This article explains the difference between validation and verification in scientific modeling and numerical simulation. Many new students and researchers use these two words as the same thing, but in the academic world, they have different meanings and different purposes. Verification asks if the model is solved correctly, while validation asks if the model represents real physical behavior. In research, both steps are required to trust the results of a numerical model, a CFD simulation, or a laboratory model. This article focuses only on these two concepts and explains their scientific definitions, their use in research, and their role in computational and experimental studies, from basic ideas to advanced understanding.
Figure 1: Conceptual difference between verification and validation in numerical modeling.
What Is Verification?
Verification means checking that a numerical model is solved correctly. In the academic world, verification answers one simple question: Are we solving the equations right? It does not check real physics or experiments. Instead, it focuses on the mathematical model, the numerical method, and the computer code. In numerical simulation and CFD, verification includes checking the governing equations, boundary conditions, discretization method, and numerical errors. Common verification steps are mesh independence study, grid convergence, and comparison with an analytical solution or a benchmark case. If verification is not done, the simulation results may look correct, but they can be numerically wrong and unreliable.
Figure 2: Typical verification steps used in numerical and CFD studies.
What Is Validation?
Validation means checking that a model represents real physical behavior. In the academic world, validation answers one clear question: Does the model match reality? Validation is done by comparing numerical results with experimental data, laboratory measurements, or trusted results from scientific articles. In scientific modeling and CFD, validation uses real data such as pressure, velocity, temperature, or force. If the numerical model shows good agreement with these data, the model is called validated. Validation does not check the code or equations, but it checks the physical accuracy of the model. Without validation, a model may be numerically correct but physically wrong, and its results cannot be trusted in research or industry.
Free Simulation Tutorials with Validation Practice
Validation is easier to understand when readers see it in practice. For this reason, we provide two free simulation courses that show how validation is applied in real numerical models. These tutorials focus on comparing numerical results with trusted reference data, which is the core idea of validation.
Lid‑Driven Cavity CFD: A FREE Fluent Tutorial for Benchmark Validation
This free tutorial presents a lid‑driven cavity CFD simulation in ANSYS Fluent. The numerical results are validated against well‑known benchmark data, which makes it a standard case for CFD validation studies in incompressible flow.
Flat Plate Boundary Layer CFD: Validating CFD Against the Blasius Solution
This free tutorial focuses on laminar flow over a flat plate. The CFD results are validated against the analytical Blasius solution, which is widely used as a reference for boundary layer validation in fluid mechanics.
Figure 3: CFD validation of laminar boundary layer flow over a flat plate using the Blasius solution, Validation of CFD results using the classical lid‑driven cavity benchmark problem.
Verification in Numerical Modeling
In numerical modeling and CFD, verification is used to check that the numerical solution is correct. This step focuses on the governing equations, the numerical method, and the computer implementation. Verification checks the mathematics of the model, not the real physics. Common verification tasks include mesh independence study, grid convergence, and checking time‑step size in unsteady cases. Researchers also compare results with an analytical solution or a benchmark solution when available. If the results change strongly with mesh size or numerical settings, the model is not verified. Proper verification reduces numerical error and increases model reliability before moving to validation. We have fully explained the issue of mesh independence in ANSYS Fluent in the article Grid Independence Test in ANSYS Fluent
Figure 4: Example of mesh independence study in CFD verification.
Validation in Numerical and Laboratory Modeling
In numerical and laboratory modeling, validation is the step where simulation results are compared with real data. This data comes from laboratory experiments, measurements, or published scientific articles. The goal is simple: to check if the numerical model represents real physical behavior. In CFD validation, researchers compare values such as velocity, pressure, temperature, or force with experimental results. Good agreement means the model is physically accurate. Poor agreement means the model setup, physical assumptions, or boundary conditions must be improved.
How Validation Is Commonly Done
Validation uses real and trusted sources, not guesses or visual results.
Table 1: Validation and Laboratory Modeling
| Validation Source | Description |
| Laboratory experiments | Measured data from wind tunnels, pipes, nozzles, or channels |
| Published scientific articles | Peer‑reviewed experimental or numerical benchmark data |
| Standard benchmark cases | Well‑known test cases accepted by the research community |
| Industrial test data | Measured data from real engineering systems |
Laboratory‑Based CFD Validation Examples
Gas‑Solid Fluidized Bed Hydrodynamics CFD Simulation
This case models the complex interaction between gas flow and solid particles in a fluidized bed. The CFD results are validated against laboratory measurements reported in published research. Key parameters such as particle distribution, velocity fields, and pressure behavior show close agreement between numerical and experimental results.
Figure 5: Validation of CFD simulations against laboratory reference data using CFDLAND benchmark studies. Numerical results show close agreement with experimental and published solutions.
Density Current CFD Simulation by ANSYS Fluent
This example studies density‑driven flow, which is common in environmental and industrial applications. The CFD simulation is validated using experimental data from laboratory studies. The validation poster compares flow structure and front propagation, showing strong agreement between simulation and experiment.
Figure 6: Validation of CFD simulations against laboratory reference data using CFDLAND benchmark studies. Numerical results show close agreement with experimental and published solutions.
