### 6.4.2. Validation Experiments

The purpose of validation tests is to check the quality of a statistical model for a given flow situation. Validation tests are the only method to ensure that a new model is applicable with confidence to certain types of flows. The more validation tests a model passes with acceptable accuracy, the more generally it can be applied to industrial flows. The goal of validation tests is to minimize and quantify modeling errors. Validation cases are often called building block experiments, as they test different aspects of a CFD code and its physical models. The successful simulation of these building blocks is a prerequisite for a complex industrial flow simulation.

#### 6.4.2.1. Description

Examples of validation cases are flows with a high degree of information required to test the different aspects of the model formulation. In an ideal case, a validation test case should be sufficiently complete to enable an improvement of the physical models it was designed to evaluate. Increasingly, validation data are obtained from DNS studies. The main limitation here is in the low-Reynolds number and the limited physical complexity of DNS data. Typically, validation cases are geometrically simple and often based on two-dimensional or axisymmetric geometries.

#### 6.4.2.2. Requirements

Validation cases are selected to be as close as possible to the intended application of the model. As an example, the validation of a turbulence model for a flat plate boundary layer does not ensure the applicability of the model to flows with separation (as is known from the model). It is well accepted by the CFD community and by model developers that no model (turbulence, multi-phase or other) will be able to cover all applications with sufficient accuracy. This is the reason why there are always multiple models for each application. The validation cases enable the CFD user to select the most appropriate model for the intended type of application.

Test case selection requires that the main features of the CFD models that are to be tested be clearly identified. They must then be dominant in the validation case. Validation cases are often ‘single physics’ cases, but it will be more and more necessary to validate CFD methods for combined effects.

The requirements for validation cases are that there should be sufficient detail to be able to compute the flow unambiguously and to evaluate the performance of the CFD method for a given target application.

Completeness of information is one of the most important requirements for a validation test case. This includes all information required to run the simulation, like:

- Geometry

- Boundary conditions

- Initial conditions (for unsteady flows)

- Physical effects involved.

While the first three demands are clearly necessary to be able to set up and run the simulation, the knowledge of all physical effects taking place in the experiment is not always considered. However, it is crucial to have a clear understanding of the overall flow in order to be able to judge the quality of a test case. Typical questions are:

- Is the flow steady-state or does it have a large-scale unsteadiness?

- Is the flow two-dimensional (axisymmetric, for example)?

- Are all the relevant physical effects known (multi-phase, transition, and so on)?

- Have any corrections been applied to the data and are they appropriate?

- Was there any measurement/wind or water tunnel interference?

Completeness of information is also essential for the comparison of the simulation results with the experimental data. A validation case should have sufficient detail to identify the sources for the discrepancies between the simulations and the data. This is a vague statement and cannot always be ensured, but a validation experiment should provide more information than isolated point measurements. Profiles and distributions of variables at least in one space dimension should be available (possibly at different locations). More desirable is the availability of field data in two-dimensional measuring planes including flow visualizations.

Completeness also relates to the non-dimensionalization of the data. Frequently the information provided is not sufficient to reconstruct the data in the form required by the validation exercise.

In case that the data provided are not sufficient, the impact of the missing information has to be assessed. Most crucial is the completeness of the data required to set up the simulation. In case of missing information, the influence of this information deficit has to be assessed. Typical examples are incomplete inlet boundary conditions. While the mean flow quantities are often provided, other information required by the method, as profiles for turbulent length scales and volume fractions is frequently missing. The importance of this deficit can be estimated by experience with similar flows and by sensitivity studies during the validation exercise.

Next to the completeness of the data, their quality is of primary importance for a successful validation exercise. The quality of the data is mainly evaluated by error bounds provided by the experimentalists. Unfortunately, most experiments still do not provide this information. Moreover, even if error estimates are available, they cannot exclude systematic errors by the experimentalist.

In addition to error bounds, it is therefore desirable to have an overlap of experimental data that enable testing the consistency of the measurements. Examples are the same data from different experimental techniques. It is also a quality criterion when different experimental groups in different facilities have carried out the same experiment. Consistency can also be judged from total balances, like mass, momentum and energy conservation. Quality and consistency can frequently be checked if validation exercises have already been carried out by other CFD groups, even if they used different models.

The availability of the data has to be considered before any CFD validation is carried out. This includes questions of ownership. For most CFD code developers, data that cannot be shown publicly are much less valuable than freely available experimental results.

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