Ever since the design and manufacture of products moved from the craftsman era where individual craftsman designed and manufactured the entire product, to the mass production era, where skilled laborers were crafting interchangeable parts or in some cases single features on interchangeable parts, variation in assemblies has been a major concern to designers, manufacturers, and in a more subtle way, customers. Variation, in the end, affects quality, performance and the cost of products. One particular type of design that is particularly robust to variation is an exactly constrained design. Several researchers have recently explored the topic of exact constraint design. An exactly constrained design is one in which each degree of freedom is constrained by a single constraint until the desired degrees of freedom for the design is attained. One attractive advantage of exactly constrained designs is that they are robust to variation. However, exactly constrained designs often require nesting forces to maintain the configuration of the design. This research develops a method for designing features that will supply robust nesting forces such that the advantages of the exactly constrained design are preserved. The method developed in this work takes advantage of a proven method for tolerance analysis and enhances this method to include the analysis of these features that supply nesting forces. Along with the enhancement, principles are developed that aid this analysis. All the examples provided in this work are verified using comparisons to Monte Carlo simulations. The comparisons show good results, typically less than 2% difference from the Monte Carlo simulations, verifying that this method accurately predicts variation and allows for the robust design of features that supply the nesting forces in exactly constrained assemblies.

Constant-force mechanisms are mechanical devices that provide a near-constant output force over a prescribed deflection range. This thesis develops various optimization-based methods for designing robust constant-force mechanisms. The configuration of the mechanisms that are the focus of this research comprises a cam and a compliant spring fixed at one end while making contact with the cam at the other end. This configuration has proven to be an innovative solution in several applications because of its simplicity in manufacturing and operation. In this work, several methods are introduced to design these mechanisms, and reduce the sensitivity of these mechanisms to manufacturing uncertainties and frictional effects. The mechanism's sensitivity to these factors is critical in small scale applications where manufacturing variations can be large relative to overall dimensions, and frictional forces can be large relative to the output force. The methods in this work are demonstrated on a small scale electrical contact on the order of millimeters in size. The method identifies a design whose output force is 98.20% constant over its operational deflection range. When this design is analyzed using a Monte Carlo simulation the standard deviation in constant force performance is 0.76%. When compared to a benchmark design from earlier research, this represents a 34% increase in constant-force performance, and a reduction from 1.68% in the standard deviation of performance. When this new optimal design is evaluated to reduce frictional effects a design is identifed that shows a 36% reduction in frictional energy loss while giving up, however, 18.63% in constant force.

