No description available.

Effective movement is central to survival and it is essential for all animals to react in response to changes around them. In many animals the rhythmic signals that drive locomotion are generated intrinsically by small networks of neurons in the nervous system which can be switched on and off. In this thesis I use a very simple animal, in which the behaviours and neuronal networks have been well characterised experimentally, to explore the salient features of such networks. Two days after hatching, tadpoles of the frog Xenopus laevis respond to a brief touch to the head by starting to swim. The swimming rhythm is driven by a small population of electrically coupled brainstem neurons (called dINs) on each side of the tadpole. These neurons also receive synaptic input following head skin stimulation. I build biophysical computational models of these neurons based on experimental data in order to address questions about the effects of electrical coupling, synaptic feedback excitation and initiation pathways. My aim is better understanding of how swimming activity is initiated and sustained in the tadpole. I find that the electrical coupling between the dINs causes their firing properties to be modulated. This allows two experimental observations to be reconciled: that a dIN only fires a single action potential in response to step current injections but the population fires like pacemakers during swimming. I build on this hypothesis and show that long-lasting, excitatory feedback within the population of dINs allows rhythmic pacemaker activity to be sustained in one side of the nervous system. This activity can be switched on and off at short latency in response to biologically realistic synaptic input. I further investigate models of synaptic input from a defined swim initiation pathway and show that electrical coupling causes a population of dINs to be recruited to fire either as a group or not at all. This allows the animal to convert continuously varying sensory stimuli into a discrete decision. Finally I find that it is difficult to reliably start swimming-like activity in the tadpole model using simple, short-latency, symmetrical initiation pathways but that by using more complex, asymmetrical, neuronal-pathways to each side of the body, consistent with experimental observations, the initiation of swimming is more robust. Throughout this work, I make testable predictions about the population of brainstem neurons and also describe where more experimental data is needed. In order to manage the parameters and simulations, I present prototype libraries to build and manage these biophysical model networks.

This research addresses a number of fundamental issues concerning convective mass transfer across fluid-fluid interfaces in porous media. Mass transfer to/from distributed sinks/sources is considered for i) the slow dissolution of liquid filaments of a wetting non-aqueous phase liquid (NAPL) held in the corners of angular pores or throats and ii) the fate of gas bubbles generated during the flow of a supersaturated aqueous phase in porous media. 1. Effects of the stability of NAPL films on wetting NAPL dissolution Wettability profoundly affects the distribution of residual NAPL contaminants in natural soils. Under conditions of preferential NAPL wettability, NAPL is retained within small pores and in the form of thick films (liquid filaments) along the corners and crevices of the pore walls. NAPL films in pore corners provide capillary continuity between NAPL-filled pores, dramatically influencing the behaviour of NAPL dissolution to the flowing aqueous phase by convection and diffusion. A pore network model is developed to explore the dissolution behaviour of wetting NAPL in porous media. The effects of initial NAPL distribution and NAPL film stability on dissolution behaviour are studied using the simulator. NAPL phase loses continuity and splits into disconnected clusters of NAPL-filled pores due to rupture of NAPL films. Quasi-state drainage and fingering of the aqueous phase into NAPL-filled pores is treated as an invasion percolation process and a stepwise procedure is adopted for the solution of flow and solute concentration fields. NAPL film stability is shown to critically affect the rate of mass transfer as such that stable NAPL films provide for more rapid dissolution. The network simulator reproduces the essential physics of wetting NAPL dissolution in porous media and explains the concentration-tailing behaviour observed in experiments, suggesting also new possibilities for experimental investigation. 2. Convective Mass Transfer across Fluid Interfaces in Straight Angular Pores Steady convective mass transfer to or from fluid interfaces in pores of angular cross-section is theoretically investigated. The model incorporates the essential physics of capillarity and solute mass transfer by convection and diffusion in corner fluid filaments. The geometry of the corner filaments, characterized by the fluid-fluid contact angle, the corner half-angle and the interface meniscus curvature, is accounted for. Boundary conditions of zero surface shear (???perfect-slip???) and infinite surface shear (???no-slip???) at the fluid-fluid interface are considered. The governing equations for laminar flow within the corner filament and convective diffusion to or from the fluid-fluid interface are solved using finite-element methods. Flow computations are verified by comparing the dimensionless resistance factor and hydraulic conductance of corner filaments against recent numerical solutions by Patzek and Kristensen [2001]. Novel results are obtained for the average effluent concentration as a function of flow geometry and pore-scale Peclet number. These results are correlated to a characteristic corner length and local pore-scale Peclet number using empirical equations appropriate for implementation in pore network models. Finally, a previously published ???2D-slit??? approximation to the problem at hand is checked and found to be in considerable error. 3. Bubble evolution driven by solute diffusion during the process of supersaturated carbonated water flooding In situ bubble growth in porous media is simulated using a pore network model that idealizes the pore space as a lattice of cubic chambers connected by square tubes. Evolution of the gas phase from nucleation sites is driven by the solute mass transfer from the flowing supersaturated water solution to the bubble clusters. Effects of viscous aqueous phase flow and convective diffusion in pore corners are explicitly accounted for. Growth of bubble clusters is characterised by a pattern of quasi-static drainage and fingering in the gas phase, an invasion percolation process controlled by capillary and gravitational forces. A stepwise solution procedure is followed to determine the aqueous flow field and the solute concentration field in the model by solving the conservation equations. Mobilization of bubbles driven by buoyancy forces is also studied. Results of bubble growth pattern, relative permeability and macroscopic mass transfer coefficient are obtained under different gas saturations and aqueous flow conditions.

