Abstract: In this paper, a novel technique for cost-efficient design optimization of microwave structures has been proposed. Our approach exploits an adaptive response scaling that ensures good alignment between an equivalent circuit (used as an underlying low-fidelity model) and an electromagnetic (EM) simulation model of the structure under design. As the adaptive scaling tracks the low-fidelity model changes both in terms of frequency and the response level, it exhibits better generalization capability than traditional (e.g., space mapping) surrogates. This translates into improved design reliability and reduced design cost. Our methodology is demonstrated using two examples of microstrip filters and compared to several variations of conventional space mapping.

Abstract: Re-designing circuits for various sets of performance specifications is an important problem in microwave and antenna engineering. Unfortunately, this is a difficult task that is normally realized as a separate design process, which is often as expensive (in computational terms) as obtaining the original design. In this work, we consider the application of inverse surrogate modeling for fast geometry scaling of microwave and antenna structures. Computational efficiency of the discussed procedure is ensured by representing the structure at the low-fidelity model level. The explicit relation between design specifications (here, operating frequency) of the structure and its geometry dimensions is determined based on a set of predetermined reference designs. Subsequently, the model is corrected to elevate the re-designed geometry to the high-fidelity electromagnetic (EM) model level. Our approach is demonstrated through a compact rat-race coupler and a patch antenna with enhanced bandwidth.

Abstract: Trawl-doors are a large part of the fluid flow resistance of trawlers fishing gear and has considerable effect on the fuel consumption. A key factor in reducing that consumption is by implementing computational models in the design process. This study presents a robust two dimensional computational fluid dynamics models that is able to capture the nonlinear flow past multi-element hydrofoils. Efficient optimization algorithms are applied to the design of trawl-doors using problem formulation that captures true characteristics of the design space where lift-to-drag ratio is maximized. Four design variables are used in the optimization process to control the fluid flow angle of attack, as well as position and orientation of a leading-edge slat. The optimization process involves both multi-point space mapping, and mixed modeling techniques that utilize space mapping to create a physics-based surrogate model. The results demonstrate that lift-to-drag maximization is more appropriate than lift-constraint drag minimization in this case and that local search using multi-point space mapping can yield satisfactory design at low computational cost. By using global search with mixed modeling a solution with higher quality is obtained, but at a higher computational cost than local search.

Abstract: A preference-based approach is proposed for Grid computing with regard to preferences given by various groups of virtual organization (VO) stakeholders (such as users, resource owners and administrators) to improve overall quality of service and resource load efficiency. Computational resources being competed for by local jobs (initiated by owners) and global (users') job flow complicate the problem of a required service quality level substantially. A specific cyclic job batch scheduling scheme is examined in the present work which enables to distribute and share resources considering all the VO stakeholders' preferences and find a balance between VO global preferences and those of its users. Two different general utility functions are introduced to represent users' preferences satisfaction.

Abstract: Parallelizing industrial simulation codes like the EUROPLEXUS software dedicated to the analysis of fast transient phenomena, is challenging. In this paper we focus on the efficient parallelization on shared memory node coupling. We propose to have each thread gather the data it needs for processing a given iteration range, before to actually advance the computation by one time step on this range. This lazy cache aware layout construction enables to keep the original data structure and leads to very localised code modifications. We show that this approach can improve execution by up to 40% when the task size is set to have the data fit in the L2 cache.