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Optimal transport theory and geometric modelling of polycrystalline materials
Full text linkThis thesis looks at an application of optimal transport theory in materials science and
computational geometry. In particular, we use optimal transport to generate Laguerre
tessellations that have cells of prescribed volumes and centroids. Laguerre tessellations
can be used to model the microstructure of polycrystalline materials, such as metals. We
study methods for generating synthetic microstructures and fitting Laguerre tessellations
to Electron BackScattering Diffraction (EBSD) images of metals.Engineering and Physical Sciences Research Council (EPSRC) grant no.
EP/S023291/
Characterisation of naturally fractured reservoirs : from geological well-testing to machine learning-assisted pressure transient analysis
Full text linkThe pressure signal recorded from dynamic well tests (e.g., pressure drawdown,
pressure build-ups) reflects reservoir properties at scales ranging from a few meters to
hundreds of meters around the wellbore. For quantifying these properties, it is essential
to leverage the pressure derivative ’ for identifying suitable reservoir models. A concave
inflexion ’ (i.e., v-shape) has been traditionally used as a diagnostic indicator of
Naturally Fractured Reservoirs (NFRs), mainly because the standard model for NFRs
produces a similar ’. However, multiple studies have shown that this model is rarely
applicable because it does not represent common geologic features of NFRs, and that
NFRs can manifest themselves in multiple different ’. Recognising the non-uniqueness
of ’ in NFRs as well as the absence of a universally applicable NFRs model, the
fundamental question is how to capture the true diversity of ’ and incorporate it into an
interpretation framework that allows correlating the observed ’ to the underlying
properties of the fractures. Addressing this question, this thesis introduces the Machine-Learning Assisted Pressure Transient Analysis in NFRs (MLA-PTA), as a novel method
for interpreting ’ in NFRs. MLA-PTA builds on the fundamental principle of the
Geological Well Testing (GWT) method for ensuring geological consistency while
interpreting ’, but enables higher flexibility to include multiple NFRs concepts or
realisations. Central to the MLA-PTA method is an innovative proof-of-concept for
classifying ’ responses using unsupervised machine learning aimed to facilitate
identifying characteristic ’ signatures of different fracture systems. The proof-of-concept was validated on a dataset encompassing over 2500 synthetic fracture models
created from a bespoke discrete fracture network generator. The results helped provide
insights about potential interpretations of two real field cases. Overall, the MLA-PTA
provides a valuable alternative for capturing and understanding ’ behaviours of different
NFRs and their correlation with real ’
Leveraging AI-driven NLP models to predict user sentiment in Dubai's urban spaces for wellbeing
Full text linkNascent city Dubai has adopted state-of-the-art urban planning across its infrastructure and
mobility systems. Yet, the integration of smart technologies to enhance human-centric planning,
sustainability, and walkability in urban spaces remains underexplored. This research explores how
AI can be leveraged to incorporate public input into urban development, promoting more inclusive
and sustainable city planning.
This study aims to develop an Artificial Intelligence (AI) driven Natural Language Processing
(NLP) model to predict the public’s emotional responses to urban features within Dubai's built
environment. The research seeks to understand how these urban features, whether frequently or
occasionally encountered, impact residents' needs, preferences, and overall quality of life. This
research is essential as it leverages AI to integrate public sentiment into urban planning, enhancing
wellness and aligning with the growing focus on smart city initiatives.
To achieve its objectives, this study investigates how traditional settlements in Dubai, such as the
Al Ras district, contend with the pressures of rapid urbanization. Despite their unchanged physical
boundaries, these areas have experienced population growth and shifts in lifestyle, leading to urban
challenges. These challenges include traffic congestion, walkability issues, navigation difficulties,
and a lack of innovative activities, problems that current urban planning processes have not
adequately addressed. This disconnect between the built environment and the community's
evolving needs highlights the necessity to reevaluate urban planning strategies within these historic
areas. Al Ras, selected as a case study represents an old settlement that carries considerable
cultural, traditional, and historical significance from the early days of Dubai.
