# 10 Common Mistakes in CFD Modeling

Author: Dr. Sharad N. Pachpute (PhD, IIT Delhi)

• There are many common mistakes in CFD Modeling even commercial software like ANSYS, Star CCM, Converge are used.
• Many commercial CFD ANSYS, Converge, etc. have limited CFD support or lack experienced engineers for industrial products. There may be limitations of CFD tools to produce correct results.
• They reduced the maintenance cost and reduced direct support by collaborating with elite channel partners that have less support in academia and product-based industries.
• The Code of CFD is not known like Open-FOAM for customization of problems
• You can refer to the following major common mistakes in CFD Modeling

## Lack of Basic Subject knowledge or literature review

Basic subject knowledge is essential to understand assumptions considered in CFD Modeling. Try to learn physical meaning of each term in governing equations before CFD analysis:

1. Governing equations in Fluid dynamics: sources of momentum acting on fluids
2. Numerical models in CFD solver: Most of CFD simulations are carried our  for classical problem of fluid flows. CFD models can not solve numerically at molecular levels.
3. Understanding of Turbulent flow physics and its models: understand assumptions considered in modeling of turbulent flow
4. Convective Heat and mass transfer:
5. Basic of combustion :  Assumptions for Turbulent -chemistry interaction models
6. Multi-phase flow and its Modeling : There are several multi-phase models in CFD solvers. Models predict the phenomenons using assumptions.

## Redundant  CFD Simulations

• Some simple problem can be solved analytically instead of time consuming CFD analysis
• CFD analysis helps to find more flow physics instead of average values calculated from analytical or experimental correlations
• Cost of CFD simulations is -2o times more than analytical solutions
• Avoid unnecessary simulations if the project or proposal is not well planned due to incomplete input data

## Selection of incorrect CFD domains

### Over simplified domain

• Many times hurry in projects, over simplified domain is selected.
• It is important to understand the flow physics are statistically symmetrical
• Ensure no gradient across the symmetry plane
• Ensure flow is periodic or not if periodic boundary is selected
• Make sure that the domain is not affected by location of inlet and outlet boundaries. This case may create unrealistic situation

### Incorrect co-ordinate system

• Use Cartesian co-ordinate system for flow through slot or rectangular duct or complex geometry which are not modeled in a cylindrical co-ordinate
• Use the axis symmetrical case for flow through the pipes or co-axial pipe

## Issues in Mesh Quality

• ### Ensure Mesh density in high gradient regions

• Fine mesh to resolve wall bounded shear layer such a way than y+ <1
• High mesh density in region whenever there is a sudden jump in domain or flow
• ### Orthogonal Quality near the walls

• Try to maintain orthogonal quality near the wall
• The mesh less orthogonal quality can be simulated with lower relaxation factors
• Highly skewed mesh would results in incorrect gradients the walls. Which may lead to unbounded solutions or divergence

• ### Skewed Mesh Cells

• Highly skewed cells may results in incorrect gradient of fluxes near the walls
• It is important avoid skewed cells near the walls

## Incorrect Boundary and Input data

• ### Inlet Conditions:

• Ensure the velocity profile at the inlet is uniform or non-uniform.
• Use the fully developed velocity profiles if inlet is the part of long duct
• Use the mass flow rate if velocity profile is important and inlet section is away from the main CFD domain
• ### Outlet Boundary

• Use a fixed pressure boundary (pressure outlet or outlet vent) if pressure field is known
• Use the outflow boundary if gradient at the outlet section is negligible

## Incorrect CFD Models

• ### Select the correct turbulence models

• The standard k-ε model cannot predict the near wall behavior in adverse pressure gradient. Hence, SST k-ω is recommended
• The standard k-ω model cannot predict the free expanding flow, The realizable k-ε model is suitable
• ### Select the correct multi-phase models

• The discrete phase model (DPM) can’t used for four way coupling if collision between particles is to be predicted
• The Eulerian- Eulerian model doesn’t predict the sharp interface
• ### Select the correct combustion models

• The turbulence –chemistry interaction should be selected based Damkohler number
• The finite rate chemistry model is suitable for slow chemistry combustion

## Incorrect CFD Solver Setting

• ### Forced/natural/mixed Convection

• Check the problem is of forced convection, natural or mixed convection. It is determined by Richardson number, Ri = Gr/Re2
• Enable the gravity for the problems of natural or mixed convection. Set the reference density and temperature
• ### In-compressible/Compressible Flow

• Pressure based solver for in-compressible or weakly
• Density based solver for compressible flow and high velocity flow ( Mach number > 0.3)

• Steady solver for statistically steady flow (example: RANS models) or some laminar flows
• Unsteady solver for URANS and LES
• ### Pressure –velocity coupling

• SIMPLE for steady or unsteady solver
• SIMPLEC for unsteady turbulent flow
• PISO for unsteady problem and poor quality mesh
• Pseudo/coupled solver for transient solver if maximum courant number is know

• ### Time steps and number of iterations:

• Select the correct time steps ( local CFL number < 1) For RANS
• Select the correct time steps ( CFL number < 0.2) For LES
• Select more iterations per time step initially. Later it can be reduced in order to get proper convergence of all the variables
• ### Initialization of variables and patching

• Incorrect initialization can lead to unbounded solutions
• Standard or hybrid initialization can be used
• Patching of variable should be correct for multi-phase or combustion problems
• Relaxation factors

