Infrastructure and Environment

Supervisor

Dr Prashant Saxena

Description 

Understanding mechanical behaviour of biological tissues is becoming increasingly important to understand biological function as well as to design effective treatments for medical conditions. Tissues such as human skin are highly deformable and demonstrate nonlinearity in their mechanical response. Phenomena such as wrinkling of skin or folding in brain tissue are associated with mechanical instabilities caused due to large strains. Furthermore all living tissues constantly remodel themselves by replacing old cells and growth of new cells – leading to changes in their mechanical properties.

This project aims at studying the various forms of mechanical instabilities that can occur in tissues due to growth and remodelling. The scope is fairly open and the exact project aims will be finalised in conjunction with your interests. You will have the opportunity to work closely with all the team members of the Mechanics of Soft Solids group at the Glasgow Computational Engineering Centre (GCEC) as well as colleagues in other schools and universities.

This PhD project is suitable for students with interests in topics such as solid mechanics, structural engineering, computational mechanics, applied mathematics, or biomechanics.

Supervisor

Dr Prashant Saxena

Description

Electro- and magneto-active smart materials are types of advanced composites that can undergo large deformations in the presence of external electromagnetic fields. Being lightweight and possessing capability of extreme deformations before any fracture, they are excellent candidates to be used as sensors, actuators, vibration suppressors, and in other structural mechanics applications. Modelling of these composites requires dealing with the mechanical and electromagnetic fields simultaneously thereby resulting in a coupled “multi-physics” problem.

This project aims at studying the behaviour of these smart materials close to instability (buckling) – a point where the electro-mechanical or magneto-mechanical behaviour of material changes drastically resulting in extreme deformations. A major outcome of this project will be the prediction of post-buckling response of smart materials. These insights will provide significant value towards the design of various devices made of these composites. You will have the opportunity to work closely with all the team members of the Mechanics of Soft Solids group at the Glasgow Computational Engineering Centre (GCEC) as well as colleagues in other schools and universities. 

This PhD project is suitable for students with interests in topics such as solid mechanics, structural engineering, computational mechanics, applied mathematics, or electromagnetics.

Supervisor

Prof. Paul Steinmann (paul.steinmann@glasgow.ac.uk)

Description

‌Computational Engineering delivers sophisticated modelling and simulation tools to predict the behaviour of complex, real-world systems. CE has a pervasive impact on engineering design and discovery-led scientific research. Postgraduate studies in CE will equip you with the skills to solve the engineering challenges of the future.

 The Glasgow Computational Engineering Centre (GCEC)is an EPSRC-supported research centre-based at the University of Glasgow. We provide a coherent focus and point of interaction for fundamental and applied research in CE. As a team of ten academics, we have exciting opportunities for motivated and talented students who want to solve challenging and relevant problems across the spectrum of science and engineering. 

 For more information on the research areas of the GCEC and information on our team, visit www.gla.ac.uk/research/az/gcec/

Supervisor: Dr. Ankush Aggarwal (ankush.aggarwal@glasgow.ac.uk)

Summary: Almost 30% of all deaths globally are related to cardiovascular diseases. The overall aim of computational cardiovascular biomechanics is to help improve the diagnosis of these diseases (faster, earlier, more precise), provide better surgical outcomes, and design devices that last longer. To achieve that aim, we study the biomechanical properties of tissues and cells comprising the cardiovascular system using a combination of in-vivo imaging, ex-vivo and in-vitro testing, and in-silico modeling. Several project topics are available, which can be categorized into model development (at organ and cellular scales) and method development (based on imaging and using data science approaches). A few examples of specific projects are:

1) Predicting aneurysm development from ultrasound images using growth and remodeling simulations
2) Modeling of endothelial cells based on in-vitro experiments
3) Uncertainty quantification of biomechanical properties based on combined ex-vivo and in-vivo dataset
4) Gaussian process modeling for cardiovascular tissue mechanics
5) Development of a digital twin of the thoracic aorta

During this project, the student will have opportunities to:

• Develop skills necessary to work at the interface of engineering and biomedical science
• Publish papers in high-quality journals
• Present research results at international conferences
• Learn about nonlinear finite element analysis, nonlinear mechanics, multiscale modeling, image-based analysis, data science, and other numerical techniques
• Learn about experimental and clinical validation
• Collaborate with our international academic and industrial partners
• Interact within the Glasgow Centre for Computational Engineering with other researchers (GCEC) and across departments with biomedical scientists and clinicians

Eligibility: Candidates must have an undergraduate degree in a relevant field, such as Mechanical Engineering, Biomedical Engineering, Civil Engineering or Mathematics, with a minimum 2.1 or equivalent final grade. A background in mechanics and knowledge of finite element modeling would be necessary. Programming skills will be required for computational modeling.

Application: The deadline for applications is 31 January 2024, and the application process consists of two parts:
1) On-line academic application: Go to https://www.gla.ac.uk/postgraduate/research/infrastructureenvironment/ and click on the ‘Apply now’ tab. Applicants should attach relevant documents such as CV, transcripts, references and a research proposal.
2) School of Engineering EPSRC/School Scholarship Application via online portal: https://www.gla.ac.uk/ScholarshipApp/ To complete the scholarship application, students will need a supporting statement from the proposed supervisor. Any queries about application procedure can be directed to eng-jws@glasgow.ac.uk

Further information: If you are interested or want more information, please contact Dr. Ankush Aggarwal (ankush.aggarwal@glasgow.ac.uk) before starting the formal application. Please visit Computational Biomechanics Research Group page for more information on our research.

Supervisor: Professor Sondipon Adhikari

Project Description: High-resolution finite element models are routinely adopted as predictive tools for complex systems such as aerospace, automotive and civil structures. Although such high-resolution numerical models can reduce discretisation errors, in numerous cases, experimental results and numerical predictions exhibit significant variabilities stemming from the random scatter in model parameters and imperfections. The scatter in the model parameters can be represented using probabilistic methods. One can fit probability density functions corresponding to available data, and consequently, the model parameters can be expressed as random variables or random processes, and the resulting problem can be solved using the stochastic finite element method. The computational cost of the solution process increases exponentially with the number of random variables and becomes a bottleneck for solving industrial-scale problems. 

This research will develop fundamentally novel bottom-up data-driven methods to address this issue. Research methodologies will include, but will not be limited to, model verification and validation, uncertainty quantification, artificial intelligence (AI) and neural network methods, machine learning, regression and model selection, classification, deep learning, surrogate modelling, model order reduction and dynamic sub-structuring. A strong inclination towards mathematical methods and computer programming is essential for this project. 

