Automated Multiobjective Optimisation in Axial Compressor Blade Design

Abstract

This paper presents an automated multiobjective design methodology for the aerodynamic optimisation of turbomachinery blades. In this approach several operating-points of the compressor are considered and the flow-characteristics of the different flow-solutions are combined to one or more objective functions. The optimisation strategy is based on multiobjective asynchronous evolutionary algorithms (MOEA’S) which are accelerated using additive local neural networks and kriging procedures. Common operators: Mutation, Crossover and Differential-Evolution are used to create a new population. In addition to these common operators the optimisation runs temporarily on the response-surface created by the neural networks and/or kriging-processes respectively. Only the Pareto-optimal solutions obtained from this metamodel are evaluated using the numerical expensive flow-solver. Therefore, the response-surface is just a new operator that creates auspicious members. One of the main differences between the presented code to usual and traditional MOEA’S is the selection of parents. While traditional codes choose potential parents of a new population from the previous population, the current method selects parents from the database of all evaluated members. This approach allows the user to run the optimisation asynchronously and with a smaller size of population, treducing numerical costs, without influencing the diversity of the optimal solutions over the whole Pareto-front. This aspect is very important when evaluating very complex and/or discontinuous fronts.

Design of an Economical Counter Rotating Fan: Comparison of the Calculated and Measured Steady and Unsteady Results

“…The experimental results were required to validate the CFD results and the reliability of the in-house optimizer AutoOpti [1].…”

“…

Within the framework of the EU funded Project VITAL, SNECMA (Group Safran), as the work package leader, developed a counter rotating low-speed fan-concept for a high bypass ratio engine. The detailed aerodynamic and mechanical optimization of one blading version (CRTF2.b) was carried out at the German Aerospace Center (DLR), by applying one of the newest design methods featuring a multi-objective automatic optimization method based on an Evolutionary Algorithm [1].The final design goals were high efficiency, a sufficient stall margin and adequate acoustic performances for the given cycle parameters. The fan stage developed was tested in an anechoic test facility at CIAM in Moscow.

…”

A Stochastic Reliability Model for Application in a Multidisciplinary Optimization of a Low Pressure Turbine Blade Made of Titanium Aluminide

“…The castability, aerodynamic efficiency, maximum equivalent stress and failure probability are used for the multi criterion objective function in a multidisciplinary optimization. The overall optimization process is realized by the DLR software AutoOpti (Voß et al, 2006), which continuously creates new geometry parameter sets that define the blade, called optimization members. 155 free geometric design parameters are varied in total for a new blade, including among others the shapes of 5 profiles at different blade span positions, the circumferential shift of these profiles and the circumferential position of the shrouding band.…”

Automated Multidisciplinary Optimization of a Transonic Axial Compressor

“…The following flowchart in Figure 1 shows the basic structure of the MPI-parallelized multiobjective evolutionary algorithm AutoOpti 5 which was developed at the Institute of Propulsion Technology in the past five years, with focus on turbomachinery applications.…”

“…The new member is stored in the database, and the Pareto rank (for a definition see Ref. 5) is updated for all stored members. In the next step some members (notation: parents) are selected from the database based on their fitness values and Pareto rank for the production of a new offspring.…”