Uncertainty is prevalent throughout the design, analysis, and optimization of aerospace products. When scientific computing is used to support these tasks, sources of uncertainty may include the freestream flight conditions of a vehicle, physical modeling parameters, geometric fidelity, numerical error, and model-form uncertainty, among others. Moreover, while some uncertainties may be treated as probabilistic, aleatory sources, other uncertainties are non-probabilistic and epistemic due to a lack of knowledge, and cannot be rigorously treated using classical statistics or Bayesian approaches. An additional complication for propagating uncertainty is that many aerospace scientific computing tools may be computationally expensive; for example, a single high-fidelity computational fluid dynamics solution may require several days or even weeks to complete. It is therefore necessary to employ uncertainty propagation strategies that require as few solutions as possible. The Non-Intrusive Polynomial Chaos (NIPC) method has grown in popularity in recent decades due to its ability to propagate both aleatory and epistemic parametric sources of uncertainty in a computationally efficient manner. While traditional Monte Carlo methods might require thousands to millions of function evaluations to achieve statistical convergence, NIPC typically requires tens to hundreds for problems with similar numbers of uncertain dimensions. Despite this efficiency, NIPC is limited in one important aspect: it can only propagate uncertainty at a particular point in a design space or flight envelope. For optimization or aerodynamic database problems that require uncertainty estimates at many more than one point, the use of NIPC quickly becomes computationally intractable. This dissertation introduces a new method entitled Spatially Accurate Polynomial Chaos (SAPC) that extends the original NIPC approach for the spatial regression of aleatory and epistemic parametric sources of uncertainty. Throughout the dissertation, the SAPC method is applied to various aerospace problems of interest. These include the regression of aerodynamic force and moment uncertainties throughout the flight envelope of a commercial aircraft, the design under uncertainty of a two-stream propulsive mixer device, and the robust design of a low-boom supersonic demonstrator aircraft. Collectively the results suggest that SAPC may be useful for a large variety of engineering applications. / Doctor of Philosophy / Uncertainty is prevalent throughout the design, analysis, and optimization of aerospace products. When scientific computer simulations are used to support these tasks, sources of uncertainty may include the speed of an aerospace vehicle, the direction of the wind, physical modeling constants or assumptions, and the vehicle shape, among others. As a result of these sources uncertainty, assessments of vehicle performance are also uncertain. For example, if the speed of a vehicle is not known precisely, then computer simulations will predict a lift force which is also imprecisely known. A challenge when assessing the uncertainty in aerospace vehicle performance is that the computer simulations which predict performance may take a long time to run, even on state-of-the-art super computers. Traditional statistical methods may require thousands or millions of simulations for the prediction of uncertainty, which does not fit within the computational budget of most aerospace analyses. A newer method called Non-Intrusive Polynomial Chaos (NIPC) is more efficient, typically requiring only tens to hundreds of simulations; however, NIPC only provides uncertainty estimates at a single point in an aircraft flight envelope or design condition. In this dissertation, a new method called Spatially Accurate Polynomial Chaos (SAPC) is developed. The SAPC method combines desirable features of NIPC with regression methods for an efficient estimation of uncertainty throughout a vehicle flight envelope or design space. Throughout the dissertation, the SAPC method is applied to various aerospace problems of interest. These include the regression of aerodynamic force and moment uncertainties throughout the flight envelope of a commercial aircraft, the design under uncertainty of a two-stream propulsive mixer device, and the robust design of a low-boom supersonic demonstrator aircraft. Collectively the results suggest that SAPC may be useful for a large variety of engineering applications.

<p dir="ltr">The rising demand for EVs, motivated by their environmental benefits, is generating increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. The high cost of these investments motivates governments to seek optimal decisions on EV-related investments including EV charging infrastructure, and such decisions include locations, capacities, and deployment scheduling of such infrastructure. Additionally, uncertainties in travel demand prediction and EV driving range constraints need to be considered in EV infrastructure investment planning. To help address these questions, this thesis developed a framework to establish optimal schedules and locations for new charging stations and for decommissioning gasoline refueling stations for any given network over a long-term planning horizon, considering uncertainties in travel demand forecasts and EV driving-range heterogeneity. To address the uncertainties, the proposed framework is formulated as a robust mathematical model that minimizes the worst-case total system travel cost and the total penalty for unused charging station capacity. This study uses an adaption of the cutting-plane method to solve the proposed model. In the numerical analyses, the performance of the robust framework and its deterministic counterpart are compared. The results show that the optimal robust plan outperforms the deterministic plan by yielding savings in the costs of travel and electricity charging. The thesis also investigates the effects of investment budget levels of robust planning. The numerical results throw light on the relationships between higher investment levels and electric charging station deployment levels and consequently, the savings in travel costs and impacts on unused charging capacity. The outcomes of this thesis can help road agencies and related private sector entities enhance preparations towards infrastructure investments to support electric charging stations in an efficient manner.</p>

Yes / The evaluation of impact of collaborative research on robust design methodologies and methods is important to both academic and industry stakeholders. This paper introduces a framework for impact evaluation which combines the broader framework adopted for the academic research impact assessment with the organisation viewpoint centred on business results, process improvement and product development teams capability improvement. A large scale empirical study conducted with evidence from technical reports on workplace projects from an automotive OEM proved the validity of the proposed framework.

