Review on Computational Fluid Dynamics and Application


Rahul P. Jadhav*1, Manohar D. Kengar1, Nikita R. Nikam1, Suraj B. Kumbhar1, Shubham B. Devkar2, Mangesh A. Bhutkar1

1Department of Pharmaceutics, Rajarambapu College of Pharmacy, Kasegaon, Maharashtra, India- 415404.

2Department of Pharmaceutics, Shree Santkrupa College of Pharmacy, Ghogaon, Maharashtra, India- 415404.

*Corresponding Author E-mail:



Computational fluid dynamics (CFD) is a simulation tool, which uses powerful computer and applied mathematics to model fluid flow situations for the prediction of heat, mass and momentum transfer and optimal design in industrial processes. It is only in recent years that CFD has been applied in the food processing industry. This paper reviews the application of CFD in food processing industries including drying, sterilisation, refrigeration and mixing. The industry is under pressure to reduce waste and improve process efficiency. The traditional approach of taking a product from laboratory scales to pilot plants and then to production is no longer attractive. Process and product development often are initiated simultaneously, and as a result, rapid prototyping and analysis are required.


KEYWORDS: Computational fluid dynamics, Refrigeration, Cooling, Drying, Steriliszation, Mixing.




The pharmaceutical industry faces new challenges associated with increased market globalization, demands for cleaner environments, higher customer expectations, the push for increased profitability, tighter FDA regulations, and the ever-increasing demand to reduce time to market. The industry is under pressure to reduce waste and improve process efficiency. The traditional approach of taking a product from laboratory scales to pilot plants and then to production is no longer attractive. Process and product development often are initiated simultaneously, and as a result, rapid prototyping and analysis are required.


To meet these challenges, the industry must implement innovation at all phases of product development.[1], a document that highlights the plans for the chemical process industries for the next 20 years, has identified three enabling technologies. Computational fluid dynamics (CFD) is one such technology that is expected to lead chemical process companies into the future. CFD methods are applied widely in various industries to examine fluid flow and heat-transfer behavior. For example, in the aerospace industry, CFD routinely is applied for aerodynamic calculations of  lift and drag. In the automotive and heavy equipment industries, CFD is used to calculate external drag, climate control, and under hood cooling. The heating and ventilation industry, power generation industry, and chemical process industries, including the pharmaceutical industry, now are beginning to apply CFD methods to gain insight into their various processes. The integration of CFD methods can lead to shortened product-process development cycles, optimization of existing processes, reduced energy requirements, efficient design of new products and processes, and reduced time to market. Unit operations in the pharmaceutical industry typically handle large amounts of fluid. As a result, small increments in efficiency may generate large increments in product cost savings. Thus, research and development staffs as well as plant and production managers should understand the benefits of CFD so that it can be integrated into the development process.CFD can be a viable tool for analyzing process equipment. Mixing, separation, drying, fluid transport, and heat generation operations are some of the processes that can benefit from CFD analysis. The flow fields associated with these processes are very complex. Conventional methods of analysis often are not adequate, and experimental measurement is not always possible. Although measurement probes provide point data, very often full-field data or data at multiple locations are required to fully diagnose a problem. Troubleshooting as well as improvements in efficiency and performance typically are achieved by trial and error on the basis of past experience. Process monitoring very often is used to identify the onset of critical conditions; however, it does not identify the underlying cause of the problem. Process-equipment failure causes undesirable downtime and loss of revenue. Hence, improved troubleshooting techniques are required so that downtime can be minimized. Unlike experimental methods,CFD provides full-field data.[2]


Performing a CFD analysis:

To perform a CFD analysis, the analyst will state the problem and use scientific knowledge to express it mathematically. Then the CFD software package will embody this knowledge and expresses the stated problem in scientific terms. Finally, the computer will perform the calculations dictated by CFD software and the analyst will inspect and interpret their results. In principle, three different major tasks should be done to perform a CFD simulation.[3]



