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Global animal law, introducing an intersectional ethical framework in order to reconceptualise legal research on international trade and animal law
Get PDFPreviously held under moratorium from 29th March 2021 until 29th March 2023This thesis seeks to answer the question: to what extent can introducing an intersectional ethical framework to global animal law help to reconceptualise legal research on international trade and animal law. This thesis provides an ethics-based, critical, intersectional and posthumanist analysis of emerging global animal law (scholarship) and the disproportionately large impact of international trade law on its normative growth. This thesis provides five novel contributions to global animal law literature. First, this thesis builds an ethical toolbox from posthumanism, feminist ethics, intersectionality theory and Earth Jurisprudence. On this basis, this thesis delineates, for the first time, a second wave of animal ethics which is utilised as an ethics-based methodology for this research. Second, this thesis crafts a new critical narrative of animal law by putting various forms of (global) animal law into dialogue with global law metatheory and second wave animal ethics, critiquing global animal law (scholarship) for ethnocentrism and coloniality. Third, this thesis problematises trade policy’s impact on animals by introducing new, critical analysis of its neoliberal underpinnings. This requires filling critical research gaps in the trade linkage debate by using complex trade data and qualitative analyses of law to assess the impact of trade on animal welfare. Fourth, this thesis critiques the unacknowledged dominance of unilateralism in trade law responses to the animal question. This critique identifies coloniality in trade law responses to the animal question which entrenches harmful norms within global animal law. Finally, this thesis utilises second wave animal ethics to reach a new set of proposals to improve global animal law’s response to trade and animal welfare issues. The recommendations are for: more diverse scholarship and critical academic spaces; multilateral and multi-level global animal law solutions to problems caused by international trade; and an incorporation of animal welfare into WTO multilateral committee work.This thesis seeks to answer the question: to what extent can introducing an intersectional ethical framework to global animal law help to reconceptualise legal research on international trade and animal law. This thesis provides an ethics-based, critical, intersectional and posthumanist analysis of emerging global animal law (scholarship) and the disproportionately large impact of international trade law on its normative growth. This thesis provides five novel contributions to global animal law literature. First, this thesis builds an ethical toolbox from posthumanism, feminist ethics, intersectionality theory and Earth Jurisprudence. On this basis, this thesis delineates, for the first time, a second wave of animal ethics which is utilised as an ethics-based methodology for this research. Second, this thesis crafts a new critical narrative of animal law by putting various forms of (global) animal law into dialogue with global law metatheory and second wave animal ethics, critiquing global animal law (scholarship) for ethnocentrism and coloniality. Third, this thesis problematises trade policy’s impact on animals by introducing new, critical analysis of its neoliberal underpinnings. This requires filling critical research gaps in the trade linkage debate by using complex trade data and qualitative analyses of law to assess the impact of trade on animal welfare. Fourth, this thesis critiques the unacknowledged dominance of unilateralism in trade law responses to the animal question. This critique identifies coloniality in trade law responses to the animal question which entrenches harmful norms within global animal law. Finally, this thesis utilises second wave animal ethics to reach a new set of proposals to improve global animal law’s response to trade and animal welfare issues. The recommendations are for: more diverse scholarship and critical academic spaces; multilateral and multi-level global animal law solutions to problems caused by international trade; and an incorporation of animal welfare into WTO multilateral committee work
Effect of added mass and damping on the response of subsea structure installation
Get PDFRemotely located deepwater fields with challenging environmental conditions are now being explored and developed because of their good energy resource. This means technology advancement in marine operations is required to influence the field development cost. This is hindered by the difficulty in estimating the added mass and damping parameters which are important influencing factors in the installation process of subsea structures required for the field development. These issues are addressed by developing an analytical calculation and Fluent simulation method to estimate the hydrodynamic coefficients of complicated subsea structures far from boundaries and in close proximity to the seabed at different KC numbers. The analytical method is developed from standard hydrodynamic theory, and the CFD analysis is based on estimating the hydrodynamic force on the structure and then splitting the force into its added mass component, while the equivalent linearized damping is derived from the sinusoidal varying force