10,727 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Offline and Online Models for Learning Pairwise Relations in Data

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    Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Exploring Potential Domains of Agroecological Transformation in the United States

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    There is now substantial evidence that agroecology constitutes a necessary pathway towards socially just and ecologically resilient agrifood systems. In the United States, however, agroecology remains relegated to the margins of research and policy spaces. This dissertation explores three potential domains of agroecological transformation in the US. Domains of transformation are sites of contestation in which agroecology interfaces with the industrial agrifood system; these material and conceptual spaces may point to important pathways for scaling agroecology. To explore this concept, I examine formal agroecology education (Chapter 1), extension services and statewide discourses around soil health (Chapter 2), and models of farmland access not based on private property (Chapter 3). While these constitute three distinct topics, I seek to demonstrate that they are linked by similar forces that enable and constrain the extent to which these domains can be sites of agroecological transformation. First, I use case study methodology to explore the evolution of an advanced undergraduate agroecology course at the University of Vermont. I examine how course content and pedagogy align with a transformative framing of agroecology as inherently transdisciplinary, participatory, action-oriented, and political. I find that student-centered pedagogies and experiential education on farms successfully promote transformative learning whereby students shift their understanding of agrifood systems and their role(s) within them. In my second chapter, I zoom out to consider soil health discourses amongst farmers and extension professionals in Vermont. Using co-created mental models and participatory analysis, I find that a singular notion of soil health based on biological, chemical, and physical properties fails to capture the diverse ways in which farmers and extension professionals understand soil health. I advocate for a principles-based approach to soil health that includes social factors and may provide a valuable heuristic for mobilizing knowledge towards agroecology transition pathways. My third chapter, conducted in collaboration with the national non-profit organization Agrarian Trust, considers equitable farmland access. Through semi-structured interviews with 13 farmers and growers across the US, I explore both farmer motivations for engaging with alternative land access models (ALAMs) and the potential role(s) these models may play within broader transformation processes. I argue that ALAMs constitute material and conceptual ‘third spaces’ within which the private property regime is challenged and new identities and language around land ownership can emerge; as such, ALAMs may facilitate a (re)imagining of land-based social-ecological relationships. I conclude the dissertation by identifying conceptual and practical linkages across the domains explored in Chapters 1-3. I pay particular attention to processes that challenge neoliberal logics, enact plural ways of knowing, and prefigure just futures. In considering these concepts, I apply an expansive notion of pedagogy to explore how processes of teaching and (un)learning can contribute to cultivating foundational capacities for transition processes

    Metaphors of London fog, smoke and mist in Victorian and Edwardian Art and Literature

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    Julian Wolfreys has argued that after 1850 writers employed stock images of the city without allowing them to transform their texts. This thesis argues, on the contrary, that metaphorical uses of London fog were complex and subtle during the Victorian and Edwardian periods, at least until 1914. Fog represented, in particular, formlessness and the dissolution of boundaries. Examining the idea of fog in literature, verse, newspaper accounts and journal articles, as well as in the visual arts, as part of a common discourse about London and the state of its inhabitants, this thesis charts how the metaphorical appropriation of this idea changed over time. Four of Dickens's novels are used to track his use of fog as part of a discourse of the natural and unnatural in individual and society, identifying it with London in progressively more negative terms. Visual representations of fog by Constable, Turner, Whistler, Monet, Markino, O'Connor, Roberts and Wyllie and Coburn showed an increasing readiness to engage with this discourse. Social tensions in the city in the 1880s were articulated in art as well as in fiction. Authors like Hay and Barr showed the destruction of London by its fog because of its inhabitants' supposed degeneracy. As the social threat receded, apocalyptic scenarios gave way to a more optimistic view in the work of Owen and others. Henry James used fog as a metaphorical representation of the boundaries of gendered behaviour in public, and the problems faced by women who crossed them. The dissertation also examines fog and individual transgression, in novels and short stories by Lowndes, Stevenson, Conan Doyle and Joseph Conrad. After 1914, fog was no more than a crude signifier of Victorian London in literature, film and, later, television, deployed as a cliche instead of the subtle metaphorical idea discussed in this thesis

    An investigation of the geothermal potential of the Upper Devonian sandstones beneath eastern Glasgow

