Engineering knowledge-based variance-reduction simulation and G-dominance for structural frame robust optimization

[EN] This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncertainties into consideration in the design variables.The simultaneous minimization of the constrained weight (adding structuralweight and average distribution of constraint violations) on the one hand and the standard deviation of the distribution of constraint violation on the other are handled with multiobjective optimization-based evolutionary computation in two different multiobjective algorithms. The optimum design values of the deterministic structural problem in question are proposed as a reference point (the aspiration level) in reference-point-based evolutionary multiobjective algorithms (here g-dominance is used). Results including

High-resolution simulation of stack pollutant emissions

[EN]A three-dimensional air pollution model for the short-term simulation of emission, transport and reaction of pollutants is presented. In the finite element simulation of these environmental processes over a complex terrain, a mesh generator capable of adapting itself to the topographic characteristics is essential, A local refinement of tetrahedra is used in order to capture the plume rise. Then a wind field is computed by using a mass-consistent model and perturbing its vertical component to introduce the plume rise effect. Finally, an Eulerian convection-diffusionreaction model is used to simulate the pollutant dispersion…