## Issues in Monitoring the Simulations

• Make ensure bounded solutions
• Constant and stable convergence
• Check the residuals of all the variables below the their limit (< 10^-6)
• Monitor the key parameters which are not changing significantly and monotonous trends with number of iteration
• Check the net fluxes of mass, total heat transfer, radiation heat transfer, total sensible heat transfer. Makes sure the net fluxes are less than 0.1 % of source values
• Time step and number of iterations affect the convergence /residuals of variables

## Incorrect Interpretation of CFD Results

• ### Validation with lab based experimental data

• Post-process the results in non-dimensional form and compare with experimental data assuming all the flow parameters are with experimental conditions
• Avoid direct comparison of CFD result with instantaneous experimental data
• For RANS, the CFD variables are averaged quantities which can be compared with averaged quantities from experimental results
• Qualitative comparison of CFD results is essential for assessment of CFD Models
• ### Expected Maximum deviation between experimental and CFD results for

•  Laminar flow Problem : 0-2 %
• Low speed turbulent flow Problem : 0-5 %
• High speed turbulent flow Problem : 0-15 %
• For Complex problems in multi-phase flow and reactive flow : 0-20%
• Example 1: CFD Modelling of a bubble column

• Example 2: Water flow over a triangular weir:  From the following figures, it is noted that the variation between experimental and CFD results is less.

• ### Comparison with Power plant experimental data

• Compare the CFD results with power plant data which is obtained over a long operation
• Understand the assumptions considered in the CFD modeling  and note practical conditions in power plant. Note what major physics in the modelling are not neglected as objective of projects
• There is always  uncertainty in the measured physical quantities. Hence, numerical and experimental can be matched well all time
• ### When you do not have proper experimental data

• Using basic knowledge of fluid mechanical and heat transfer, we can compare with average velocity and surface heat transfer rate
• Using the available simple correlation we can estimate velocity , temperature and pressure drop across the from inlet to outlet
• Intuition of engineering can be applied to judge the predicted CFD results are correct or not
• Example 1: Cooling of electronics chip sets
• In this, experimental date over small chips is difficult  to measure over the  small ships
• However, based on the values of voltage and current, we generally give as a source iof heat generation to each chips
•  Average velocity and temperature at inlet and outlet can be measured and compared with the CFD values

## Lack of concrete Conclusion from CFD results

• Due to uncertainty in CFD results, sometimes it is difficult for CFD engineer to provide a conclusion with confidence
• ### Reasons for uncertainty for CFD result

• Simplified geometry for complex problems because of limited resources
• Assumption in CFD Modelling : RANS modelling is used instead of LES to get results in less time
• Uncertainty in CFD Models for complex flows and experimental data
• Uncertainty in experimental data
• Lack of stability in convergence of residuals
• Lack of credibility in CFD results by considering practical situations
• Limited CFD input data from process industries
• Insufficient data for comparison: Inadequate measurement of flow parameters in process industries which results in Limited practical database.
• Differences in the interpretation of data by CFD engineers and practical persons or operators at plants

Don’t simply match your CFD results to satisfy your supervisor or manager without strong technical justifications

A basic understanding of physics (fluid flows and heat transfer) and numerical modeling is essential along with good skills in CFD Tools

## CFD Training to Overcome Mistakes

• Training in Computational Fluid Dynamics (CFD) is crucial for overcoming common mistakes and improving the accuracy and efficiency of simulations.
• Here are some key areas to focus on during CFD training to avoid common pitfalls:

### Understanding the Basics Subjects

• Governing Equations: Make sure you understand the Navier-Stokes equations and other relevant fluid dynamics equations.
• Mesh Generation: Learn how to create and refine a computational mesh that balances accuracy and computational cost.
• Boundary Conditions: Properly setting boundary conditions is crucial for realistic simulations.

### Software Proficiency

• Learning the Tools: Gain proficiency in popular CFD software like ANSYS Fluent, OpenFOAM, or COMSOL Multiphysics.
• Pre-Processing Skills: Develop skills in geometry preparation and mesh generation using tools like ANSYS Meshing or Gmsh.
• Post-Processing Skills: Learn to analyze and visualize results using software such as ParaView or Tecplot.

### Advanced Topics in CFD Course

• Turbulence Modeling: Understand different turbulence models (RANS, LES, DNS) and their appropriate applications.
• Multi-phase Flow: Learn how to handle simulations involving multiple phases (e.g., liquid-gas interactions).
• Heat Transfer: Incorporate heat transfer mechanisms if they are relevant to your study.
• Combustion and Pollution Modeling
• Turbo-machinery Modeling

### Advanced CFD Course To fill the gaps

• The advanced CFD Course will overcome your mistakes by gaining proficiency in software and advanced topics in the above topics
• This course is as good as a Master’s degree if you finish all assignments with dedicated CFD support teams
• The affordable and valuable course at Rs 3800 for students and USD 80 for overseas students for seven months
• Trusted by hundreds of students from top universities and professionals
• Click for the detailed syllabus

### Continuous Learning and Practice

• Workshops and Courses: Attend workshops, webinars, and courses to stay updated on the latest techniques and software updates.
• Community Engagement: Participate in CFD forums and user groups to share experiences and learn from others.
• Hands-On Projects: Apply your knowledge to real-world projects and case studies to gain practical experience.

### Documentation and Reporting

• Detailed Documentation: Keep detailed records of your simulation setup, assumptions, and results for future reference and reproducibility.
• Reporting Results: Learn to effectively communicate your findings through reports and presentations.

Focusing on these areas during CFD training can significantly reduce common mistakes and lead to more reliable and accurate simulations.