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    The urban development of the city of Glasgow is a consequence of its economic development, in part fuelled by local coalfields which exploited rocks in the same sedimentary basin within which geothermal resources in flooded abandoned mine workings, and deeper hot sedimentary aquifers (HSA), are present. This creates an opportunity to provide geothermal heating to areas of dense urban population with high heat demand. The depth of the target HSA geothermal resource, in Upper Devonian aged sandstones of the Stratheden Group, beneath eastern Glasgow was determined by gravity surveying and structural geological modelling. The estimated depth of the geothermal resource ranged from c.1500-2000 m, in the eastward deepening sedimentary basin. To reliably estimate the temperature of the geothermal resource, rigorous corrections to account for the effects of palaeoclimate and topography on heat flow were applied to boreholes in the Greater Glasgow area. The mean regional corrected heat flow was calculated as 75.7 mW m-2, an increase of 13.8 mW m-2 from the uncorrected value of 61.9 mW m-2, emphasising the extent to which heat flow was previously underestimated. Extrapolation of the geothermal gradient, calculated from the mean regional corrected heat flow, results in aquifer temperatures of c. 64-79 °C at depths of c.1500-2000 m beneath eastern Glasgow. The geothermal resource may, therefore, be capable of supporting a wide variety of direct heat use applications if sufficient matrix permeability or fracture networks are present. However, diagenetic effects such as quartz and carbonate cementation were found to restrict the porosity in Upper Devonian sandstones in a borehole and outcrop analogue study. These effects may likewise reduce porosity and intergranular permeability in the target aquifer, although this crucial aspect cannot be fully understood without deep exploratory drilling. To quantify the magnitude of the deep geothermal resource, the indicative thermal power outputs of geothermal doublet wells located in Glasgow’s East End were calculated for the first time, with outputs ranging from 1.3-2.1 MW dependent upon the aquifer depth. This, however, is predicated upon an aquifer permeability of c. 40 mD, which if reduced to 10 mD or less due to the effects of diagenesis, significantly reduces the thermal power outputs to 230-390 kW. The lack of assured project-success, given uncertainties related to the aquifer properties at depth, coupled with high capital costs of drilling, pose barriers to the development of deep geothermal energy in Glasgow. Further investigation of the economic viability of geothermal exploration, and alternative technological solutions is therefore required to mitigate the technical and economic risks. However, if sufficient matrix permeability or fracture networks are present at depth in the Upper Devonian sandstone sequence, then the potential contribution that geothermal energy could make to meeting local heat demand, reducing greenhouse gas emissions, and addressing the ‘energy trilemma’ in Glasgow is significant

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    El desmantelamiento de las fronteras. Análisis comparativo de las actividades mercantiles de las mujeres en Brabante y Vizcaya, ca. 1420-ca. 1550

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    RESUMEN En Dismantling the Borders comparo las actividades laborales de mujeres en Brabante y Bizkaia de 1420 a 1550. Historiadores especializados en género han estado debatiendo las diferencias en las oportunidades laborales de mujeres premodernas en el norte y en el sur de Europa. Algunos investigadores argumentan que las diferencias en las estructuras legales y familiares conllevaron menos oportunidades económicas para las mujeres en el sur de Europa. En mi estudio, reevalúo este debate y argumento que se han de reevaluar los contextos locales, en concreto, las instituciones responsables de la organización del trabajo en las diferentes regiones. En las ciudades Brabantinas – Malinas y Amberes – la mayor parte de las ocupaciones estaban organizadas en gremio, unas instituciones extremadamente patriarcales y masculinizadas. Por ende, el trabajo de las mujeres dependía en gran medida de su estatus familiar y social. La ausencia de estas instituciones en Bilbao – la ciudad Bizkaína estudiada – favoreció unas oportunidades laborales más independientes para las mujeres activas en sectores del mercado laboral poco lucrativos.ABSTRACT In Dismantling the Borders I compare women’s labour opportunities in Brabant and Biscay from 1420 to 1550. Scholars studying gender in premodern Europe have been debating differences in premodern women’s labour opportunities in northern and southern Europe. Some scholars have argued that diverging family structures and legal structures in the two regions resulted in better economic positions for women in northern Europe. In my research project, I reassess this debate and argue for involving local contexts in the regional comparison – more specifically the institutions responsible for the organization of work. In the Brabantine case studies, Antwerp and Mechelen, most occupations were organized in guilds – extremely patriarchal and masculine institutions. As a result of this organization in guilds, women’s labour opportunities in Brabant were highly dependent on their social status and marital status. In Bilbao – the Biscayan case study – occupations were rarely organized in such corporations, resulting in more independent labour opportunities for women active in less lucrative market sectors in the town
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