Application of CFD in the industry
CFD for process-technological flow problems

by
dr. ir. A.H.M. Eisenga
Cyclone Fluid Dynamics B.V.

Contents

1. Introduction
2. CFD modeling of process-technological flows
3. Complex mesh generation for CFD
4. Flow visualization
5. Examples
Example 1: Flow simulation and residence times in UV-disinfection systems
Example 2: Hygienic design of flow installations in the food industry
Example 3: Heat exchanger
6. Final remarks

1. Introduction

Fluid flows play an important role in various equipment and processes in the industry. Flows of air or water are often used for cooling purposes. To localize regions of deficient cooling or to improve the cooling performance of an apparatus insight in the cooling flow pattern is necessary. In general, information about the structure of the flow in a process or an apparatus can be obtained from measurements in experimental test facilities or from flow visualization studies. Although these techniques have proven to be of great importance, there are also limitations and a full picture of the flow field is often hard to obtain in this way. Computational Fluid Dynamics, commonly abbreviated as CFD, is a technique to model fluid flow using a computer simulation. Due to the recent rapid grow of powerful computer resources and the development of general purpose CFD software packages CFD can nowadays be applied to solve industrial flow problems. Today, CFD has already proven to be a valuable tool to complement experimental findings in flow structure studies. In a computational simulation the flow structure is computed by solving the mathematical equations that govern the flow dynamics. The result is a complete description of the three-dimensional flow in the entire flow domain in terms of the velocity field and pressure distribution, including profiles of temperature variations, density and other related physical quantities. Today's CFD codes include in their basic flow computations effects of heat and mass transfer and a range of physical and chemical models. These extensions are indispensable for application of CFD in process-technological flow problems.

2. CFD modeling of process-technological flows

Flows in process installations are usually very complex. Many processes deal with flows consisting of multiple phases or mixtures of several components and these flow properties have to be included in the numerical simulation. The motion of bubbles or droplets in a flow can be modeled by seeding the computed flow field with particles, which tracks are then traced as part of the solution of the flow computation. The mean time the particles spent in the flow domain provides the residence times of the droplets in the process. In mixing vessels the mixing of multi-component flows is modeled by introducing passive scalars for each flow component. These scalars are advected by the main flow and the scalar concentration is computed as part of the solution. The dispersion of these scalars in the flow is a measure for the mixing performance in the vessel. In heat exchangers, furnaces or cooling equipment a combination of effects of heat convection by the flowing medium and heat conduction in the solid material requires inclusion of the solid material properties in the computation of the temperature profile. The conjugate heat transfer between the fluid and the solid is based on a model for the thermal boundary layer along the solid surface. In fans and turbines a part of the flow geometry rotates while in engines the flow domain changes in time by the motion of pistons and valves. The time-variation of these flow geometries is programmed in the numerical simulation and the transient flow field is solved according to the geometrical variations.

3. Complex mesh generation for CFD

Applying CFD to industrial flow problems in the past has been of limited value, mainly because of the complexity of the flow geometries. Fortunately, today's CFD solvers have been designed to deal with geometries of any arbitrary structure. To perform a numerical simulation the flow geometry has to be represented by a computational mesh consisting of a large number of computational cells (representing the elements in the popular family of Finite Volume Methods). The flow field is solved in all these cells according to the conservation equations describing the flow dynamics. The accuracy and resolution of the results obtained depend on the number of cells defined: using more cells yields more details of the flow field on the expense of more computational effort (i.e. computer memory and CPU-time). At this moment meshes with cell numbers in the range between 100,000 to several millions are very commonly used; this number is sufficient for solving practical industrial flow problems. It should be remarked, however, that these meshes are still too coarse to resolve all length scales appearing in full-turbulent flows: additional turbulence models are still necessary to account for these effects.

The quality of the computation depends on the quality of the mesh and therefore the generation of a good mesh is another important stage in the preparation of a CFD simulation. Cells have to be distributed so that cells of small sizes are clustered in regions of interest with severe flow gradients, leaving larger cells in the ambient far field. Therefore, an impression of the flow field to be computed is necessary in advance and the mesh has to be adjusted accordingly. Special mesh generating software produces meshes based on CAD-data representing the three-dimensional geometry. These mesh generators enable easy adaptation of the mesh to modifications in the CAD-data. While the offered mesh generation facilities are mostly (semi)-automatically, fine-tuning of the cell size distribution has still to be done manually.

