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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]

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]