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Experimental and computational studies of fused deposition modelling for the fabrication of microstructure patterns
Get PDFFused Deposition Modelling (FDM) is an additive manufacturing process used in 3D
printers for the fabrication of complex 3D objects by layer deposition of molten
thermoplastic filaments. The FDM technology has the potential to produce customised
micro/nano structured patterned surfaces with applications in medical, microfluidic, bio surfaces, etc. FDM is widely used due to its simplicity, low costs and high throughput.
However, high printing accuracy and micro-manufacturing are still challenging due to the
nature of the process. Even though progresses on numerical simulations of FDM have
been made, there is a lack of comprehensive research on swelling and solidification of
the filament and microstructures by considering the temperature dependant properties of
polymers.
This project aims to improve the accuracy of printed parts by controlling the polymer
swelling phenomena as well as developing a cost-effective manufacturing technique, in
terms of predicting the evolution of the extrusion process for the fabrication of
microstructures using commercial desktop 3D printers which utilise the FDM technology.
To achieve this, the temperature dependant rheological and thermal properties of
polylactic acid, within the boundary of printing conditions are first obtained and analysed
numerically. The mechanisms and the effects of operation and design parameters on the
dimensional accuracy of the extruded filaments are then investigated by numerical
simulations based on the Finite Element Method developed in COMSOL Multiphysics
software. Moreover, the free surface of the polymer is determined by applying force
balance and energy equations on the interface to obtain the filament swell and phase
change behaviours. Finally, the model is validated using theoretical results from the
literature and experimental measurements and further used to investigate the evolution of
the microstructures on the filament surfaces during the extrusion process.
From this study, it is identified that both printing process parameters, especially printing
speed, and polymer properties have a significant impact on the formation of extrudate
swell and the shape of the microstructures. The deformation of the polymer increases
with the rise in temperature as the viscosity is highly affected by temperature changes.
Through enhancing the solidification rate and injection rate, the accuracy of filaments and
microstructures can be improved. It is discovered that the swelling phenomena can be
reduced by as much as 21% through cooling the filament with water. Also, in terms of
the micromanufacturing with FDM, several microstructures with different shapes including rectangles, triangles and semi-circles are simulated. The hydraulic swell value
of unity is possible to achieve through adjusting the printing speed for each geometrical
shape. Swell value of 1 is obtained at to 45,50 and 70 mm/s printing speed for rectangles,
semi-circles and triangles respectively which can be used as a reference point for printing
microstructures.
This study helps to predict the shape of the filament and its microstructures using
temperature dependant polymer data through simulations. This eliminates the need for
time-consuming experimentations for the determination of surface topography of the
filaments.
To further improve this study, the effect of layer deposition on the surface topography
needs to be considered
Developing a new design approach to estimate design flow rate in non-residential buildings
Get PDFMaking an accurate estimation of peak water demand in buildings is essential for
engineers and designers in order to ensure proper sizing of water supply systems, storage
tanks, boilers, and booster pumps. Over recent years, the amount of potable water used in
buildings has reduced considerably as a result of the prevalence of water-efficient
appliances and a heightened awareness of the need to conserve water. This has, in part,
led to oversizing of water supply networks; a phenomenon that has given cause for
concern to those responsible for the design of building plumbing systems. This oversizing
problem does not only result in a material and financial cost, it also has negative health
consequences.
In the UK, despite a clear reduction in consumption at end-use points, traditional design
approaches are still used for determining design flow rate. Although different design
methods have been presented in various British standards and guidance documents, all
use the Loading Unit (LU) approach, which is based on the application of probabilistic
techniques, to estimate the design flow for both residential and non-residential buildings.
In recent studies, the focus has generally been on residential buildings and there has been
little, if any, research to assess the validity of current design methods for non-residential
buildings. This study, therefore, focuses on developing a new design approach to estimate
demand flow in non-residential buildings.