Supervisor: Professor Sondipon Adhikari

Project Description: Metamaterials are designer media with periodic units comprised of unique tailor-made geometry and pattern to accomplish exceptional and unusual bulk properties, unprecedented in conventional materials. Mechanical metamaterials have surged to the forefront over the past five years against the backdrop of unprecedented developments in optical, electromagnetic and acoustic metamaterials. Significant promise and outstanding growth predicted for mechanical metamaterials crucially hinge upon knowledge, insight, and exhaustive comprehension of three pivotal issues: random disorder, damping, and nonlinearity. These combined challenges will be addressed using synchronised, ground-breaking theoretical approaches and unconventional manufacturing techniques. The aim is to deliver a transformative understanding of dynamic characteristics, wave propagation and homogenised mechanical properties of metamaterials with the simultaneous presence of random disorder, damping and nonlinearity. Radically new analytical and computational frameworks will be developed for dynamic homogenisation of disordered media with sub-wavelength scale resonators using a probabilistic paradigm from the outset.

The impact of this research will be new frontiers in characterising metamaterials with respect to unusual attributes such as non-reciprocity, complex/negative elastic moduli, complex/anisotropic mass density, mode localisation, damped cloaking, ergodicity in the presence of random disorder, damping and nonlinearity. The core research capabilities to be employed will include: analytical mechanics, structural dynamics, nonlinear systems, vibration damping, engineering design, additive layer manufacturing, wave propagation, homogenisation theory, band-gap analysis, buckling and post-buckling, computational mechanics, finite element method, experimental modal analysis, experimental mechanics. Applicants should have strong interests in these areas. 

Background: Triboelectric nanogenerators (TENG) are modern devices that use repeated cycles of contact between suitably chosen surfaces to transform mechanical energy into electrical energy. TENG have attracted significant attention in recent years as autonomous clean energy harvesters. Various sources of mechanical energy can be used: from human motion (wearable textile systems for charging medical sensors) to ocean waves (large-scale networks for "blue energy" harvesting). 

Optimisation of TENG performance is an active area of research requiring a combination of experimental and computational approaches. Experiments show that TENG output depends on the frequency of contact-separation cycles and the applied mechanical load. At the same time, accurate simulation of TENG is challenging and includes coupling of contact mechanics and electrostatics. It is further complicated if the surface roughness and the material heterogeneity are taken into account.

Objectives: The primary objective of this project is to develop a numerical model of an actual TENG, which requires solving a nonlinear, multi-scale, multi-physical, and time-dependent problem. This work will be underpinned by advanced scientific computing tools available in MoFEM, an open-source finite element library developed at GCEC. As a result, the framework will enable massively parallel simulations with 10-100M of unknowns required to demonstrate the model's predictive capabilities.

Moreover, the project will include collaboration with colleagues at the Materials & Manufacturing Research Group who are doing experimental research on TENG. Therefore, the second objective is to create a "virtual engineering lab" for TENG based on the developed numerical framework, accelerating the design and prototyping of new devices. 

Supervisor: Andrei Shvarts (andrei.shvarts@glasgow.ac.uk)

Supervision team: Dr Caroline Gauchotte-Lindsay (University of Glasgow), Dr William Peveler (University of Glasgow) and Dr Nathalie Lidgi-Guigui (Université Sorbonne Paris Nord)

Context of the Project Hospital wastewaters have been shown to be responsible for the release of drugs and antibiotics to environmental waters. This has potentially severe impacts on environmental microbial populations and could accelerate the development and spread of antibiotic resistance. The project aims to develop a new sensing technique combining Surface Enhanced Raman Scattering (SERS) and high precision thin layer chromatography (HPTLC). Raman scattering is a widely used chemical analysis technique that provides spectra which act as fingerprints of the molecule under study. Its sensitivity can be extraordinarily enhanced by adding metallic nanostructures in the direct vicinity of the molecules reaching sensitivity up to the single molecule level. However, when used in complex system such as wastewaters, SERS provides spectra with multiple peaks corresponding to the whole range of compound found in the sample. HPTLC is a portable chromatography technique, that allow the separation of different compound in a liquid mixture. However, it does not give a chemical identification. Therefore, the combination of both techniques would fill the drawbacks of each and allow the development of small, sensitive, rapid response analytical tools for the exhaustive characterization of pharmaceuticals in water.

Aim of the project The literature reports of some promising results when SERS is performed on HPTLC place where a solution of metallic nanoparticles has been injected on the fluorescent spots revealed by HPTLC. Instead, we propose to elaborate HPTLC plates decorated with nanoparticle fabricated by lithography. Nanofabrication techniques allow to control the size, the shape and the distribution of the nanoparticles on the surface. In Université Sorbonne Paris Nord (Nathalie Lidgi-Guigui), we have developed several techniques of soft lithography for the fabrication of large assembly of nanostructures on all kind of surfaces including insulating or flexible. At the University of Glasgow, we have developed leading edge method for two-dimensional chromatography of complex environmental samples and specialize in multivariate analysis method to interpret analytical signals. The researcher will be based two third of the time at the University of Glasgow and the rest of the time in Paris.

Candidate profile We are looking for a candidate with interest in environmental sciences. They will have a demonstrable background in analytical chemistry, materials or physical chemistry, candidates familiar with HPTLC or Raman spectroscopy would be particularly appreciated. The candidate will have a strong motivation for interdisciplinary projects. Interested candidates should contact Dr Caroline Gauchotte-Lindsay (Caroline.Gauchotte-Lindsay@glasgow.ac.uk) before January 14th. 

Supervisors

Dr Umer Zeeshan Ijaz (James Watt School of Engineering)

Professor Barbara Mable (Institute of Biodiversity, Animal Health & Comparative Medicine)

Project Description

Background: The discovery of networks is a fundamental problem arising in numerous fields of science and technology, including, biology, communication systems, sociology, and neuroscience. In terms of microbial communities, syntrophies and antagonistic behavior exists amongst members of microbial consortia, either because of environmental pressure (including both biotic and abiotic factors), or because of competition for resources. Continuing technological advancements in development of massively parallel sequencing of millions of reads, accompanied by parallel development of bioinformatics strategies that give unprecedented accuracy, provides an unprecedent opportunity to use these big datasets for understanding the underlying biological processes that govern microbes. Network modeling in microbial community is at an incipient stage, primarily focusing on “co-occurences”, i.e., which network components (microbes) have associations, but do not indicate the order in which they communicate, i.e., causality. Thus, there are critical gaps in our understanding of interdependencies between microbes within complex communities. One applied area where this could be particularly important is in the assessment of how agricultural practices influence the dynamics of soil communities associated with crops. For example, irrigation could promote overall soil health and growth of beneficial microbes but could also inadvertently increase the frequency of pathogens. Building a network framework using such a soil community dataset would thus also help to advance the theory and have a direct application to aiding in the design of management intervention strategies.

Aims: Using data collected as part of a BBSRC consortium grant focused on management of bacterial pathogens of potato crops, the aim of this PhD project is to fuse some of the key concepts from the latest developments in Inverse Problems with those in Network Science, in order to advance the network inferences possible from complex soil communities. Specifically, the objectives are to:

a) develop robust network inference strategies to obtain both static and dynamic networks for microbial communities;

b) develop network-wide statistical measures on the obtained topologies to identify keystone microbial species and the subcommunities they belong to;

c) develop meta-analyses workflows to associate the above statistics with meta-data (including physico-chemical data); and

d) develop interactive visualisation strategies to improve interpretability of the obtained networks.