All the tasks that take place before the numerical solution process are called pre-processing. This includes problem thinking, meshing and generation of a computational model. Problem thinking is the first stage in using CFD. Before committing to practice, it is worth thinking about the physics of the problem that is faced. In this stage the analyst should consider the flow problem and try to understand as much as possible about it. The second stage is meshing. In this stage the analyst should create the shape of the problem domain that needs to be analyzed. This can usually be done with the help of a standard CAD program. It is possible to import data generated by such program into a CFD package. Then the problem domain is sub-divided into numerous cells, also known as volumes and elements. Most CFD packages have the program to do meshing and define the shape simultaneously shows an example which is the meshing structure of a commercial air blast chiller with a ham inside.[4] Once meshing has been completed, the boundaries of the problem domain can be found and the necessary boundary conditions, determined in the initial stage, should be applied. These conditions together with some fluid parameters and physical properties specify the actual flow problem to be solved. Advanced CFD software packages have the program to carry out the following operations: defining a grid of points, also volumes or elements, defining the boundaries of the geometry, applying the boundary conditions, specifying the initial conditions, setting the fluid properties and setting the numerical control parameters. However, it is not easy to generate a complicated mesh. For example, despite the steadily increasing power of computers, it is still difficult to discretise the solution domain in the case of 3-D turbulent problems with a grid fine enough for the solution to be truly independent.[5]



Processing involves using a computer to solve mathematical equations of fluid flow. Once the meshing is completed, the model input values should be specified and then the software can solve the equations of state for each cell until an acceptable convergence is achieved. This is a very intensive process and usually it requires the computer to solve many thousands of equations. In each case, the equations are integrated and the boundary conditions are applied to it. This is known as equation discretisation and is applied to each individual cell of the mesh. The process is repeated in an iterative manner until a required accuracy is achieved. This step can be a time-consuming process and although it is the core of any CFD software package, little of its operation can be seen.



The post-processing program is used to make evaluation of the data generated by the CFD analysis. When the model has been solved, the results can be analyzed both numerically and graphically. Post-processing tools of the powerful CFD software can create visualization ranging from simple 2-D graphs to 3-D representations. Typical graphs obtained with the post-processor might contain a section of the mesh together with vector plots of the velocity field or contour plots of scalar variables such as pressure. In such graphs, colors are used to differentiate between the different size of the values. The second stage is meshing. In this stage the analyst should create the shape of the problem domain that needs to be analyzed. This can usually be done with the help of a standard CAD program. It is possible to import data generated by such program into a CFD package. Then the problem domain is sub-divided into numerous cells, also known as volumes and elements. Most CFD packages have the program to do meshing and define the shape simultaneously. provide numerical details of mesh generation, discretization, and CFD solver techniques.[6,7]



CFD, as a tool of research for enhancing the design process and understanding of the basic physical nature of fluid dynamics.[8] can provide benefits to the food processing industry in many areas, such as drying, sterilization, mixing, refrigeration and other application areas. In the past few years’ great development has taken place in these areas.



Drying is a common food manufacturing process. The drying rate is a strong function of air flow or air velocity. Therefore, it is of great importance to know the air flow and velocity in the drying chamber, thus leading to know the areas of adequate air velocities for proper drying. However, air flow and air velocity are difficult to measure during operation because several sensors are needed to be placed at various directions of air flow and locations. Since there are some difficulties in modeling the complex phenomena, especially the gas turbulence.[9] CFD is a powerful tool to aid the prediction of drying process.CFD has been used to predict the air flow and velocity during drying.[10] used CFD to simulate the air movement inside an industrial batch-type, tray air drier. Drying tests of several fruits were performed and the result showed that the degree of fruit dryness depended on its position within the drier. Determination of pressure profiles and air velocities by CFD showed that the main cause of the variations in drying rates and moisture contents was the lack of spatial homogeneity of air velocities within the drier. With the aid of CFD,[11] studied velocity fields in a modern sausage drier in order to provide information on air circulation inside the drier, which showed that CFD was able to predict the effects of filling level on air-flow patterns and also to identify measurement errors in areas where the main air flow direction was horizontal. However, the quantitative comparison between the simulated and measured air velocities showed wide discrepancy with means of absolute differences of about 0.6m s−1. Although, the flow pattern and air velocity in the drier can be predicted using CFD modeling, further study on how to control the drying process and to reduce the energy cost is still a research topic for CFD modelling. Meanwhile, more attention should be paid on the assumptions such as spatial homogeneity  because of such assumptions could lead to inaccuracy in prediction