This research starts by providing background information on the water situation in the
UK and discusses the reasons for oversizing and its consequences. Water demand is also
discussed, as is water conservation, per capita water consumption and the demand from
micro-components. In addition, the history of system design and the most commonly used
UK design approaches are discussed. After undertaking a critical review and
comprehensive investigation of statistical methods and recent studies used to estimate
demand flow, a new design methodology for estimating water demand, specifically for
non-residential buildings, is introduced. This has also allowed for the presentation of a
new stochastic model, namely the Water Demand Estimation Model (WDEM).
The model is underpinned by the interaction between users and the provision of sanitary
appliances in conjunction with the generation of a comprehensive range of probabilities
to capture all possible simultaneous uses of appliances. The Monte Carlo technique has been applied to calculate flow rate values based on a given number of users. A specific
type of non-residential building i.e. the ‘workplace’ has been selected for application of
the model and for which new design equations have been derived. Taking into account
the water saving appliances used in modern plumbing systems, five design equations have
been derived based on efficiency levels of corresponding appliances. In order to validate
the model and to assess its accuracy, high quality flow rate data was gathered from three
case study buildings. The effectiveness of the WDEM and its impact on the oversizing of
water systems has been confirmed by comparing simulated, measured and design flow
rates. The results show that the simulated demand is very close to the measured flow rate,
and that its use results in a significant reduction of design flow rate compared to those
determined by using current design codes.
The main outcome of this study is hence a novel approach for the estimation of demand
flow for non-residential buildings and a set of design equations to estimate the
simultaneous demand flow rate for workplaces. This new approach will be of value to all
engineers and designers who seek to establish a more accurate estimation of water
demand in buildings
Communication, behavioural biases and financial markets ; a case study of Tesla Inc.
Get PDFThis aim of this research is to evaluate the effect of corporate communication on setting
investor expectations about a stock´s fair valuation. By means of a case study analysis of
the Tesla Inc. stock price since the initial public offering (IPO), it seeks to empirically
explore the extent to which investor sentiment was influenced by fundamentals or by
behavioural aspects. The increased internet connectivity, the additional information
available to economic agents or investors (Stigler, 1961) as well as higher transparency
(Leff, 1984) of companies has impacts in the context of the efficient market hypothesis
(Fama, 1970) and Behavioural Finance (Odean 1998a, Kahneman and Tversky 1973,
1979, Scharfenstein and Stein 1990). The methodology employed is positivist, utilizing
statistical time-series techniques/models on the basis of Arbitrage Pricing Theory (Ross,
1976), Vector Autoregression (VAR), Vector Error Correction Models (VECM) and
Impulse Response Functions. The data sources, by means of web-crawlers, algorithms
and manual interpretation of media, operationalize the sentiment indicators required
(Nisar and Yeung, 2018). Accordingly, it seeks to determine whether stock price
movements were pre-dominantly explained by aggregated or individual sentiment
variables representing a meaningful tool to proxy emotions and social media in response
to communication. Through a deductive approach, patterns and theoretical underpinnings
are sought to be explained by communication-driven deviations from Tesla Inc.´s intrinsic
value. Ultimately, it seeks to re-emphasize that traditional finance theories require the
adoption of behavioural proxies to appropriately capture short-term movements as
informational advantages can still yield additional results. In so doing, this research
evaluates indicators of selected behavioural biases associated to communication that may
impact the value of incorporating fundamental information and help explain changes in
share prices. Therefore, this research is geared to establishing an understanding of and
extent to which sentiment determinant corporate communication should be focused and
provide a basis to be replicated for similar case studies
Discretionary decision making : understanding the response dynamic between keyworkers and residents in supported accommodation
Get PDFSupported accommodation, and congregate hostels in particular, remains the default form
of provision for most young people experiencing homelessness, despite increasing
evidence of its negative effects on residents. Existing evidence also suggests that such
environments negatively impact hostel staff and thereby reduce their ability to provide
effective support. The role of a keyworker in supported accommodation is to support and
manage residents’ behaviour. Because this behaviour is not always predictable and
keyworkers operate independently, judgements cannot be universally applied and
keyworkers must employ discretion in their decision making and response. However,
there is a dearth of literature regarding the impact of this discretion on the keyworker role
and the formation and implementation of organisational policy. This thesis aims to
examine the influences on keyworkers’ discretionary decision making and map the ways
in which they respond to service users. It also explores how residents respond to this use
of discretion, and the factors that influence their response.
