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what does it mean? It often happens that opposite needs occur during the design process, like, for example trying to minimize a beam's weight, and also its deformation under load: a multi-objective dilemma. modeFRONTIER’s optimization algorithms identify the solutions which lie on the trade-off curve known as Pareto Frontier: none of them can be improved without prejudicing another. In other words the best possible solutions: the optimal solutions. We believe that optimization is not a watchword, but a process that can reinstate creativity into the design project.

modeFRONTIER’s optimization tools help focusing on the stream of creativity leading to innovation. The software is a multidisciplinary and multi-objective optimization and design environment, written to allow easy coupling to almost any computer aided engineering (CAE) tool, whether commercial or in-house.

TURN THE PAGE to read an example of multi-objective optimization: a 4-stroke 4 cylinder engine optimization problem.

Multi-objective Optimization: an example As an example of multi-objective optimization, consider the 4-stroke 4 cylinder engine optimization problem illustrated in the image below.

The engine has to be designed using a commercial software (GT-Power), and several parameters have to be taken in account including, to mention a few, intake and exhaust cam timing, intake runners length, percentage of burn point, geometry of valves, and many others. The purpose of the designer is to find a combination for these parameters in order to match at the same time contrasting objectives, such as maximization of brake power and minimization of NOx emissions. After the application of the multiobjective optimization by modeFRONTIER, each single configuration proposed by the selected algorithm is represented in the picture as a point in the objectives chart (Power vs NOx). The red dotted line includes several solutions which represent a different optimal compromise between the contrasting objectives. All the other points in the front are however as optimal solutions as the former two, because none of them is better than another for both objectives at the same time. All of them instead dominates any other possible configuration, represented by the points staying below the line, because the results of the former are better than the ones of the latter for both objectives at the same time. The designer is therefore free to select arbitrary one of the solutions belonging to this set as definitive design, accordingly to his preferences and criteria. The optimal solutions belonging to this front are called Pareto Frontier designs. 

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Optimization: what does it mean?