over a time record by fitting a line that touches the peak of these forces. The results from the analytical method and CFD analysis were found to be satisfactory after validating with existing literature and through numerical flow visualisation. The added mass and damping of the structure show KC dependency. As KC increases, the flow field around the vertically oscillating structure and the vortex shedding pattern changes. The increase in KC leads to an independent and interactive vortex shedding regime for the different heights above seabed. The installation analysis performed, showed increasing response of the subsea protective structure at different KC as it progresses to the seabed, which is useful in understanding the influence of submergence on the added mass and damping of subsea structures oscillating in heave direction at various KC number.Remotely located deepwater fields with challenging environmental conditions are now being explored and developed because of their good energy resource. This means technology advancement in marine operations is required to influence the field development cost. This is hindered by the difficulty in estimating the added mass and damping parameters which are important influencing factors in the installation process of subsea structures required for the field development. These issues are addressed by developing an analytical calculation and Fluent simulation method to estimate the hydrodynamic coefficients of complicated subsea structures far from boundaries and in close proximity to the seabed at different KC numbers. The analytical method is developed from standard hydrodynamic theory, and the CFD analysis is based on estimating the hydrodynamic force on the structure and then splitting the force into its added mass component, while the equivalent linearized damping is derived from the sinusoidal varying force over a time record by fitting a line that touches the peak of these forces. The results from the analytical method and CFD analysis were found to be satisfactory after validating with existing literature and through numerical flow visualisation. The added mass and damping of the structure show KC dependency. As KC increases, the flow field around the vertically oscillating structure and the vortex shedding pattern changes. The increase in KC leads to an independent and interactive vortex shedding regime for the different heights above seabed. The installation analysis performed, showed increasing response of the subsea protective structure at different KC as it progresses to the seabed, which is useful in understanding the influence of submergence on the added mass and damping of subsea structures oscillating in heave direction at various KC number
Time-resolved emission spectra of intrinsic Tyrosine during the early stages of ß-amyloid aggregation
Get PDFThe aggregation of beta-amyloids (Aß) is one of the key processes responsible for the development of Alzheimer's disease (AD). Early molecular-level detection of beta-amyloid oligomers may help in early diagnosis and in the development of new intervention therapies. Previous studies on the changes in beta-amyloid's single tyrosine intrinsic fluorescence response during aggregation can be efficiently used to indicate the extent of the aggregation at its earliest stages before the beta-sheets are formed. To better understand the underlying kinetics of Aß aggregation we present a complementary approach based on the time-resolved emission spectra (TRES) of amyloid's tyrosine. TRES can sufficiently demonstrate structural changes on the nanosecond time scale after excitation. Aß monomers can be distinguished from oligomers by means of the position of their emission spectra. Further spectral shift caused by dielectric relaxation can be useful for determining the size of the oligomers since their spectral shift gradually decreases as the aggregates grow larger.Aß1-40 self-assembly was also studied in the presence of additional compounds affecting the progress of aggregation such as copper ions and glucose or factors that can potentially prevent aggregation like quercetin. In the presence of copper ions, time‐resolved fluorescence techniques demonstrated the formation of beta amyloid‐copper complexes and the accelerated peptide aggregation. The shifts in the emission spectral peaks indicated that the peptides exhibit different aggregation pathways than in the absence of copper. In the presence of high glucose concentrations TRES exhibit multiple peaks, their position and shifts reveal the impact of glycation on Aβ1– 40 oligomerisation. The results show that formation of the advanced glycation end products (AGEs) alters the aggregation pathway. These changes are highly relevant to our understanding of the pathophysiology of AD and the implication of AGE and diabetes in these pathways. In the presence of quercetin, TRES exhibit multiple peaks with characteristic spectral shifts, indicating a different aggregation pathway. At a molar ratio of 1:1 (Aß1-40 : Quercetin), TRES results showed early formation of Aß-Quercetin complexes, which seem to inhibit further Aß aggregation. This makes it a potential nutrient that may help prevent or delay the development of Alzheimer’s disease.Other techniques like fluorescence anisotropy decay and nanoparticle tracking analysis (NTA) were investigated in order to study Aβ aggregation and to explore synergy resulting from combining different experimental techniques.The aggregation of beta-amyloids (Aß) is one of