Rich qualitative data collected through participant observation, interviews, and focus
groups conducted in three supported accommodation services within Scotland are used to
examine the role of keyworker discretionary decision making in their responses to young
residents. Drawing upon street-level bureaucracy, social control, and judgement and
decision-making theory, this thesis develops an innovative conceptual framework through
which to map out and deepen understanding of the keyworker-resident response dynamic.
In doing so, it finds an irreconcilable tension within the keyworker role between: firstly,
rule enforcer and, secondly, resident confidant. It argues that in conjunction with the high
levels of discretionary decision making keyworkers hold, this constitutes an inherent and
harmful impact of hostel accommodation for both residents and keyworkers. The thesis
concludes by arguing against the continued use of the dominant congregate model of
supported accommodation, while making recommendations that might mitigate the
negative impact of discretionary decision making within these settings given the likely
continued use of this form of provision
Integrated prospect evaluation and characterisation using pre-stack seismic data, Mansoura area, onshore Nile Delta, Egypt
Get PDFThe Mansoura area locates in the north of the Nile Delta Hinge Zone. It geologically
comprises a thick sequence of deltaic sediments from Recent to Oligocene age
overlying older Mesozoic sequences which are probably too deep for hydrocarbon
exploration. The middle and late Miocene in the onshore Nile Delta is dominated by
siliclastic sediments with excellent reservoir quality, which allows the researchers to
introduce different techniques for reservoir properties prediction for the sensitivity of
this type of sediments to the reservoir characterization studies. West Dikirnis (WD)
and West Khilala (WKH) fields were selected for the reservoir characterization
studies done in this thesis because they are typical examples for the onshore Nile delta
geological setting.
In 2003, a successful aggressive drilling program started in the Mansoura area in the
onshore Nile Delta with a high success ratio depending on the direct hydrocarbon
indicators from the post-stack seismic data. However, after proving hydrocarbon
presence and many fields discovered, some challenges appeared. Lately, the most
important ones are the lithology and fluid discrimination due to the inconvenient shale
behavior, reliability of more than half the available pre-stack seismic data, and the
delineation of different reservoir properties like clay content, water saturation, and
porosity. One of the big challenges facing different operators in the on-shore Nile
Delta is the discrimination between residual gas saturations and mobile commercial
gas. Analysis of pre-stack seismic data for different reservoir properties prediction is
commonly used for reservoir geophysics. As a result of the problems facing the
acquisition and processing in the Mansoura area, the output pre-stack seismic data
needs some improvements especially for the ultra-far data; consequently, the gathers
pre-conditioning becomes essential before proceeding in any reservoir delineation
process. After a crucial review of the gathers, it was found that there is a potential to
improve the signal to noise ratio and to prove the reliability of the ultra-far data, after
several iterations and testing several processing parameters, a conditioning workflow
was developed and applied to the prestack seismic data. The clean ultra-far data was
an output that resulted from the gathers conditioning developed a workflow that was
applied to the whole common depth point (CDP) gathers for Mansoura area in the onshore Nile Delta. This workflow can be applied simply in the whole onshore Nile
Delta, not just the Mansoura area. The application of spectral decomposition and
frequency attributes on pre-stack seismic data has opened the door to think about the
relationship between frequency attenuation and reservoir properties. We will see later
in the thesis how spectral decomposition can be used to discriminate between low gas
saturations and mobile, commercial gas in the area of study. The popped-out
amplitudes are vital to be analyzed. Understanding the phase and polarity of the
seismic data is critical; the seismic survey design should take into consideration the
special geophysical techniques. Building up a rock physics model in the onshore Nile
Delta can play an important role in linking the elastic properties to the reservoir
properties and applying the results in the unexplored areas within the concession,