The level of air pollution in urban areas, which is largely affected by road traffic, is an issue of high political relevance. Congestion is most prevalent in urban areas and a common and increasingly present phenomenon worldwide. The first four chapters of this study have investigated how and to what extent models, which are used to predict emissions on road links in urban road networks, include the effects of congestion on emissions. In order to make this assessment, traffic engineering literature and empirical studies have been examined and used as a basis to review (current) emission models that exist or have been used around the world. Congestion causes changes in driving patterns of individual vehicles in a traffic stream, and these changes are subsequently reflected in changes in congestion indicators and changes in emission levels. This consideration and a literature review has led to a proposed 'congestion typology' of emission models, which reflects the different ways in which and the extent to which congestion has been incorporated in these models. The typology clarifies that six of in total ten families of emission models that were investigated in this thesis explicitly consider congestion in the modelling process (i.e. model variables are related to congestion), although this is done in different ways. For the remaining four families of emission models it was not possible to determine the extent to which congestion has been incorporated on the basis of literature review alone. Two families fell beyond the scope of this work since they cannot be used to predict emission on road links. For the other two families it became clear in the course of the thesis that the extent can be determined through analysis of driving pattern data (and other information with respect to e.g. data collection) that were used in the model development. A new methodology is presented in this thesis to perform this analysis and to assess the mean level of congestion in driving patterns (driving cycles). The analysis has been carried out for one important family of emission models, the so-called travel speed models ('average speed models'), which are used extensively in urban network modelling. For four current models (COPERT III, MOBILE 6, QGEPA 2002, EMFAC 2000), it is concluded that these models implicitly (i.e. congestion is inherently considered) take varying levels of congestion into account, but that this conclusion is subject to a number of limitations. It became clear in the course of this study that prediction of (the effects of) congestion in both traffic models and emission models is generally restricted to certain modelling dimensions. As a consequence, the effects of congestion are only partially predicted in current air emission modelling. Chapter 5 has attempted to address the question whether congestion is actually an important issue in urban network emission modelling or not. It also addressed the question if different types of emission models actually predict different results. On the basis of a number of selection criteria, two types of models were compared, i.e. one explicit model (TEE-KCF 2002) and two implicit models (COPERT III, QGEPA 2002). The research objectives have been addressed by applying these emission models to a case-study urban network in Australia (Brisbane) for which various model input attributes were collected from different sources (both modelled and field data). The findings are limited by the fact that they follow from one urban network with particular characteristics (fleet composition, signal settings, speed limits) and application of only a few particular emission models. The results therefore indicate that: 1. Changes in traffic activity (i.e. distribution of vehicle kilometres travelled on network links) over the day appear to have the largest effect on predicted traffic emissions. 2. Congestion is an important issue in the modelling of CO and HC emissions. This appears not to be the case for NOx emissions, where basic traffic composition is generally a more important factor. For the most congested parts in the urban network that have been investigated, congestion can more than double predicted emissions of CO and HC. 3. Different types of emission models can produce substantially different results when absolute (arithmetic) differences are considered, but can produce similar results when relative differences (ratio or percent difference) are considered.