The literature review, which forms the initial part of the study, explores four key areas: the use of
smart technology to enhance well-being, the impact of urban spaces on people's emotions, the
application of AI in urban contexts, and the transformation of Dubai into a futuristic city marked
by economic growth, technological advancements, master planning, and governance. This
theoretical framework establishes the foundation for the empirical component of the study, where
data is collected through questionnaires and interviews..
To analyze this data, a textual dataset was prepared from the field survey and augmented with
additional data for machine learning purposes. Four machine learning models were developed and evaluated through a comparative analysis to select the most effective one. This model excels at
analyzing people’s responses with high accuracy and provides a method for predicting sentiment.
The study concludes by proposing a roadmap that aims to harmonize technological integration,
participatory governance, and human-centric urban design. This approach is intended to advance
sustainable development and enhance well-being in urban environments, providing actionable
insights for policymakers and urban planners
Direct C-H amidations of N-heterocycles
Full text linkN-Heterocycles are well-studied structures in synthetic organic chemistry and their
prevalence in the pharmaceutical industry, for example, has led to this great interest. Therefore,
the ability to directly C-H functionalisation N-heterocycles would be of great interest to the
synthetic organic chemistry community, allowing access to valuable targets without the need
for non-productive chemical steps (such as protection/deprotection, functional group
interconversions (FGI) or oxidation manipulation chemistry). This thesis will describe the
development of two complementary C-H amidations of N-heterocycles (one thermally mediated and one light-mediated) which will allow for the C-H amidation of phenanthrolines,
purines and 1,3-azoles. The term “amidation” is predominately used in this thesis to refer to
carbamoylation. Please note that the more specialist term “carbamoylation” was initially used
in Chapters 1 and 2 (and the publications contained within them). Recently, however, the more
general and well-known term “amidation” tended to be the preferred terminology to refer to
carbamoylation, and this change in notation is reflected in the publications contained within
Chapters 3 and 4.* Strictly speaking, there are two possible types of amidation:
carboxyamidation (bond-forming at the C of the amide) and N-amidation.) Amidation in this
thesis refers to carboxyamidation throughout, unless otherwise stated.
Chapter 1 will provide an introduction to the work discussed within this thesis. More
specifically, chapter 1 will introduce the Minisci reaction, a powerful method for C-H
functionalisation of electron-deficient N-heterocycles.
Chapter 2 presents a metal- and light-free C-H diamidation of 1,10-phenanthrolines.
This is the first C-H diamidation of 1,10-phenanthrolines which is capable of directly installing
primary, secondary and tertiary amides. This method is cheap, operationally simple and
scalable. Moreover, the facile 2-step (vs. previous 11-step) synthesis of a diamidated 1,10-
phenanthroline target is demonstrated.
Chapter 3 showcases the development of a direct C-H amidation of purines. As well as
being able to install primary, secondary and tertiary amides, this method is the first to
demonstrate a direct C-H amidation on a wide range of purines (e.g. xanthines, guanines,
adenines, including guanosine- and adenosine-type nucleosides). This procedure is not only cheap, operationally simple and scalable as before, but is also applicable to the late-stage
functionalisation of many biologically important molecules.
Chapter 4 communicates the development of two complementary methods (one
thermally-mediated and one light-mediated) for the direct C-H amidation of 1,3-azoles. This
work is applicable to the four most important 1,3-azoles in medicinal chemistry:
benzothiazoles, thiazoles, benzimidazoles and for the first time, imidazoles. As with the
previous chapters, the methods developed are cheap, operationally simple and scalable but also
allow for the first late-stage C-H functionalisations of 1,3-azoles. The light-mediated method
presented is the first photosensitiser-free direct Minisci-type amidation which proceeds via an
EDA. Furthermore, this newly developed photosensitiser-free Minisci-type amidation is not
only limited to azoles but can be applied to other N-heterocycles.