A gas turbine engine design process was developed for the design of a common core engine family. The process considers initial and projected variant engine applications, likely technology maturation, and various sources of uncertainty when making initial core design considerations. A physics based modeling and simulation environment was developed to enforce geometric core commonality between the core defining design engine and a common core variant engine. The environment also allows for upgrade options and technology to be infused into the variant engine design. The relationships established in the model enable commonality to be implicitly enforced when performing simultaneous design space explorations of the common core design and all corresponding variant engine designs. A robust design simulation process was also developed, enabling probabilistic surrogate model representations of the common core engine family design space to be produced. The probabilistic models provide confidence interval performance estimates with a single function call for an inputted set of core and variant design settings and the uncertainty distribution shape parameter settings representative of an uncertainty scenario of interest. The unique form of the probabilistic surrogate models enables large numbers of common core engine family applications to be considered simultaneously, each being simulated under a unique uncertainty scenario. Implications of core design options can be instantaneously predicted for all engine applications considered, allowing for favorable common core design regions to be identified in a highly efficient manner.

Estimativas robustas de parâmetros populacionais de cetáceos são necessárias para a criação de estratégias de manejo e de conservação eficazes. Dentre os métodos comumente utilizados para estimar parâmetros demográficos, o método de captura e recaptura tem se destacado devido às suas vantagens e precisão. O objetivo deste estudo foi estimar parâmetros populacionais do boto-cinza, Sotalia guianensis, no estuário de Cananéia (25° 03\' S; 47° 55\' W), mediante o uso de modelos de captura-recaptura. O delineamento robusto de Pollock foi utilizado para estimar abundância, sobrevivência, emigração temporária e probabilidades de captura. Levantamentos de fotoidentificação sistemáticos foram conduzidos em três temporadas entre janeiro de 2015 e fevereiro de 2016. O modelo que melhor representou os dados mostrou uma população com sobrevivência constante, emigração temporária aleatória e probabilidades de captura com heterogeneidade variando no tempo e entre as estações. Foram estimados 430 (95% CI: 410-451) indivíduos no verão/2015; 384 (95% CI: 366-403) indivíduos no inverno/2015; e 414 (95% CI: 392-438) indivíduos no verão de 2016. A taxa de emigração temporária (&#947;\'\'=&#947;\') foi de 0,05 (&#177; 0,03) e a estimativa de sobrevivência aparente foi de 0,86 (&#177; 0,06). As probabilidades de captura variaram entre as temporadas e os períodos secundários, com valores médios de 0,24 (&#177; 0,02) no verão de 2015; 0,34 (&#177; 0,03) no inverno de 2015; e 0,24 (&#177; 0,03) no verão de 2016. Esses resultados sugerem que as variáveis ambientais entre as estações do ano têm pouco efeito sobre o tamanho da população local, a qual se encontra em equilíbrio e utiliza uma área ainda protegida de ameaças à sua perpetuação. / Reliable estimates of cetacean population parameters are necessary for effective management and conservation strategies. Capture-recapture methods have been commonly used to estimate demographic parameters because of their advantages and accuracy under a set of assumptions. The aim of this study was to estimate population parameters of Guiana dolphins, Sotalia guianensis, in the Cananeia estuary (25°03\' S; 47°55\' W) using capture-recapture models. The Pollock\'s robust design was used to estimate abundance, survival, temporary emigration and capture probabilities. Systematic boat-based photo-identification surveys were conducted in three seasons between Jan/2015 and Feb/2016. The best fitting model showed a population with constant survival, random temporary emigration and heterogeneous time-varying capture probabilities. Population sizes were of 430 (95% CI: 410-451) individuals in summer of 2015, 384 (95% CI: 366-403) individuals in winter of 2015 and 414 (95% CI: 392-438) individuals in summer of 2016. Temporary emigration rate (&#947;\'\'=&#947;\') was 0.05 (&#177; 0.03) and apparent survival estimate was 0.86 (&#177; 0.06). Capture probabilities varied greatly among seasons and secondary periods with mean values of 0.24 (&#177; SE 0.02) in summer of 2015, 0.34 (&#177; SE 0.03) in winter of 2015 and 0.24 (&#177; SE 0.03) in summer of 2016. These results suggest that environmental variables between seasons have little effect on the size of the local population, which is stable and uses an area that is still protected from threats.

