007) reported one of the first examples of the application of this methodology to illustrate the nonuniformity of quench tank fluid flow. Garwood (1992) performed similar, but much more rigorous work with model validation on a commercial quench system with subsequent model validation but no extensive flow distribution impact was carried out. CFD modelling has been conducted on a classic laboratory apparatus used for cooling curve analysis. In summary, a literature survey has clearly illustrated the enormous potential in improving quenching performance by utilizing CFD analysis. This methodology can be used to analyze fluid flow in existing quench tanks with the objective of retrofitting for improved performance or it can be used to design new and innovative quench systems. The main objective of such exercises is optimize quenching performance for distortion control and reduction of cracking problems (Canale, 2005) The mathematics of CFD The differential equations which are solved express a principle of conservation and are known as continuity (1), momentum (2) and energy equations (3). To discretize the governing equations the software ANSYS CFX makes use of an element-based finite volume method, which firstly involves discretizing the spatial domain using a mesh (Figure 2). The mesh is used to construct finite volumes, which are used to conserve relevant quantities such as mass, momentum, and energy. A control volume is constructed around each mesh node and these equations are integrated over each control volume (ANSYS CFX Guide, 2006). ρ + ( ρu) ( ρh ) tot ( ρu ) = 0 + ( ρu U) = p + τ + S M p + ( ρuhtot ) = ( λ T ) + ( U τ ) + U S M + S E (1) (2) (3)

3 Simulation to Analyze Two Models of Agitation System in Quench Process 2. Methodology 2.1. Tank design: CAD modeling The tank design was carried in CAD (Computer Aided Design) software, originally sketched with its original features, and then the assumption was made for replacement of the nozzle ejectors only for regions that represent the nozzles. This hypothesis was adopted to reduce the size of the mesh, because to represent all pipe was necessary a greater number of elements in the mesh, which would require more computational time. Additionally, taking into account the purpose of this work, the flux that occurs inside the pipe were not of interest. The Figure 1 represents the geometric model used in the simulation. This adopted geometry was transferred by Villares Metals industry. Figure 1. Tank model prepared in CAD software 2.2. Creating the geometry/mesh The objective of this process is to produce a mesh to serve as input to the physical preprocessor. Before a mesh can be produced, a closed geometric solid is required. The geometry and mesh can be created in CFX-Mesh or any of the other geometry/mesh creation tools. In ANSYS CFX, a tank CAD geometry was imported from CAD and the mesh was generated (Figure 2). In this study, the regions of ejector nozzles and block surface were refined since they are region of interest. The numerical meshes applied in this simulation are shown in Figures 2. Figure 2. Cut of mesh shown details of models: the left case 1, and the right case 2.

4 A.R.R. Bineli et al Boundary Conditions The boundary conditions are used to create input required by the Solver. The fluid utilized in this simulation was water, considering that their properties vary with the temperature and were found in (Kreith, 2000). The mesh file was loaded into the physics pre-processor, and the thermal properties of steel block and fluid were implemented. The initial temperature of steel block was 900ºC. The entrance velocity for the water in the pipes, for case one, were 5 m/s in direction of bottom s face of steel block (axis Y) and the temperature was 35ºC. For the case two, the entrance velocity was 5 m/s in direction of lateral of steel block and temperature of 35ºC. The entry velocity of refrigerant fluid (water) was 1 m/s and temperature of 25ºC in direction of bottom s tank. The two regions of fluid output were defined with relative pressure 0 Pa, because the velocity in the output is unknown. The surfaces of the wall tank were no slip because the fluid velocity near the wall is affected by friction effects and temperature 35ºC. The top of the tank that is in contact as atmosphere was defined as free slip surface, which the velocity near the wall is not delayed by the effects of friction; this represents an approximation of the atmospheric condition. In this simulation the flows conditions used are turbulence and the model implemented was k-ε. The model of Kader (1981) is used by ANSYS CFX to calculate heat flux and heat transfer coefficient at wall. 3. Results and Discussion The cooling analysis and heat transfer coefficients of the block was made with the calculations of the averages of the variables of each face of the block. Figure 3, shows the identification of each one used. The average estimated was made by CFX Expression Language (CEL) and this was necessary because the values of variables (temperatures and heat transfer coefficient) vary greatly in each face. Face 1 Face 2 Face 6 Face 3 Face 4 Face 5 Figure 3. Faces Identification. Figures 4 and 5 compare the simulation of the two agitation arrangements. For the case one, the face 4 (below), directly exposed to the ejectors nozzles lead to a more rapid cooling and face 2 (upper block) suffered a slower cooling. The faces 1, 3, 5 and 6 have an intermediate cooling. For case two, the cooling temperature profile is clearly more uniform when compared to the first arrangement (case one). The heat transfer coefficients determined for the case one (Figure 6) and two (Figure 7) showed consistency with the cooling profile. The face 4 showed the highest values whereas the face 2 (upper block) presented the lowest values. In case two the heat transfer coefficients were more uniform than the first case.

5 Simulation to Analyze Two Models of Agitation System in Quench Process Figure 4. Cooling profile each face of the steel block for the case one. Figure 5. Cooling profile each face of the steel block for the case two. Figure 6. Heat transfer coefficient each face of the steel block for the case one.

6 A.R.R. Bineli et al. Figure 7. Heat transfer coefficient each face of the steel block for the case one. 4. Concluding Remarks The results of this work showed that the CFD is a suitable procedure to evaluate the flow variation in the quench zone, taking into account the positioning of the steel block and different flow arrangements which impact the agitation system in the tank. It was shown that different arrangements of ejector nozzles in specific positions lead to different cooling curves. For the arrangement one, where no cooling flow was available at the bottom of the block a relatively wide variations in the cooling curves was observed. For the case two, with the ejector nozzles positioned at next side of the tank, a significant improvement in uniformity of temperature profile was achieved. Consequently, the developed simulation environment allows to understanding the flow regime in an existing industrial quench tank. Also, the use of this tool provides a suitable procedure to evaluate innovative design concepts for a new quench system and the performance of the various kinds of geometry and agitation system could be analyzed. 5. Acknowledgements The authors acknowledge the financial support provide by CNPq (National Council for Scientific and Technological Development) and Villares Metals Industry. References H.K. Versteeg, W. Malalasekera, 1995, An introduction to Computational Fluid Dynamics (The Finite Volume Method). Prentice Hall, 257. C.R Maliska, 2004, Transferência de Calor e Mecânica dos Fluidos Computacional, second ed. LTC editora, 452. L.C.F Canale, G.E. Totten, 2005, Quenching Technology: A Selected Overview of the Current State-of-the-art. Materials Research, v. 8, n.4, G.E. Totten, 2007, Steel heat treatment handbook, second ed. Portland: CRC Press Taylor & Francis Group, 820. C.T. Shaw, 1992, Using Computational Fluid Dynamics, Prentice Hall, 325. D.R. Garwood, J.D. Lucas, R.A. Wallis, J. Ward, 1992, Modeling of flow distribution in an oil quench tank. Journal Mat. Eng. And Perf, v.1, n.6, ANSYS CFX, 2006, Users Guide F. Kreith, 2000, Handbook of Thermal Engineering. Boca Raton, EUA: CRC Press B.A. Kader, 1981, Temperature and concentration profiles in fully turbulent boundary layers, International Journal of Heat and Mass Transfer, v.24, n.9,