which help in di-risking the delineated prospects. The rock physics model built in the
study area helps significantly in the discrimination of sand and shale, wet and gas
sands. The amplitude versus offset (AVO) simultaneous inversion applied to the ultra-far seismic data helped in qualitative interpretation for the reservoir in both WD and
WKH fields by combining the results of the Zp, Zs, Vp/Vs and density above, within
and below the reservoir. The middle and late Messinian are typically sand-rich
sections with excellent reservoir quality encountered in the drilled wells within both
fields. Different reservoir properties have been predicted using advanced inversion
techniques. The well data showing strong correlation at the well locations increases
the confidence in using this prediction in prospects drilling. Developing workflows to
increase the reliability of the far and ultra-far seismic data (one third of the recorded
seismic data was useless before) is one of the innovations of this study. The cross-plot
of the acoustic impedance versus shear impedance represents one of the best tools in
the onshore Nile Delta for sand and shale discrimination. Using Elastic impedance
logs, we can discriminate the water bearing sand, hydrocarbon sand and shale,
especially at the ultra-far angle (in this case, 45 degrees). The application of spectral
decomposition, frequency attributes and amplitude attributes on the ultra-far stacked
data shows excellent results in terms of the tuning frequency response. The strong
relationship between gas saturation and frequency attenuation was proven and will be
shown in this study. The work done in this study will broaden the role of spectral
decomposition and frequency attribute analysis beyond its use as a hydrocarbon
indicator by further emphasizing its role in reservoir properties delineation
Robust optimization of well placement and control : enhancing operational applications and computational efficiency
Get PDFField optimization aims to find the field design and management strategy that maximizes
economic profit while honoring multiple constraints. This results in a complex
optimization problem with a large number of correlated variables of various types (e.g.
well locations and control settings) and a computationally expensive and uncertain
objective function based on a simulated field model. Two practical limitations of the
existing optimization workflows are: 1) the forthcoming field production limits are
ignored during at least the early field design optimization stages, and 2) they provide a
single optimal solution strategy, whereas later operational problems often add unexpected
constraints changing this strategy and reducing this inflexible solution to a sub-optimal
scenario in the application. This thesis develops efficient field development and control
optimization techniques to provide operationally flexible solutions while considering
production constraints and geological uncertainty.
The first part of the thesis presents a robust, multi-level framework that considers fluid
processing capacity constraints during both well placement and control optimization
levels. To accomplish this, an integrated model of the reservoir and production network
is simulated to impose the production limit while capturing the flow behavior in the
integrated system. A systematic realization selection process, tailored to the objective of
the subsequent optimization stage, is proposed to select a small representative ensemble
of reservoir model realizations to account for the reservoir description uncertainty during
robust optimization. The proposed realization selection technique is shown to outperform
the alternative approaches. Various scenarios are investigated to identify the impact of
field production constraints on the optimal field development and control strategy. The
developed integrated optimization workflow is applied to optimize the infill well
placement in a real North Sea field.
A robust, multi-solution optimization framework is then developed to offer operational
flexibility by providing multiple optimal field development and control solutions. The
developed workflow is based on the sequential optimization of well placement and
control at multiple levels. An ensemble of close-to-optimum solutions is chosen from
each level and transferred to the next level of optimization, and this loop continues until
no significant improvement is observed in the (expected) objective value. Fit-for-purpose
clustering techniques are developed to systematically select an ensemble of solutions, with maximum differences in decision variables but close-to-optimum objective values,
at each optimization level.