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility, in order to consolidate existing network inference strategies and advance the field. The specific training outcomes include:

a) proficiency in developing bioinformatics workflows on a high-performance cluster using workflow management systems such as SnakeMake and Nextflow;

b) proficiency in developing statistical toolboxes in R (microbiomeSeq package, see “Supervisory Team”; and

c) proficiency in developing bespoke methods and visualisation tools in Python, Perl, C++, and D3.js.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project https://github.com/KociOrges/cviewer. Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally (http://userweb.eng.gla.ac.uk/umer.ijaz/#visitors) and will enable the candidate to stay up-to-date with the latest methods. The student will be provided with a dataset from the recently funded BBSRC consortium grant (BB/T010657/1; led by the James Hutton Institute) on which both Dr Ijaz and Professor Mable are Co-Is, entitled, “Building a Decision Support Tool for Potato Blackleg Disease (DeS-BL)”. By working on this dataset, the student will gain an in-depth insight into soil ecology.

Supervisory Team: Professor Mable is an evolutionary geneticist whose main focus is on understanding the role of genetic diversity in adaptation to environmental variation, including interactions between hosts, pathogens and other elements of biotic communities. Dr Ijaz has vast experience in developing network inference strategies, for instance, GlobalView software developed by him at University of Oxford implements Probabilistic Graphical Models (those that are based on Markov Random Fields, and some that are based on Dynamic Bayesian Networks). These were initially implemented to work with time series data, however, Graphical Lasso Method [1] was successfully applied to 16S rRNA microbiome datasets. More recently, microbiomeSeq, an R package (http://userweb.eng.gla.ac.uk/umer.ijaz/projects/microbiomeSeq_Tutorial.html) from Dr Ijaz’s lab implements generation of co-occurrence networks through simple correlations as well as Biweight Midcorrelations to infer network topologies, on which subcommunity detection algorithms are applied. Prof. Mable and Dr. Ijaz previously co-supervised an LKAS student, Elizabeth Mittell, who was trained by Ijaz in bioinformatic approaches to resolving community structure of soil bacteria associated with wild cabbages and worked with Mable on quantifying population genetic diversity of hosts using reduced representation sequencing.

References:

[1] U. Z. Ijaz. Graphical Lasso Method for 16SrRNA datasets http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/GraphicalLasso.tar.gz

[2] Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. nature, 433(7028), 895.

[3] Li, J., & Convertino, M. (2019). Optimal Microbiome Networks: Macroecology and Criticality. Entropy, 21(5), 506.

[4] K. Harris et al. Linking statistical and ecological theory: Hubbell's unified neutral theory of biodiversity as a hierarchical Dirichlet process. Proceedings of the IEEE, 105(3):516-529, 2017.

Supervisor

Dr Umer Zeeshan Ijaz (James Watt School of Engineering) http://userweb.eng.gla.ac.uk/umer.ijaz

Project Description

Background: Whilst microbial community surveys using both short-sequence amplicons and whole-genome shotgun metagenomics offer unprecedented insights into taxonomic and functional ecology of environmental communities, important ecological questions such as “what” drives the observed changes in these communities remain largely unexplored. Unravelling the ecological processes that assemble local communities in response to environmental change or perturbation has always been of great research interest. Traditionally, microbial community assembly is thought to be influenced by either “stochastic” or “deterministic” processes. Stochastic neutral theory assumes that species respond to chance colonisation, extinction, and ecological drift not because of theirs and their competitors’ traits but rather because of random changes and thus have no interspecific trade-offs (i.e. species are neutral). Niche theory, on the other hand, assumes that site-to-site variations in species composition is determined entirely by their specific traits, local habitat conditions, and interspecific relations, which in turn creates different niches that benefit different groups of species (determinism).

Null models are statistical tests that have been widely used to describe diversity patterns in macroecology and biogeography, acting as pattern-generating models that are based on the randomisation of ecological data or random sampling from a known or imagined distribution. Recent advancements in the broader numerical ecology field have made it possible to apply theoretical macroecological concepts to microbial metagenomics in order to better understand and quantify the mechanisms and patterns controlling the complexity of microbial ecology.

Aims: Using data collected as part of several grants running in Dr Ijaz’s group, the aim of this project is to develop and advance null modeling techniques for microbial community assembly. Specifically, the objectives are to:

• explore and consolidate null modeling techniques that rely on incidences (presence/absence), abundances, and phylogeny of microbial consortia;
• optimise the randomisation procedures by exploiting instruction level parallelism (ILP) utilizing a low-level programming language (traditional null modeling techniques are implemented in R and sometimes take hours and weeks to run); and
• explore their utility to understand global biogeographical patterns on microbial community datasets at both spatial and temporal scales

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility. The specific training outcomes include:

• proficiency in applying numerical ecology principles;
• proficiency in scripting metagenomics workflows on a high-performance computing environment (Orion Cluster: http://userweb.eng.gla.ac.uk/umer.ijaz/#orion); and
• proficiency in developing bespoke methods and visualisation tools in R, Python, Perl, and C++.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project https://github.com/KociOrges/cviewer. Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally  (http://userweb.eng.gla.ac.uk/umer.ijaz/#visitors) and will enable the candidate to stay up-to-date with the latest methods.

Supervisory Team:  Dr Ijaz has vast experience in developing and advancing statistical tools for microbial community analyses, for instance, RvLab [1] offers an intuitive virtual environment interface enabling users to perform analysis of ecological and microbial communities based on underlying parallel implementation of numerical ecology methods; and microbiomeSeq is an R package (http://userweb.eng.gla.ac.uk/umer.ijaz/projects/microbiomeSeq_Tutorial.html) with easy to use functions to explore diversity patterns and also includes enrichment analyses.

References:

[1] C. Varsos, et al. Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab). Biodiversity Data Journal, 4:e8357, 2016. DOI:10.3897/BDJ.4.e8357.

Supervisor

Dr Umer Zeeshan Ijaz (James Watt School of Engineering) http://userweb.eng.gla.ac.uk/umer.ijaz

Project Description

Background: Use of high-throughput sequencing is widespread in efforts to understand the microbial communities in natural and engineered systems. These studies (underlying sequences and meta-data) are routinely deposited in online repositories such as the National Center for Biotechnology Information (NCBI) short read archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra). There is a wealth of information held in this available data that goes beyond the more specific questions addressed by any one study and offers promising historical data to complement newer studies. By using and combining these previously published datasets, we can discern large-scale patterns that have the potential to give global perspective. Dedicated repositories such as Earth Microbiome Project (https://earthmicrobiome.org/), a massively collaborative effort to characterize microbial life on this planet, is currently underway with their protocols and methods standardized to achieve seamless collation of the newer datasets. Nonetheless, NCBI’s SRA still remains the largest of repositories and  examining its expanse shows that there is huge variation from extraction method right through to bioinformatic workflows. Methodological biases are often introduced at every step – from biomass sampling through to data processing, from the nucleic acid extraction method to the choice of v-region (for short-sequence amplicons). Thus, there are critical gaps in the methodology to collate existing studies without the loss of beta diversity patterns. Additionally, the meta-data associated with the datasets in the recent years have been following an in-depth, controlled description of the samples that microbial community sequences are taken from, and also includes contextual data such as environmental conditions (latitude, longitude, physico-chemical parameters) or clinical observations. Thus, there is an opportunity to use text mining engines to discern patterns based on controlled vocabulary/ontologies.

