A product's architecture can affect many aspects of product and process quality, from technical performance to the design effort required, production costs and satisfaction of later lifecycle requirements. This paper explores how computational tools can augment creative methods in product architecture design. Based on an empirical study aiming to understand the context of product architecture design, a new computational method is proposed to support this activity. In the method, product architectures-networks of components linked by connections- can be synthesised using constraints on the structure of the network to define the set of 'realisable' architectures for a product. An example illustrates how the method might be used on a real design problem, including the construction of an appropriate set of network structure constraints and the identification of promising architectures from the synthesis results. Preliminary evaluation of the method's usability, assessed through a laboratory experiment, and its utility, assessed through application to a real historical design problem, supported by initial validation by an engineer from the case study company, suggests that the method has value for engineering design practice.

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The complexity of modern gas turbine engines has led to the adoption of a modular design approach, in which a conceptual design phase fixes the values for a number of parameters and dimensions in order to facilitate the subdivision of the overall task into a number of simpler design problems. While making the overall problem more tractable, the introduction of these process-intrinsic constraints (such as flow areas and radii between adjacent stages) at a very early phase of the design process can limit the level of performance achievable, neglecting important regions of the design space and concealing important trade-offs between different modules or disciplines. While this approach has worked satisfactorily in the past, the continuous increase in components' efficiencies and performance makes further advances more difficult to achieve. Part I of this paper described the development of a system for the integrated design optimization of gas turbine engines: postponing the setting of the interface constraints to a point where more information is available facilitates better exploration of the available design space and better exploitation of the trade-offs between different disciplines and modules. In this second part of the paper, the proposed approach is applied to several test cases from the design of a three-spool gas turbine engine core compression system, demonstrating the risks associated with a modular design approach and the consistent gains achievable through the proposed integrated optimization approach.

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The design of gas turbine engines is a complex problem. This complexity has led to the adoption of a modular design approach, in which a conceptual design phase fixes the values for some global parameters and dimensions in order to facilitate the subdivision of the overall task into a number of simpler subproblems. This approach, while making a complex problem more tractable, necessarily has to rely on designer experience and simple evaluations to specify these process-intrinsic constraints at a point in the design process where very little knowledge about the final design exists. Later phases of the design process, using higher-fidelity tools but acting on a limited region of the design space, can only refine an already established design. While substantial improvements in performance have been possible with the current approach, further gains are becoming increasingly hard to achieve. A gas turbine is a complex multidisciplinary system: a more integrated design approach can facilitate a better exploitation of the trade-offs between different modules and disciplines, postponing the setting of these critical interface parameters (such as flow areas, radii, etc.) to a point where more information exists, reducing their impact on the final design. In the resulting large, possibly multimodal, highly constrained design space, and with a large number of objectives to be considered simultaneously, finding an optimal solution by simple trial-and-error can prove extremely difficult. A more intelligent search approach, in which a numerical optimizer takes the place of the human designer in seeking optimal designs, can enable the design space to be explored significantly more effectively, while also yielding a substantial reduction in development times thanks to the automation of the design process. This paper describes the development of a system for the integrated design and optimization of gas turbine engines, linking a metaheuristic optimizer to a geometry modeler and to evaluation tools with different levels of fidelity. In recognition of the substantial increase in design space size required by the integrated approach, an improved parameterization based on the concept of principal components' analysis was implemented, allowing a rotation of the design space along its most significant directions and a reduction in its dimensionality, proving essential for a faster and more effective exploration of the design space.

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Gas turbine compression systems are required to perform adequately over a range of operating conditions. Complexity has encouraged the conventional design process for compressors to focus initially on one operating point, usually the most commonor arduous, to draw up an outline design. Generally, only as this initial design is refined is its offdesign performance assessed in detail. Not only does this necessarily introduce a potentially costly and timeconsuming extra loop in the design process, but it also may result in a design whose offdesign behavior is suboptimal. Aversion of nonintrusive polynomial chaos was previously developed in which a set of orthonormal polynomials was generated to facilitate a rapid analysis of robustness in the presence of generic uncertainties with good accuracy. In this paper, this analysis method is incorporated in real time into the design process for the compression system of a three-shaft gas turbine aeroengine. This approach to robust optimization is shown to lead to designs that exhibit consistently improved system performance with reduced sensitivity to offdesign operation.