It is known that consumer demands for food products focus on safety, product quality and cost. Therefore, it is of great necessity to enhance quality and assure safety of the food supply. Sterilization is an important technique for food storage and preservation. CFD can be used to study both temperature distribution and flow pattern of food in the process of sterilization so as to optimize the quality of food products. Thermal processing remains the most significant technique of sterilization which results in microbial inactivation, but in the mean time, quality loss and flavor development. Excessive heating will affect food quality and its nutritive properties. With the application of CFD, there has been a number of studies to optimize the thermal sterilization of foods.[12,13] These studies had led to substantial improvement on the optimal control of the process and the retention of the nutritional and sensory quality of the food. Abdul Ghania[14,15] carried out a series of research work in canned food sterilization with CFD simulation. The work varied from those simulating the changes of bacteria diffusion and their transient spatial distribution during sterilization process to those simulating natural convection heating within a can of liquid food during sterilization. It is only in recent years that the food pouches have been introduced to the market and, therefore, little or no study has been executed on sterilization of food in pouches. CFD code was used for the purpose to simulate the transient temperature, velocity profiles and the shape of the slowest heating zone in sterilization of carrot soup in pouches. The modelling of a continuous sterilization process to optimize the quality of safe food has also been developed[16] and the results showed that CFD modelling could be of significant help to the liquid food sterilization.



In the food processing industry, mixing is one of the most common operations. Mixing applications involve the substances of gas, liquid and solid. And the mixing of fluids is one of the most important unit operations for the food processing industry. However, mixing is a complicated process as regards to the multiphase turbulence during mixing and the design of a mixer. CFD is a powerful tool for the modelling of mixing processes. It provides a natural method to link food process and fluid flow information. With the help of CFD, the phenomena in an agitated vessel can be predicted.[17] During mixing, a common method of enhancing the process is to use some kind of stirrer or paddle. CFD codes have been applied in optimising the mixing process to minimise energy input and to shorten the processing time. Therefore, research has been carried out on the distribution of energy in mixing vessel and on the effects of mixing quality when the stirrer is in different position. Such prediction of the mixing process within these units was impossible in the past.[18] Recently, CFD modelling of mixing in stirred tanks has been carried out by Sahu  with several important points about impeller-vessel geometry, energy balance and the linkage between the flow field and the design objective being addressed. Although no experiments were carried out in the study, the predicted values of mixing time were compared with published experimental data and the agreement was within 5–10%. This study will benefit the design of the stirred tanks,and some technical problems about the impeller types, mixing time and equipment size can be avoided.[19]



The consumption of frozen foods has increased continually in the past years because frozen foods have demonstrated good food quality and safety record. Refrigeration can slow down bacterial growth and preserve food. Therefore, researchers have recently applied CFD in the modelling of heat and mass transfer in foods during refrigeration (chilling and freezing). They have developed the modeling of air blast and vacuum cooling[20,21,22], chilling[23,24], cold chain, cold store, refrigerated room and refrigerated display cabinets.[25] Since refrigerated foods require strict temperature control, the design of equipment or stores for refrigerated foods is very important. With the utilisation of CFD, designers can examine the whole range of modifications before manufacturing and designing at a minimal cost and in a short time.[26] A large amount of research work has been accomplished about the simulation and optimization of the design of refrigerated cases or stores using CFD[27,28,29],  Cortella et al., (2001) analysed the velocity and temperature distributions in refrigerated open cabinets based on CFD simulation. The average value of the predicted temperature (6.54°C) did not differ much from the average measured value (6.3°C) showing good agreement.[31] However, since the commercial CFD software packages lack several features that are important for the design of cool store, such as turbulence modelling for mixed convection problems with heat and mass transfer, more efforts on the modelling should be made.[32] Nevertheless, these researches will lead to the optimizing of the design of equipment or store for refrigerated foods and the increase of the confidence of food safety and quality.