the key processes responsible for the development of Alzheimer's disease (AD). Early molecular-level detection of beta-amyloid oligomers may help in early diagnosis and in the development of new intervention therapies. Previous studies on the changes in beta-amyloid's single tyrosine intrinsic fluorescence response during aggregation can be efficiently used to indicate the extent of the aggregation at its earliest stages before the beta-sheets are formed. To better understand the underlying kinetics of Aß aggregation we present a complementary approach based on the time-resolved emission spectra (TRES) of amyloid's tyrosine. TRES can sufficiently demonstrate structural changes on the nanosecond time scale after excitation. Aß monomers can be distinguished from oligomers by means of the position of their emission spectra. Further spectral shift caused by dielectric relaxation can be useful for determining the size of the oligomers since their spectral shift gradually decreases as the aggregates grow larger.Aß1-40 self-assembly was also studied in the presence of additional compounds affecting the progress of aggregation such as copper ions and glucose or factors that can potentially prevent aggregation like quercetin. In the presence of copper ions, time‐resolved fluorescence techniques demonstrated the formation of beta amyloid‐copper complexes and the accelerated peptide aggregation. The shifts in the emission spectral peaks indicated that the peptides exhibit different aggregation pathways than in the absence of copper. In the presence of high glucose concentrations TRES exhibit multiple peaks, their position and shifts reveal the impact of glycation on Aβ1– 40 oligomerisation. The results show that formation of the advanced glycation end products (AGEs) alters the aggregation pathway. These changes are highly relevant to our understanding of the pathophysiology of AD and the implication of AGE and diabetes in these pathways. In the presence of quercetin, TRES exhibit multiple peaks with characteristic spectral shifts, indicating a different aggregation pathway. At a molar ratio of 1:1 (Aß1-40 : Quercetin), TRES results showed early formation of Aß-Quercetin complexes, which seem to inhibit further Aß aggregation. This makes it a potential nutrient that may help prevent or delay the development of Alzheimer’s disease.Other techniques like fluorescence anisotropy decay and nanoparticle tracking analysis (NTA) were investigated in order to study Aβ aggregation and to explore synergy resulting from combining different experimental techniques
Investment benchmarks for alternative asset classes
Get PDFThis thesis is a collection of essays that explore current theory and practice in respect of benchmarks for alternative asset classes. A benchmark is required in order to measure and attribute the performance of professionally managed investment funds. A principal component analysis (PCA) based index, based on factor weights determined by eigenvalues, is proposed to address identified weaknesses in current indices. The approach uses linear combinations of factor returns to construct alternative asset indices.;These are statistically correlated with the principal components identified. The resultant indices provide an attributable benchmark, particularly for commodity futures. Collectively the essays identify and suggest enhancements to index construction methodology as applied to alternative assets. Increased investment in such assets has created a need for such new and innovative benchmarks. The essays focus on a variety of unique features present in alternative assets and the proxies used to invest in them. It was found that the lack of reporting on leverage and liquidity is the biggest impediment to index refinement in real estate and hedge fund indices.;The gearing and lack of liquidity in the investment proxies make indices harder to replicate. Principal component derived factor weights can partially address this issue. The approach also proves useful to address identified problems in peer group benchmarking. It is concluded that PCA can be used to benchmark commodity futures and help in the classification of hedge fund strategies. The chapters herein have important implications for asset allocation, manager selection, index construction, portfolio risk assessment, alternative asset pricing, the testing of commodity market efficiency and the synthesis of hedge fund strategies. They point to the need for a more bespoke treatment of the benchmarking of investments in alternative assets.This thesis is a collection of essays that explore current theory and practice in respect of benchmarks for alternative asset classes. A benchmark is required in order to measure and attribute the performance of professionally managed investment funds. A principal component analysis (PCA) based index, based on factor weights determined by eigenvalues, is proposed to address identified weaknesses in current indices. The approach uses linear combinations of factor returns to construct alternative asset indices.;These are statistically correlated with the principal components identified. The resultant indices provide an attributable benchmark, particularly for commodity futures. Collectively the essays identify and suggest enhancements to index construction methodology as applied to alternative assets. Increased investment in such assets has created a need for such new and innovative benchmarks. The essays focus on a variety of unique features present in