Aims: Using online repositories for microbial community datasets as a source, the aims of the project are to:

• develop meta-analyses workflow that seamlessly download existing sequencing datasets from online repositories, and any meta-data associated with existing studies;
• develop bioinformatics pipelines and methodology to remove inter-study biases in microbial community datasets introduced by variability in terms of sequencing platforms, library protocols, and choice of genomic regions, etc.;
• develop numerical ecology framework to reveal patterns of interest in a global perspective; and
• advance the existing text mining strategies (see “Supervisory Team”) by supplementing existing work in Dr Ijaz’s lab with Natural Language Parsers.

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility. The specific training outcomes include:

• proficiency in applying numerical ecology principles;
• proficiency in scripting metagenomics workflows on a high-performance computing environment (Orion Cluster: http://userweb.eng.gla.ac.uk/umer.ijaz/#orion); and
• proficiency in developing bespoke methods and visualisation tools in R, Python, Perl, and C++.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project https://github.com/KociOrges/cviewer. Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally  (http://userweb.eng.gla.ac.uk/umer.ijaz/#visitors) and will enable the candidate to stay up-to-date with the latest methods.

Supervisory Team:  Dr Ijaz has vast experience in developing meta-analyses workflows and text-mining strategies, for instance, SeqEnv (https://github.com/xapple/seqenv) [1] and its extension SeqEnv-Ext [2] utilizes Environmental Ontology (https://www.ebi.ac.uk/ols/ontologies/envo) to annotate microbial sequences with knowledge about environments, environmental processes, ecosystems, habitats, and related entities by mining text fields from NCBI repository. More recently, Pytag (https://github.com/KociOrges/pytag) [3] was developed as a general-purpose tool to annotate PubMed abstracts with a far expansive list of ontologies, thus enabling systematic literature surveys.

References:

[1] L. Sinclair, U. Z.  Ijaz et al. Seqenv: linking microbes to environments through text mining. PeerJ, 4:e2690, 2016. DOI:10.7717/peerj.2690

[2] A. Z. Ijaz et al. Extending SEQenv: A taxa-centric approach to environmental annotations of 16S rDNA sequences. PeerJ, 5:e3827, 2017. DOI:10.7717/peerj.3827

[3] O. Koci et al. An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights PeerJ, 6:e5047, 2018. DOI:10.7287/10.7717/peerj.5047

Supervisor

Prof Bill Sloan

Project Description

Naturally occurring microbial communities are ubiquitous fundamental catalysts in nature. They inhabit almost every environment on Earth from the deep subsurface to the upper atmosphere and play an essential part in controlling the chemistry of the Earth’s natural environment. Moreover, many years of empirical research has taught us that these barely visible communities can be exploited for the benefit of man. However, our fundamental understanding of how the communities form, evolve and function is poor, which limits our potential to make radical leaps in new emerging technologies for cleaning the environment, power generation, fighting climate change, chemical production and health care.

In this PhD you will explore the fundamental drivers of community assembly in microbial communities that are used in environmental biotechnologies, such as wastewater treatment. Building on established theory and using emerging experimental tools such as microfluidics and flow cytometry you will derive mathematical descriptions of the relationship between the functioning of microbial communities and environmental variables, like the energy in their food  or how open they are to immigration, that can be used in the design of engineering solutions.

The research forms part of a large research initiative on the development of novel biotechnologies for water and wastewater treatment for rural populations in Scotland and in the developing world.

Primary supervisor: Dr. Maggie J. Creed

Description: In recent decades, there has been widespread construction of embankments and flood defences to alleviate flood risk in the alluvial plains downstream of the Himalaya. Sediment dynamics are often omitted in flood protection projects, even in these sediment-laden Himalayan river systems. Sediment build up in confined channels can lead to the overtopping or breaching of embankments, with disastrous consequences for heavily populated settlements on the Gangetic floodplains. Predicted long-term changes in sediment flux in the Himalayas due to climate change, coupled with predictions of increased frequency and intensity of monsoonal rainfall events could compound future flood risk.

The aim of this project is to examine the long-term effect of channel confinement on sediment dynamics and flood risk in the context of climate change and increasing populations living in the Gangetic plains. The successful candidate will develop a physical and/or numerical model of an embanked river based on Himalayan river(s) as they exit the mountain front. The model(s) will be calibrated against existing field data of sediment and discharge flux, and validated against historical flood events. There will be scope in this project to explore alternative measures to typical hard engineering solutions to alleviate flood risk in dynamic, sediment laden river systems. Physical experiments will be run in the Water Engineering Laboratory at the University of Glasgow.

We are looking for a stellar candidate with an interest in Environmental Engineering or Geomorphology to apply for the James Watt School of Engineering Doctoral Scholarship Scheme (UK or EU student). The candidate must have, or is expected to receive, a first class honours degree in Civil/Environmental Engineering, Geosciences or Geomorphology, or a related physical science discipline. A strong background or interest in numerical modelling techniques or physical experiments would be an advantage.

Supervision Team: Dr Caroline Gauchotte-Lindsay (University of Glasgow), Dr Kate McAulay (Glasgow Caledonian University)

Context: Hospital wastewaters have been shown to be responsible for the release of drugs and antibiotics to environmental waters. This has potentially severe impacts on environmental microbial populations and could accelerate the development and spread of antibiotic resistance. Contrast agents for X-ray imaging (X-Ray CAs), while not the most frequently used, have been demonstrated both to be recalcitrant to remediation and to present an environmental risk. These drugs are principally taken in hospital as in or out-patients. This provides us with an opportunity to treat the waste separately from other pharmaceuticals, focusing on either recovering pharmaceuticals directly from urine.

Aim of the project: A new novel approach for the removal of contrast agents involves utilizing a combination of electrochemical techniques and low molecular weight gelators. Low molecular weight gels (LMWGs) are a promising new class of materials for the treatment of wastewater. These differ from polymer hydrogels as they do not contain physical chemical bonds between the structures. However, in many cases the general term “hydrogel” is liberally applied to both of these distinctively different gel systems. Recently, within the literature there have been reports in the excellent ability of polymer hydrogels to remove pollutants such as oil, toxic metals, dyes and pharmaceuticals. However, there are fewer reports into LMWGs. Chemically the structures of various contrast agents are comparable to those of LMWGs. The proposed tasks are broken into three sections. The first investigation is to manipulate contrast media to assess their potential to form gels electrochemically. The second is to incorporate pollutants into existing LMWGS. Both of these approaches will result in the pollutants forming a hydrogel. When in gel from these gel/pollutants can then be easily removed from the water. Due to the nature of these LMWGs these can be easily destroyed and reused. The third section of the electrochemical investigation will be producing a method to reverse the pollutant/gel mixtures (normally by using a simple pH switch). This will result in the removal of pollutants from water and consequently the ability to recover the high value pharmaceutical. Due to the vast number of parameters that can be controlled electrochemically it is also possible to explore the selective removal of different pollutants from wastewaters containing multiple contaminants.

