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a b s t r a c tA method is presented for generating a well-distributed Pareto set in nonlinear multiobjective optimization. The approach shares conceptual similarity with the Physical Programming-based method, the Normal-Boundary Intersection and the Normal Constraint methods, in its systematic approach investigating the objective space in order to obtain a well-distributed Pareto set. The proposed approach is based on the generalization of the class functions which allows the orientation of the search domain to be conducted in the objective space. It is shown that the proposed modification allows the method to generate an even representation of the entire Pareto surface. The generation is performed for both convex and nonconvex Pareto frontiers. A simple algorithm has been proposed to remove local Pareto solutions. The suggested approach has been verified by several test cases, including the generation of both convex and concave Pareto frontiers.

The paper is devoted to numerical implementation of the wall functions of Robin-type for modeling near-wall turbulent flows. The wall functions are based on the transfer of a boundary condition from a wall to some intermediate boundary near the wall. The boundary conditions on the intermediate boundary are of Robin-type and represented in a differential form. The wall functions are formulated in an analytical easy-to-implement form, can take into account the source terms, and do not include free parameters. The relation between the wall functions of Robin type and the theory of Calderon-Ryaben'kii's potentials is demonstrated. A universal robust approach to the implementation of the Robin-type wall functions in finite-volume codes is provided. The example of an impinging jet is considered.

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