Multi-objective aerodynamic design optimisation for axial compressors

This paper considers the aerodynamic design optimisation of turbomachinery blades from a multi-objective perspective. The aim is to improve the performance of a specific stage and eventually of the whole engine. The integrated system developed for this purpose is described. It combines an existing geometry parameterisation scheme, a well-established CFD package and a novel multi-objective variant of the Tabu Search optimisation algorithm. Its performance is illustrated through a case study in which the flow characteristics most important to the overall performance of turbomachinery blades are optimised.

A preliminary study of a new multi-objective optimization algorithm

This paper presents a preliminary study which describes and evaluates a multi-objective (MO) version of a recently created single objective (SO) optimization algorithm called the "Alliance Algorithm" (AA). The algorithm is based on the metaphorical idea that several tribes, with certain skills and resource needs, try to conquer an environment for their survival and to ally together to improve the likelihood of conquest. The AA has given promising results in several fields to which has been applied, thus the development of a MO variant (MOAA) is a natural extension. Here the MOAA's performance is compared with two well-known MO algorithms: NSGA-II and SPEA-2. The performance measures chosen for this study are the convergence and diversity metrics. The benchmark functions chosen for the comparison are from the ZDT and OKA families and the main classical MO problems. The results show that the three algorithms have similar overall performance. Thus, it is not possible to identify a best algorithm for all the problems; the three algorithms show a certain complementarity because they offer superior performance for different classes of problems. © 2012 IEEE.

The design of multi-element airfoils through multi-objective optimization techniques

This paper presents the development and the application of a multi-objective optimization framework for the design of two-dimensional multi-element high-lift airfoils. An innovative and efficient optimization algorithm, namely Multi-Objective Tabu Search (MOTS), has been selected as core of the framework. The flow-field around the multi-element configuration is simulated using the commercial computational fluid dynamics (cfd) suite Ansys cfx. Elements shape and deployment settings have been considered as design variables in the optimization of the Garteur A310 airfoil, as presented here. A validation and verification process of the cfd simulation for the Garteur airfoil is performed using available wind tunnel data. Two design examples are presented in this study: a single-point optimization aiming at concurrently increasing the lift and drag performance of the test case at a fixed angle of attack and a multi-point optimization. The latter aims at introducing operational robustness and off-design performance into the design process. Finally, the performance of the MOTS algorithm is assessed by comparison with the leading NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization strategy. An equivalent framework developed by the authors within the industrial sponsor environment is used for the comparison. To eliminate cfd solver dependencies three optimum solutions from the Pareto optimal set have been cross-validated. As a result of this study MOTS has been demonstrated to be an efficient and effective algorithm for aerodynamic optimizations. Copyright © 2012 Tech Science Press.

Multi-objective optimization for multiphase orbital rendezvous missions

A multi-objective optimization approach was proposed for multiphase orbital rendezvous missions and validated by application to a representative numerical problem. By comparing the Pareto fronts obtained using the proposed method, the relationships between the three objectives considered are revealed, and the influences of other mission parameters, such as the sensors' field of view, can also be analyzed effectively. For multiphase orbital rendezvous missions, the tradeoff relationships between the total velocity increment and the trajectory robustness index as well as between the total velocity increment and the time of flight are obvious and clear. However, the tradeoff relationship between the time of flight and the trajectory robustness index is weak, especially for the four- and five-phase missions examined. The proposed approach could be used to reorganize a stable rendezvous profile for an engineering rendezvous mission, when there is a failure that prevents the completion of the nominal mission.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Multi-objective optimization of a fluid structure interaction benchmarking

The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed uid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in laminar channel ow. The optimized ow characteristics of the aforementioned geometrical arrangement illustrate the performance of the system in two dimensions. Special emphasis is given to the analysis of the simulation package, which is of high accuracy and is the core of application. The design process identifies the best combination of ow features for optimal system behavior and the most important objectives. In addition, the presented methodology has the potential to run in parallel, which will significantly speed-up the elapsed time. Finite Element Method (FEM), Fluid-Structure Interaction (FSI), Multi-Ojective Tabu search (MOTS2). Copyright © 2013 Tech Science Press.