Algorithmic techniques for the acoustical analysis of exhaust systems
thesisposted on 2013-08-13, 13:54 authored by John F. Dowling
One dimensional, linear, plane-wave modelling of silencer systems in the frequency domain provides an efficient means to analyse their acoustic performance. Software packages are available to analyse silencers within these modelling parameters; however, they are heavily restricted. The thesis develops an algorithm that increases the computational efficiency of the silencer analysis. The thesis concentrates on how data, within a software package, is stored, retrieved and analysed. The computational efficiency is increased as a result of the predictable patterns caused by the repetitive nature of exhaust system analysis. The work uses the knowledge gained from the construction of two previous algorithms of similar parameters; it isolates and maximises their advantages whilst minimising their associated disadvantages. The new algorithm is dependent on identifying consecutively sequenced exhaust components and sub-systems of such components within the whole exhaust system. The algorithm is further generalised to include multiple time-variant sources, multiple radiation points and exhaust systems that have a balance pipe. Another feature of the improved algorithm encompasses the option of modelling secondary noise sources such as might arise from flow generated noise or be included for active noise cancellation systems. The validation of these algorithmic techniques is demonstrated by comparison of the theoretical noise predictions with experimental or known results. These predictions are achieved by writing computational code using object orientated programming techniques in the language of c++ to implement the algorithms.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Publisher© John F Dowling
NotesA Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University
EThOS Persistent IDuk.bl.ethos.421920