Chapter 5 will draw an overall conclusion of the work presented within this thesis and
discuss the possible future work.Engineering and Physical Sciences Research Council (EPSRC) fundin
Smart construction and industry 4.0 challenges and opportunities : a strategic and operational framework to unlock digital transformation
Full text linkThe construction industry is always fragmented, labour-intensive, and has slow technology adoption
and low-profit margins. Nevertheless, with the development of different global sectors, improvement
becomes mandated, especially given the complexity, tight budgets, and squeezed time frames.
Rethinking the delivery of construction projects became vital to planning, operating, delivering, and
measuring success as the world witnessed a historically unprecedented industrial disruption. This
revolution entails transforming humankind.
Unlike any predecessor with a linear pace of expansion, Industry 4.0 is spreading exponentially,
renovating all traditional strategies and aiming to transform business models across countries,
governments, companies, industries, and societies. Like other industries, the construction industry
has struggled to take advantage of technology significantly.
A fourth industrial revolution and its subsequent "Industry 4.0 and construction 4.0" terminology
are invading advanced industries, calling for fully integrated digitalised value chains across
ecosystems. The Internet of Things (IoT), digitalisation and offsite construction became the essence
of overcoming industry pitfalls. Construction is moving from a Resource-based industry to a
knowledge-based sector, considering the borderless boundaries between different sectors by
adapting the cross-industry cooperation concept.
The research investigates industry 4.0 opportunities and their potential impact on the construction
industry throughout the project life cycles from inception to asset management. It seeks to improve
physical site delivery by reviewing related concepts, technologies, and supply chains.
Emerging technology priority areas are identified, implementation mandates are explored, and
challenges and enablers are distinguished. A framework and Roadmap for incorporating industry 4.0
concepts and technologies are proposed.
A triangulation approach was adopted throughout the research to integrate results from a literature
review and other field works. Focus group workshops helped to understand industry issues and were
a key input towards questionnaire design. Survey questionnaires highlighted industry drawbacks and expected improvement of emerging technology implementation, concentrating on linking potential
technology and industry inefficient practices.
Interviews with industry experts were crucial in associating the findings with corporate strategic
objectives and initiating the roadmap. The fieldwork was conducted to satisfy pre-defined research
objectives and Initiate/validate the proposed framework for successful implementation at both
operational and strategic levels.
The primary outcome of the research is a framework and roadmap for digital transformation
initiatives with a concentration on the main contractors operating in the GCC area to empower the
utilisation of industry 4.0 technologies; both were developed from the field/desk works and validated
with different stakeholders operating in the construction industry as the pandemic situation of
COVID-19 enforced the need for more technology utilisation and enhanced industry professional
awareness and willingness to adapt and change the conventional methods of entire project delivery
Exploring the potential and control of mixed culture fermentations in brewing
Full text linkMixed culture fermentation with non-conventional yeast strains is a promising strategy for use in
the brewing industry. Well-paired strains can work together to result in mixed cultures with
improved overall fermentation performance, and can result in the enhanced production of the esters
and higher alcohols that give beer its characteristic aroma. Fermentation of standard-gravity
(1.044) wort by mixed cultures of Schizosaccharomyces pombe and Saccharomyces cerevisiae
enhanced concentrations of isoamyl acetate, ethyl octanoate, and isoamyl acetate, important
