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The design of large complex systems, such as warehouses, requires multiple experts and analyses as well as methods to organise and integrate their knowledge.While there are many models for optimising individual aspects of warehouses, there is not, today, a comprehensive design methodology that incorporates and supports all of the design decisions and provides a method to effectively integrate the solutions to these subproblems into a complete warehouse system specification. In this research, we propose a hierarchical design decision support methodology based on decomposing the design problem into a set of subproblems and using a formal model of the system to integrate the solutions to these subproblems. The methodology enables a thorough search of the design space and the identification ofmany candidate designs for consideration by the design decision maker. The hierarchical design methodology is demonstrated with an example of designing a forward pick area. [ABSTRACT FROM AUTHOR]

Two vehicle chassis design tasks were solved by decomposition-based multi-disciplinary optimisation (MDO) methods, namely collaborative optimisation (CO) and analytical target cascading (ATC). A passive suspension system was optimised by applying both CO and ATC. Multiple parameters of the spring and damper were selected as design variables. The discomfort, road holding, and total mass of the spring–damper combination were the objective functions. An electric vehicle (EV) powertrain design problem was considered as the second test case. Energy consumption and gradeability were optimised by including the design of the electric motor and the battery pack layout. The standard single-level all-in-one (AiO) multi-objective optimisation method was compared with ATC and CO methods. AiO methods showed some limitations in terms of efficiency and accuracy. ATC proved to be the best choice for the design problems presented in this paper, since it provided solutions with good accuracy in a very efficient way. The proposed investigation on MDO methods can be useful for designers, to choose the proper optimisation approach, while solving complex vehicle design problems. [ABSTRACT FROM AUTHOR]

Historically, squeak and rattle (S&R) sounds have been among the top quality problems and a major contributor to the warranty costs in passenger cars. Geometric variation is among the main causes of S&R. Though, geometric variation analysis and robust design techniques have been passively involved in the open-loop design activities in the predesign-freeze phases of car development. Despite the successful application of topometry optimisation to enhance attributes such as weight, durability, noise and vibration and crashworthiness in passenger cars, the implementation of closed-loop structural optimisation in the robust design context to reduce the risk for S&R has been limited. In this respect, the main obstacles have been the demanding computational resources and the absence of quantified S&R risk evaluation methods. In this work, a topometry optimisation approach is proposed to involve the geometric variation analysis in an attribute balancing problem together with the dynamic response of the system. The proposed method was used to identify the potential areas of a door component that needed structural reinforcement. The main objective was to enhance the design robustness to minimise the risk for S&R by improving the system response to static geometrical uncertainties and dynamic excitation.

Historically, squeak and rattle (S&R) sounds have been among the top quality problems and a major contributor to the warranty costs in passenger cars. Geometric variation is among the main causes of S&R. Though, geometric variation analysis and robust design techniques have been passively involved in the open-loop design activities in the predesign-freeze phases of car development. Despite the successful application of topometry optimisation to enhance attributes such as weight, durability, noise and vibration and crashworthiness in passenger cars, the implementation of closed-loop structural optimisation in the robust design context to reduce the risk for S&R has been limited. In this respect, the main obstacles have been the demanding computational resources and the absence of quantified S&R risk evaluation methods. In this work, a topometry optimisation approach is proposed to involve the geometric variation analysis in an attribute balancing problem together with the dynamic response of the system. The proposed method was used to identify the potential areas of a door component that needed structural reinforcement. The main objective was to enhance the design robustness to minimise the risk for S&R by improving the system response to static geometrical uncertainties and dynamic excitation. [ABSTRACT FROM AUTHOR]

Two vehicle chassis design tasks were solved by decomposition-based multi-disciplinary optimisation (MDO) methods, namely collaborative optimisation (CO) and analytical target cascading (ATC). A passive suspension system was optimised by applying both CO and ATC. Multiple parameters of the spring and damper were selected as design variables. The discomfort, road holding, and total mass of the spring–damper combination were the objective functions. An electric vehicle (EV) powertrain design problem was considered as the second test case. Energy consumption and gradeability were optimised by including the design of the electric motor and the battery pack layout. The standard single-level all-in-one (AiO) multi-objective optimisation method was compared with ATC and CO methods. AiO methods showed some limitations in terms of efficiency and accuracy. ATC proved to be the best choice for the design problems presented in this paper, since it provided solutions with good accuracy in a very efficient way. The proposed investigation on MDO methods can be useful for designers, to choose the proper optimisation approach, while solving complex vehicle design problems.

Piezoelectric materials are excellent transducers in converting vibrational energy into electrical energy, and vibration-based piezoelectric generators are seen as an enabling technology for wireless sensor networks, especially in selfpowered devices. This paper proposes an alternative method for predicting the power output of a bimorph cantilever beam using a finite element method for both static and dynamic frequency analyses. Experiments are performed to validate the model and the simulation results. In addition, a novel approach is presented for optimising the structure of the bimorph cantilever beam, by which the power output is maximised and the structural volume is minimised simultaneously. Finally, the results of the optimised design are presented and compared with other designs.

