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Inaugural lecture Niels Horsten: Gradient-based optimization in CFD: from nuclear fusion to small modular fission reactors

immc | Louvain-la-Neuve

immc
4 February 2025, modified on 5 February 2025

Gradient-based optimization is a powerful tool for advancing engineering applications. 

Its success relies on three key ingredients: 

  • accurate simulation models, 

  • efficient gradient calculations, 

  • and carefully chosen optimization methods. 

In this seminar, I will demonstrate how I developed fast, physics-based CFD models for nuclear fusion that are now being used for the design of future reactors. A particular focus will be on the challenges and solutions for obtaining gradients from Monte Carlo particle simulations, which are critical for modeling neutral-plasma interactions in fusion and neutron transport in fission systems. Lastly, I will discuss the application of these methods to the design of heat exchangers for lead-cooled small modular reactors (SMRs). The unique properties of liquid lead, particularly its low Prandtl number, introduce additional complexities for the optimization.

Évènement associé

Inaugural lecture : Gradient-based optimization in CFD (Niels Horstens)
25 Feb
Gradient-based optimization is a powerful tool for advancing engineering applications. Its success relies on three key ingredients: accurate simulation models, efficient gradient calculations, and carefully chosen optimization methods. In this seminar, I will demonstrate how I developed fast, physics-based CFD models for nuclear fusion that are now being used for the desig
Inaugural lecture : Gradient-based optimization in CFD (Niels Horstens)
25 Feb
Gradient-based optimization is a powerful tool for advancing engineering applications. Its success relies on three key ingredients: accurate simulation models, efficient gradient calculations, and carefully chosen optimization methods. In this seminar, I will demonstrate how I developed fast, physics-based CFD models for nuclear fusion that are now being used for the desig