It might be easy to get a single simulation for your case of interest, and you might even successfully compare the simulation data point to experimental data.  But what does this really mean, and what level of trust can be placed on the results for your product development process? It is not uncommon to get the “right” single simulation for wrong reasons, which sounds okay until you consider a new design or operating condition.

ISimQ’s BPP start from analysing your needs. Fidelity of the simulation is chosen to match the engineering requirements, dictating the physics models, the extent of the simulation, and geometric defeaturing. Accuracy is quantified to understand response to uncertainties arising from mesh, convergence level, numerics, and systematic differences between simulation and reality. Once sensitivities are understood, a quality assurance process can be implemented.

An important module of ISimQ’s BPP is Quality Control. It is about hierarchically quantifying and reducing uncertainties arising from numerics, models and systematic uncertainties.

Discretisation errors arise from too coarse grids or from inadequate discretisation schemes. They can be quantified by comparing solutions on different grids or solutions obtained with different discretisation schemes. Here is an example for a compressor blade.

Step 1: Scalable grids – finer grids @ constant grid quality
Step 2: Comparison of results – identification of uncertainties
Step 3: Uncertainty quantification – Richardson extrapolation theory

Interested in more information on Best Practice Procedures & quantification of uncertainties? Contact us at info@isimq.com

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