alternative assets and the proxies used to invest in them. It was found that the lack of reporting on leverage and liquidity is the biggest impediment to index refinement in real estate and hedge fund indices.;The gearing and lack of liquidity in the investment proxies make indices harder to replicate. Principal component derived factor weights can partially address this issue. The approach also proves useful to address identified problems in peer group benchmarking. It is concluded that PCA can be used to benchmark commodity futures and help in the classification of hedge fund strategies. The chapters herein have important implications for asset allocation, manager selection, index construction, portfolio risk assessment, alternative asset pricing, the testing of commodity market efficiency and the synthesis of hedge fund strategies. They point to the need for a more bespoke treatment of the benchmarking of investments in alternative assets
Maximum power point tracking and control of grid interfacing PV systems
Get PDFGrid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power.Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power
Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations
Get PDFThe global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical BayesianAnalysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical BayesianAnalysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation
Investigating the effects of thrombin on SIM-A9 microglial cell line : relevance to haemorrhagic stroke
No full textHaemorrhagic stroke accounts for approximately 20% of the total stroke cases annually and is characterised by high mortality rates. The activated pro-inflammatory microglia are protagonists in the development of neuroinflammation following stroke. Thrombin has been shown to be one of the blood products driving microglial activation, neurotoxic reactions, contributing to neuroinflammation. Activation of microglia by thrombin induces pro-inflammatory signalling pathways and production of cytokines, such as IL-1β. Thrombin can also influence the activation of the NLRP3 inflammasome, while inflammasome-dependent release of HMGB1 following thrombin stimulation has not been studied. The present study, focused on investigating the effects of thrombin on the induction of microglial pro-inflammatory phenotypes, as well as the expression of NLRP3 and HMGB1, using the novel spontaneously immortalised microglial cell line (SIM-A9). The cells were cultured and exposed to thrombin (10, 20 and 40 U/ml), as well as LPS (1µg/ml, as positive control). Pro-inflammatory populations, NLRP3 and HMGB1 expression, MAPK and NF-κB activations, as well as IL-1β released were them examined using flow cytometry, RT-qPCR, immunoblotting and ELISA respectively. The results obtained showed no thrombin-induced effect on the induction of proinflammatory phenotypes in SIM-A9, as the levels of pro-inflammatory populations remained high in all groups, including the untreated ones. Similarly, the expression of the NLRP3 and HMGB1 remained relatively stable before and after thrombin stimulation. The western blot analysis revealed an activated signal of the MAPK pathway in both treated and untreated cells (n=1), while the ELISA analysis revealed that there were no detectable levels of IL-1β following the stimulation with thrombin. The results suggest that thrombin does not affect neither the phenotypical profile of the SIM-A9, or the expression of the NLRP3 and HMGB1. However, the results from the FACS analysis and the western blots, suggest that in the pro-inflammatory phenotypes could pre-exist in the specific microglial line. This could be an important limitation for the experiments conducted and it should be considered for future studies.Haemorrhagic stroke accounts for approximately 20% of the total stroke cases annually and is characterised by high mortality rates. The activated pro-inflammatory microglia are protagonists in the development of neuroinflammation following stroke. Thrombin has been shown to be one of the blood products driving microglial activation, neurotoxic reactions, contributing to neuroinflammation. Activation of microglia by thrombin induces pro-inflammatory signalling pathways and production of cytokines, such as IL-1β. Thrombin can also influence the activation of the NLRP3 inflammasome, while inflammasome-dependent release of HMGB1 following thrombin stimulation has not been studied. The present study, focused on investigating the effects of thrombin on the induction of microglial pro-inflammatory phenotypes, as well as the expression of NLRP3 and HMGB1, using the novel spontaneously immortalised microglial cell line (SIM-A9). The cells were cultured and exposed to thrombin (10, 20 and 40 U/ml), as well as LPS (1µg/ml, as positive control). Pro-inflammatory populations, NLRP3 and HMGB1 expression, MAPK and NF-κB activations, as well as IL-1β released were them examined using flow cytometry, RT-qPCR, immunoblotting and ELISA respectively. The results obtained showed no thrombin-induced effect on the induction of proinflammatory phenotypes in SIM-A9, as the levels of pro-inflammatory populations remained high in all groups, including the untreated ones. Similarly, the expression of the NLRP3 and HMGB1 remained relatively stable before and after thrombin stimulation. The western blot analysis revealed an activated signal of the MAPK pathway in both treated and untreated cells (n=1), while the ELISA analysis revealed that there were no detectable levels of IL-1β following the stimulation with thrombin. The results suggest that thrombin does not affect neither the phenotypical profile of the SIM-A9, or the expression of the NLRP3 and HMGB1. However, the results from the FACS analysis and the western blots, suggest that in the pro-inflammatory phenotypes could pre-exist in the specific microglial line. This could be an important limitation for the experiments conducted and it should be considered for future studies
