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Long-term performance assessment and future proofing of a raingarden for stormwater management
Full text linkRaingardens are a type of Sustainable Drainage Systems (SUDS) that provides a nature-based
solution to stormwater control. Research into the performance of raingardens is relatively
scarce in the UK and effort is needed to provide data and improved scientific understanding to
deliver a strong evidence-base. The overarching goal of this study is to evaluate the long-term
performance of a raingarden, designed over a low-conductivity soil. To this effect, an
experimental raingarden with two amended soil types (Sandy loam with 24% fines content;
Loamy sand with 13% fines) was installed within the Royal Botanic Garden Edinburgh
(RBGE) in Scotland, and monitored between Summer 2019 and Winter 2022-23.
This study documents the qualitative and quantitative methods in evaluating a field scale
raingarden. Antecedent soil moisture between storm events, saturated hydraulic conductivity,
and climate season were found to be key factors that controlled surface ponding in the sandy
loam media. The time to drain ponded water in this media in Winter was on average twice
longer than that of Summer due to 71% lesser median percolation in the cold season. In
comparison, loamy sand media exhibited a lesser influence of temperature on infiltration, with
average values in Winter being 40% lower than those in summer.
Computer model of the raingarden developed in this study predicts that infiltration in the sandy
loam soil must be at a minimum of 64 mm/h to prevent overflow from the raingarden under a
reasonable worst- case scenario projected for 2050. The efficiency of evapotranspiration (ET)
removal on rainfall was on average of 43% in summer highlighting the significance of the role
of vegetation in the hydrological cycle of the raingarden. Species Festuca altissima, Primula
posioni and Ligularia fischeri, Rodgersia pinnata and Gunnera manicata were found not
suitable in a raingarden setting due to either stringent habitat requirements or prone to wilting
during prolonged dry periods. Organic wood mulch is preferred over gravel mulch as the later
potentially increases the solar radiation exacerbating heat stress in times of drought.
Overall, the results of this work indicate that raingardens installed over low-conductivity soils
can still be an effective technology for stormwater management, when designed suitably. The
findings of this work provide clear and transferable insights that can be applied to the design,
monitoring and maintenance of raingardens in similar climatic and soil conditions, both in the
UK and around the world
The effect of in-situ fiberglass reinforcements on the mechanical and tribological properties of 3D printed parts
Full text linkFused filament fabrication (FFF) has been a widely used manufacturing method in the
past decade. It operates through additive manufacturing (AM), where thermoplastic
material is melted layer by layer to create a product, competing with traditional
methods like injection moulding or subtractive techniques such as milling, drilling,
and turning, Nevertheless, thermoplastic products produced through FFF often exhibit
inferior mechanical properties when compared to their injection moulded counterparts.
This research addresses these limitations by employing fibre-reinforced thermoplastics
to enhance the mechanical strength of printed parts, improving interlaminar bonds and
reducing voids between layers. A prototype fibre-doser was developed and optimized
to deposit in-situ short fibre reinforcement during the FFF process, enabling the
fabrication of fibre-reinforced thermoplastic composite parts. The fibre-doser was
constructed using high strength materials to ensure precision and durability. It is able
to produce composites with varying fibre contents, offering adaptability for diverse
applications.
Specimens prepared following ASTM standards underwent thermogravimetric
analysis (TGA), scanning electron microscopy (SEM), and mechanical tests, including
tensile, flexural, impact, and tribological assessment. Results showed that the inclusion
of glass fibre enhanced tensile strength by up to 45% and impact resistance by
approximately 60%, with a modest 15% improvement in flexural strength.
Tribological test revealed a 35% reduction in wear rate due to the reinforcing effect of
the fibres. SEM analysis confirmed uniform fibre distribution and improved
interlaminar bonding, while TGA indicated enhanced thermal stability.
These findings demonstrate that the fibre-doser effectively produces high-strength
composite materials using FFF, overcoming the limitations of traditional FFF parts.
The advancements achieved in mechanical and tribological properties expand the
potential of 3D-printed components for demanding engineering applications such as
prostheses, gears, bearings, and linkages, thus extending the capabilities of FFF
technology
Aspects of braided field theories from homotopy algebras and the double copy of noncommutative gauge theories
Full text linkThe quantum Batalin–Vilkovisky (BV) formalism and its connection to homotopy
Lie algebras is reviewed. The Drinfel’d twist deformation procedure is used to define
braided homotopy Lie algebras, leading us to perform a detailed study of the braided
cubic scalar and the braided quantum electrodynamics model in the first part of
this thesis. The braided BV formalism is used to compute correlation functions
for these models and we show the absence of UV/IR mixing up to one-loop order
and three-point multiplicity. New homological techniques are presented to study
the Schwinger–Dyson equation and the Ward–Takahashi identities of these braided
theories respectively. In the later theory, the braided gauge symmetry is verified to
be non-anomalous.