Acknowledging the goal of reduced aircraft weight, there is a need to improve on conservative design techniques used in industry. Minimisation of laminate in-plane elastic energy is used as an appropriate in-plane performance marker to assess the weight saving potential of new design techniques. MATLAB optimisations using a genetic algorithm were used to find the optimal laminate variables for minimum in-plane elastic energy and/or damage tolerance for all possible loadings. The use of non-standard angles was able to offer equivalent, if not better in-plane performance than standard angles, and are shown to be useful to improve the ease of manufacture. Any standard angle laminate stiffness was shown to be able to be matched by a range of two non-standard angle ply designs. This non-uniqueness of designs was explored. Balancing of plus and minus plies about the principal loading axes instead of themanufacturing axes was shown to offer considerable potential for weight saving as the stiffness is better aligned to the load. Designing directly for an uncertain design load showed little benefit over the 10% ply percentage rule in maintaining in-plane performance. This showed the current rule may do a sufficient job to allow robustness in laminate performance. This technique is seen useful for non-standard angle design that lacks an equivalent 10% rule. Current use of conservative damage tolerance strain limits for design has revealed the need for more accurate prediction of damage propagation. Damage tolerance modelling was carried out using fracture mechanics for a multi-axial loading considering the full 2D strain energy and improving on current uni-axial models. The non-conservativeness of the model was evidenced to be from assumptions of zero post-buckled stiffness. Preliminary work on conservative multi-axial damage tolerance design, independent of thickness, is yet to be confirmed by experiments.

Estimativas robustas de parâmetros populacionais de cetáceos são necessárias para a criação de estratégias de manejo e de conservação eficazes. Dentre os métodos comumente utilizados para estimar parâmetros demográficos, o método de captura e recaptura tem se destacado devido às suas vantagens e precisão. O objetivo deste estudo foi estimar parâmetros populacionais do boto-cinza, Sotalia guianensis, no estuário de Cananéia (25° 03\' S; 47° 55\' W), mediante o uso de modelos de captura-recaptura. O delineamento robusto de Pollock foi utilizado para estimar abundância, sobrevivência, emigração temporária e probabilidades de captura. Levantamentos de fotoidentificação sistemáticos foram conduzidos em três temporadas entre janeiro de 2015 e fevereiro de 2016. O modelo que melhor representou os dados mostrou uma população com sobrevivência constante, emigração temporária aleatória e probabilidades de captura com heterogeneidade variando no tempo e entre as estações. Foram estimados 430 (95% CI: 410-451) indivíduos no verão/2015; 384 (95% CI: 366-403) indivíduos no inverno/2015; e 414 (95% CI: 392-438) indivíduos no verão de 2016. A taxa de emigração temporária (&#947;\'\'=&#947;\') foi de 0,05 (&#177; 0,03) e a estimativa de sobrevivência aparente foi de 0,86 (&#177; 0,06). As probabilidades de captura variaram entre as temporadas e os períodos secundários, com valores médios de 0,24 (&#177; 0,02) no verão de 2015; 0,34 (&#177; 0,03) no inverno de 2015; e 0,24 (&#177; 0,03) no verão de 2016. Esses resultados sugerem que as variáveis ambientais entre as estações do ano têm pouco efeito sobre o tamanho da população local, a qual se encontra em equilíbrio e utiliza uma área ainda protegida de ameaças à sua perpetuação. / Reliable estimates of cetacean population parameters are necessary for effective management and conservation strategies. Capture-recapture methods have been commonly used to estimate demographic parameters because of their advantages and accuracy under a set of assumptions. The aim of this study was to estimate population parameters of Guiana dolphins, Sotalia guianensis, in the Cananeia estuary (25°03\' S; 47°55\' W) using capture-recapture models. The Pollock\'s robust design was used to estimate abundance, survival, temporary emigration and capture probabilities. Systematic boat-based photo-identification surveys were conducted in three seasons between Jan/2015 and Feb/2016. The best fitting model showed a population with constant survival, random temporary emigration and heterogeneous time-varying capture probabilities. Population sizes were of 430 (95% CI: 410-451) individuals in summer of 2015, 384 (95% CI: 366-403) individuals in winter of 2015 and 414 (95% CI: 392-438) individuals in summer of 2016. Temporary emigration rate (&#947;\'\'=&#947;\') was 0.05 (&#177; 0.03) and apparent survival estimate was 0.86 (&#177; 0.06). Capture probabilities varied greatly among seasons and secondary periods with mean values of 0.24 (&#177; SE 0.02) in summer of 2015, 0.34 (&#177; SE 0.03) in winter of 2015 and 0.24 (&#177; SE 0.03) in summer of 2016. These results suggest that environmental variables between seasons have little effect on the size of the local population, which is stable and uses an area that is still protected from threats.

