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At present, optimization is an enabling technology in innovation. Multi-objective and multidisciplinary optimization tools are essential in the design process for real-world applications. In turbomachinery design, these approaches give insight into the design space and identify the tradeoffs between the competing performance measures. This paper describes the application of a novel multi-objective variant of the tabu search algorithm to the aerodynamic design optimization of turbomachinery blades. 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 the optimizer with an existing geometry parameterization scheme and a well-established computational fluid dynamics package. Its performance is illustrated through a case study in which the flow characteristics most important to the overall performance of turbomachinery blades are optimized. Nomenclature B= blockage C = blade tip clearance C lim = blade tip clearance limit F = vector of objective functions f = objective function i local = local iteration counter (tabu search parameter) im size = intensification-memory size counter (tabu search parameter) im thresh = intensification-memory size threshold (tabu search parameter) _ m = mass flow rate n = number of design variables n regions = number of regions used for each variable in long-term memory (tabu search parameter) n sample = number of points sampled in the local search (tabu search parameter) n stm = short-term memory size (tabu search parameter) R LE = blade leading-edge minimum radius SS = initial step size as a percentage of the variable range (tabu search parameter) SSRF = factor by which step sizes are reduced on restarting (tabu search parameter) ssr = step-size-reduction counter used in the restart strategy (tabu search parameter) ssr thresh = step-size-reduction threshold used in the restart strategy (tabu search parameter) x = vector of design variables = mass-averaged flow turning Subscript 0 = datum blade parameter

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Real-world simulation challenges are getting bigger: virtual aero-engines with multistage blade rows coupled with their secondary air systems & with fully featured geometry; environmental flows at meta-scales over resolved cities; synthetic battlefields. It is clear that the future of simulation is scalable, end-to-end parallelism. To address these challenges we have reported in a sequence of papers a series of inherently parallel building blocks based on the integration of a Level Set based geometry kernel with an octree-based cut-Cartesian mesh generator, RANS flow solver, post-processing and geometry management & editing. The cut-cells which characterize the approach are eliminated by exporting a body-conformal mesh driven by the underpinning Level Set and managed by mesh quality optimization algorithms; this permits third party flow solvers to be deployed. This paper continues this sequence by reporting & demonstrating two main novelties: variable depth volume mesh refinement enabling variable surface mesh refinement and a radical rework of the mesh generation into a bottom-up system based on Space Filling Curves. Also reported are the associated extensions to body-conformal mesh export. Everything is implemented in a scalable, parallel manner. As a practical demonstration, meshes of guaranteed quality are generated for a fully resolved, generic aircraft carrier geometry, a cooled disc brake assembly and a B747 in landing configuration.

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