The developed multi-solution optimization framework requires significantly higher
computation time especially when aiming to maximize diversity among well placement
solutions. Convolutional Neural Networks (CNNs) are used as surrogate models (SMs)
to enhance the computational efficiency by partly substituting the time-consuming
reservoir simulation runs. An ensemble of CNNs is employed to enhance the robustness
of the surrogate modeling as well as to allow estimation of the SM’s prediction quality
for new data points. The ensemble of CNNs is adaptively updated during the optimization
process using new data points, to improve its prediction accuracy. The efficiency of the
developed optimization frameworks is demonstrated using several benchmark case
studies
Experimental and numerical analysis of high-speed railway infrastructure
Get PDFThe demand for higher train speeds and heavier axle loads proved that the performance
of ballasted tracks and conventional embankments are suboptimal. Such train loading
triggers high stress levels and elevated vibration levels of the track when subjected to
forces of high-speed and heavy axle trains. An effective superstructure type, known as
slab track, was developed in the last decades and has already been in use all over the
world. In addition to the state-of-the-art superstructure types, improvements in
substructures have also become increasingly popular.
The experimental research work presented in this thesis evaluates the performance of a
Geosynthetic Reinforced Soil Retaining Wall (GRS-RW) system as an alternative to the
conventional railway embankment. Significant savings in Carbon emissions, cost and
time could be achieved if the capital costs of the track construction and the land take
could be reduced. The GRS-RW substructure offers this opportunity. However, such
technology requires significant performance evaluation and the development of
appropriate design guidelines before the rail industry can justifiably implement it in
projects.
Full-scale testing is carried out on three-sleeper sections of ballasted and slab tracks by
simulating moving loads at 360km/h in the Geopavement and Railway Accelerated
Fatigue Testing (GRAFT-II) facility. The tracks are supported by a low-level fully
confined conventional embankment and a GRS-RW substructure. First, a three-sleeper
section of a precast concrete Max-Bögl slab track was tested under controlled laboratory
conditions, followed by a ballasted track. Both superstructures are supported by a 1.2m
deep subgrade and frost protection layer, in accordance with high-speed railway design
standards. Two different axle load magnitudes are applied statically, and then
cyclically/dynamically, using 6 actuators to replicate moving train axle loads. The overall
aim is to assess the performance of the tracks, in terms of transient displacements and
total settlements, as well as stress levels at different locations of the substructures.
The experimental results show that the pressure levels on the GRS-RW wall are negligibly
small for the particular test setup, proving the GRS substructure under the action of
compaction reached its active state. This means that the reinforced soil was self-supporting under its self-weight and train loads, implying there was minimal pressure on
the walls. Therefore, GRS-RW systems have the potential to be better alternatives to
traditional earth embankments due to the enhanced soil stabilisation and lower land take. It is concluded that the slab track performs significantly better than the ballasted track in
terms of elastic and plastic deformation, under both static and cyclic loading.
The full-scale experimental work is complimented by a numerical part, which aims to
develop three-dimensional train-track-soil models using the finite element (FE) method.
The commercial software Abaqus was used to create these models. First, the GRAFT-II
tested samples were modelled replicating all the track components and geotechnical
parameters. The New Ballastless Track (NBT), developed by Alstom, was also simulated
in the FE models, which were calibrated using the laboratory results, under the
considered cyclic loading.