La conception des structures multi-échelle s’appuie sur des modélisations expérimentales et prédictives. Pour accéder à des niveaux de précision élevés, ces modélisations reposent sur des campagnes expérimentales nombreuses et des développements prédictifs sophistiqués analytiques ou numériques qui intègrent des connaissances sur les paramètres d’intérêt. L’intégration de connaissances diminue l’incertitude sur les grandeurs d’intérêt et impacte de façon significative le coût de modélisation des structures multi-échelle, facteur majeur du coût de conception. Le concepteur doit alors être en mesure de maîtriser la pertinence de l’intégration de connaissances pour la prédiction des grandeurs d’intérêt et son impact sur le coût de modélisation. Les recherches menées sont structurées autour du développement d’une méthodologie d’aide à la conception sous incertitudes permettant au concepteur de choisir des combinaisons de modèles prédictifs et expérimentaux, appelées chemins de modélisation, présentant des compromis différents entre le coût de modélisation et l’incertitude sur les paramètres d’intérêt. Le travail se base sur une représentation pyramidale des modélisations expérimentales et prédictives. Les incertitudes aléatoires et épistémiques liées aux matériaux, aux modèles ainsi qu’aux tolérances géométriques sont agrégées et propagées dans la pyramide jusqu’aux grandeurs d’intérêt de la structure. Une méthode adaptative d’estimation du coût de modélisation, basée sur la logique floue, a été proposée. Le problème multi objectif visant à minimiser les incertitudes sur les paramètres d’intérêt et le coût de modélisation est résolu au moyen d’un algorithme « NSGA-II » permettant l’identification de chemins optimisés robustes. Les travaux sont appliqués au cas d’un réservoir composite épais destiné au stockage d’hydrogène. La méthodologie proposée démontre qu’il est possible de rationaliser les modélisations expérimentales et prédictives menées pour obtenir la pression d’éclatement du réservoir avec une précision maîtrisée. Dans un second temps, la méthodologie est utilisée pour obtenir des solutions de reconception sur des réservoirs présentant des volumes plus importants ou plus faibles et atteignant des pressions cibles différentes. Les chemins de modélisations robustes obtenus délivrent des solutions de dimensionnement adaptées aux exigences de reconception présentant un coût de modélisation et un niveau d’incertitude maitrisés. / The design of multi-scale structures is based on predictive and experimental modelling. To achieve a high level of precision, modelling rest on a high number of experimental tests and sophisticated analytical and numerical developments integrating all possible knowledge about the quantity of interest. Adding knowledge into models diminishes the uncertainty on quantities of interest and significantly impacts the cost of modelling, a high impact factor on the design cost. The designer must be able to control the suitability of the integration of knowledge into the prediction of quantities of interest and its impact on the cost of modelling. The research carried out in this work is structured around the development of a methodology of assistance to the design under uncertainties allowing the designer to choose combinations between several predictive and experimental models, called modelling paths, presenting different compromises between the cost of modelling and the uncertainty on quantities of interest. The work is based on a pyramidal representation of experimental and predictive modelling. Random and epistemic uncertainties related to materials, models and geometrical tolerances are aggregated and propagated in the pyramid up to the quantities of interest of the structure. An adaptive method based on fuzzy logics for estimating the cost of modelling has been proposed. The multi objective problem aiming to minimizing the uncertainties on the quantities of interest and the cost of modelling is solved by means of the « NSGA-II » genetic algorithm, allowing to identify robust optimized modelling paths. This methodology is applied to a thick composite vessel for hydrogen storage. The proposed methodology demonstrates the possibility of rationalization of experimental and predictive models carried out to obtain the burst pressure of the vessel with a controlled precision. In a second step, the methodology is used to redesign the vessel considering larger or smaller volumes and with different burst pressure targets. Robust modelling paths obtained deliver design solutions adapted to the redesign requirements with a controlled modelling cost and a managed level of uncertainty.