Online defect detection using a high-speed multi-sensor data fusion system for laser metal deposition
Get PDFLaser metal deposition (LMD) is an advanced additive manufacturing technology that is also known as metal 3D printing. It has many industrial applications which include building parts with complex geometry from scratch, Remanufacturing, and coating parts with a variety of materials. It is being adopted by the industry at a very quick rate, however quality assurance is still a hurdle that is being investigated. Defects are an inherent part of the process and are caused by many factors which may be unacceptable to industries like the Aerospace industry. For this the state of the art presents many NDT solutions to detect defects during the deposition process however, these methods have limitations due to the type of sensor being used, the high rate of change of the phenomenon the sensor is observing, the level of information that can be extracted from the sensor data about the defects and obviously the accuracy of the extracted information. This research investigates and develops a defect detection methodology that uses a multisensory array in collaboration with a custom data fusion algorithm that allows for the detection of defects and predicts features of the detected defects. This includes total number of defects, types and quantity of the defect, Max defect size and total defected area. This is achieved by first designing and developing a multisensory architecture capable of monitoring different parts of the defect development cycle at high enough sampling speeds so that information that indicates defects can be effectively captured. This is followed by monitoring defect provocation experiments to capture signals from a defected sample and training the system on these signals. In the training run the system takes the signals from these defect provocation experiments and stiches them onto defect information extracted from the XCT scans from the provocation experiments. From the stitched data sets events that exhibit anomalous behaviour which might indicate the presence of a defect are extracted and plugged into a K means clustering algorithm which sorts them into clusters. For every cluster a predictor table is formed which are used to predict defect features for any new event that is assigned to that cluster. The online data fusion algorithm takes in values from the predictor table once a new defect is introduced and outputs a predicted range between which the actual value of the defect feature lies in. The reliability of the range is also quantified using another value called % confidence which is formed using a unique scoring system. The results of this system show a relatively higher accuracy than solutions that use a single sensor approach and predicts further information about the defects.Laser metal deposition (LMD) is an advanced additive manufacturing technology that is also known as metal 3D printing. It has many industrial applications which include building parts with complex geometry from scratch, Remanufacturing, and coating parts with a variety of materials. It is being adopted by the industry at a very quick rate, however quality assurance is still a hurdle that is being investigated. Defects are an inherent part of the process and are caused by many factors which may be unacceptable to industries like the Aerospace industry. For this the state of the art presents many NDT solutions to detect defects during the deposition process however, these methods have limitations due to the type of sensor being used, the high rate of change of the phenomenon the sensor is observing, the level of information that can be extracted from the sensor data about the defects and obviously the accuracy of the extracted information. This research investigates and develops a defect detection methodology that uses a multisensory array in collaboration with a custom data fusion algorithm that allows for the detection of defects and predicts features of the detected defects. This includes total number of defects, types and quantity of the defect, Max defect size and total defected area. This is achieved by first designing and developing a multisensory architecture capable of monitoring different parts of the defect development cycle at high enough sampling speeds so that information that indicates defects can be effectively captured. This is followed by monitoring defect provocation experiments to capture signals from a defected sample and training the system on these signals. In the training run the system takes the signals from these defect provocation experiments and stiches them onto defect information extracted from the XCT scans from the provocation experiments. From the stitched data sets events that exhibit anomalous behaviour which might indicate the presence of a defect are extracted and plugged into a K means clustering algorithm which sorts them into clusters. For every cluster a predictor table is formed which are used to predict defect features for any new event that is assigned to that cluster. The online data fusion algorithm takes in values from the predictor table once a new defect is introduced and outputs a predicted range between which the actual value of the defect feature lies in. The reliability of the range is also quantified using another value called % confidence which is formed using a unique scoring system. The results of this system show a relatively higher accuracy than solutions that use a single sensor approach and predicts further information about the defects