CFD for packaging:

Liquid pharmaceutical products primarily are supplied in bottles, and decreasing filling time can shorten time-to-market and increase productivity. To save time, filling equipment can be adapted to package various products, but splashing, spillover, and frothing are some of the problems associated with such filling lines. CFD can be applied to conduct virtual experiments before changes are made to the filling lines or to the package geometry. This method allows a wide range of conditions to be tested and leads to an optimized filling process the filling of a container. The shows are typical of solution results that are used to optimize filling processes to increase throughput and reduce foaming.[33]



Authors are highly Acknowledge the help of teaching staff of Rajarambapu College of Pharmacy, Kasegaon. For providing necessary information required for research work. Also we are highly Acknowledge the help and guidance of Dr. Mangesh A. Bhutkar.



1.      Technology Vision 2020: The US Chemical Industry (American Chemical Society, 1998).

2.      R.H. Perry and D.Green, Chemical Engineers ‘Handbook (McGraw-Hill, New York, NY 1984).

3.      Shaw, C.T., 1992. Using Computational Fluid Dynamics. Prentice Hall, New Jersey, USA.

4.      Hu, Z., Sun, Da-Wen, 2001b. Predicting local surface heat transfer coefficients by different turbulent k–_models to stimulate heat and moisture transfer during air-blast chilling. International Journal of Refrigeration 24 (7), 702–717.

5.      Mirade, P.S., 2001. Personal communication

6.      S.V. Patankar, Numerical Heat Transfer and Fluid Flow (Hemisphere Publishing Corp., Bristol, PA, 1983).

7.      D.A. Anderson, J.C. Tannehill, and R.H. Pletcher, Computational Fluid Mechanics and Heat Transfer (McGraw-Hill,New York,NY,1984).

8.      Anderson, J.D., 1995. Computational Fluid Dynamics: the Basics with Applications. McGraw-Hill, Singapore.

9.      Oakley, D.E., 1994. Scale-up of spray dryers with the aid of computational fluid dynamics. Drying Technology 12 (1-2), 217–233.

10.   Mathioulakis, E., Karathanos, V.T., Belessiotis, V.G., 1998. Simulation of air movement in a dryer by computational fluid dynamics: application for the drying of fruits. Journal of Food Engineering 36 (2), 183–200.

11.   Mirade, P.S., Daudin, J.D., 2000. A numerical study of the airflow patterns in a sausage dryer. Drying Technology 18 (1–2), 81–97.

12.   Datta, A.K., Teixeira, A.A., 1987. Numerical modelling of natural convection heating in canned liquid foods. Transactions of the ASAE 30 (5), 1542–1551

13.   Akterian, S.G., Fikiin, K.A., 1994. Numerical simulation of unsteady heat conduction in arbitrary shaped canned foods during sterilization processes. Journal of Food Engineering 21 (3), 343–354.

14.   Abdul Ghania, A.G., Farid, M.M., Chen, X.D., Richards, P., 1999a. Numerical simulation of natural convection heating of canned food by computational fluid dynamics. Journal of Food Engineering 41(1), 55–64.

15.   Abdul Ghania, A.G., Farid, M.M., Chen, X.D., Richards, P., 1999b. An investigation of deactivation of bacteria in a canned liquid food during sterilization using computational fluid dynamics (CFD). Journal of Food Engineering 42 (4), 207–214

16.   Jung, A., Fryer, P.J., 1999. Optimising the quality of safe food: computational modelling of a continuous sterilization process. Chemical Engineering Science 54 (6), 717–730.