Power transformers are key components of electric system networks; their performance inevitably influences the reliability of electricity transmission and distribution systems. To comprehend the thermal ageing of transformers, hot-spot prediction becomes of significance. As the current method to estimate the hot-spot temperature is based on empirical hot-spot factor and is over-simplified, thermal network modelling has been developed due to its well balance between computation speed and approximation details. The application of Computational Fluid Dynamics (CFD) on transformer thermal analysis could investigate detailed and fundamental phenomena of cooling oil flow, and the principle of this PhD thesis is then to develop more accurate and reliable network modelling tools by utilising CFD.In this PhD thesis the empirical equations employed in network model for Nusselt number (Nu), friction coefficient and junction pressure losses (JPL) are calibrated for a wide range of winding dimensions used by power transformer designs from 22 kV to 500 kV, 20 MVA to 500 MVA, by conducting large sets of CFD simulations. The newly calibrated Nu equation predicts a winding temperature increase as the consequence of on average 15% lower Nu values along horizontal oil ducts. The new friction coefficient equation predicts a slightly more uniform oil flow rate distribution across the ducts, and also calculates a higher pressure drop over the entire winding. The new constant values for the JPL equations shows much better match to experimental results than the currently used 'off-the-shelf' constants and also reveals that more oil will tend to flow through the upper half of a pass if at a high inlet oil flow rate. Based on a test winding model in the laboratory, the CFD calibrated network model's calculation results are compared to both CFD and experimental results. It is concluded that the deviation between the oil pressure drop over the pass calculated by the network model and the CFD and the measured values is acceptably low. It proves that network modelling could deliver quick and reliable calculation results of the oil pressure drop over windings and thereby assist to choose capable oil pumps at the thermal design stage. However the flow distribution predicted by network model deviates from the one by CFD; this is particularly obvious for the cases with high flow rates probably due to the entry eddy circulation phenomena observed in CFD. As no experiment validation has been conducted, further investigation is necessary. The CFD calibrated network model is also applied to conduct a set of sensitivity studies on various thermal design parameters as well as loads. Because the studies are on a directed oil cooling winding case, an oil pump model is incorporated. From the studies recommendations are given for optimising thermal design, e.g. narrowed horizontal ducts will reduce average winding and hot-spot temperatures, and narrowed vertical ducts will however increase the temperatures. Doubled oil block washers are found to be able to significantly reduce the disc temperatures, although there is a slight reduction of the total oil flow rate, due to the increase of winding hydraulic impedance. The impact of different loadings, 50%~150% of rated load, upon the forced oil flow rate is limited, relative change below 5%. The correlations between the average winding and hot-spot temperatures versus the load factors follow parabolic trends.

yes / A 9 × 18 × 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1·028 and 0·154, respectively, for simple deep beams, and 1·0 and 0·122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations.

Yes / A 9 x 18 x 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations.