Candidate profile We are looking for a candidate with interest in sustainable solutions. They will have a demonstrable background in organic chemistry, materials or physical chemistry, candidates familiar hydrogel formation would be particularly appreciated. The candidate will have a strong motivation for interdisciplinary projects. Interested candidates should contact Dr Caroline Gauchotte-Lindsay (Caroline.Gauchotte-Lindsay@glasgow.ac.uk) before January 14th. 

Supervision Team: Dr Caroline Gauchotte-Lindsay (University of Glasgow), Dr Umer Ijaz (University of Glasgow), Dr Juan Ye (University of St Andrews)

Context: Industrialisation has left behind a legacy of pollution that can present serious risks to human health and the environment. The traditional approach for dealing with contaminated soil has been disposal to landfill (dig and dump), however, rising landfill tax means that this is becoming increasingly costly. Bioremediation, which uses microorganisms to treat contaminated soil, is a cost effective and sustainable alternative that has been deployed for petroleum hydrocarbon contamination. However, the challenge is now to apply bioremediation to soils containing more complex hydrocarbon mixtures, particularly polycyclic aromatic hydrocarbons (PAHs). Understanding and ultimately engineering the biodegradation of PAH contamination requires us to profile these intricate mixtures as they change in time and space and correlate the emergent patterns with microbial diversity. This means we need to deploy new analytical tools.

Aim of the project: Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GCxGC-MS) is an analytical method that has a resolution power that is at least an order of magnitude higher than that of common gas chromatography methods, which means that thousands of compounds can be separated in one analysis with minimal sample preparation. It is therefore the ideal tool for studying complex hydrocarbon samples. The limitation of current state of the art in GCxGC studies, however, is that the amount of information provided is so large that data reduction or targeted analysis are still the most common approaches to data analysis. However, these techniques might not be sufficient to handle non-linear relationships between parameters. Advanced analytical techniques need to be developed to assess the full potential of GCxGC-MS data. Guided by information theoretic approaches, we intend to develop dimensionality reduction techniques that offer a tradeoff between discrimination and correlation by consolidating additional dataset such as 16S rRNA sequencing and metagenomics with GCxGC-MS. Additionally, we want to extend GCxGC-MS by developing meta community ecology framework for GCxGC-MS by focusing on potential biochemical transformation networks. To this end, we have already developed novel integrative'omics tools to seamlessly integrate chemical and the DNA data, and will be the starting point. As a targeted application of these approaches, the student will run micro or mesocosm on soil contaminated with coal tar. The mechanism of the overall degradation of the samples will be investigated by using comprehensive characterisation of the samples for chemical composition and microbial communities. The additional aim is then to understand the enzymatic processes involved in the co-degradation of contaminants to eventually be able to model the end-products of biodegradation in complex samples.

Candidate profile We are looking for a candidate with interest in environmental sciences. They will have a demonstrable background in analytical chemistry or environmental chemistry, candidates should be comfortable with or willing to learn to use statistical tools. The candidate will have a strong motivation for interdisciplinary projects. Interested candidates should contact Dr Caroline Gauchotte-Lindsay (Caroline.Gauchotte-Lindsay@glasgow.ac.uk) before January 14th. 

Supervisors: Dr Marta Vignola, Prof. Cindy Smith, Dr. Caroline Gauchotte-Linsay, Dr Umer Zeeshan Ijaz 

Project Description 

Brazil is the world first consumer of pesticides. Runoff processes, occurring after their application to agricultural soil, actively transport these compounds into water sources and, as many of these are recalcitrant to traditional drinking water treatments (DWTs), they affect the quality and the safety of the drinking water supplied to people. Against a backdrop of water scarcity and deterioration in the quality of water sources, water companies are struggling to produce safe and affordable drinking water. Biological treatments are promising, sustainable alternatives; however, they suffer from uncertainty in performance and their implementation on existing DWT plants is difficult. A growing body of work indicates that the microbial communities that naturally establish on biofilters might develop the ability to degrade pesticides and other micropollutants. However, this ability seems to be site-specific and often insufficient to fully remove the pesticides present in raw waters. Developing an understanding of filter microbial communities and their micropollutant removal abilities is of great interest and could potentially help to improve the performance of new and existing biofilters. Advanced biofiltration and its potential application in low-and middle-income countries (with a focus on Brazil) are the focus of this PhD project. 

The aim of this project is to gain knowledge on pesticides degradation through biofiltration process by: (1) characterising the microbiome of full scale biofilters (UK and Brazil) and quantifying their biodegradation potential ; (2) examining how ecological and/or environmental and operational parameters influence the microbial diversity within the biofilter microbiome and their biodegradation potential, and (3) enhance pesticides degradation by manipulating the microbial diversity of the biofilter community.  

The research methods will include the use of lab-scale bioreactors, molecular biology and microbiology techniques (qPCR, DNA sequencing, Flowcytometry) and the application of ecological models.  

References 

1. J. Hedegaard et al. (2018) Water Res., 129:105–114; T. L. Zearley and R. S. Summers. (2012). Environ. Sci. Technol., 46 (17):9412–9419; J. Vandermaesen, et al. (2019) Chemosphere, 228:427–436.

Supervisor: Professor Sondipon Adhikari

Project Description: This research aims to develop digital twins for engineering dynamic systems related to complex civil infrastructures such as offshore wind turbines and bridges. A digital twin is a virtualized proxy of an actual physical system. While a numerical model of a physical system attempts to closely match a physical system's behaviour, the digital twin also tracks the temporal evolution of the physical system. Once a digital twin has been trained and developed, it can be used to make crucial decisions at a point in time, significantly far from implementing/constructing a dynamic engineering system. In the heart of this research lies techniques based on multiple timescales, which differentiate the intrinsic timescale of a dynamic system and the timescale of its operational life. The intrinsic timescale of a dynamic system is short (in seconds or minutes), while the operational timescale is long (in months or years). Physics-based methods (e.g., the finite element method) will be used for dynamic evolution in the intrinsic timescale, while data-based methods (e.g., surrogate models) will be used for dynamic evolution in the operational timescale. Thus, the digital twin of a complex dynamic system will arise from the fusion of physics and data-based approaches. 

The core research capabilities to be employed will include: finite element modelling, model verification and validation, uncertainty quantification, sensor data assimilation, model updating, risk, reliability analysis, prognosis, damage detection, time-series analysis, artificial intelligence (AI) and neural network methods and machine learning. A strong interest in mathematical methods and computer programming is essential for this project. 