4. Flow visualization

The results of a CFD simulation consist of an enormous amount of computed data that have to be ordered and presented conveniently. Several of the CFD codes have developed their own visualization tools, although special visualization packages are available that produce fancy (almost artistic) pictures of the flow field. Velocity fields are best presented by vector plots, although an impression of the three-dimensional structure of the flow is better gained from a bunch of streamlines guiding the fluid flow, as seen in figure 1. Even more dynamically are animations, which are indispensable for representing transient flow fields. Vector plots or contour plots (colour shading plots of scalar properties like pressure, temperature or density distribution) can also be produced in slices through the geometry, enabling the user to study the internal flow structure. The cross-section shown on the right in figure 1 opens a view into the core of the swirling flow.

Figure 1: Ribbons visualizing the swirling flow in a cyclone separator. The cross-sectional view on the right shows the flow structure inside the core of the vortex.

5. Examples

In the next three examples the benefit of CFD for several process-technological flow problems is demonstrated.

Example 1: Flow simulation and residence times in UV-disinfection systems

UV-disinfection systems are often used for disinfection of potable water. In such a system water flows along several mercury vapor discharge lamps. The performance of the system depends on the UV-C dose to which the bacteria in the water are exposed. This dose depends, in turn, on the UV- intensity in combination with the residence time of the water in the system. Stagnant zones in the water flow or local short-circuits involving short residence times in regions of low UV-intensity reduce the efficiency of the system considerably. Therefore, detailed insight in the structure of the water flow through the system is necessary to localize these deficiencies and to improve the system performance. CFD is an excellent tool to achieve this.

Appearance of such injurious effects in the design of a UV-intensity system has been examined using numerical simulations of the entire three-dimensional flow field. From the computed velocity field the path of individual fluid parcels along flow streamlines is deduced, providing the residence times of each parcel in the system. Combining these results with separate calculation of the UV-intensity distribution in the system yields the UV-dose experienced by each flow parcel. This technique has been applied to compare the performance of two UV-disinfection systems. Figure 2 shows the computed streamlines for a large number of fluid parcels in both systems. In the system with parallel flow along the cylindrical lamps computation of the residence time demonstrates that short-circuits occur in the system. Despite the shorter residence times in the new design with cross- flow, the average UV-dose is larger than in the parallel flow system.

Figure 2: Particle tracks in parallel and cross flow UV-disinfection systems.

Example 2: Hygienic design of flow installations in the food industry

Process installations in the food industry have to meet specific requirements related to hygiene in addition to their functional and process-technically efficient design. Only in 1995 European prescriptions have first been stated on requirements for ‘Hygienic Design’ of all parts of machinery and other equipment that are in direct contact with the food product. Individual components should be mounted as to permit easy access for appropriate cleaning. For this two issues should be pursued. First, no danger may result due to inappropriate hygiene, for instance infection of the product by micro-organisms and other polluting remnants which are left behind at the walls. Secondly, cleaning of the equipment should be optimal with minimal disinfectant to limit environmental load. Cleaning of individual parts is affected by several factors. Pure mechanically, cleaning means that the force with which the pollutants stuck to the wall have to be exceeded by the local flow shear stresses in order to be relieved and transported in the fluid flow. Flow conditions in the process equipment are thus important. More specifically, stagnant zones in which the main flow does not penetrate or small gaps or ridges need to be avoided.

To fulfill these requirements hygiene-tests are necessary. The method of the European Hygienic Equipment Design Group (the EHEDG-Cleanability-Test) provides a well-established standard guide for these tests. Such a test shows all locations along the wall were the cleaning is insufficient. In figure 3 this is shown for two T-junctions that are closed on one side; such junctions are often installed to enable insertion of measurement probes. Zones of improper cleaning are clearly shown.

Figure 3: Zones with poor cleaning after Cleanability-tests in two T-junctions.

Performing a numerical simulation of the flow through the junction yields the velocity distribution from which the shear stresses along the walls are deduced. Figure 4 shows these shear stresses for both junctions. Regions with low shearing rate (indicated in blue) nicely agree to the earlier experimental findings. Note that quantification of the cleaning performance related to local shear stress values is difficult, especially due to the statistical nature of the test results and the modeling of the turbulent boundary layer along the walls in the simulation. However, after a preliminary qualification of the results of the numerical simulations CFD can be used to compare results for geometrically different junctions and hence enables the engineer to improve the design at an early stage, without the necessary tests: these tests are limited to the final, optimized design only.

Figure 4: Computed wall shear stresses related to the cleaning performance.