Computational fluid dynamics software have gained in popularity the past few decades. They use numerical analysis and data structures to analyze and solve problems that involve fluid flows. This remains a field under development, with ongoing research to improve the accuracy and speed of complex simulation scenarios.
New methods such as topology optimisation have been recently added to CFD software, paving the path to design highly efficient engineering components. The design of an engineering component is updated using a mathematical algorithm to maximise a performance value while being subject to specific boundary conditions. This seminar will explain the basis of topology optimisation and it will go through a specific application for valve and robust optimisation. CFD software enables a first prediction before relying on experimental data to confirm an engineering component performance. Discrepancies are a common occurrence, thus accuracy of experimental data and CFD software can be improved by the utilization of uncertainty quantification. This will be shown on a cantilever beam test case, where the exceedance probability is estimated while experimental data are subject to epistemic and aleatory uncertainties.  Accuracy of models prediction in CFD software can also be improved by using data from higher fidelity models. Machine learning algorithms have been gaining in popularity in the past couple years. They offer the opportunity to leverage data coming from high fidelity simulation to develop models for low fidelity simulation. This enables low fidelity to gain in accuracy while keeping a low computational cost. However, dealing with large data can be challenging in itself, which is going to be shown on an AI data driven turbulence model test case, where data from DNS and LES are used to build a turbulence model for RANS simulations.