The unwanted sound, noise, can lead to health problems, e.g. hearing loss and stress-related problems. A pre-knowledge of noise generation by machines is of great importance due to the ever-shorter product development cycles and stricter noise legislation. The noise from a machine radiates to the environment indirectly via the foundation structure and directly via the surrounding fluid. A fluid machine converts the energy from the fluid into mechanical energy or vice versa. Examples of the fluid machines are internal combustion engines (IC-engines), pumps, compressors, and fans. Predicting and controlling noise from a fluid machine requires a model of the noise sources themselves, i.e. acoustic source data. In the duct systems connected to the fluid machines, the acoustic source interacts strongly with the system boundaries, and the source characteristics must be described using in-duct methods. Above a certain frequency, i.e. first non-plane wave mode cut-on frequency, the sound pressure varies over the duct cross-section and non-plane waves are introduced. For a number of applications, the plane wave range dominates and the non-plane waves can be neglected. But for machines connected to large ducts, the non-plane wave range is also important. In the plane wave range, one-dimensional process simulation software can be used to predict, e.g. for IC-engines, the acoustic in-duct source characteristics. The high frequency phenomena with non-plane waves are so complicated, however, that it is practically impossible to simulate them accurately. Thus, in order to develop methods to estimate the sound produced, experimental studies are also essential. This thesis investigates the acoustic in-duct source characterization of fluid machines with applications to exhaust noise from medium speed IC-engines.  This corresponds to large engines used for power plants or on ships, for which the non-plane wave range also becomes important. The plane wave source characterization methods are extended into the higher frequency range with non-plane waves. In addition, methods to determine non-plane wave range damping for typical elements in exhaust systems, e.g. after-treatment devices, are discussed. / <p>QC 20151119</p>

Coronary artery disease (CAD) remains the leading cause of death in both the United States and the world at large. This is primarily due to the extreme difficulty associated with preemptive diagnosis of CAD. Currently, only about 20% of all patients are diagnosed with CAD prior to the occurrence of a heart attack. This is the result of limitations in current techniques, which are either noninvasive, extremely expensive, or have very poor correlation with the actual disease state of the patient. Phonoangiography is an alternative approach to the diagnosis of CAD that relies upon detection of the sound generated by turbulent flow downstream from occlusions. Although the technique is commonly used for the carotid arteries, in the case of the coronary arteries the technique is hampered by signal-to-noise problems as well as uncertainty regarding the spectral characteristics associated with CAD. To date, these signal processing difficulties have prevented the use of the technique clinically. This research introduces an alternative approach to the processing of phonoangiographic data based upon knowledge of the acoustic transfer within the chest. The validity of the proposed approach was examined using transfer functions which were calculated for 14 physiologically relevant locations within the chest using a 2-D Finite Element Model (FEM) generated from physiologic data. These transfer functions were then used to demonstrate the technique using test cases generated with the FEM. Finally, the vulnerability of the technique to noise was quantified through calculation of matrix condition numbers for the chest acoustic transfer at each frequency. These results show that while in general the technique is susceptible to noise; noise tolerance is greatly improved within the frequency range most likely to correspond to an occlusion. Taken together, these results suggest that the proposed technique has the potential to make phonoangiography viable as a screening technique for CAD. Such a technique would greatly reduce the cost of CAD, measured in terms of both financial cost as well as lives. / Master of Science

Acoustic source localization (ASL) is pinpointing an acoustic source. ASL can reveal the point of impact of a foreign object or the point of crack initiation in a structure. ASL is necessary for continuous health monitoring of a structure. ASL in an anisotropic plate is a challenging task. This dissertation aims to investigate techniques that are currently being used to precisely determine an acoustic source location in an anisotropic plate without knowing its material properties. A new technique is developed and presented here to overcome the existing shortcomings of the acoustic source localization in anisotropic plates. It is done by changing the analysis perspective from the angular dependent group velocity of the wave and its straight line propagation to the wave front shapes and their geometric properties when a non-circular wave front is generated. Especially, 'rhombic wave front' and 'elliptical wave front' are dealt with because they are readily observed in highly anisotropic composite plates. Once each proposed technique meets the requirements of measurement, four sensor clusters in three different quadrants (recorded by 12 sensors) for the rhombus and at least three sensor clusters (recorded by 9 sensors) for the ellipse, accurate Acoustic Source Localization is obtained. It has been successfully demonstrated in the numerical simulations. In addition, a series of experimental tests demonstrate reliable and robust prediction performance of the developed new acoustic source localization technique.