Carbonate rocks are of global importance as they contain about 50% of the world’s remaining hydrocarbon reserves and are also a major host to the world’s groundwater resources. Therefore, understanding and modelling the fluid flow processes in carbonates are of great importance. A critical problem is that, unlike homogenous media (such as sandstones), carbonates often show features, including porosity, that span across a wide spatial range, from sub-micron porosity to fractures of meters length-scale. In this study X-ray computed micro-tomography (μCT) has been utilised as a tool to monitor two phase (oil-brine) flow in porous carbonate (dolomite) plugs at ambient temperature and pressures smaller than 690 kPa. A simple, low-cost and highly X-ray transparent core-holder was utilised for which the design is introduced. Capillary end effects were recognised and avoided in data analysis. Displacement processes that occur in the dolomite under water-wet, oil-wet, and partially mixed-wet states were investigated. The experiments consisted of a series of drainage and imbibition processes occurring under capillary and viscous dominated flow regimes. Pore-scale mechanisms of piston-like displacement and snap-off (or at least clear results of them), that were previously observed in sandstones and 2D micro-models, were observed in the dolomite under study. In addition, a new pore-scale mechanism was identified which occurred at high capillary numbers, referred to as droplet-fragmentation. This new pore-scale mechanism may provide an explanation to the capillary-desaturation process for heterogeneous media. In the experiments performed on the oil-wet plug formation of a stable water in oil emulsion was observed which appears to be the first 3D observation of in situ emulsion formation made using μCT. Direct visualisation of the oil-brine-rock configurations and measurement of the contact angles are presented. A comparison was made for the contact angle distributions measured for the water-wet and oil-wet conditions. Observation of fluid displacement processes as well as oil-brine-rock contact angle distributions demonstrate that pore-scale imaging provides a promising tool for wettability characterisation on both pore and core scales. Such detailed wettability data can also be used in pore-scale flow models. For the dolomite under study multiple-scale pore network models were constructed by integrating single-scale networks extracted from μCT images acquired at different length-scales. Mercury injection capillary pressure laboratory measurements were used to evaluate the capillary pressure (vs. saturation) curves calculated using single, two-scale, and three-scale network models of this dolomite. The integrated networks displayed an improved match to the laboratory measurements in comparison with the single-scale network model. The three-scale network provided the closest simulated curve, this result confirms that a more representative model displays closer properties. While simulated capillary pressure curves are close (converging) for the integrated networks the calculated relative permeability curves show variability for different multiple-scale networks. The present work demonstrates that the pore-scale fluid displacement processes occurring in heterogeneous porous media are more complex than those occurring in homogeneous media. In addition, successful fluid flow simulations require construction of multiple-scale models as well as consideration of the pore-scale processes (such as droplet-fragmentation) that are specific to such complex pore systems.