Optimal operation an sizing of a combined heat and power system integrated with demand side response in a smart energy hub
Get PDFAs a distributed high-efficient generation technology, combined heat and power(CHP) has been widely applied to generate thermal and electrical energy in residentialand commercial buildings. This thesis aims to investigate the efficient operation and optimal sizing problems of CHP to guide optimal operations of a smart energy hub (S.E. Hub).As a distributed high-efficient generation technology, combined heat and power(CHP) has been widely applied to generate thermal and electrical energy in residentialand commercial buildings. This thesis aims to investigate the efficient operation and optimal sizing problems of CHP to guide optimal operations of a smart energy hub (S.E. Hub)
Effects of fractional time derivatives in predator-prey models
Get PDFThis thesis is concerned with the effects of fractional derivatives in predator-prey like systems, including models of plant water interaction. In Chapter 3, a fractional order predator-prey model is introduced, and we show how fractional derivative order can change the system from monostable to bistable. The observable domains of attraction of the two stable points will also be considered, in particular how they change as the fractional order is changed. In Chapter 4, we will generalise the predator-prey model studied in Chapter 3 by considering different fractional orders for each species. This system is referred to as an incommensurate system. We will explain how the different fractional orders affect the stability of this model. Then, in order to see if this change in stability is a more general result, we will consider a plant-herbivore incommensurate system and study the stability of this system. We will also find an approximate analytical solution for the characteristic equation of the incommensurate system when the two fractional orders α and β are similar and both close to the critical value of the fractional order of the commensurate system.;In this case, we are able to map out the stable and unstable boundary as a functionof both parameters. We will compare the analytical and numerical solutions in these two incommensurate systems. In Chapter 5, we consider two different modelsof the interaction between surface water, soil water and plants. The first is similar to the model of Dagbovie and Sherratt, without spatial derivatives. We study the steady states of this model and observe the effect of adding the fractional order on the system. In the second model the soil water equation is replaced with the more realistic the Richards equation. In this model, we will also study the steady state and dynamic behaviour in the integer model and then consider the incommensurate fractional system. In this case, we see that a fractional order can affect the transient behaviour of the system.This thesis is concerned with the effects of fractional derivatives in predator-prey like systems, including models of plant water interaction. In Chapter 3, a fractional order predator-prey model is introduced, and we show how fractional derivative order can change the system from monostable to bistable. The observable domains of attraction of the two stable points will also be considered, in particular how they change as the fractional order is changed. In Chapter 4, we will generalise the predator-prey model studied in Chapter 3 by considering different fractional orders for each species. This system is referred to as an incommensurate system. We will explain how the different fractional orders affect the stability of this model. Then, in order to see if this change in stability is a more general result, we will consider a plant-herbivore incommensurate system and study the stability of this system. We will also find an approximate analytical solution for the characteristic equation of the incommensurate system when the two fractional orders α and β are similar and both close to the critical value of the fractional order of the commensurate system.;In this case, we are able to map out the stable and unstable boundary as a functionof both parameters. We will compare the analytical and numerical solutions in these two incommensurate systems. In Chapter 5, we consider two different modelsof the interaction between surface water, soil water and plants. The first is similar to the model of Dagbovie and Sherratt, without spatial derivatives. We study the steady states of this model and observe the effect of adding the fractional order on the system. In the second model the soil water equation is replaced with the more realistic the Richards equation. In this model, we will also study the steady state and dynamic behaviour in the integer model and then consider the incommensurate fractional system. In this case, we see that a fractional order can affect the transient behaviour of the system
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