The distributed point source method, or DPSM, developed in the last decade has been used for solving various engineering problems-such as elastic and electromagnetic wave propagation, electrostatic, and fluid flow problems. Based on a semi-analytical formulation, the DPSM solution is generally built by superimposing the point source solutions or Green's functions. However, the DPSM solution can be also obtained by superimposing elemental solutions of volume sources having some source density called the equivalent source density (ESD). In earlier works mostly point sources were used. In this paper the DPSM formulation is modified to introduce a new kind of ESD, replacing the classical single point source by a family of point sources that are referred to as quantum sources. The proposed formulation with these quantum sources do not change the dimension of the global matrix to be inverted to solve the problem when compared with the classical point source-based DPSM formulation. To assess the performance of this new formulation, the ultrasonic field generated by a circular planer transducer was compared with the classical DPSM formulation and analytical solution. The results show a significant improvement in the near field computation.

No description available.

The overall goal of this thesis is to develop non-linear Bayesian methods for three-dimensional tracking of a moving acoustic source in shallow water despite environmental uncertainty, with application to data from a horizontal line array (HLA) of hydrophones. As a precursor, Bayesian geoacoustic inversion is applied to estimate seabed model parameters and their uncertainties. A simulation study examines the effect of source and array factors on geoacoustic information content in matched-field inversion of HLA data, as quantified in terms of model parameter uncertainties. Bayesian geoacoustic inversion is applied to both controlled-source and ship-noise data from a HLA deployed on the seafloor in a shallow-water experiment conducted in the Barents Sea. A new approach is introduced to account for data error reduction due to averaging data over time-series subsegments (snapshots), based on empirically apportioning measurement and theory error, with effects on inversion results compared to those of existing approaches. It is further demonstrated that combining data from multiple, independent time-series segments (for a moving source) in the inversion can significantly reduce geoacoustic parameter uncertainties. Geoacoustic uncertainties are also shown to depend on ship range and orientation, with lowest uncertainties for short ranges and for the ship stern/propeller oriented toward the array. Sediment sound-speed profile and density estimates from controlled-source and ship-noise data inversions are found to be in good agreement with values from geophysical measurements. Two non-linear Bayesian matched-field inversion approaches are developed for three-dimensional source tracking despite environmental uncertainty. Focalization-tracking maximizes the posterior probability density (PPD) over track and environmental parameters. Synthetic test cases show that the algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those achieved with exact environmental knowledge. Marginalization-tracking integrates the PPD over environmental parameters to obtain joint marginal distributions over source coordinates, from which track uncertainty estimates and the most probable track are extracted. Both approaches are applied to data from the Barents Sea experiment. Focalization-tracking successfully estimates the tracks of the towed source and a surface ship in cases where simpler tracking algorithms fail. Marginalization-tracking generally outperforms focalization-tracking and gives uncertainty estimates that encompass the true tracks.

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

L’objectif de ces travaux est le développement d’une méthode d’imagerie de sources optoacoustiques en milieu solide. Afin d’analyser l’influence de la géométrie de la source sur les ondes acoustiques qu’elle génère et ainsi prévoir qualitativement si l’image de la source peut être obtenue avec une précision suffisante, la directivité d’une source acoustique est d’abord considérée. Pour quantifier plus précisément cette influence, la résolution complète de la propagation des ondes générées par cette source est ensuite menée. L’analyse de l’influence de l’incidence oblique d’un faisceau laser lors de la génération photoacoustique est proposée en illustration. La seconde partie de ce manuscrit est consacrée à la résolution du problème inverse qui permet d’obtenir l’image de la source initiale. Un algorithme de rétropropagation est alors mis en place. Il est basé sur les principes du retournement temporel et de Huygens, et simule la propagation, en milieu solide, des ondes mesurées vers la position initiale de la source acoustique. / The purpose of this work is to develop a method to image optoacoustic sources in solid media. To analyze the influence of the source geometry on laser-generated acoustic waves and thus qualitatively predict if the image of the source can be obtained with sufficient accuracy, the directivity of an acoustic source is considered first. In order to quantify more precisely this influence, the complete resolution of the propagation of acoustic waves generated by this source is then achieved. The analysis of the effect of oblique incidence of a laser beam in the photoacoustic generation process is proposed as an illustration. The second part of this thesis is devoted to the inverse problem resolution, which provides the image of the initial acoustic source. A backpropagation algorithm is then implemented. It is based on the time-reversal principle and the Huygens’ principle, and simulates, in a solid medium, the propagation of the measured acoustic waves back to the initial position of the acoustic source.

No description available.