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The design of a gas turbine, or one of its constituent modules, is generally approached with some specific operating condition in mind (its design point). Unfortunately, engine components seldom exactly meet their specifications and do not operate at just one condition, but over a range of power settings. This simplification can then lead to a product that exhibits performance worse than nominal in real-world conditions. The integration of some consideration of robustness as an active part of the design process can allow products less sensitive to the presence of the noise factors commonly found in real-world environments to be obtained. To become routinely used as a design tool, minimization of the time required for robustness analysis is paramount. In this study, a nonintrusive polynomial chaos formulation is used to evaluate the variability in the performance of a generic modular-core compression system for a three-spool modern gas turbine engine subject to uncertain operating conditions with a defined probability density function. The standard orthogonal polynomials from the Askey scheme are replaced by a set of orthonormal polynomials calculated relative to the specific probability density function, improving the convergence of the method.

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In real-world optimization problems, large design spaces and conflicting objectives are often combined with a large number of constraints, resulting in a highly multi-modal, challenging, fragmented landscape. The local search at the heart of Tabu Search, while being one of its strengths in highly constrained optimization problems, requires a large number of evaluations per optimization step. In this work, a modification of the pattern search algorithm is proposed: this modification, based on a Principal Components' Analysis of the approximation set, allows both a re-alignment of the search directions, thereby creating a more effective parametrization, and also an informed reduction of the size of the design space itself. These changes make the optimization process more computationally efficient and more effective - higher quality solutions are identified in fewer iterations. These advantages are demonstrated on a number of standard analytical test functions (from the ZDT and DTLZ families) and on a real-world problem (the optimization of an axial compressor preliminary design).@en

Given the focus on incremental change in existing empirical aerodynamic design methods, radical, unintuitive, new optimal solutions in previously unexplored regions of design space are very unlikely to be found using them. We present a framework based on an implicit shape representation and a multiobjective evolutionary algorithm that aims to produce a variety of optimal flow topologies for a given requirement, providing designers with insights into possibly radical solutions. A revolutionary integrated flow simulation system developed specifically for design work is used to evaluate candidate designs.@en

In the last 15 years, automatic design optimisation has been the subject of ever growing interest, thanks to the development of ever more reliable analysis software, efficient optimisation methods and powerful computers. Even in industrial environments, design optimisation is extensively used in a number of disciplines, such as aerodynamics, structures and many more applications. Better designs in shorter times and clearer trade-offs between different objectives and disciplines are the most generally recognised advantages.22 The parameterisation represents the " critical enabling factor" for an efficient exploration of the design space: it is essential to ensure that the parameterisation scheme is able to cover all feasible designs, in order not to lose potentially good designs, but also that the minimum possible number of parameters is used, since these affect the size of the design space and thus the convergence time of the optimiser. Irrespective of the specific parameterisation technique, the optimisation of complex products is likely to translate into a large design space and an a priori reduction is not advisable, as this could reduce the generality of the paramenterisation and lead to the loss of potentially good designs. Kipouros et al.16 have presented a method for selecting only the most significant components based on the results of a preliminary optimisation run. In the present study, a method based on Principal Components' Analysis is introduced: given a generic parameterisation, this allows an optimal representation to be derived that will facilitate a faster and more complete exploration of the design space. The advantages of this approach are demonstrated through two optimisation test cases from the design of a core compression system for a three-spool modern turbofan engine.@en

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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Engineering design commonly assumes nominal values for uncertain parameters to simplify the design process: the design of a gas turbine, or one of its modules, is generally approached with some specific operating conditions in mind (its design point). Unfortunately, engine components never exactly meet their specifications and do not operate at just one condition, but over a range of power settings. This simplification can then lead to a product that exhibits performance significantly worse than nominal in real-world conditions. This problem is exacerbated in the presence of heavily optimised designs, which tend to lie in extreme regions of the design space.15 In gas turbine design, safe and satisfactory off-design operation must be guaranteed and is generally evaluated before moving to the next phase of the design process. This approach, while guaranteeing that some minimum requirements are met, introduces a further loop in the design process and does not ensure the final design will be optimal with respect to this new requirement. The introduction of some robustness considerations into the design process can reduce the level of fragmentation and iteration typical of gas turbine engine design and produce further (and more robust) improvements relative to the traditional method. In this study, two approaches for dealing with off-design performance analysis are presented, integrated into an automatic optimisation system and applied to the preliminary design of a core compression system from a three-spool modern turbofan engine. Designs that are more robust than those found if only design-point performance is considered are successfully identified.@en