This research addresses a number of fundamental issues concerning convective mass transfer across fluid-fluid interfaces in porous media. Mass transfer to/from distributed sinks/sources is considered for i) the slow dissolution of liquid filaments of a wetting non-aqueous phase liquid (NAPL) held in the corners of angular pores or throats and ii) the fate of gas bubbles generated during the flow of a supersaturated aqueous phase in porous media. 1. Effects of the stability of NAPL films on wetting NAPL dissolution Wettability profoundly affects the distribution of residual NAPL contaminants in natural soils. Under conditions of preferential NAPL wettability, NAPL is retained within small pores and in the form of thick films (liquid filaments) along the corners and crevices of the pore walls. NAPL films in pore corners provide capillary continuity between NAPL-filled pores, dramatically influencing the behaviour of NAPL dissolution to the flowing aqueous phase by convection and diffusion. A pore network model is developed to explore the dissolution behaviour of wetting NAPL in porous media. The effects of initial NAPL distribution and NAPL film stability on dissolution behaviour are studied using the simulator. NAPL phase loses continuity and splits into disconnected clusters of NAPL-filled pores due to rupture of NAPL films. Quasi-state drainage and fingering of the aqueous phase into NAPL-filled pores is treated as an invasion percolation process and a stepwise procedure is adopted for the solution of flow and solute concentration fields. NAPL film stability is shown to critically affect the rate of mass transfer as such that stable NAPL films provide for more rapid dissolution. The network simulator reproduces the essential physics of wetting NAPL dissolution in porous media and explains the concentration-tailing behaviour observed in experiments, suggesting also new possibilities for experimental investigation. 2. Convective Mass Transfer across Fluid Interfaces in Straight Angular Pores Steady convective mass transfer to or from fluid interfaces in pores of angular cross-section is theoretically investigated. The model incorporates the essential physics of capillarity and solute mass transfer by convection and diffusion in corner fluid filaments. The geometry of the corner filaments, characterized by the fluid-fluid contact angle, the corner half-angle and the interface meniscus curvature, is accounted for. Boundary conditions of zero surface shear (‘perfect-slip’) and infinite surface shear (‘no-slip’) at the fluid-fluid interface are considered. The governing equations for laminar flow within the corner filament and convective diffusion to or from the fluid-fluid interface are solved using finite-element methods. Flow computations are verified by comparing the dimensionless resistance factor and hydraulic conductance of corner filaments against recent numerical solutions by Patzek and Kristensen [2001]. Novel results are obtained for the average effluent concentration as a function of flow geometry and pore-scale Peclet number. These results are correlated to a characteristic corner length and local pore-scale Peclet number using empirical equations appropriate for implementation in pore network models. Finally, a previously published “2D-slit” approximation to the problem at hand is checked and found to be in considerable error. 3. Bubble evolution driven by solute diffusion during the process of supersaturated carbonated water flooding In situ bubble growth in porous media is simulated using a pore network model that idealizes the pore space as a lattice of cubic chambers connected by square tubes. Evolution of the gas phase from nucleation sites is driven by the solute mass transfer from the flowing supersaturated water solution to the bubble clusters. Effects of viscous aqueous phase flow and convective diffusion in pore corners are explicitly accounted for. Growth of bubble clusters is characterised by a pattern of quasi-static drainage and fingering in the gas phase, an invasion percolation process controlled by capillary and gravitational forces. A stepwise solution procedure is followed to determine the aqueous flow field and the solute concentration field in the model by solving the conservation equations. Mobilization of bubbles driven by buoyancy forces is also studied. Results of bubble growth pattern, relative permeability and macroscopic mass transfer coefficient are obtained under different gas saturations and aqueous flow conditions.

The growing uptake of wind and photovoltaic technologies requires further sources of system-level flexibility to avoid or defer significant investments. The ability to control, to some extent, customer demand (load management, LM) is one of these sources of flexibility. However, the direct involvement of a large number of customers makes the scalability of such approach a major challenge. A mostly unexplored solution to overcome the challenges of managing thousands or millions of customers is to leverage the positive correlation between voltage and demand. More precisely, Distribution Network Operators (DNOs) can control existing regulation devices to reduce customer voltages and so triggering a reduction in demand. This scheme, hereafter called voltage-led LM, avoids the direct involvement of customers overcoming one of the major barriers of traditional LM solutions. To understand whether this approach can be of any significance, a methodology able to quantify such reduction in demand need to be developed. However, the few methodologies available in the literature neglect the interactions across voltage levels and their influence on the benefits of the scheme. Moreover, time-varying demand profiles and load models are not always considered. Finally, the impact that the widespread adoption of distributed energy resources might have, is also neglected. This thesis addressed these gaps by developing a four-stage approach in which the time-varying volume of demand reduction that the scheme can unlock is quantified considering for the first time the influences among all voltage levels in distribution network. To reduce the complexity each voltage level is analysed separately whilst maintaining the corresponding dependencies. The methodology, also able to extrapolate the results at national scale, can quantify the impact that the uptake of residential scale PV units might have on the scheme. The methodology is demonstrated with a real UK case study where 10-min resolution time-series daily and seasonal analysis are performed. For the first time real network models across the whole distribution network, from 132 kV to 400 V, have been adopted. The interactions across voltage levels, the adoption of realistic load models, the variety of network models and the use of a time-varying approach, all aspects simultaneously considered for the first time in a case study, have shown to play a key role in the quantification. In Great Britain the scheme is expected to provide a significant volume of flexibility of around 1.8GW (60 GW of peak demand). The presence of PV, at least in the short term, has shown to have only a marginally effect on the benefits unlocked by the voltage-led LM scheme, making such scheme promising even in a low carbon future.