L’imagerie acoustique est une technique performante pour la localisation et la reconstruction de puissance des sources acoustiques en utilisant des mesures limitées au réseau des microphones. Elle est largement utilisée pour évaluer l’influence acoustique dans l’industrie automobile et aéronautique. Les méthodes d’imagerie acoustique impliquent souvent un modèle direct de propagation acoustique et l’inversion de ce modèle direct. Cependant, cette inversion provoque généralement un problème inverse mal-posé. Par conséquent, les méthodes classiques ne permettent d’obtenir de manière satisfaisante ni une haute résolution spatiale, ni une dynamique large de la puissance acoustique. Dans cette thèse, nous avons tout d’abord nous avons créé un modèle direct discret de la puissance acoustique qui devient alors à la fois linéaire et déterminé pour les puissances acoustiques. Et nous ajoutons les erreurs de mesures que nous décomposons en trois parties : le bruit de fond du réseau de capteurs, l’incertitude du modèle causée par les propagations à multi-trajets et les erreurs d’approximation de la modélisation. Pour la résolution du problème inverse, nous avons tout d’abord proposé une approche d’hyper-résolution en utilisant une contrainte de parcimonie, de sorte que nous pouvons obtenir une plus haute résolution spatiale robuste à aux erreurs de mesures à condition que le paramètre de parcimonie soit estimé attentivement. Ensuite, afin d’obtenir une dynamique large et une plus forte robustesse aux bruits, nous avons proposé une approche basée sur une inférence bayésienne avec un a priori parcimonieux. Toutes les variables et paramètres inconnus peuvent être estimées par l’estimation du maximum a posteriori conjoint (JMAP). Toutefois, le JMAP souffrant d’une optimisation non-quadratique d’importants coûts de calcul, nous avons cherché des solutions d’accélération algorithmique: une approximation du modèle direct en utilisant une convolution 2D avec un noyau invariant. Grâce à ce modèle, nos approches peuvent être parallélisées sur des Graphics Processing Unit (GPU) . Par ailleurs, nous avons affiné notre modèle statistique sur 2 aspects : prise en compte de la non stationarité spatiale des erreurs de mesures et la définition d’une loi a priori pour les puissances renforçant la parcimonie en loi de Students-t. Enfin, nous ont poussé à mettre en place une Approximation Variationnelle Bayésienne (VBA). Cette approche permet non seulement d’obtenir toutes les estimations des inconnues, mais aussi de fournir des intervalles de confiance grâce aux paramètres cachés utilisés par les lois de Students-t. Pour conclure, nos approches ont été comparées avec des méthodes de l’état-de-l’art sur des données simulées, réelles (provenant d’essais en soufflerie chez Renault S2A) et hybrides. / Acoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensor array. This technique can provide meaningful insights into performances, properties and mechanisms of acoustic sources. It has been widely used for evaluating the acoustic influence in automobile and aircraft industries. Acoustic imaging methods often involve in two aspects: a forward model of acoustic signal (power) propagation, and its inverse solution. However, the inversion usually causes a very ill-posed inverse problem, whose solution is not unique and is quite sensitive to measurement errors. Therefore, classical methods cannot easily obtain high spatial resolutions between two close sources, nor achieve wide dynamic range of acoustic source powers. In this thesis, we firstly build up a discrete forward model of acoustic signal propagation. This signal model is a linear but under-determined system of equations linking the measured data and unknown source signals. Based on this signal model, we set up a discrete forward model of acoustic power propagation. This power model is both linear and determined for source powers. In the forward models, we consider the measurement errors to be mainly composed of background noises at sensor array, model uncertainty caused by multi-path propagation, as well as model approximating errors. For the inverse problem of the acoustic power model, we firstly propose a robust super-resolution approach with the sparsity constraint, so that we can obtain very high spatial resolution in strong measurement errors. But the sparsity parameter should be carefully estimated for effective performance. Then for the acoustic imaging with large dynamic range and robustness, we propose a robust Bayesian inference approach with a sparsity enforcing prior: the double exponential law. This sparse prior can better embody the sparsity characteristic of source distribution than the sparsity constraint. All the unknown variables and parameters can be alternatively estimated by the Joint Maximum A Posterior (JMAP) estimation. However, this JMAP suffers a non-quadratic optimization and causes huge computational cost. So that we improve two following aspects: In order to accelerate the JMAP estimation, we investigate an invariant 2D convolution operator to approximate acoustic power propagation model. Owing to this invariant convolution model, our approaches can be parallelly implemented by the Graphics Processing Unit (GPU). Furthermore, we consider that measurement errors are spatially variant (non-stationary) at different sensors. In this more practical case, the distribution of measurement errors can be more accurately modeled by Students-t law which can express the variant variances by hidden parameters. Moreover, the sparsity enforcing distribution can be more conveniently described by the Student's-t law which can be decomposed into multivariate Gaussian and Gamma laws. However, the JMAP estimation risks to obtain so many unknown variables and hidden parameters. Therefore, we apply the Variational Bayesian Approximation (VBA) to overcome the JMAP drawbacks. One of the fabulous advantages of VBA is that it can not only achieve the parameter estimations, but also offer the confidential interval of interested parameters thanks to hidden parameters used in Students-t priors. To conclude, proposed approaches are validated by simulations, real data from wind tunnel experiments of Renault S2A, as well as the hybrid data. Compared with some typical state-of-the-art methods, the main advantages of proposed approaches are robust to measurement errors, super spatial resolutions, wide dynamic range and no need for source number nor Signal to Noise Ration (SNR) beforehand.