Main Content Region

Ongoing Research Projects supported by Research IT

Listing of project codes and abstracts, describing work undertaken which use the resources of the compute clusters hosted by the Research IT team.

New codes can be obtained by filling in this resource application form.

Search Results
Showing 10 of 419 Results
Project Title QoS optimisation for IoT Service Composition
Project Code HPC_17_01003
Principal Investigator Professor Siobhan Clarke
Start Date 2017-11-13
End Date 2018-09-01
Abstract IoT services have a number of QoS attributes that are constantly changing. When used in a composed service, the QoS needs to be continuously optimised to account for the variability in resources.
Project Title Autotune – Determining the Feasibility of using Objective Electrophysiological Audiometry to develop an Integrated Sel
Project Code HPC_17_01002
Principal Investigator Assistant Professor Edmund Lalor
Start Date 2017-11-10
End Date 2018-04-30
Abstract Currently audiologists rely on time consuming behavioural measures to assess a person’s hearing. The aim of project is to explore a rapid, automatic technique. The idea was founded on a closed-loop ‘self-tuning hearing aid’ that uses electrophysiological signals (electroencephalography, EEG) from the auditory nervous system to measure hearing to tune the hearing aid to the user’s audiogram. The project is based on converging scientific, technological and market trends in the hearing aid sector. The aim is to evaluate the feasibility of integrating an objective electrophysiological hearing test into a self-tuning hearing aid using a bench prototype. The first stage aims to replicate and improve upon the results of clinical audiometry by using multi-channel EEG measures to characterise the function of the auditory nervous system. The viability of such an approach has been established by the auditory neuroscience community. However, integrating a full EEG system into a hearable is not feasible. Therefore, the second aim is to evaluate the feasibility of using a more integration-friendly approach, which utilises single- or bi-channel EEG, to replicate clinical audiometry. If such an approach performs as well as clinical audiometry, then we believe that the miniaturisation and integration into a hearing aid is technically feasible.
Project Title FERROVOLT
Project Code HPC_17_00999
Principal Investigator Prof Stefano Sanvito
Start Date 2017-10-26
End Date 2019-10-25
Abstract For a better understanding and design of ferroelectric photovoltaics: First-principles study of optical absorption and charge-carrier transport at ferroelectric domain walls in BiFeO3 The goal of this project is to help find the rules for a domain-wall engineering that optimizes photovoltaic efficiency of potential future-generation ferroelectric solar cells. The material to be studied is BiFeO3 as the most promising photovoltaic ferroelectric material known. Does the photovoltaic effect in BiFeO3 occur at the domain walls or in the bulk? What does it take a domain-wall to conduct electrons? The project aims at establishing the necessary conditions for electric fields and electrical conductivity at ferroelectric domain walls. Since experimental evidence is inconclusive, state-of-the-art ab initio methods will be applied. Electric fields have a long spatial range, so we will go beyond the standard supercell approach to obtain the spatial gradient of the band structure at the domain wall, needed to obtain charge-carrier distributions and electric fields. The Green's-function method for electronic quantum transport will be used for this purpose because it is suitable for extended, non-periodic systems. We will obtain the electrical conductivity as a function of the domain-wall type, structure, and purity. Conclusions for the role of the domain walls in BiFeO3 will be generalized as far as possible in order to apply them to other ferroelectric semiconductors as well. The project is positioned where fundamental condensed-matter physics meets applied solar-cell research. It is expected to advance the frontier of knowledge in basic research and to lay the ground for further research on ferroelectric photovoltaics. It is a contribution to the efforts of the European Union to develop innovative solutions for a sustainable energy supply that help achieve independence of fossil energy. In addition, high-throughput screening and computational characterization for promising photovoltaic absorber materials is planned, including, but maybe not limited to, perovskites and related compounds.
Project Title Finding Metastable magnets
Project Code HPC_17_00998
Principal Investigator Prof Stefano Sanvito
Start Date 0000-00-00
End Date 0000-00-00
Abstract In Physics it is not always the most energetically favourable structure the one that forms. In fact, there are many situations in which together with the most stable structure many others also exist. These are called metastable. For example, diamond is a metastable form of carbon at standard temperature and pressure, with graphene being the most stable one. Thus, at least in principle, all diamonds will eventually turn into graphite. In practice, however, such metastable structures take extremely long times to be converted into their thermodynamically stable counterparts, so that to all purposes they can be considered as stable. There are even several examples, where only metastable structures are found in nature, simply because the most stable one is not accessible during the growth process. Since the minimum energy criterion does not apply any longer, predicting the existence of metastable structures is a complicate task. In this project we will explore possible high-throughput ways to predict metastable structures for metallic and magnetic alloys. In particular we will try to identify random metallic alloys, which can sustain a magnetic ground state. The tools for the investigation will be advanced electronic structure theory, in the form or density functional theory, and a statistical analysis of the possible alloy configurations. We will focus on a specific class of materials known as Heusler alloys, which contains a large number of known members, with many presenting a large degree of disorder. We will evaluate whether known compounds do exist because of their low energy or because of their high entropy, and estimate the probability to find new ones.