Analytical and Benchmark‑Based CFD Validation Examples
Cyclone Separator CFD Validation Study
In this case, CFD is used to predict flow patterns and separation behavior inside a cyclone separator. Validation is performed by comparing numerical results with well‑established analytical and experimental reference data from the literature. The results confirm the accuracy of CFD in predicting particle separation performance.
Figure 7: Validation of CFD simulations against analytical reference data using CFDLAND benchmark studies. Numerical results show close agreement with experimental and published solutions.
Validation and CFDLAND Resources
Validation is best understood through well‑defined reference cases. CFDLAND provides a set of fundamental CFD simulations that are widely used in education, research, and engineering practice. These examples demonstrate how numerical results are validated using experimental data, analytical solutions, or trusted literature. Each case focuses on a different physical phenomenon and shows how validation is applied in a clear and systematic way.
- S809 Airfoil CFD Validation: Fluent Aerodynamic Performance Analysis
This case studies the aerodynamic behavior of the S809 airfoil, which is commonly used in wind turbine applications. The CFD results are validated against experimental data, allowing comparison of lift and drag coefficients and flow behavior. - Double‑pipe Heat Exchanger CFD: A Complete Fluent Validation Tutorial
This simulation focuses on internal flow and heat transfer in a double‑pipe heat exchanger. Validation is performed by comparing temperature and heat transfer results with published numerical and theoretical data. - Rotating Water Container CFD: A Fluent Validation Tutorial
This tutorial models a fluid in a rotating container, where centrifugal effects shape the free surface. The numerical results are validated against analytical solutions, making it a classic and reliable validation case. - Uniform Magnetic Field CFD, MHD Fluent Validation Tutorial
This example demonstrates magneto hydrodynamic (MHD) flow under a uniform magnetic field. Validation is carried out by comparing CFD results with high‑impact research data, focusing on the effect of the Lorentz force on fluid motion.
Figure 8: Examples of fundamental CFD validation cases available on CFDLAND, covering aerodynamics, heat transfer, rotating flows, and magneto hydrodynamics.
Key Differences Between Verification and Validation
Although verification and validation are closely related, they answer different scientific questions. New researchers often confuse them, but in academic research, they are separate and sequential steps. Verification checks the numerical correctness of the model, while validation checks the physical accuracy of the model. Understanding this difference is essential for scientific credibility and trustworthy results.
Table 2: Verification vs Validation – Clear Comparison
| Aspect | Verification | Validation |
| Main question | Are the equations solved correctly? | Does the model represent reality? |
| Focus | Numerical method and code | Physical model and real behavior |
| Uses experimental data | No | Yes |
| Uses laboratory data | No | Yes |
| Uses analytical solution | Yes | Sometimes |
| Uses benchmark cases | Yes | Yes |
| Related to mesh and time step | Yes | No |
| Related to physics assumptions | No | Yes |
| Typical tools | Grid convergence, mesh independence | Experiment comparison, literature data |
| Result | Numerical correctness | Physical accuracy |
In the academic world, the correct order is:
- Verification – make sure the numerical solution is correct
- Validation – make sure the model matches real physical behavior
Skipping verification can hide numerical errors. Skipping validation can lead to physically wrong conclusions.
Why Verification and Validation Matter in Research
For a reputable academic article or journal, verification and validation are not optional steps. They provide clear evidence that the numerical work is reliable and trustworthy. Verification proves that the numerical solution is mathematically correct, while validation proves that it represents real physical behavior. Journals and reviewers use these two steps to judge whether numerical results can be accepted, cited, and reused by other researchers. Without proper verification and validation, numerical results may look correct but cannot support scientific conclusions. When both steps are clearly reported, the numerical work becomes credible, reproducible, and suitable for high‑quality scientific publication.
Common Mistakes in Verification and Validation
Even experienced researchers can make mistakes in verification and validation. These mistakes reduce the scientific value of numerical work and may lead to rejection by journals. Knowing these issues helps researchers improve their studies before submission.
Table 3: Frequent Mistakes in Practice
| Mistake | Why It Is a Problem |
| Skipping verification | Numerical errors remain hidden |
| Using only one mesh | Results may depend on grid size |
| Calling a simulation “validated” without data | Validation needs experimental or literature data |
| Comparing only visual contours | Quantitative comparison is required |
| Ignoring boundary condition sensitivity | Results may change with small setup changes |
| Using unverified turbulence models | Model assumptions may not fit the physics |
| Mixing verification and validation | Causes confusion for reviewers |
| Not citing reference data | Validation must be traceable and reproducible |
From a reviewer’s point of view, the most common reason for rejection is this sentence: “The results are validated by CFD simulation.” This statement is scientifically incorrect. A simulation cannot validate itself. Validation must always involve external and trusted data such as laboratory measurements or published articles.
Conclusion
Verification and validation are essential steps in numerical modeling and CFD research. Verification ensures that the numerical equations are solved correctly, while validation ensures that the model represents real physical behavior. Together, they form the foundation of credible, reproducible, and publishable numerical work. Scientific journals and reviewers rely on these steps to judge the quality and reliability of simulation results. By clearly separating verification from validation and reporting both in a structured way, researchers can increase the trustworthiness, impact, and acceptance of their numerical studies in academic and industrial applications.