15,334 research outputs found

    Geometric Properties of the 2-D Peskin Problem

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    The 2-D Peskin problem describes a 1-D closed elastic string immersed and moving in a 2-D Stokes flow that is induced by its own elastic force. The geometric shape of the string and its internal stretching configuration evolve in a coupled way, and they combined govern the dynamics of the system. In this paper, we show that certain geometric quantities of the moving string satisfy extremum principles and decay estimates. As a result, we can prove that the 2-D Peskin problem admits a unique global solution when the initial data satisfies a medium-size geometric condition on the string shape, while no assumption on the size of stretching is needed

    A family of total Lagrangian Petrov-Galerkin Cosserat rod finite element formulations

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    The standard in rod finite element formulations is the Bubnov-Galerkin projection method, where the test functions arise from a consistent variation of the ansatz functions. This approach becomes increasingly complex when highly nonlinear ansatz functions are chosen to approximate the rod's centerline and cross-section orientations. Using a Petrov-Galerkin projection method, we propose a whole family of rod finite element formulations where the nodal generalized virtual displacements and generalized velocities are interpolated instead of using the consistent variations and time derivatives of the ansatz functions. This approach leads to a significant simplification of the expressions in the discrete virtual work functionals. In addition, independent strategies can be chosen for interpolating the nodal centerline points and cross-section orientations. We discuss three objective interpolation strategies and give an in-depth analysis concerning locking and convergence behavior for the whole family of rod finite element formulations.Comment: arXiv admin note: text overlap with arXiv:2301.0559

    Underwater optical wireless communications in turbulent conditions: from simulation to experimentation

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    Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel. This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated. Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link. Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions

    Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention

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    a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..

    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

    Response of saline reservoir to different phaseCOâ‚‚-brine: experimental tests and image-based modelling

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    Geological CO₂ storage in saline rocks is a promising method for meeting the target of net zero emission and minimizing the anthropogenic CO₂ emitted into the earth’s atmosphere. Storage of CO₂ in saline rocks triggers CO₂-brine-rock interaction that alters the properties of the rock. Properties of rocks are very crucial for the integrity and efficiency of the storage process. Changes in properties of the reservoir rocks due to CO₂-brine-rock interaction must be well predicted, as some changes can reduce the storage integrity of the reservoir. Considering the thermodynamics, phase behavior, solubility of CO₂ in brine, and the variable pressure-temperature conditions of the reservoir, there will be undissolved CO₂ in a CO₂ storage reservoir alongside the brine for a long time, and there is a potential for phase evolution of the undissolved CO₂. The phase of CO₂ influence the CO₂-brine-rock interaction, different phaseCO₂-brine have a unique effect on the properties of the reservoir rocks, Therefore, this study evaluates the effect of four different phaseCO₂-brine reservoir states on the properties of reservoir rocks using experimental and image-based approach. Samples were saturated with the different phaseCO₂-brine, then subjected to reservoir conditions in a triaxial compression test. The representative element volume (REV)/representative element area (REA) for the rock samples was determined from processed digital images, and rock properties were evaluated using digital rock physics and rock image analysis techniques. This research has evaluated the effect of different phaseCO₂-brine on deformation rate and deformation behavior, bulk modulus, compressibility, strength, and stiffness as well as porosity and permeability of sample reservoir rocks. Changes in pore geometry properties, porosity, and permeability of the rocks in CO₂ storage conditions with different phaseCO₂-brine have been evaluated using digital rock physics techniques. Microscopic rock image analysis has been applied to provide evidence of changes in micro-fabric, the topology of minerals, and elemental composition of minerals in saline rocks resulting from different phaseCO₂-br that can exist in a saline CO₂ storage reservoir. It was seen that the properties of the reservoir that are most affected by the scCO₂-br state of the reservoir include secondary fatigue rate, bulk modulus, shear strength, change in the topology of minerals after saturation as well as change in shape and flatness of pore surfaces. The properties of the reservoir that is most affected by the gCO₂-br state of the reservoir include primary fatigue rate, change in permeability due to stress, change in porosity due to stress, and change topology of minerals due to stress. For all samples, the roundness and smoothness of grains as well as smoothness of pores increased after compression while the roundness of pores decreased. Change in elemental composition in rock minerals in CO₂-brine-rock interaction was seen to depend on the reactivity of the mineral with CO₂ and/or brine and the presence of brine accelerates such change. Carbon, oxygen, and silicon can be used as index minerals for elemental changes in a CO₂-brine-rock system. The result of this work can be applied to predicting the effect the different possible phases of CO₂ will have on the deformation, geomechanics indices, and storage integrity of giant CO₂ storage fields such as Sleipner, In Salah, etc

    Applications and practical considerations of polarisation structuring by a Fresnel cone