The calibrated models are then extended to create 3D linear dynamic models, considering
train-track-soil interaction, simulating train passages at various speeds. The Ledsgård
case was used to validate the models. Trains travelling at low and high speeds are
considered to investigate the track deflections and the wave propagation in the soil. The
issues associated with critical speeds were observed in the presence of both ballasted and
slab tracks.Engineering and Physical Sciences Research Council (EPSRC) grant EP/N009207/
Hidden properties identification and text diversity translation of people’s names
Get PDFDevelopments in the ability to analyse online data of people’s names have provided
breakthroughs for social research, however, there are several existing challenges. This
thesis proposes several novel approaches for identifying hidden properties and text
diversity translation of people’s names. We start by studying the hidden properties
of people’s names and found that there are limited existing methods to identify more
than one hidden property of people’s names at one time. We, therefore, propose
a ’Hidden Property Bayes’ model that achieves identifying more than one hidden
property of people’s names in Kanji and Hanzi at one time. In addition, our model
performs better than an existing system on name origin identification. We then
moved on to text diversity translation and found that translating romanised names
to the original language is a challenge. Therefore, we propose two novel models to
translate Pinyin names to Hanzi names. These two novel models perform better
than ’google translate’ on Mandarin name translation. We next investigated gender
prediction of people’s names and found that limited existing tools can predict and
analyse the data in one process. Therefore, we propose a ’Name-Gender’ tool that
achieves predicting the gender of people’s names and also provides a statistical graph
directly. In addition, our tool has better performance than an existing system on
predicting the genders of people’s names in Latin and Hanzi characters. We also
provide novel findings of gender analysis in computer science using our ’Name-Gender’
tool approaches. Overall, our contributions provide effective novel approaches to
support social researchers analysing online data sources of people’s names to aid
them in understanding real-world events
Artificial intelligence for decision making in energy demand-side response
Get PDFThis thesis examines the role and application of data-driven Artificial Intelligence
(AI) approaches for the energy demand-side response (DR). It follows the point of
view of a service provider company/aggregator looking to support its decision-making
and operation. Overall, the study identifies data-driven AI methods as an essential
tool and a key enabler for DR. The thesis is organised into two parts. It first provides
an overview of AI methods utilised for DR applications based on a systematic review
of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR
application area of the AI methods. The end of the first part of the thesis discusses
the advantages and potential limitations of the reviewed AI techniques for different
DR tasks and how they compare to traditional approaches. The second part of the
thesis centres around designing machine learning algorithms for DR. The undertaken
empirical work highlights the importance of data quality for providing fair, robust,
and safe AI systems in DR — a high-stakes domain. It furthers the state of the art
by providing a structured approach for data preparation and data augmentation in
DR to minimise propagating effects in the modelling process. The empirical findings
on residential response behaviour show better response behaviour in households with
internet access, air-conditioning systems, power-intensive appliances, and lower gas
usage. However, some insights raise questions about whether the reported levels of
consumers’ engagement in DR schemes translate to actual curtailment behaviour and
the individual rationale of customer response to DR signals. The presented approach
also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an
aggregator in leveraging small-scale flexibility resources by providing an automated
end-to-end framework to select the set of consumers for demand curtailment during
Demand-Side Response (DR) signals in a dynamic environment while considering a
long-term view of their selection process
Benthic ecosystem functioning of the western Clarion-Clipperton Zone, Pacific Ocean, and the West Antarctic Peninsula : a study to assess the effectiveness of Areas of Particular Environmental Interest (APEIs) in the context of deep-sea mining and the effects of climate change
Get PDFThe deep sea encompasses the largest ecosystem on Earth and remains largely unexplored.
With plans for deep-sea mining and the increasing impacts of climate change on our oceans,
there is a growing necessity to understand and safeguard deep-sea biodiversity and ecosystem
functioning. The cycling of carbon (C) by deep-sea benthic communities is a key ecosystem
function and pulse-chase experiments are aimed to measure this process I conducted pulse-chase experiments in situ at abyssal depths (4800-5300 m) in three no-mining areas, called
Areas of Particular Environmental Interest (APEIs), in the Clarion-Clipperton Zone (CCZ) and
in ex situ experiments using sediments collected from a bathyal (500-600 m) fjord, Andvord
Bay, and the continental shelf of the West Antarctic Peninsula (WAP). My results underline
the importance of organic C in driving ecosystem dynamics at the abyssal seafloor and support
the notion that Antarctic fjords are hotspots of benthic biomass and ecosystem functions. The
microbial community was shown to be a key player in the short term (1.5 d) cycling of C on
the abyssal plain of the western equatorial Pacific Ocean, which is consistent with other
published studies, while the macrofaunal community (>300 µm) dominated the initial (~1 d)
degradation of phytodetritus in Andvord Bay. My study provides important information on
benthic ecosystem functioning in the western CCZ, an area targeted for commercial-scale deep-sea mining, and the WAP, a region that is becoming increasingly impacted by climate change
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