The purpose of this paper is to compare k‐ω shear‐stress transport (SST) and large eddy simulation (LES) turbulence model application effect on numerical computation of…
The purpose of this paper is to compare k‐ω shear‐stress transport (SST) and large eddy simulation (LES) turbulence model application effect on numerical computation of flow pattern and heat exchange characteristics through the neutron beam window region for European spallation source setup model.
Transient hydrodynamic and thermal calculations with appropriate heat sources are performed using both turbulence models and typical discrepancies in flow and thermal patterns are discussed, as well as, simulation results are qualitatively compared with experimental data for heat transfer coefficient distribution α at the window surface.
Contribution of greater k‐energy field obtained with LES calculation leads to prediction of more intensive heat transfer in comparison to k‐ω SST.
The paper illustrates discrepancies of thermal patterns caused by application of k‐ω SST and LES turbulent models.
Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori…
Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori known that some functions have greater importance than other. This paper aims to present a novel multi-surrogate, multi-objective, decision-making (DM) optimization algorithm, which is suitable for time-consuming simulations. Its performances have been compared, on the one hand with a standard decision-making algorithm (iTDEA), on the other with a self-adaptive evolutionary algorithm (AMALGAM*). The comparison concerns numerical tests and an optimal control task in induction heating.
In particular, the algorithm makes use of surrogates (meta-models) to concentrate the field evaluations at the most promising areas of the design space. The effect of the decision-maker is instead to drive the search to given regions of the Pareto front. The synergy between surrogates and the decision-maker leads to a greater effectiveness of the optimization search. For the field analysis of the optimal control task, a coupled electromagnetic-thermal FEM model has been developed.
The novel algorithms outperform both iTDEA and AMALGAM* in all done tests.
The algorithm could be applied to other computationally intensive multi-objective real-life problems whenever a preference between the objectives is known.
The combination of surrogates and a decision-maker is beneficial with time-consuming multi-objective optimization problems.