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    The polarisation property of light has been known about for hundreds of years. Often its use in technology has been limited to uniform states, however, more recently light with structured polarisation has gained interest. This is largely prompted by availability of spatial light modulators for generation, and increased computation speed to model complex focal fields. My PhD research has extended upon work carried out during a master’s project where we investigated the use of a solid glass cone (so-called Fresnel cone) for generating vector vortex beams. The aim of this thesis is to report on the potential use of a Fresnel cone in microscopy and polarimetry applications, and practical implications discovered. Expanding on the previous work, enhanced fidelity polarisation states are measured and a newly developed Fresnel cone coupling technique is shown, allowing high-efficiency annular vector vortex beam generation. We demonstrate through simulations based on vector diffraction theory that azimuthally polarised light with OAM generated using a Fresnel cone can provide sub-diffraction limited focal spots, below those of more well-known radially polarised light. Practical implications were encountered, prompting investigation into the effects of phase aberrations on resulting focal spots, and experimental measurement of cone surface topology. We find the uniformity of the Fresnel cone shape and apex angle is crucial to the focussing properties. For polarimetry application, full details are provided for a single-shot full-Stokes polarimeter technique and proof-of-principle experiment, where broadband operation is demonstrated. I conclude by summarising the findings of my research and suggest potential future work in this area

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer

    Understanding the Relationship among Durable Goods, Academic Achievement, and School Attendance in Colombia

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    A joint report from the United Nations Development Program and the Oxford Poverty and Human Development Initiative indicates that while the number of people living with less than 1.90adaydeclinedglobally,droppingfrom2billionin1990to736millionin2015,thenumberofpeoplewhoexperiencednon−incomepovertyreached1.3billionin2020.Non−incomepoverty,referredtoasmultidimensionalpoverty,assessestheextenttowhichpeoplearedeprivedfromaccessingbasicservicessuchashealth,education,orattainingdecentlivingstandards,despitehavingincomelevelswellabove1.90 a day declined globally, dropping from 2 billion in 1990 to 736 million in 2015, the number of people who experienced non-income poverty reached 1.3 billion in 2020. Non-income poverty, referred to as multidimensional poverty, assesses the extent to which people are deprived from accessing basic services such as health, education, or attaining decent living standards, despite having income levels well above 1.90. Research on development and welfare economics points to assets as the missing piece in the poverty puzzle because they can build capacity. In general, assets can be used to generate income or to enhance quality of life. Income-generating assets such as bonds, credit, or home ownership help people gain economic stability, acquire other assets, and prepare for economic shocks. Quality-of-life-enhancing assets help people improve their living standards, develop agency, and participate in political as well as in social life. Examples of quality-of-life-enhancing assets include education, social capital, and durable goods such as TVs or computers. Most research on assets examines the relationship either between financial assets and poverty or between financial assets and education. An exploration of durable goods and education was the focus of this dissertation. Although not a nascent field, most studies in this area have focused on analyzing how durable goods relate to academic achievement and school attendance mainly in African and Asian countries. From a methodological standpoint, these studies have modeled durable goods utilizing a binary approach, where ownership of durable goods is measured as possession of any durable good, or as an index, using principal component analysis (PCA), which research suggests is not the most robust method for index creation. Such methodological decisions have provided only a partial understanding of the relationship between durable goods and education. For example, findings indicate that possession of durable goods improves achievement in reading, but not in math. However, further research is needed to assess whether different types of durable goods have differential effects on educational outcomes. Hence, this study explored the relationship among durable goods, academic achievement, and school attendance in Colombia through three methodological approaches to operationalize durable goods: inventory, attributional, and index approaches. Data come from the 2017 SABER test, a nation-wide examination that assesses reading and math skills, for fifth and ninth grade students, (N = 621,218). Students with complete durable goods information (N = 364,436) were included. This research added to the existing literature on this field by using different methodological approaches to model durable goods, including the construction of a durable goods index employing exploratory factor analysis (EFA), and by expanding the geographic scope to Latin America. By using hierarchical linear and nonlinear modeling, this study found that, overall, durable goods were positively associated with reading and math outcomes, particularly for fifth graders. Similarly, results indicated that students whose families owned washing machines, computers, or who had Internet access were more likely to go to school

    Evaluation of clamp-on ultrasonic liquid flowmeters

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    The clamp-on ultrasonic flowmeter measures the fluid flow velocity and flowrate with the help of ultrasonic waves. Flow profile distortion due to pipe network disturbances cause uncertainty in the flowrate measurement. A numerical and experimental investigation is conducted to model the performance of a clamp-on ultrasonic flowmeter onto a straight pipe and at x/d=1 downstream of a 900 elbow for the flowrate range of 0.3-2.5m3/hr. The average percentage error in the flowrate at x/d=1 downstream of the elbow estimated from the numerical and experimental study is 8.6% and 10.8% respectively. The correction factors suggested for the numerical and experimental data reduces the average percentage error to 0.7% and 2.3% respectively. The repeatability tests show ±1.8% uncertainty in the flowrate. Integrating velocity along the acoustic path can roughly estimate measurement uncertainty due to flow profile without simulating the ultrasonic wave propagation numerically. This research will help increase the use of clamp-on ultrasonic flowmeters in practical applications with reduced uncertainty
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