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dc.rights.licenseCC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorXiang, Ning
dc.contributorBraasch, Jonas
dc.contributorScarton, Henry A.
dc.contributorMarkov, Ivan
dc.contributorKnuth, Kevin H.
dc.contributor.authorFackler, Cameron Jeff
dc.date.accessioned2021-11-03T08:16:57Z
dc.date.available2021-11-03T08:16:57Z
dc.date.created2015-03-09T09:49:25Z
dc.date.issued2014-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1258
dc.descriptionDecember 2014
dc.descriptionSchool of Architecture
dc.description.abstractTraditional porous materials are widely used as sound absorbers. Additionally, other substances such as soils or sediments may be modeled as porous materials. When studying and attempting to predict the acoustic properties of such materials, knowing the physical properties of the material is essential. A Bayesian approach to infer these physical parameters from an acoustic measurement is developed. In addition to determining the values and associated uncertainties of the physical material parameters, the Bayesian method is able to determine the number of porous layers present when applied to data measured from a multilayer material.
dc.description.abstractNew methods for the analysis and design of multilayer sound absorbers, utilizing a model-based Bayesian inference approach, are proposed. Additionally, a Bayesian method for calibrating impedance tubes, widely used to measure the acoustic properties of sound absorbing materials, is developed. Impedance tubes provide a convenient way to characterize the normal-incidence acoustic properties of materials. These measurements rely on accurately knowing the positions of microphones sensing the sound field inside the tube; these positions must be determined acoustically since the physical dimensions of the microphones are larger than the required precision. Using a calibration measurement of the empty tube, the method developed here determines the acoustic positions and their uncertainties for the microphones of an impedance tube.
dc.description.abstractMicroperforated panel absorbers are an exciting, relatively new type of sound absorber, requiring no traditional fibrous materials. The provided absorption, however, has a narrow frequency bandwidth. To provide a more broadband absorption, multiple microperforated panels may be combined into a multilayer absorber, but this yields a difficult design challenge. Here, the Bayesian framework is used to design such multilayer microperforated panels. This provides a method that automatically determines the minimum number of layers required and the design parameters for each layer of a multilayer arrangement yielding a desired acoustic absorption profile.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectArchitectural sciences
dc.titleBayesian model selection for analysis and design of multilayer sound absorbers
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid174646
dc.digitool.pid174647
dc.digitool.pid174648
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentSchool of Architecture


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CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.