Calibrating the Discrete Boundary Conditions of a Dynamic Simulation: A Combinatorial Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) Approach.

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters.

Microphone array analysis of the first non-axisymmetric mode for the detection of pipe conditions.

This paper reports on the use of a circular microphone array to analyze the reflections from a pipe defect with enhanced resolution. A Bayesian maximum a posteriori algorithm is combined with the mode decomposition approach to localize pipe defects with six or fewer microphones. Unlike all previous acoustic reflectometry techniques, which only estimate the location of a pipe defect along the pipe, the proposed method uses the phase information about the wave propagated in the form of the first non-axisymmetric mode to estimate its circumferential position as well as axial location.

Boundary admittance estimation for wave-based acoustic simulations using Bayesian inference.

Acoustic surface admittance/impedance at domain boundaries is essential for wave-based acoustic simulations. This work applies two levels of Bayesian inference to estimate the order and the parameter values of the multipole admittance model. The frequency-dependent acoustic admittance is experimentally measured.

Low frequency attenuation of acoustic waves in a perforated pipe.

This paper presents new experimental and numerical evidence that perforations in a pipe wall result in a low-frequency bandgap within which sound waves rapidly attenuate. These perforations are modelled as an acoustically soft boundary condition on the pipe wall and show that a low frequency bandgap is created from 0 Hz. The upper bound of this bandgap is determined by the dimensions and separation of the perforations.

Microphone array analysis for simultaneous condition detection, localization, and classification in a pipe.

An acoustic method for simultaneous condition detection, localization, and classification in air-filled pipes is proposed. The contribution of this work is threefold: (1) a microphone array is used to extend the usable acoustic frequency range to estimate the reflection coefficient from blockages and lateral connections; (2) a robust regularization method of sparse representation based on a wavelet basis function is adapted to reduce the background noise in acoustical data; and (3) the wavelet components are used to localize and classify the condition of the pipe. The microphone array and sparse representation method enhance the acoustical signal reflected from blockages and lateral connections and suppress unwanted higher-order modes.