As set out in the UK’s effort for a Green Industrial Revolution, infrastructure investment is fundamental to achieving net zero. This is relevant to transport infrastructure (roads, railways) which is highly used in the UK and worldwide. The performance of the road and rail networks is critically dependent on the performance of slopes and tunnels, which largely relies on ground/rock reinforcement. Climate change-induced temperature increase (e.g., the recent heatwaves in Europe and worldwide) and extreme rainfall are exacerbating the risks of ground stability. Globally, approximately 5000 km of new tunnels are excavated per year for infrastructure, and the demand for underground space is growing with the increasing urbanization. It is therefore important to develop cost-effective, low-carbon solutions for ground reinforcement to mitigate risks and contribute to reversing climate change.

This PhD project offers a unique solution involving the use of low-carbon (composite) materials in ground engineering, tunneling and reinforcement for sustainable infrastructure. The project will underpin our ongoing research on engineering net zero and will use a combination of lab measurements and numerical models to develop a low-cost, low-carbon composite rock bolt for sustainable rock reinforcement. The successful candidate will develop interdisciplinary skills.

Candidates should hold a first-class or a strong upper second class degree in rock mechanics, material sciences, geotechnics, or a related discipline, ideally with experience in Discrete Element numerical modelling. Excellent English communication and team-working skills are a must.

The scholarship covers tuition fees at the UK home rate and a tax-free stipend (£16,125 pa for 2023/24) for 3.5 years. The successful candidate is expected to start in October 2023 or in October 2024. Due to the nature of funding, the position is only open to UK home students. We also welcome inquiries from international students for this project, but please mention your funding sources when emailing us.

For informal inquiries, please email Dr Junlong Shang (junlong.shang@glasgow.ac.uk)

Title: Multi-level simulation of crack process for uncertainty quantification

Supervisors: Dr Ji-Eun Byun, Dr Peter Grassl and Dr Eky Febrianto 

Description: 

Supervisor 

Dr Junlong Shang (Junlong.Shang@glasgow.ac.uk)

Description 

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project entitled ‘Investigating Coupled Processes in Fractured Rocks to Inform Geothermal Energy Extraction’. This PhD project will underpin the NERC DEEPHEAT project (NE/W004127/1), led by Dr Shang, and you will have the opportunity to work with a team of scientists from the University of Glasgow, University of Wisconsin-Madison (USA), Lawrence Berkeley National Laboratory (USA), German Research Centre for Geosciences (Germany), Sinopec (China), and Stanford University (USA). 

Actively seeking viable “clean/green” energy sources is essential for humans to maintain sustainable development. Deep geothermal energy provides an important alternative to fossil fuels in our life. This PhD project will promote deep heat extraction, and you will develop a THMC (thermo-hydro-mechanical-chemical) model by coupling the discontinuum method and open-access multiphysics flow simulators to understand the coupled flow processes in fractured geothermal reservoir rocks. You will have the opportunity to get access to the X-ray CT scanner co-owned by the Universities of Glasgow and Strathclyde and the newly established rock shearing facility at the University of Glasgow. The outcome of the project can be used as a benchmark for geoscientists, engineers, and policymakers in further exploring and optimising enhanced geothermal systems (EGS).  

Please get in touch with Dr Junlong Shang if you are interested: 

https://www.gla.ac.uk/schools/engineering/staff/junlongshang/#biography 

Supervisors Dr Junlong Shang (Junlong.Shang@glasgow.ac.uk) Dr Peter Grassl (Peter.Grassl@glasgow.ac.uk)

Description

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project entitled ‘Characterisation and modelling of fractured rocks: integrity, deformation, and fluid flow’.

Nuclear power is low-carbon energy. It now provides about 20% of the UK’s electricity and about 10% of the world’s electricity, contributing enormously to global Net Zero emissions. One main disadvantage of nuclear power is that its generation process produces radioactive waste that can remain hazardous for hundreds of thousands of years. Geological disposal of radioactive waste is UK Government’s policy. A global consensus has also been reached in this regard: isolate radioactive waste that are incompatible with surface disposal permanently deep underground in a geological disposal facility (GDF). This project aims to increase confidence in the design, construction, and operation of a GDF, by integrating imaging analysis, laboratory experiments, and numerical modelling to understand the influence of mineral-filled fractures on the integrity, deformation, and fluid flow behaviour of veined rocks that are often ignored or less researched so far.

Please get in touch with Dr Junlong Shang if you are interested: https://www.gla.ac.uk/schools/engineering/staff/junlongshang/#biography

Supervisor 

Dr Junlong Shang (Junlong.Shang@glasgow.ac.uk)

Description 

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project entitled ‘Understanding Spatial Heterogeneity of Fractured Rock Media Using Machine Learning’.   

Mechanical and Physical properties of rocks are very complex in nature, which can vary significantly from location to location, exhibiting spatial heterogeneity (SH). A better characterization of SH of fractured rock media is fundamentally important to many geotechnical engineering applications (e.g., underground engineering design, rock slope stability analysis), and energy-related activities in the subsurface (e.g., geothermal energy extraction, nuclear waste disposal). This PhD project will develop a novel Python-based algorithm, which will improve our understanding of the SH of fractured rock media. Data will be collected from a range of sources including the Glasgow Geoenergy Observatory, open-access database (i.e., OpenGeoscience, British Geological Survey), and published literature and technical reports. The developed model will be used to predict SH of fractured rocks under broader geological contexts. 

Please get in touch with Dr Junlong Shang if you are interested: 

https://www.gla.ac.uk/schools/engineering/staff/junlongshang/#biography 

Nuclear power is low-carbon energy. It plays an important role in the global transition to a low-carbon economy. One major disadvantage of nuclear power is that its generation process produces radioactive waste, which can remain hazardous for hundreds of thousands of years. Over the past more than 60 years of utilisation of nuclear power in the UK and worldwide, many radioactive wastes have accumulated. Most of the waste are stored temporarily in storage near nuclear power plants. It is important to deal with the waste in a manner that protects both human health and the environment. One solution is to permanently isolate the waste that is incompatible with surface disposal in suitable underground rock formations by developing a geological disposal facility (GDF).

The development of GDF involves a series of underground excavations of tunnels and drifts in host geological media, which will lead to Excavation-Disturbed Zones (EDZ) where in-situ stress is redistributed, with the formation of fractures at different scales. EDZ introduces pathways for groundwater, gas, and radionuclides, which modulates the safety barrier function of host geological media (e.g., crystalline, clay, and salt). Constraining the long-term spatial evolution of EDZ under complex coupled subsurface conditions will inform the operational and closure stages of GDF, which is critically important for the sustainable geological disposal of radioactive waste.

This PhD project aims to develop a 3D coupled numerical model to examine the spatiotemporal evolution of EDZ in fractured geological media. This PhD research is closely related to our recent EPSRC-funded INFORM project (Influence of fracture heterogeneity on rock deformation and failure: a mechanics-based multi-scale framework for radioactive waste disposal). The successful candidate will join the INFORM project team and will have opportunities to engage with our excellent academic and industry partners.