contributing compounds to the aroma of beer. Fermentation of very high gravity wort (1.088) by
the same culture again enhanced aroma-active volatile compound concentration, and attenuated
the wort more fully, offering a potential route for improving efficiency and sustainability. Mixed
cultures of Saccharomyces bayanus and S. cerevisiae had more favourable fermentation kinetics
than the S. pombe mixed culture and resulted in the enhancement of 2-phenylethyl acetate and
other esters. Dynamic monitoring of the consumption of sugars and amino acids and the production
of higher alcohols and esters during fermentation allowed further analysis of metabolism during
mixed culture fermentation. We have shown that mixed cultures enable the brewer to exploit the
natural diversity of yeast strains to improve product characteristics and process efficiency,
representing a key avenue for continued research. On an industrial level, this approach could lead
to process economy benefits and the production of beer with innovative sensory properties
Experimental investigation on surface chemistry and optical properties of tin(IV) oxide and tin(IV) oxide-molybdenum disulfide nanocomposites
Full text linkThe rapid advancement of material science and nanotechnology has opened up
numerous avenues in the realm of nonlinear optics. An expanding array of
nanomaterials showcases exceptional nonlinear optical properties, catalyzing the
innovation and production of optoelectronic and photonic devices at the nano and micro
scales. Among these advancements lies the investigation of a material's saturable
absorption properties, a subtype of nonlinear optical properties pivotal in realizing Q switching and mode-locking lasers. A problem statement has been identified that
despite many materials demonstrating promising saturable absorption properties, their
processing remains costly and complex, introducing significant challenges in
fabrication and practical applications. This highlights the critical need for ongoing
research to identify materials that offer simpler, more cost-effective processing
methods while still maintaining strong saturable absorption properties. Exploration into
metal oxides has unveiled novel possibilities within the photonics domain. Among
these oxides, SnO2, a common metal oxide in diverse applications, remains relatively
underexplored in the realm of nonlinear optical properties.
Herein, the research endeavors focus on the investigation of SnO2 by developing SnO2
into polymer composite films via the solution casting method, aiming to study their
saturable absorption properties, particularly modulation depth and saturation intensity.
SnO2 was chosen due to to its notable attributes, including high electron mobility and
a wide band gap of 3.6eV. These characteristics result in substantial third-order optical
nonlinearities, measuring at 3.8 × 10−12 esu. Theoretically, this magnitude should confer
saturable absorption properties, far surpassing the typical range observed in silica fibre,
which usually falls around ~10−14 esu. Moreover, an optimization strategy was
employed by integrating exfoliated MoS2 nanosheets with SnO2, resulting in the
formation of a SnO2-MoS2 nanocomposite. This approach aimed to enhance the
saturable absorption properties of the material and prove the versatility of SnO2 in the
photonics realm.
The research primarily addresses the knowledge gap concerning the insufficient
understanding of developing SnO₂ with saturable absorption properties and optimizing
SnO₂ composites to enhance these properties. Within this broader scope, two additional research gaps are identified: (1) the lack of knowledge regarding compatible solvents
with SnO₂ to achieve good dispersion stability, as well as the impact of surfactants on
the preparation of SnO₂ nanodispersions; and (2) insufficient understanding of how the
mass composition of SnO₂ and MoS₂ affects the optical bandgap of the nanocomposite.
Addressing these two knowledge gaps is crucial to advancing the research and
achieving its final objective in this research. The research was conducted in three stages,
each aimed at addressing a specific knowledge gap.
