Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.722875
Title: Inverse methods in cochlear mechanics
Author: Sun, Luyang
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2016
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Abstract:
Cochlear modelling is used to provide insight into the physical mechanics of the cochlea. The complicated, three dimensional geometry of the fluid chambers in the cochlea is often represented in models of its mechanics by a box with a uniform area along its length. The first part of this thesis is concerned with the development of a tapered box model of the cochlea, in which the geometry of the cochlea is assumed to vary in a linear way along its length. Previous measurements of the variation in area of the two fluid chambers along the length of the cochlea in various mammals has been used to calculate a linear fit to the variation in the "effective area" that determines the 1D fluid coupling. The width of the basilar membrane is also assumed to vary linearly along the length of the model. The analytic form of the 1D fluid pressure distribution due to elemental BM motion is derived for this tapered box model, together with the added mass due to near field acoustic coupling. The coupled response in the 1D and 3D, uniform and tapered box model of passive cochlea can then be readily calculated. Although the form of the fluid coupling are very different in the uniform and tapered box models, the distribution of the basilar membrane vibration in the coupled models are very similar. The second part of the thesis is concerned with deriving the parameters of cochlear models from measured data using inverse methods. Previous inverse methods are first reviewed before a novel direct method is introduced, based on modelling the poles and zeros of the micromechanical response. This is compared with other inverse methods, using previously measured data on basilar membrane vibration in the cochlea, and relatively simple models are shown to provide a good fit to the data.
Supervisor: Elliott, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.722875  DOI: Not available
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