Aeroengines are designed using fractured processes. Complexity has driven the design of Such machines to be subdivided by specialism, customer and function. While this approach has worked well in the past, with component efficiencies, current material performance, and the possibilities presented by scaling existing designs for future needs becoming progressively exhausted, it is necessary to reverse this process of disintegration. Our research addresses this aim. The strategy we use has two symbiotic arms. The first is an open data architecture from which existing disparate design codes all derive their input and to which all send their output. The second is a dynamic design process management system known as "SignPosting." Both the design codes and parameters are arranged into complementary multiple level hierarchies: fundamental to the successful implementation of our strategy is the robustness of the mechanisms we have developed to ensure consistency in this environment as the design develops over time. One of the key benefits of adopting a hierarchical structure is that it confers not only the ability to use mean-line, throughflow, and fully 3D computational fluid dynamics techniques in the same environment, but also to cross specialism boundaries and to insert mechanical, material, thermal, electrical, and structural codes, enabling, exploration of the design space for multi-disciplinary nonlinear responses to design changes and their exploitation. We present results from trials of an early version of the system applied to the redesign of a generic civil aeroengine core compressor. SignPosting has allowed us to examine the hardness of design constraints across disciplines which has shown that it is far more profitable not to strive for even higher aerodynamic performance, but rather to improve the commercial performance by maintaining design and part-speed pressure ratio stability and efficiency while increasing rotor blade creep life by up to 70%.

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Modern turbofan engines are designed with pressure ratios as high as 40 and with bypass ratios in excess of 8. Multiple spools with substantial radial offset are essential to achieve the desired design and off-design performance and s-shaped ducts are necessary to deliver the flow from the exit of a turbomachine to the inlet of the following one in the minimum possible space to reduce the weight and the size of the gas turbine, without the risk of incurring in flow separation. In the absence of practical design rules or performance correlations for annular s-shaped ducts, a practical design approach could be the use of Computational Fluid Dynamics (CFD) for performance evaluation together with optimisation techniques for seeking the best design. Numerical optimisation methods (either deterministic or stochastic) can be linked to CFD solvers to provide a more thorough exploration of the design space, trying to produce a better design than the datum one, subject to a number of constraints. This paper reports the development and testing of an automatic framework for design optimisation of s-shaped ducts and its initial application to the optimisation of an axial-symmetric inter-compressor duct.

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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 first reviews the application of design optimization techniques to axial compressor blade design, commenting on the feasibility of this approach for highly constrained, highly dimensional problems. It then analyzes an innovative method to characterize the design space, based on proper orthogonal decomposition (POD). The method was used to generate a reduced order model (ROM) from the true complex system, and revealed its potential to find optimum solutions in selected regions of the design space.

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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

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

In 2002 the UK Department of Health and the Design Council jointly commissioned a scoping study to deliver ideas and practical recommendations for a design approach to reduce the risk of medical error and improve patient safety across the National Health Service (NHS). The research was undertaken by the Engineering Design Centre at the University of Cambridge, the Robens Institute for Health Ergonomics at the University of Surrey and the Helen Hamlyn Research Centre at the Royal College of Art. The research team employed diverse methods to gather evidence from literature, key stakeholders, and experts from within healthcare and other safety-critical industries in order to ascertain how the design of systems - equipment and other physical artefacts, working practices and information - could contribute to patient safety. Despite the multiplicity of activities and methodologies employed, what emerged from the research was a very consistent picture. This convergence pointed to the need to better understand the healthcare system, including the users of that system, as the context into which specific design solutions must be delivered. Without that broader understanding there can be no certainty that any single design will contribute to reducing medical error and the consequential cost thereof.

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Industry is facing ever-increasing demands for more performance at less cost. Current emphasis is on improving the design process to yield designs, at least technically equivalent to the present, more quickly. The surge-stagnate model (SSM) is introduced to provide an analysis framework, describing the way in which progress in design occasionally plateaus before striding forward. It has been found that there are two methods of accelerating the design, both may be achieved by the use of 'understanding'. The existence of the SSM has been verified through observation of case studies, and the results of one such study of a large steam turbine design are presented.@en

It is widely acknowledged that a company's ability to aquire market share, and hence its profitability, is very closely linked to the speed with which it can produce a new design. Indeed, a study by the U.K. Department of Trade and Industry has shown that the critical factor which determines profitability is the timely delivery of the new product. Late entry to market or high production costs dramatically reduce profits whilst an overrun on development cost has little significant effect. This paper describes a method which aims to assist the designer in producing higher performance turbomachinery designs more quickly by accelerating the process by which they are produced. The adopted approach combines an enhanced version of the 'Signposting' design process management methodology with industry-standard analysis codes and Computational Fluid Dynamics (CFD). It has been specifically configured to enable process-wide iteration, near instantaneous generation of guidance data for the designer and fully automatic data handling. A successful laboratory experiment based on the design of a large High Pressure Steam Turbine is described and this leads on to current work which incorporates the extension of the proven concept to a full industrial application for the design of Aeroengine Compressors with Rolls-Royce plc.

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