We next study how conventional noncommutative field theories fit into the ho motopy double copy paradigm whose central idea is the factorisation of homotopy
algebras. To perform this operation, a new twisted colour-kinematics duality is
identified. This twist captures the (conventional) noncommutativity of the theory,
hence the double copied theories are shown to match with their commutative coun terparts; a result that is expected from open-closed string duality in the presence
of a background B-field. We conjecture that performing the homotopy double copy
within the category of braided L∞-algebras is the correct way to probe twisted
noncommutative gravity
Bayesian methods for online real-time 3D imaging in challenging environments using single-photon LiDAR data
Full text linkSingle-photon LiDAR (SPL) continues to gain interest in a variety of different
applications. With LiDAR technology being deployed more outside of lab based conditions, it is critical to investigate methods for providing real-time scene reconstruction while reducing, in a principled way, the effects of noise and uncertainties caused
by photon scattering environments, which is the aim of this thesis. Traditional 3D
ranging methods for SPL usually perform surface detection and range estimation sequentially, alleviating the computational burden of joint detection and estimation.
Furthermore, traditional approaches construct and process detected photon time of
arrival (ToA) histograms to obtain final target depth estimates. However processing large data volumes over long temporal sequences results in undesirable costs in
memory requirement and computational time. Adopting a Bayesian formalism, the
initial joint detection/estimation problem is formulated as a single inference problem. Intractable integrals involved with variable marginalization in the Bayesian
calculations are avoided by discretising variables, recasting the resulting problem
as a model selection/averaging problem. A further approach is then investigated
by using online Assumed Density Filtering (ADF) strategies to process SPL data
on-chip without the need for histogram data construction. Additional benefits of the
proposed methods are demonstrated by providing a conservative approach to uncertainty quantification of the calculated depth estimates, and real time analysis from
the results. Statistical approaches can be limited by user defined input parameters
and prior information. Finally, an approach is proposed using recursive Bayesian
estimation to implement a detect-and-track method to SPL data processing which
incorporates the inference information obtained from the previously mentioned joint
detection/estimation approach. To avoid intractable calculations when computing
the model parameters, a spatio-temporal correlation approach is proposed between
individual model parameters to improve the quality of scene reconstruction. The
benefits of the proposed methods are illustrated using synthetic, real SPL data for
outdoor targets at up to 8.6 km as well as real data of underwater targets at up to
7.5 attenuation lengths from the LiDAR system
Deep learning for size-agnostic two-phase flow simulation with realistic pore structures and rock-fluid properties
No full textThe study of pore-scale flow in porous media is essential across numerous fields, including
petroleum engineering, environmental science, chemical engineering, and biomedicine.
Recently, deep learning techniques have shown significant potential in enhancing pore-scale
flow modelling. However, existing research predominantly addresses single-phase flow, and
studies focusing on the prediction of two-phase flow fields remain sparse. Current deep
learning research in two-phase flow typically involves simplified pore structures, limited
training datasets, and fixed rock-fluid and flow parameters. In this work, I develop deep
neural networks as data-driven proxy models for generating phase distributions during a two-phase, capillary-dominated drainage process, where a non-wetting phase invades a wetting-phase-saturated porous rock. My approach integrates complex Computerised Tomography
(CT) images and incorporates pixel size (i.e., imaging resolution), interfacial tension, contact
angle (wettability), and capillary pressure as direct inputs. Leveraging these capabilities, I
showcase several real-world applications of the trained models.
First, I construct an extensive and diverse dataset by subsampling both synthetic and real rock
images. Next, an efficient morphology-based drainage simulator is developed, providing
phase distributions for each sub-image. I evaluate various deep learning architectures and
analyse their accuracy and adherence to physical principles. A recurrent encoder-decoder
model outperforms the commonly used U-Net in capturing phase connectivity, though it
exhibits flow-direction bias and high computational demands. I subsequently introduce a
hybrid transformer-convolutional neural network that performs drainage based solely on pore
size, with phase connectivity enforced as a post-processing step. This approach facilitates
inference for images of various sizes and accommodates any fluid inlet-outlet configuration.