In this research, the uniformity of the resulting SnO2 composite film is crucial when
employing the solution casting method to attain the desired saturable absorption
properties. The uniformity is largely influenced by the nanoparticles dispersion
stability. Hence, in the first stage of the research, it prompted an initial exploration of
solvent options, including NMP, water, and DMF, to identify the most suitable solvent
for maintaining optimal dispersion stability of SnO2 nanoparticles during the solution
casting process. In the first stage of the research, an initial exploration of solvent
options—specifically NMP, water, and DMF—was conducted to identify the most
suitable solvent for maintaining optimal dispersion stability of SnO₂ nanoparticles
during the solution casting process. The key finding from this stage indicated that water
and NMP effectively facilitated uniform dispersion and prevented particle
agglomeration with 1-2 hours of sonication, thereby enabling the formation of
homogeneous nanodispersions. Additionally, the impact of adding a surfactant was
assessed using Response Surface Methodology, which revealed minimal effects,
resulting in its exclusion from the preparation of SnO₂ polymer composite films. In the
second stage of the research, the incorporation of SnO₂ with exfoliated MoS₂
nanosheets was conducted to form SnO₂-MoS₂ nanocomposites as part of an
optimization effort aimed at exploring the versatility of SnO₂ in greater depth. This
process was motivated by the principle that the optical bandgap of a material can be
significantly reduced to enhance its saturable absorption properties. The findings from
this stage established a systematic approach for tuning the optical bandgap of SnO₂-
MoS₂ composites, revealing a linear relationship between SnO₂ mass and optical
bandgap tuning, ranging from 1.8 eV to 3.0 eV. This successful tuning demonstrated
the capability to adjust the optical bandgap across a wide range. In the third stage of the
experiment, both SnO₂ and SnO₂-MoS₂ dispersions were fabricated into polymer
composite films using the solution casting method, with fabrication parameters optimized to successfully achieve the desired saturable absorption properties. The
resulting composite films demonstrated improved modulation depth and reduced
saturation intensity, highlighting the effectiveness of the optimization strategy. The key
findings indicated that the SnO₂-based films achieved saturable absorption properties
with an 11% modulation depth and a saturation intensity of 30 kW/cm², while the SnO₂-
MoS₂ films exhibited a modulation depth of 27% and a saturation intensity of 0.13
kW/cm²
Solubility of light ends in heavy condensates
Full text linkThe research work aims to improve the prediction of the vapour-liquid distribution of light
hydrocarbons in heavy raw condensate production streams by gathering and evaluating the
available experimental data. The principal motivation of this research initiative was to generate
a comprehensive data set to assist in the efficient and economic design and running of
condensate stabilization facilities. Vapour liquid equilibrium (VLE) data for binary systems of
light hydrocarbons (ethane, propane and n-butane) in heavier hydrocarbons were gathered from
the literature. The gathered datasets were evaluated using predictive Peng Robinson (PPR78)
and multifluid Helmholtz energy approximation (MFHEA) equations of state. Experiments
were conducted to generate VLE data for binary and multicomponent systems of light and
heavy gas condensate ends up to 373 K and 10 MPa using an isochoric setup. This effort
yielded entirely new datasets for binary systems of ethane and propane in n-decane, n-undecane, n-dodecane and n-tridecane respectively. Six multicomponent systems composed of
varying amounts of n-alkane components were synthesised as part of this study and their VLE
properties (bubble point) determined. The new binary and multicomponent systems VLE data
generated were correlated/predicted using the PPR78 EoS, MFHEA EoS and PC SAFT EoS
with good agreement observed between the experimental data and model
correlations/predictions. The gathered VLE data were used to fit the βT_ij and γT_ij parameters
of the MFHEA EoS while keeping the βv_ij and γv_ij BIPs equal to unity. This formed the basis
for the development of a model for predicting the βT_ij and γT_ij BIPs for the MFHEA EoS using
group contribution (GC) method. The model covers systems composed of n – alkanes with
future studies proposed to extend its application to components such as cycloalkanes, aromatics
and non-hydrocarbon components such as CO2, H2S and N2.The GC model was validated using
binary and multicomponent systems with good agreement between the experimental data and
model predictions
Investigation on advanced signal processing techniques in high-speed secure optical fibre communication and underwater wireless optical communication systems
Full text linkOptical communication is pivotal to modern networks, offering low latency, high
security, and extensive bandwidth capabilities, essential for distributed nodes and base
stations. It enhances data transmission speeds across various domains. Innovations in
signal processing, notably through machine learning algorithms and time-domain spectral
phase encoding (TDSPE), have significantly advanced channel performance and security
in open communication networks.
This thesis presents a comprehensive exploration of TDSPE and Underwater Optical
Wireless Communication (UWOC) systems through theoretical research, simulation
validation, and experimental demonstration. It details the development and testing of
prototype systems for Quantum Key Distribution (QKD) seeded TDSPE system and
Underwater monitoring system (UMS) implemented by UWOC and installed satellite
systems, with the goal of enhancing the security and efficiency of optical communication
systems. The research culminates in the creation of three system prototypes—a coherent
optical receiver, a QKD-TDSPE system, and a UWOC system—each attaining a
Technology Readiness Level (TRL) of 4.