Candidates should hold a first-class or a strong upper second class degree in rock mechanics, geosciences, geotechnics, or a related discipline, ideally with experience in Discrete Element Method (e.g., PFC), coupled numerical modelling, and coding. Excellent English communication and team-working skills are a must.

The scholarship covers tuition fees at the UK home rate and a tax-free stipend (£16,125 pa for 2023/24) for 3.5 years. The successful candidate is expected to start in October 2023. Due to the nature of funding, the position is only open to UK home students. We also welcome inquiries from international students for this project, but please mention your funding sources when emailing us.

For informal inquiries, please email Dr Junlong Shang (junlong.shang@glasgow.ac.uk)

Supervisor: Dr Zhiwei Gao (Zhiwei.gao@glasgow.ac.uk) 

Description  

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project on constitutive modelling for offshore wind turbine foundation design in sand.  

Offshore wind power is being exploited by many countries around the world for clean energy production. The UK government has revealed an ambitious plan to increase its offshore wind power capacity to 40GW by 2030, from the previous target of 30GW. This will provide electricity for every home in the UK based on the current demand. The wind turbine foundations are an important part of the wind farm because they are crucial for the safety of wind turbine towers. The construction cost of OWTF accounts for about 25% of the overall capital cost. Optimization of the foundation design is thus of pivotal importance.  

Offshore wind turbine foundations (OWTF) are now routinely designed using 3D finite element (FE) modelling, where constitutive models for describing the stress and strain relationship of soils are essential. While reasonable prediction of the OWTF response under monotonic loading is key, designers must also account for the millions of cyclic loads applied over the lifetime of the foundation by wind, wave and rotating turbine blades. In the existing FE software packages for geotechnical design (e.g., Plaxis), some practical models for fine-grained soils are available. But there is not a proper model which can capture the stress-strain relationship of sand under monotonic and cyclic loading. Therefore, engineers have to choose some simplistic models which cannot describe the mechanical response of sand reasonably. Knowing the limitations of such models, engineers always make very conservative assumptions in their design to minimize the risks, leading to more steel use in the foundations and higher capital costs. Meanwhile, thousands of advanced sand models with excellent predictive capability have been proposed but their uptake by engineers in geotechnical design is rare. There are two major reasons for this. First, most advanced sand models use complex formulations to achieve better predictive capability. This leads to the employment of many model parameters, some of which are hard to determine using the test data that is available in most offshore wind farm projects. Secondly, it is infeasible to use these models for FE analysis of foundations subjected to millions of cyclic loads due to the excessive amount of computation time required. A practical 'engineer' sand model that can offer reasonable prediction of sand element response and have relatively simple formulations is thus urgently needed by the offshore wind industry for improving the FE modelling of OWTF. 

This project aims to propose an 'engineer' sand model for offshore foundation design. The model will be implemented in a finite element software package for modelling the response of OWTF in cyclic loading. The model will balance the complexity of formulations and the predictive capability, such that it can be used by geotechnical engineers for OWTF design.  

Please get in touch with Dr Zhiwei Gao if you are interested (https://www.gla.ac.uk/schools/engineering/staff/zhiweigao/). He will give you more advice on the project and scholarships.

Supervisor: Dr Zhiwei Gao (Zhiwei.gao@glasgow.ac.uk) 

Description  

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project on multiphysics modelling of gas migration in fine-grained soils.  

Marine sediments with free gas bubbles are widely seen throughout the world, including the Bristol Channel, North Sea, Gulf of Mexico, Gulf of Guinea and Eastern China Sea. The gas is mainly methane produced by the breakdown of organic matter, thermal cracking of complex organic/inorganic compounds, gas exploration or gas hydrate melting. Free gas in fine-grained marine soil is a major hazard for offshore ground engineering. In 2010, a vessel in Hangzhou Bay of China was 'sucked’ into the sea and some workers were killed. This accident was caused by failure of the anchors for the vessel, which were built in soft soils with a high concentration of free methane. After the anchor failure, a huge amount of methane was released into the sea, reducing the density of water and buoyancy force. Lower buoyancy force eventually made the vessel sink. Gas bubbles produced by gas hydrate melting can also cause large submarine landslides/sediment movement in the deep sea, which is a significant hazard to seafloor infrastructure like internet cables, pipelines and foundations.  

There is concern that global warming will make more gas hydrates melt in the sea. Many of these problems are caused by gas migration in soils, which damages the soil structure and degrades the soil strength. But a big knowledge gap remains in this area. In this project, multiphysics modelling will be used to investigate gas migration in fine-grained marine sediments. Impact of gas migration on the soil structure and mechanical properties will be explored. The knowledge will then be used to assess the failure of offshore foundations and submarine slopes. 

Please get in touch with Dr Zhiwei Gao if you are interested (https://www.gla.ac.uk/schools/engineering/staff/zhiweigao/). He will give you more advice on the project and scholarships.

Supervisor: Dr Zhiwei Gao (Zhiwei.gao@glasgow.ac.uk) 

Description  

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project on constitutive modelling of sand. 

The mechanical behaviour of sand is time dependent. Time effects in granular materials comprise rate effects and aging effects. Rate effects include rate dependent stress-strain relation, creep (time-delayed accumulation of strain at constant stress) and stress relaxation (stress change at constant strain). The aging effects, which are used to describe changes in engineering properties (e.g., stiffness and stress-strain relation) of sand with time, could be attributable to changes in internal structure of sand associated with static fatigue at particle contacts, contact force homogenization, formation of interlocking or cementing agents and/or time-delayed particle movement. These time effects have significant influence on practical geotechnical design. For instance, long term observations showed that sand creep can induce large settlement of foundations built on sand. The settlement after construction may reach 35% of the total one. Neglecting such settlement can result in dangerous designs. Consideration of aging effects on mechanical properties of sand can lead to safer and more cost-efficient design. Abundant experimental and field tests indicate that the bearing capacity of driven piles in sand increase with time, which is known as pile setup. In some cases, the increase can reach 100% in just 3 months. This is attributable to sand aging.  

This project aims to develop a comprehensive constitutive model to describe time effects on mechanical behaviour of sand. The main objectives are: (a) Development of an elasto-visco-plastic constitutive model for describing the time effects on mechanical behaviour of sand. Since the time effects on mechanical behaviour of sand cannot be described by a conventional rate-dependent model which assumes that all the time effects are due to time-delayed accumulation of strain, distinction between the rate and aging effects will be made in the proposed model. (b) After the model is developed, it will be implemented in the finite element package Abaqus to solve practical boundary value problems associated with time-dependent mechanical behaviour of sand. Examples are back analysis on increase of bearing capacity of driven piles with time, increase of liquefaction resistance of sand with time and long-term deformation of sand embankments/dams. 