The trained models exhibit high efficiency and accuracy across unseen and larger sandstone
and carbonate images. I further validate the models against data from microfluidic
experiments and Lattice-Boltzmann (LBM) simulations, demonstrating similar capillary
pressure curves and phase distributions with significantly faster performance. These models
can replace slow direct simulations or costly experiments, generate finer pressure steps
between existing results, and serve as data validation tools. They deliver results in seconds to
minutes with minimal preprocessing across a range of realistic rock types, rock-fluid
properties, resolutions, and image sizes.
I show that the final deep learning models can integrate with an efficient optimiser to estimate
wettability if phase distributions are already available. I apply this inverse-problem technique
to determine the average contact angle from an LBM-generated phase distribution image in a
core-scale Bentheimer sandstone, where supercritical CO2 displaces brine. This scenario has
applications in CO2 sequestration. I find that the model achieves results comparable to the
GPU-accelerated LBM method, 5,000 times faster. I then generate phase distributions over
101 pressure steps and build the complete capillary pressure curve in minutes. Through these
studies, it becomes clear that the developed models can be seamlessly integrated into
downstream workflows to provide further insight into pore-scale flow.James Watt Scholarshi
Instanton partition functions in eight-dimensional cohomological gauge theory
Full text linkWe elaborate on the analysis of noncommutative instantons on C4 with SU(4)-
holonomy and their generalized ADHM construction. They are realized in a dimensional reduction of supersymmetric Yang–Mills theory from ten dimensions and
also in string theory. The instanton partition function can be evaluated using torus
equivariant localization, and we extend it to Calabi-Yau orbifolds C4/Γab with Γab
a finite abelian subgroup of SU(4). For some classes of Γab, we exhibit the dimensional reduction to orbifold partition functions of Donaldson–Thomas theory
on the toric Kähler three-orbifold C3/Γab. Through such reduction we conjecture
closed formulas for the instanton partition function on the orbifolds C2/Zn × C2
and C3/(Z2 × Z2) × C.
Solutions of the noncommutative instanton equations localized on collections of
hyperplanes C3
in C4 are called tetrahedron instantons. They can be similarly studied as noncommutative six-dimensional instantons. We investigate their partition
functions on orbifolds, generalising the discussion on Calabi-Yau orbifolds C4/τ (Γ),
where τ is a homomorphism from a finite group Γ inside SU(4). When Γ is a finite
abelian subgroup of SL(2, C), we show the reduction of of the 8d instanton partition
functions on C2/Γ × C2
to tetrahedron instanton partition functions on C2/Γ × C2
.
For Γ = Zn we expand our conjecturesEngineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership grant
Characterisation of porosity and permeability in reservoir seals using an experimental and upscaled modelling approach
Full text linkCarbon capture, utilisation, and storage (CCUS) provide a safe option to achieve net zero carbon
emission in 2050. Captured CO2 is usually stored in a deep geological reservoir formation, overlain
by a sealing formation known as caprock. Understanding caprock integrity is important in ensuring
safe and long-term CO2 containment and storage. Therefore, this research aims to reduce the
uncertainties/risk due to the caprock integrity via comprehensive characterisation
analysis. However, since caprock is comprised of micropores and nanopores with very low
permeability (less than one microDarcy or 10E-18 m2
), conventional porosimeter and permeameter
are not suitable to determine its porosity and permeability. In addition, coring operation in the
caprock section is very difficult and expensive, leading to the limited or unavailability of preserved
core samples for laboratory analyses. Hence, data from drill cuttings and well logs are used as
alternatives when core samples are limited or unavailable. In this research, caprock core samples,
drill cuttings, and well log data were selected from S Field, located in offshore East Malaysia,
because it is one of the candidates for a CO2 storage site, with a total of 4 wells, including an
appraisal well drilled in 2015.
This study is comprised of experimental rock characterisation, analyses of well-log data integrated
with lab data, and numerical modelling of advective CO2 transport. The rock characterisation
analyses include x-ray diffraction (XRD), x-ray fluorescence (XRF), particle size analysis (PSA),
thin section petrography, scanning electron microscopy with energy dispersive x-ray (SEM-EDX),
low-pressure N2 (LP-N2) sorption analysis, mercury intrusion capillary pressure (MICP), nuclear
magnetic resonance (NMR), unsteady state (USS) pulse decay, and helium pycnometry (HP). In
addition, broad ion beam (BIB) and focused ion beam scanning electron microscopy (BIB-SEM)
were used for digital core analysis (DCA) of caprock samples. Next, well log data from S Field
was analysed and integrated with lab data to generate porosity and permeability trends of the
caprock of S Field. The data was also used to calculate capillary entry pressure and CO2 column
height as part of the caprock integrity assessment. Finally, we studied advective transport of CO2
in S Field using Peng-Robinson (PR) equations of state (EOS) and multiphase fluid flow method.
