A cold extrusion process has been developed to tailor a porous structure from polymeric waste. The use of an extruder to manufacture acoustic materials from recycled waste is a novel idea and the author is not aware of any similar attempts. The extruder conveys and mixes the particulates with a reacting binder. The end result is the continuous production of bound particulates through which a controlled amount of carbon dioxide gas that is evolved during the reaction is used to give the desired acoustic properties. The cold extrusion process is a low energy consuming process that reprocesses the post manufacturing waste into new vibro-acoustic products that can be used to meet the growing public expectations for a quieter environment. The acoustical properties of the developed products are modelled using Pade approximation and Johnson-Champoux-Allard models. Applications for the developed products are widespread and include acoustic underlay, insulation and panels in buildings, noise barriers for motorways and railway tracks, acoustic insulation in commercial appliances and transport vehicles.

A cold extrusion process has been developed to tailor a porous structure from polymeric waste. The use of an extruder to manufacture acoustic materials from recycled waste is a novel idea and the author is not aware of any similar attempts. The extruder conveys and mixes the particulates with a reacting binder. The end result is the continuous production of bound particulates through which a controlled amount of carbon dioxide gas that is evolved during the reaction is used to give the desired acoustic properties. The cold extrusion process is a low energy consuming process that reprocesses the post manufacturing waste into new vibro-acoustic products that can be used to meet the growing public expectations for a quieter environment. The acoustical properties of the developed products are modelled using Pade approximation and Johnson-Champoux-Allard models. Applications for the developed products are widespread and include acoustic underlay, insulation and panels in buildings, noise barriers for motorways and railway tracks, acoustic insulation in commercial appliances and transport vehicles.

Avec le développement rapide des modèles mathématiques et des outils de simulation, le besoin des processus de quantification des incertitudes a été bien augmenté. L'incertitude paramétrique et la groupe des nombreux décisions sont aujourd’hui les deux barrières principales dans la résolution des grandes problèmes systématiques.Capable de proportionner l'incertitude de la sortie sur celle des entrées, l’Analyse de Sensibilité Globale (GSA) est une solution fiable pour la quantification de l’incertitude. Parmi plusieurs algorithmes de GSA, Fourier Amplitude Sensitivity Analysis (FAST) est l’un des choix les plus populaires des chercheurs. Basé sur ANOVA-HDMR (ANalysis Of VAriance - High Dimensional Model Representation), il est solide en mathématique est efficace en calcul.Malheureusement, la décomposition unique d’ANOVA-HDMR se dépend sur l’indépendance des entrées. À cause de cela, il y a pas mal de cas industriels qui ne peut pas se traiter par FAST, particulièrement pour ceux qui donnent uniquement les échantillons mais sans lois de distribution. Sous cette demande, deux méthode extensifs de FAST avec design de corrélation sont proposées et étudiées dans la recherche. Parmi les deux méthodes, FAST-c s’est basé sur les distributions et FAST-orig s’est basé sur les échantillons.Comme applications et validations, multiples problèmes vibroacoustiques se sont traités dans la recherche. Les matériaux acoustiques avec soustructures, sont des candidats parfaits pour tester FAST-c et FAST-orig. Deux application sont présentées dans la première partie de la thèse, après l’état de l’arts. Les modèles choisis sont matérial poroélastique et structures composite sandwich, dont les propriétés mécaniques sont tous fortement influencées par les paramètres géométriques microscopique ou mesoscopique. D’avoir la méthode de FAST originale comparée avec les deux nouvelles, on trouve bien plus d’information sur la performance vibroacoustique de ces matériaux.Déjà répondu à la demande de GSA sur les modèles avecs les variables dépendantes, la deuxième partie de la thèse contient plus de recherches reliées avec FAST. D’abord FAST est pris en comparaison avec Random Forest, une algorithme bien connu de data-mining. Leurs erreurs potentiels et la possibilité de fonctioner ensemble sont discutés. Et dans les chapitres suivies, plus d’application de FAST sont présentées. Les méthodes sont appliquées sous plusieurs différente conditions. Une modèle de structure périodique qui contient des corrélation parmi les unités nous a en plus forcé à développer une nouvelle FAST-pe méthode. Dans ces applications, les designs des processus préliminaires et les stratégies d’échantillonages sont des essences à présenter. / With rapid development of mathematical models and simulation tools, the need of uncertainty quantification process has grown higher than ever before. Parametric uncertainties and overall decision stacks are nowadays the two main barriers in solving large scale systematic problem.Global Sensitivity Analysis (GSA) is one reliable solution for uncertainty quantification which is capable to assess the uncertainty of model output on its inputs’. Among several GSA algorithms, Fourier Amplitude Sensitivity Test (FAST) is one of the most popular choices of researchers. Based on ANOVA-HDMR (ANalysis Of VAriance - High Dimensional Model Representation), it is both mathematically solid and computationally efficient.One unfortunate fact is that the uniqueness of ANOVA-HDMR relies on the independency of input variables. It makes FAST unable to treat many industrial cases especially for those with only datasets but not distribution functions to be found. To answer the needs, two extended FAST methods with correlation design are proposed and further studied in this research. Among them FAST-c is distribution-based and FAST-orig is data-based.As a frame of validation and application, a number of vibroacoustic problems are dealt with in this research. Vibroacoustic materials with substructures, are perfect test candidates for FAST-c and FAST-orig. Two application cases are presented in the first part of this thesis, following the literature review. The models chosen here are poroelastic material and sandwich composite structures, both having their mechanical properties hugely influenced by their microscopic and mesoscopic geometric parameters. Getting the original FAST method compared to the two with correlation design, many different features on materials’ vibroacoustic performance are latter discovered.Having got an answer for GSA on models with dependent variables, the second part of this thesis contains more extended researches related to FAST. It is taken into comparison with Random Forest, a well-known data-mining algorithm. The potential error of both algorithms are analyzed and the possibility of joint application is discussed. In the following chapters, more applications of FAST-series methods are reported. They are applied under various conditions where another improved version named FAST-pe is developed to treat a model of periodic structures with correlation among each units. Upon these FAST application cases, the design of preliminary process and the sampling strategies is the core part to be introduced.