Please get in touch with Dr Zhiwei Gao if you are interested (https://www.gla.ac.uk/schools/engineering/staff/zhiweigao/). He will give you more advice on the project and scholarships.

Supervisor: Dr. Róisín Buckley

Description: The UK is the world leader in offshore wind energy and has set a new ambitious target of 40GW of wind power by 2030, producing enough offshore wind capacity to power every home. Offshore wind turbine (OWT) foundations make up a significant portion of the capital cost of a project and optimisation of their design is paramount. Recent research has led to significant advances through theoretical developments combined with high-quality field-testing. There remains significant uncertainty in the measurement and interpretation of key soil and rock deformation parameters that underpin design approaches. Improving offshore site characterisation will contribute to economising foundation geometries, lowering OWF capital costs and meeting the UK’s renewable energy targets.

A new programme of research in the School of Engineering aims to quantify and reduce the uncertainty associated with soil/rock in situ stiffness measurements and minimise the impact on the predictive capability of OWT foundation design methods. This will be achieved through rigorous measurement and interpretation in the field and the laboratory. This PhD project will focus on field testing at three UK test sites - processing, analysing and interpreting large datasets from a range of geophysical and insitu testing methods carried out by third parties including: seismic CPT, cross-hole, down-hole and surface wave geophysics, P-S logging and pressuremeter tests. There will be a particular focus on intra- and inter- method variability between field methods to determine small strain stiffness and on correlation between in situ and laboratory results. The research outcomes will be applied to design with an opportunity to re-analyse designed monopile foundations and determine the effect of the results on design life.

How to Apply: Please refer to the following website for details on how to apply:

http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/.

Informal inquiries can be directed to the first supervisor (roisin.buckley@glasgow.ac.uk)

Supervisor: Dr Arianna Gea Pagano (arianna.pagano@glasgow.ac.uk)

Description: Soils are discrete geomaterials, composed of i) a particulate solid phase, and ii) a fluid phase, filling up the porosity. Despite their discrete nature, soils are regarded as continua in the theories of soil mechanics and in conventional geotechnical design. This simplifying assumption allows to successfully capture soil responses in a variety of scenarios, where an extensive engineering database of macroscopic experimental evidence to inform the continuum models is available.

Nonetheless, the macroscopic response of soil is intrinsically related to its particulate nature. The particle-to-particle interactions, electrochemical and/or mechanical, affect particle arrangements and kinematics, and ultimately control the macroscopic behaviour. Elucidating the particle-scale mechanisms is crucial to the prediction of soil responses where these cannot be easily reproduced within the framework of continuum mechanics, due to a poor fundamental understanding and/or limited experimental evidence. Some examples include: i) peculiar aspects of soils subjected to mechanical loading, including anisotropic behaviour of clays, strain localisation, non-unicity of critical state lines in transitional soils; ii) the response of soils subjected to “environmental” loads, e.g. chemical loading (relevant to clay liners and caps for landfill sites) and thermal loading (relevant to engineered barriers for radioactive waste disposal, and for soils interacting with energy geo-structures for renewable energy production).

This PhD project will focus on the discrete numerical modelling and experimental investigation of fine-grained and course-grained soils, and mixtures thereof. The successful candidate will: 1) acquire and implement programming skills using the Discrete Element Method (DEM) to explore the behaviour of virtual soil samples made of interacting particles, with the aim to unveil the particle-scale mechanisms leading to specific macroscopic responses; 2) obtain experimental evidence to calibrate and validate the numerical framework, by means of geotechnical tests (e.g. oedometer, direct shear, triaxial) and laboratory techniques to characterise the porous and solid microstructure (e.g. mercury intrusion porosimetry, x-ray imaging). One or more of the aforementioned aspects of soil behaviour under mechanical and environmental loads will be explored, depending on the preliminary results and the interests of the candidate.

The candidate must have a first class honours degree or equivalent in Civil/Environmental Engineering, or a related physical science discipline. A strong background and/or interest in numerical modelling and physical experiments is desirable. For informal inquiries and information on how to apply, please contact the first supervisor (arianna.pagano@glasgow.ac.uk).

Project title: Data-driven constitutive modelling of sand

Supervisor: Dr Zhiwei Gao (Zhiwei.gao@glasgow.ac.uk)

Description: The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project on data-driven constitutive modelling of sand.

Finite element (FE) analysis is now routinely used in geotechnical design, from deep excavation to tunnelling and offshore wind turbine foundations (OWTFs). Constitutive models representing the stress-strain relationship of soils are regarded as the most important component of FE modelling. In a geotechnical design, choosing the appropriate constitutive model and getting the right model parameters for sand is never easy, mainly due to the complexity of sand behaviour. It is believed that sand behaviour is more complicated than quantum mechanics from the perspective of physics. Lack of proper engineering judgement and high-quality test data can lead to the wrong choice of constitutive models and parameters.

This project aims to develop a data-driven sand model with built-in physics for FE modelling. The built-in physics will include principles of soil mechanics and plasticity theory. The model does not require complex formulations or parameters. The research project will include database construction for real sand using results from the literature and publications and data-driven modelling. The new data-driven model will be used in the finite element analysis of classical geotechnical problems. This will bring paradigm changes in the way we do constitutive modelling and finite element analysis in geotechnical engineering.

Please get in touch with Dr Zhiwei Gao if you are interested (https://www.gla.ac.uk/schools/engineering/staff/zhiweigao/). He will give you more advice on the project and scholarships.

Project title: How does free gas in the seabed trigger large submarine landslides and impact the offshore renewable energy infrastructure?

Supervisor: Dr Zhiwei Gao (Zhiwei.gao@glasgow.ac.uk)

Description: The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project on submarine landslides in gassy soils.

Marine sediments with free gas bubbles are widely seen throughout the world, including the Bristol Channel, North Sea, Gulf of Mexico, Gulf of Guinea and Eastern China Sea. The gas is mainly methane produced by the breakdown of organic matter, thermal cracking of complex organic/inorganic compounds, gas exploration or gas hydrate melting. Free gas in fine-grained marine soil is a major hazard for offshore ground engineering. In particular, it is believed some large-scale submarine landslides were triggered by free gas. There is concern that global warming will make more gas hydrates melt in the sea, which can potentially trigger more submarine landslides in the seabed. Since submarine landslides are much bigger than those onshore, a small landslide in the seabed can bring devastating damage to our offshore infrastructure, such as wind farms and submarine cables.

But the mechanism behind these landslides remains poorly understood. Some research has shown that the high-pressure gas movement is the main cause of submarine landslides. However, this conclusion is based on simple 2D numerical analysis, which may not represent reality. Meanwhile, many researchers believe that landslide development is a complex process which needs to be explained using the knowledge of chemistry, soil mechanics and fluid mechanics. The aim of this project is to carry out multiphysics modelling of landslides in soils with free gas bubbles.

Please get in touch with Dr Zhiwei Gao if you are interested (https://www.gla.ac.uk/schools/engineering/staff/zhiweigao/). He will give you more advice on the project and scholarships.