17.   Delaplace, G., Torrez, C., Andre, C., Leuliet, J.C., Fillaudeau, L., 2000. CFD simulation of foodstuff flows in an agitated vessel. In Proceedings of the 1st International Conference on Simulation in Food and Bio-industries 2000. Society of Computer Simulation International, The Netherlands, pp.179–18.

18.   Quarini, J., 1995. Applications of computational fluid dynamics in food and beverage production. Food Science and Technology Today 9 (4), 234–237.

19.   Sahu, A.K., Kumar, P., Patwardhan, A.W., Joshi, J.B., 1999. CFD modelling and mixing in stirred tanks.Chemical Engineering Science 54 (13–14), 2285–2293.

20.   Hu, Z., Sun, Da-Wen, Bryan, J., 1998. Modelling of an experimental air-blast freezer using CFD code. In: Advance in Refrigeration System, Food Technologies and Cold chain. International Institute of Refrigeration, Paris, France, pp. 395–400.

21.   Hu, Z., Sun, Da-Wen, 1999. The temperature distribution of cooked meat joints in an air-blast chiller during cooling process: CFD simulation and experimental verification. Paper presented at the 20th International Congress of Refrigeration, Sydney, Australia.

22.   Hu, Z., Sun, Da-Wen, 2000a. Simulation of heat and mass transfer for vacuum cooling of cooked meats by using computational fluid dynamics code. Paper presented at the 8th International Congress on Engineering and Food, Paper no. O-130, Puebla, Mexico.

23.   Davey, L.M., Pham, Q.T., 1997. Predicting the dynamic product heat load and weight loss during beef chilling using a multi-region finite difference approach. International Journal of Refrigeration 20 (7),470–482.

24.   Davey, L.M., Pham, Q.T., 2000. A multi-layered two-dimensional finite element model to calculate dynamic product heat load and weight loss during beef chilling. International Journal of Refrigeration 23 (6), 444–456.

25.   Foster, A., 1996. The costs involved in modelling retail display cabinets. Paper presented at the Seminar on Achievements with CFD in the Food Industry, Leatherhead Food Research Association, Leatherhead,UK.

26.   Foster, A., James, S.J., 1996. Using CFD in the design of food cooking, cooling and display plant equipment. Paper presented in Second European Symposium on Sous Vide, Belgium.

27.   Stribling, D., Eng, B., Tassou, S.A., Marriott, D., 1996. Optimisation of the design of refrigerated display cases using computational fluid dynamics. The Institute of Refrigeration. Session , 1995–1996, p.7-1–7-10

28.   Xiang, W., Tassou, S.A., 1998. A dynamic model for vertical multideck refrigerated display cabinets. In: Advances in the Refrigeration Systems, Food Technologies and Cold Chain. International Institute of  Refrigeration, Paris, France, pp. 637–644

29.   Wang, H., Touger, S., 1990. Distributed dynamic modelling of a refrigerated room. International Journal of Refrigeration 13, 214–222

30.   Cortella, G., Manzan, M., Comini, G., 1998. Computation of air velocity and temperature distributions in open display cabinets. In: Advanced in the Refrigeration Systems, Food Technologies and Cold Chain. International Institute of Refrigeration, Paris, France, pp. 617–625.

31.   Cortella, G., Manzan, M., Comini, G., 2001. CFD simulation of refrigerated display cabinets. International Journal of Refrigeration 24 (3), 250–260.

32.   Nicolai, B.M., Verboven, P., Scheerlinck, N., Hoang, M.L., Haddish, N., 2001. Modelling of cooling and freezing operations. Paper presented at the IIR International Conference on Rapid Cooling of Food, March 2001, Bristol, UK, pp. 28–30.






Received on 04.07.2019         Accepted on 09.08.2019

© Asian Pharma Press All Right Reserved

Asian J. Pharm. Res. 2019; 9(4):263-267.

DOI: 10.5958/2231-5691.2019.00043.1