Design by decomposition makes complex design problems tractable. However, decomposition also requires fixing key interdisciplinary design variables, decisions that often lock in much of the final product performance, at the very beginning of the design process. We suggest that a design process based on decomposition thus introduces "processintrinsic " constraints, in addition to the "hard" constraints driven by physical or customer requirements, which can have important and unexpected implications on the achievable performance in later design stages. Using an integrated design methodology which preserves the inherent multidisciplinary coupling between components and disciplines and does not require assumption of the relevant interactions, the advantages of removing, or "softening," process-intrinsic constraints can be explored. In this paper we investigate how removing these constraints may improve the adiabatic efficiencies of a high pressure compressor and turbine spool and thus reduce specific fuel consumption. The results indicate that integrating the aerothermal and mechanical analyses can reduce specific fuel consumption by 0.20% but if the process-intrinsic constraints on shaft work, component weight, shaft speed, and bleed air cooling flows are removed a reduction of 0.42% is possible. The cost performance benefit of removing constraints exhibited bimodal behavior. Conservative performance improvements were achieved with and without process-intrinsic constraints but design time decreased as more constraints were removed. More aggressive improvements were achievable only by removing constraints but only with significant computational expense.

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With the advent of maintenance contracts and stricter noise and emissions regulations, the need to visualize and understand the effects of multidisciplinary design trade-offs on the economics of producing and maintaining the commercial and environmental performance of an aero engine has never been more important. By integrating multidisciplinary, multi-component design tools early in the design process, the effects of the early design decisions on performance trade-offs can be visualized and better understood. Based on the SignPosting methodology, the Integrated Engine Design (IED) model is used to demonstrate the benefits of a hierarchical optimization strategy for reducing the weight of a core compressor and turbine spool. By visualizing the constraints throughout the optimization history, limiting constraints are identified and "softened" to improve the optimization convergence and outcome. Trade-off visualization is used to explore the selection of an operating speed for a high pressure turbine and compressor spool. The design space is then modified and spool performance improved by changing turbine design variables.@en

The aviation industry is under significant commercial and environmental pressure to produce a revolution in design. However, despite the significant advances in automatic design optimization made over the last 30 years, the industry is still largely conducting design by evolution. The complexity of a modern aeroengine encourages the separation of its conceptual from its detailed design: this limits the utility of powerful design optimization tools solving the "classical" optimization problem (of design space search for the global optimum) to the detailed designer who is more usually tasked with reaching a specification. One of the principal difficulties of modifying the design to reach a particular specified goal is that, though the desired improvement might be achieved, it often comes at unacceptable detriment to other performance indicators. We present results of our orthogonal design technique (that assists the designer in producing improvements in specific attributes of the design without penalty in other aspects) applied to the redesign of a generic core engine compressor for two "real-world" design problems: reducing the part count without aerodynamic penalty and increasing the efficiency without reduction in surge margin, pressure rise or mass flow. The two resulting designs, while meeting their constraints, exhibit a reduction in blading equivalent to two rotor rows and an increase in adiabatic efficiency of 1.0 percentage point respectively. The design changes which produce these improvements, together with how these compare with design rationale, are discussed.@en

The complexity of the modern aeroengine has led to a fragmented, modular, and evolutionary design process which, due to the lack of methods for generating understanding about the design space, results in a potentially holistically sub-optimal design. Technological maturity, environmental pressures and changing business models are among the challenges facing the industry today; thus, the need to understand and exploit the trade-offs between the economics of production, maintenance, and operating costs and understand their effects during the design process is increasingly important. The first steps toward increased design space exploration and understanding can be achieved by integrating multidisciplinary engine design tools. By coupling engine design tools via SignPosting, a design process management and optimisation technique with a hierarchical database to manage multiple fidelity data and confidences, we have developed an integrated engine design model (IED) with which "better" designs and multidisciplinary trade-offs can be explored and visualized. As aeroengine aerodynamics mature and business models change, the benefit of focusing on other performance variables increases and the definition of what constitutes "better" changes. Accordingly, we have used the IED to minimize the weight of an intermediate pressure compressor and uncooled turbine spool while maintaining aerodynamic performance. By taking three different approaches to the optimisation and using weight objectives of different resolutions (complete spool weight, turbine and compressor weight, and both blade and disc weights for the turbine and compressor separately), the implications of a more traditional approach, which uses the turbine and compressor weights, can be visualized. We also present an analysis of how multidisciplinary non-linearities are exploited to reduce spool weight.@en

This paper describes the implementation of a parallel Tabu Search algorithm for multi-objective continuous optimisation problems. We compare our new algorithm with a leading multi-objective Genetic Algorithm and find it exhibits comparable performance on standard benchmark problems. In addition, for certain problem types, we expect Tabu Search to outperform other algorithms and present preliminary results from an aerodynamic shape optimisation problem. This is a real-world, highly constrained, computationally demanding design problem which requires efficient optimisation algorithms that can be run on parallel computers: with this approach optimisation algorithms are able to play a part in the design cycle.

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