The thesis sets two primary achievements: (i) the design and validation of a novel
dual-layer transmission security approach that utilizes QKD to seed TDSPE, thereby
significantly strengthening the defence of traditional optical communication systems
against brute-force attacks. The effectiveness of this QKD-TDSPE strategy is validated
within a 40Gbps coherent QPSK optical communication system, demonstrating its ability
to preserve data confidentiality without compromising transmission capabilities. A
detailed theoretical analysis underscores the robustness of this security framework,
especially for the resilience to brute-force attack. ii) Present the deep echo state network
(DeepESN) as a pioneering digital signal processing approach to improve the
transmission performance of UWOC systems. A DeepESN configuration is
recommended by the post analysis of the equalization performance, which balances
equalization performance and training efficiency. Furthermore, developing an FPGA-based long-haul remote underwater monitoring system using UWOC technology. This
system demonstrated an UMS with an 8000-km remote real-time underwater video
transmission
Computationally efficient uncertainty quantification for flood hazard assessments
No full textFlood events pose the largest natural hazard risk to the United Kingdom with over
5.4 million properties currently exposed to some degree of flood risk. Climate change
induced variations in natural river processes are expected to increase the frequency
and magnitude of flood events in the future. This will increase the number of
vulnerable areas and the severity of inundation impacts. Governments have an
obligation to prepare effectively for these impacts taking into account the uncertain
changes to global climates. This work has analysed methods to perform probabilistic
fluvial flood hazard assessments in less time than previously thought possible.
Prior to robust testing of different methods, the work herein has analysed the
multiple stages of future flood modelling and identified climate model variations as
the dominant uncertainty factor. Several uncertainties associated with future flood
modelling have been quantified and analysed. Results suggest a strong dependence
upon climate models which cascades through extreme value estimations into hydrograph input uncertainty. Having identified hydraulic model input uncertainty as
the most important, advanced quantification techniques such as multi-level Monte
Carlo and Kriging have been applied to rapidly quantify the output uncertainty.
This thesis investigates a range of advanced uncertainty quantification methods
to speedup probabilistic assessments thus reducing the dichotomy between accuracy
and costs. Results are presented across three diverse case studies using traditional
Monte Carlo (FMC) methods as a gold-standard output to which accuracy and costs
can be compared. Sampling approaches (Latin Hypercube Sampling, Markov chains)
are examined and shown to reduce costs by up to 98.75% over FMC. Following an
analysis of discretisation errors, multi-level Monte Carlo was found to reduce the
costs of high resolution flood hazard assessments. MLMC costs have been shown
to be lower than LHS with a 1.4 times speedup. Application of a Kriging proxy
model was found to further reduce computational costs, requiring only 0.1% of
FMC costs. Testing commonly used quantification methods for the first time in
flood modelling produced significant improvements however further cost reductions
are needed to ensure accuracy and flexibility across every topography. As such, a novel multi-fidelity Monte Carlo (MFMC) method for rapid probabilistic flood
hazard assessments was developed. MFMC combines proxy models built across
different resolutions with the aim of further reducing computational costs. However,
in this low-dimensional problem Kriging was found to be a more effective approach
with MFMC requiring slightly larger costs. The significantly increased efficiency of
every method tested particularly Kriging has potential to improve future policy and
engineering decisions, reducing the impacts of future flood events.
Given the results of this work, a Kriging method has been identified as the most
efficient probabilistic flood modelling technique tested for the three Scottish case
studies considered. This allows accurate high resolution outputs with quantified uncertainties in as few as 10 simulations. Application of these methods will reduce the
likelihood of unpredictable flood hazards and thus increase resilience in vulnerable
communities
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