Embedding measured data within a computational framework for vibro-acoustic design

The design of products to achieve acceptable levels of noise and vibration is a major concern across a range of industries. In many cases there is a large trade off between cost and performance, and this means that achieving an efficient design is crucial to commercial success. In principle design optimisation can be achieved through testing and improving physical prototypes, but the production of a prototype is time consuming and costly. For this reason there is a pressing need for virtual design methodologies, in which computational models are used to produce a near-final design before a physical prototype is built. Computational models used for noise and vibration analysis must be able to predict the performance of the system over a wide frequency range, potentially ranging from low frequency vibration problems at several hertz to high frequency noise problems at several kilohertz, and this presents severe difficulties. High frequency motions require a very detailed computer model, and this leads to long run times that are not ideal for iterative design. Furthermore, the high frequency performance of a system can be very sensitive to small manufacturing imperfections, and hence the predicted performance may not match the performance of the actual system. These difficulties can be largely overcome by employing recent advances in noise and vibration modelling in which a technique known as Statistical Energy Analysis (SEA) is combined with more conventional analysis methods such as the finite element method (FEM) or the boundary element method (BEM); this approach is known as the Hybrid Method. The Hybrid Method leads to a very large reduction in the run time of the model, while also providing an estimate of the variance in the performance caused by manufacturing imperfections. However, this approach does not fully solve the prediction problem, as a further major difficulty remains: some components in a system can be so complex that it is not possible to produce a detailed computational model of the component, and hence some degree of physical testing is unavoidable. Frequently experimental measurements are used to validate a computational model, or to update the parameters in a computational model, but the requirement here is quite different: the measured data must be used to complete the computational model by coupling a representation of the missing complex component to the other parts of the model. This issue forms the core of the current research proposal.

The aim of the present work is to add "experimental" components to the Hybrid Method, and one way to do this is to model a component as a grey or black box: a grey box model consists of mathematical equations with experimentally determined parameters, while a black box model is based purely on measured input-output properties. These models must be capable of being coupled to either FEM, BEM, or SEA component models, and the project will address this issue. A major challenge is to determine the appropriate experimental tests and machine learning algorithms that are required to produce such models in the context of complex vibro-acoustic components. A second major challenge is to quantify the uncertainty in such models, and to include this uncertainty in the combined system model. The model must predict outputs that are useful to the designer, and such outputs include noise and vibration levels, together with uncertainty bounds on the predictions. In some cases "sound quality" rather than the overall noise level is of concern, and the project will develop techniques for the "auralisation" of the output of the combined model. A number of case studies will be developed with industrial partners to explore the application of the proposed approach.

The present research programme will produce an efficient and reliable vibro-acoustic "design by science" prediction tool that meets the needs of a wide range of industrial sectors.

In the medium and long term the proposed research will benefit the environment by enabling design of quieter products with lower weight and reduced energy consumption. Quieter products are important because noise is known to impact negatively on quality of life and health so many people will benefit through quieter environments and workplaces, reduced stress and improved ability to communicate in private and public transport.
The main benefits though will be economic, benefitting partner companies directly and feeding into the UK economy. 'Design by science' is of major importance to the UK economy; around half our exports are manufactured goods in which design engineering plays a major role in, for example, automotive, aerospace, rail and domestic products etc. Companies working in the sector are able to generate wealth by selling products in global markets and major part of their work is to design for better performance and lower cost.

Nowadays rather than build physical prototypes, which is relatively slow and expensive, designers want to give themselves a competitive advantage by doing all their design virtually, with computer modelling. 'Virtual prototyping' has advanced enormously in the last decade or so, and even very complex performance parameters can be predicted. However, modelling of product sound is not as reliable as most other performance parameters. Some individual components can be modelled vibro-acoustically but others can't which leaves gaps in the overall model of the assembled product: with only a partial model of the assembly it is difficult or impossible to predict the sound of the product.

Is product sound important? Design engineering companies think so, for three main reasons. First, nearly all products cannot be sold unless they meet noise limits. Secondly, a product that sounds 'wrong' is likely to be returned even if it works perfectly and returns are very expensive for the manufacturer. Thirdly, sound is very important for brand image even to the extent that it can make or break the reputation of a product. So acoustic design is highly important for designers, but the risks are high because problems cannot be predicted until late in the design cycle when it may be too late to fix them economically.

The proposal aims to help the designers to pursue their virtual prototyping philosophy by using measured data to fill the gaps left by components that can't be modelled. With a full model of the assembled product, the product sound can then be 'auralised' to allow the virtual product to be heard. Because of the importance of product sound and the difficulty of modelling, the techniques should offer competitive advantages through reduced time to market and prototyping costs.

Two of the partners are important contributors to the UK economy in different engineering sectors: automotive (Bentley), high value domestic products (Dyson). The new techniques will be developed in conjunction with these companies to ensure that they generate a positive impact on their virtual prototyping developments and hence benefit the UK economy.

There are two other companies in the consortium both of which produce design tools: Bruel&Kjaer supply a vehicle simulator to the automotive industry which can be enhanced by incorporating measured data for vehicle components that can't be modelled. WaveSix produce software for vibro-acoustic prediction. The tools developed by these companies are expected to generate much wider impact by making the techniques available across the whole range of engineering companies.

We will also make data from our experiments available online together with worked examples to help other design engineering companies to adopt the techniques.