At the preliminary design phase of a gas turbine, time is crucial in capturing new business opportunities. In order to minimise the design time, the concept of Preliminary Multi-Disciplinary Optimisation (PMDO) was used to create parametric models, geometry and cooling flow correlations towards a new design process for turbine housing and shroud segments. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterised models allows more design iterations and reduces set-up and design time. A user interface was developed to interact with the parametric models and improve the design time. Second, geometry correlations were created to minimise the number of parameters used in turbine housing and shroud segment design. Third, a correlation study was conducted to minimise the number of engine parameters required in cooling flow predictions. The parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% compared to the existing design system. For the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process. [ABSTRACT FROM PUBLISHER]

Piezoelectric materials are excellent transducers for converting mechanical energy from the environment for use as electrical energy. The conversion of mechanical energy to electrical energy is a key component in the development of self-powered devices, especially enabling technology for wireless sensor networks. This paper proposes an alternative method for predicting the power output of a bimorph cantilever beam using a finite-element method for both static and dynamic frequency analyses. A novel approach is presented for optimising the cantilever beam, by which the power density is maximised and the structural volume is minimised simultaneously. A two-stage optimisation is performed, i.e., a shape optimisation and then a 'topology' hole opening optimisation. [ABSTRACT FROM AUTHOR]

Abstract: Aircraft manufacturers are facing several challenges in the pre-design of aircraft structures. This early stage of the aircraft design has a very multi-disciplinary character. Different competence centres need input data, which is at this point in time to a large extent undefined. Therefore, a large variety of specialised tools is used in order to estimate and predict the required data. If these tools are not compatible, interface problems are the consequence. A permanent improvement of the applied processes with regard to the informal value as well as the applicability remains a continuous challenge. The objective of a collaboration project between Airbus Germany GmbH, the DLR Braunschweig, and the ETH Zurich is to find new methods and approaches to improve accuracy, efficiency, and flexibility of data prediction for primary aircraft structures. The use of modern CAE systems together with the integration of finite element methods into the early pre-design process is a very promising approach [F. Bianconi, P. Conti, N. Senin, D.R. Wallace, CAE systems and distributed design environments, in: XII ADM International Conference, Italy, 5–7 September, 2001 ; M. Pellicciari, G. Barbanti, A.O. Andrisano, Functional requirements for a modern CAD system, in: XII ADM International Conference, Italy, 5–7 September, 2001 ; T. Richter, H. Mechler, D. Schmitt, Integrated parametric aircraft design, in: ICAS 2002 Congress, Institute of Aeronautical Engineering, TU Munich]. The modular and knowledge-based architecture of modern CAE systems allows to represent complex assemblies like aircraft structures by parametric associative and very dynamic models. Design knowledge can be integrated into the modelling [M. Mäntylä, S. Finger, T. Tomiyama, Knowledge Intensive CAD, vol. 2, Chapman & Hall, 1997 ] and different characteristics or individuals of the same structure can be mapped through parameters. This document presents concepts, which allow to design comprehensive digital models of novel aircraft structures whereas the level of the modelling detail shall be variegated flexibly [D.E. Whitney, R. Mantripragada, J.D. Adams, S.J. Rhee, Designing assemblies, Res. Engrg. Design 11 (1999) 229–253 ; P. Aspettati, S. Barone, A. Curcio, M. Picone, Parametric and feature-based assembly in motorcycle design: from preliminary development to detail definition, in: XII ADM International Conference, Italy, 5–7 September, 2001]. The strongly parameterised structures allow calculating and assessing different individuals of a given structure in a very efficient and automated way. This makes parametric associative structures very suitable for optimisation. After structural optimisation tasks have successfully been performed with parametric models [U.M. Fasel, O. König, M. Wintermantel, N. Zehnder, P. Ermanni, DynOPS – an approach to parameter optimization with arbitrary simulation software, Centre of Structure Technologies, ETH Zurich; O. König, R. Puisa, M. Wintermantel, P. Ermanni, CAD-entity based evolutionary design optimization, Centre of Structure Technologies, ETH Zurich, and VGTU, Faculty of Mechanics, Vilnius, Lithuania; U.M. Fasel, O. König, M. Wintermantel, P. Ermanni, Using evolutionary methods with a heterogeneous genotype representation for design optimization of a tubular steel trellis motorbike-frame, Centre of Structure Technologies, ETH Zurich], multi-disciplinary optimisations are gaining importance, since they have the potential to find global optima instead of the discipline-dependent optimal configurations and solutions. [Copyright &y& Elsevier]