Project Title Persistency and population dynamics in perennial ryegrass (Lolium perenne)
Project Code HPC_17_00997
Principal Investigator Dr Trevor Hodkinson
Start Date 2017-10-23
End Date 2020-10-31
Abstract Perennial ryegrass (Lolium perenne) is an important forge crop species in global temperate regions. It underpins the beef and dairy industry in Ireland. As its name suggests, perennial ryegrass (PRG) is perennial in nature and swards can remain in the ground up to decadal periods. Population structure can change dramatically over time, as multiple selective pressures acting on the highly heterogeneous PRG population results in survival of the most persistent individuals. Persistent plants are often not productive, reducing yield and over all sward productivity. Persistency is a complex trait which refers to the ability of PRG individuals to survive in swards and has been identified as a trait of economic performance in Teagasc's Pasture Profit Index. By tracking changes in allele frequencies in monovarietal and mixed swards over time using a genotyping-by-sequencing (GBS) approach, we aim to identify persistent genotypes. GBS is an effective high throughput assay for SNP discovery. However, for some applications the level of coverage which GBS offers is unnecessarily large and in some cases a more cost effective, targeted approach would be more appropriate. Genotyping-in-thousands by sequencing (GT-seq) . For applications such as varietal discrimination, association mapping genotyping thousands of individuals at many thousand this approach may be more suitable. The potential of this assay in these applications in PRG will be investigated as part of this project
Project Title Optimisation via simulation for stochastic set covering problem
Project Code HPC_17_00996
Principal Investigator Prof Stephan Onggo
Start Date 2017-11-01
End Date 2018-11-01
Abstract Set covering problem is an optimisation problem where we want to maximize coverage subject to a set of policy and operational constraints. The problem is often solved using deterministic optimisation algorithms such as integer linear programming and integer non-linear programming. However, many real-world set covering problems are stochastic. In this case, the objective function can only be estimated using a simulation model. In addition, some constraints may need to be estimated using a simulation model too. This research applies optimisation via simulation algorithm to solve stochastic set covering problem
Project Title Transmit Beamforming in mmWave networks
Project Code HPC_17_00995
Principal Investigator Prof Linda Doyle
Start Date 2017-10-18
End Date 2017-10-30
Abstract In this work, we want to perform analysis of small-cell ultra-dense networks, using transmit beamforming in mmWave bands.
Project Title Improving Social Communication through Executive Function Training in Autism
Project Code HPC_17_00994
Principal Investigator Assistant Professor Clare Kelly
Start Date 2017-10-06
End Date 2018-09-01
Abstract The proposed research project will explore the benefits of a computerized training (CT) as intervention for individuals with autism spectrum disorder (ASD), it will add to the literature regarding the interplay between higher level executive function skills and social functioning in ASD, and it will utilise state-of-the-art neuroimaging techniques to elucidate associated brain behaviour relationships. The project was designed to be a natural progression of the my current skill set, but to also expand, elevate, and introduce techniques and tactics to help me achieve mastery of skills that are required to be a successful and innovative independent investigator. In addition to the research outcomes of the project, there will also be positive societal and economic impacts. Validation of the proposed CT programme will increase the availability and quality of therapy for adolescents with ASD; the independence of individuals with ASD; and the employability of high functioning individuals with ASD.
Project Title High Entropy Magnetic Alloys
Project Code HPC_17_00993
Principal Investigator Prof Stefano Sanvito
Start Date 2017-09-26
End Date 2017-12-31
Abstract Steel is usually considered as a highly mechanically performing material, with a large yield strength and fracture toughness. Recently a new class of metallic alloys have seriously challenged such performances and have established themselves as new star materials for a wide range of applications. These are the so-called high-entropy alloys [1, 2], made by mixing in equal proportion five or more transition metals (either from the 3d or the 4d family). Remarkably such alloys, whose thermodynamical stability at high temperature is driven by their large entropy, have remarkable mechanical properties.
Project Title High Entropy Magnetic Alloys
Project Code HPC_17_00992
Principal Investigator Prof Stefano Sanvito
Start Date 2017-09-26
End Date 2017-12-31
Abstract Steel is usually considered as a highly mechanically performing material, with a large yield strength and fracture toughness. Recently a new class of metallic alloys have seriously challenged such performances and have established themselves as new star materials for a wide range of applications. These are the so-called high-entropy alloys [1, 2], made by mixing in equal proportion five or more transition metals (either from the 3d or the 4d family). Remarkably such alloys, whose thermodynamical stability at high temperature is driven by their large entropy, have remarkable mechanical properties.