The caprock of S Field has been identified as siltstones since it is dominated by quartz and silts
from mineralogical analyses. The caprock is split into two facies, Seal A and Seal B, with differing percentages of clay minerals (20% and 40%, respectively). Seal A is shallower and lies between
800 and 1400 meters below the seafloor. Seal B, on the other hand, is situated between Seal A and
the carbonate reservoir and has a burial depth of around 1400 to 1900 meters. The permeability
and porosity values determined in the lab, however, do not differ substantially between the two
facies. This could be because Seal B is considerably over-pressured compared to Seal A. This
excessive pressure could lead to the preservation of porosity during compaction, consequently
resulting in enhanced permeability. This finding is consistent with the time-to-depth conversion
from seismic data, which identifies Seal B as being less compacted than Seal A. Based on the data
integration of the calculated porosity, permeability, capillary entry pressure, and column height, it
can be summarised that the seal layers of S Field can contain injected CO2 as long as the reservoir
capacity is not exceeded. This finding is supported by the numerical flow models, which show no
leakage across the seal in 10,000 years and contained leaks in 1 million years
Computationally-enhanced electro-optical sensing
No full textAbstract and full text unavailable. Restricted access until 28.02.2035. Please refer to the PDF
Strategic drivers for market penetration in Zambia’s insurance industry
Full text linkThis study investigated the key strategic drivers for market penetration, explicitly
focusing on Zambia’s insurance sector. Insurance penetration in Zambia is still relatively
low compared to the African average and those in developed markets. Several strategic
factors are hypothesised to influence this lower penetration. However, some studies on
strategic management suggest a positive correlation between effective strategy
formulation and implementation and market penetration. There is no documented
evidence suggesting this theory has been critically investigated in Zambia’s insurance
sector. Thus, this study sought to examine this theory regarding the Zambian insurance
sector and understand the strategic factors that impact insurance uptake. Specific strategic
management factors explored were leadership competency score, organisational
structure, and culture, including the effect of innovation and technology and government
policies on other variables and market penetration.
The research followed a positivist paradigm employing a quantitative mode of inquiry
with a cross-sectional survey design, where a sample of respondents from 30 insurance
firms was used for primary data collection. Structural Equation Modelling (SEM) using
Smart Partial Least Square (Smart PLS4) and Statistical Package for Social Sciences
(SPSS) software facilitated data analysis.
The study findings suggest that organisational culture exerts a positive and statistically
significant impact on market penetration. However, leadership competency and
organisational structure within insurance firms exhibit a counterintuitive impact, as they
were found to have a statistically insignificant effect on market penetration. Further, the
research uncovered the nuanced interplay of experience as an additional determinant of
market penetration in the Zambian insurance landscape.
The findings underscore the significance of strategic management in influencing market
penetration and contribute to the literature by adding the Zambian insurance industry
perspective. These findings contribute to an enhanced understanding of the Zambian
insurance sector and hold relevance for a broader spectrum of industries. The insights
from this study put forth a practical model to guide effective strategy formulation and
implementation, fostering sustainable market penetration. The study provides a valuable
resource for insurance industry practitioners, policymakers, and academics seeking to
navigate Zambia's intricate landscape of market penetration and strategic development.
The findings encourage a nuanced perspective on strategy and market dynamics, offering
a foundation for future research and industry enhancement
Investigation of biofouling characteristics and corrosion behaviour in tropical waters of Indonesia for marine renewable energy infrastructures
Full text linkAs one of the emerging sectors, marine renewable energy (MRE) in Indonesia offers a
high potential to contribute to the national energy mix and greenhouse gas emission reduction,
while at the same time providing an opportunity to increase equal access to clean energy as
part of a just energy transition. However, despite the abundant resources and early stage of
technological development in the country, there is a gap between what knowledge base is
needed to help inform the design of MRE technologies and what research has been conducted
to date on marine biofouling in Indonesian waters. The main aim of this study is to provide
preliminary baseline evidence on the biofouling characteristics and corrosion behaviour in
tropical waters of Indonesia. By deploying sets of frames with different types of materials,
submersion durations, and seawater depths, the investigation found the key biofouling
communities with the most dominant settlement and their correlation with the corrosion
behaviour on steel panels. This preliminary evidence can be used to determine the selection of
materials with its appropriate type of protection according to the conditions of the marine
environment, and the frequency of monitoring activities required to maintain the performance
of the technology
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