<div>New approaches in topology optimization and manufacturing techniques are generating multi-scale, physically realized mechanical components from advanced materials. Current optimization formulations do not consider the dependence of strength on feature size. By failing to account for the mechanical models of this behavior, sub-optimal structures are generated.</div><div><br></div><div>A currently available academic density-based topology optimization code is extended to incorporate strength constraints. A continuum theory of failure novel to the optimization field is implemented to account for both general yielding and fracture dominated failure. The fracture limit is then formulated in terms of well-established models of brittle and quasi-brittle size dependence. Additional models of size dependence based on assumed flaw sizes are considered using the theory of linear elastic fracture mechanics. To unify the optimized topology and the empirical geometric-scaling models used, a novel geometric measure of local size is proposed. This measure interprets the evolving density field using a consistent domain of support and maintains consistency with gradient-based methods of optimization. The geometric measure is evaluated using test-problems which consider a minimum compliance objective under geometric constraints.</div><div><br></div><div>The resulting optimized structures are presented for the geometric and size-dependent strength constrained formulations. The geometrically constrained results illustrate the flexibility and robustness of the proposed local size measure. The various models of size-dependent strength illustrate the impact and necessity of considering physical models of material within the topology optimization formulation. Results which exhibit clear "micro-structural" features and scale transitioning architectures are presented for limited multi-scale optimization studies.</div><div><br></div><div>An attempt at physical validation considering a single model of quasi-brittle material failure is made. Existing approaches for generating 3D volumetric meshes from image data are leveraged to yield CAD interpretations of optimized structures. Structures are then printed using a 3D printing PolyJet process with a previously established size-dependent material. Structures are destructively evaluated under displacement controlled load testing. The resulting tests indicate that the stress states in the structure fail to induce the expected size-dependent material characteristics. Furthermore, the testing results indicate the difficulty in properly accounting for boundary conditions in the topology optimization approach.</div>

Climate change results to more extreme and frequent flood events that induce extra risk to flood protection structures such as levees. Thus, estimation of the probability of levee failure is of utmost importance when it comes to structural safety and flood risk assessment. This master thesis focuses on the estimation of the probability of levee failure owing to backward erosion at the foundation of the levee. For the estimation of the probability of failure three breach models of different complexity were developed and site-specified data were used from the Paglia river area and an assessment of the results followed. Besides the breach models, a 2-D hydraulic model was also built where breach scenarios were used to assess the associated flood risk. The results showed that the complexity of the breach model plays significant role to the estimated probability of failure. The simplest model estimated the lowest probability, while the complex the highest. Consequently, the associated to the complex model flood risk was higher than the other two. / Klimatförändringarna är ett allmänt problem som bland annat resulterar i ökad risk för extrema hydrologiska händelser. Av dessa extrema händelser är översvämningar bland de mest förekommande. För att tackla problemen med översvämningsriskerna används skyddsvallar som en av de vanligaste anläggningarna bland olika översvämningsskydd. Vid ett tillräckligt högt flöde kan dock den ökade belastningen på skyddsvallen orsaka att skyddsvallen havererar. En skyddsvall kan haverera på grund av flera fysikaliska fenomen, varav de vanligaste är inre erosion, otillräcklig släntstabilitet samt överspolning. Risken för att en skyddsvall havererar, det vill säga brottrisken, kan beräknas med olika metoder. Sannolikhetsbaserade metoder har nyligen växt fram som ett allt viktigare tillvägagångssätt. Det här examensarbetet handlar om brottsannolikhetsberäkningar för skyddsvallar i syfte att kartlägga översvämningsrisker. Arbetet fokuserar på fenomenet med inre erosion i skyddsvallars grundläggning. Det praktiska exemplet i examensarbetet studerar nybyggda skyddsvallar utmed den italienska floden Paglia längs en flodsträcka om 4,5 km mellan städerna Ciconia och Orvieto Scalo i regionen Umbria i centrala Italien. Ur ett hydrogeologiskt perspektiv karaktäriseras regionen av fint jordmaterial, med inslag av slutna akviferer. Sannolikhetsberäkningar med hjälp av ramverket för Monte Carlo-analys genomfördes för tre olika modeller för att beskriva inre erosion i skyddsvallarna till följd av ett 200-årsflöde. De tre olika numeriska modellerna kännetecknades av olika grad av komplexitet. Den enklaste modellen baserades på en metod av Mazzoleni et al. (2015) och tog endast hänsyn till skyddsvallens porositet. Den mellankomplexa modellen var baserad på metoden ifrån Khilar et al. (1985). Den mest komplexa modellen var tidsberoende och baserades på metoderna från Sellmeijer et al. (2011) och Scheuermann (2005), för att ta hänsyn till de två ömsesidigt beroende fenomenen bakåterosion, så kallad ”piping” och läckage, så kallat ”seepage”, vilka tillsammans ger upphov till inre erosion. Resultaten visar att graden av komplexitet i den numeriska modellen spelar en viktig roll när det gäller brottsannolikheten för skyddsvallars grundläggning. Den enklaste modellen beräknade en lägre brottsannolikhet än de andra två modellerna, och den mest komplexa modellen beräknade högst brottsannolikhet av de tre modellerna. Vidare visar resultaten på att några parametrar har en stor inverkan på brottsannolikheten. Dessa är bland annat partikeldiametern som påverkar skjuvhållfastheten i materialet, permeabiliteten, jordlagrets tjocklek under skyddsvallen samt strömningsvaraktigheten. Parametern strömningsvaraktighet kan endast beaktas i tidsberoende modeller, vilket är en av fördelarna med den mest komplexa modellen i denna studie. Förutom de numeriska brottsannolikhetsmodellerna byggdes en tvådimensionell hydraulisk modell upp för att kartlägga de resulterande översvämningsriskerna. Tre olika haveriscenarier studerades för att representera de tre brottsannolikhetsmodellerna. Dock visade det sig att den enklaste av de tre modellerna beräknade så låg brottsannolikhet att det inte var meningsfullt att studera de resulterande översvämningsriskerna enligt den enklaste brottsannolikhetsmodellen i den hydrauliska modellen. Av de två mer komplexa brottsannolikhetsmodellerna visade det sig att den mest komplexa av de två gav mer omfattande haverier i skyddsvallarna och dessutom haverier i flera av skyddsvallarna. Vidare gav den mest komplexa modellen upphov till högst vattennivåer bakom de havererade skyddsvallarna. Slutligen kan det nämnas att staden Scalo Orvieto är utsatt för en högre översvämningsrisk än staden Ciconia enligt resultaten i denna studie.

A new approach to the evaluation of the reliability of electrical systems is presented. In this approach a Graph Trace Analysis based approach is applied to integrated system models and reliability analysis. The analysis zones are extended from the traditional power system functional zones. The systems are modeled using containers with iterators, where the iterators manage graph edges and are used to process through the topology of the graph. The analysis provides a means of computationally handling dependent outages and cascading failures. The effects of adverse weather, time-varying loads, equipment age, installation environment, operation conditions are considered. Sequential Monte Carlo simulation is used to evaluate the reliability changes for different system configurations, including distributed generation and transmission lines. Historical weather records and loading are used to update the component failure rates on-the-fly. Simulation results are compared against historical reliability field measurements. Given a large and complex plant to operate, a real-time understanding of the networks and their situational reliability is important to operational decision support. This dissertation also introduces using an Integrated System Model in helping operators to minimize real-time problems. A real-time simulation architecture is described, which predicts where problems may occur, how serious they may be, and what is the possible root cause. / Ph. D.

This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.