ERC FUNDED PROJECTS

"Combustion is essential to the world’s energy generation and transport needs, and will remain so for the foreseeable future. Mitigating its impact on the climate and human health, by reducing its associated emissions, is thus a priority. One significant challenge for gas-turbine combustion is combustion instability, which is currently inhibiting reductions in NOx emissions (these damage human health via a deterioration in air quality). Combustion instability is caused by a two-way coupling between unsteady combustion and acoustic waves - the large pressure oscillations that result can cause substantial mechanical damage. Currently, the lack of fast, accurate modelling tools for combustion instability, and the lack of reliable ways of suppressing it are severely hindering reductions in NOx emissions. This proposal aims to make step improvements in both fast, accurate modelling of combustion instability, and in developing reliable active control strategies for its suppression. It will achieve this by coupling low order combustor models (these are fast, simplified models for simulating combustion instability) with advances in analytical modelling, CFD simulation, reduced order modelling and control theory tools. In particular: * important advances in accurately incorporating the effect of entropy waves (temperature variations resulting from unsteady combustion) and non-linear flame models will be made; * new active control strategies for achieving reliable suppression of combustion instability, including from within limit cycle oscillations, will be developed; * an open-source low order combustor modelling tool will be developed and widely disseminated, opening access to researchers worldwide and improving communications between the fields of thermoacoustics and control theory. Thus the proposal aims to use analytical and computational methods to contribute to achieving low NOx gas-turbine combustion, without the penalty of damaging combustion instability."

1 489 309 €

Start date: 2013-01-01, End date: 2017-12-31

In the last ten years there has been a surge of interest in non-modal analysis applied to canonical problems in fundamental fluid mechanics. Even in simple flows, the stability behaviour predicted by non-modal analysis can be completely different from and far more accurate than that predicted by conventional eigenvalue analysis. As well as being more accurate, the tools of non-modal analysis, such as Lagrangian optimization, are very versatile. Furthermore, the outputs, such as receptivity and sensitivity maps of a flow, provide powerful insight for engineers. They describe where a flow is most receptive to forcing or where the flow is most sensitive to modification. The application of non-modal analysis to canonical problems has set the scene for step changes in engineering practice in fluid mechanics and thermoacoustics. The technical objectives of this proposal are to apply non-modal analysis to high Reynolds number flows, reacting flows and thermoacoustic systems, to compare theoretical predictions with experimental measurements and to embed these techniques within an industrial design tool that has already been developed by the group. This research group s vision is that future generations of engineering CFD tools will contain modules that can perform non-modal analysis. The generalized approach proposed here, combined with challenging scientific and engineering examples that are backed up by experimental evidence, will make this possible and demonstrate it to a wider engineering community.

1 301 196 €

Start date: 2010-12-01, End date: 2016-06-30

The goal of crystal engineering is the design of functional crystalline materials in which the arrangement of basic structural building blocks imparts desired properties. The engineering of organic molecular crystals has, to date, relied largely on empirical rules governing the intermolecular association of functional groups in the solid state. However, many materials properties depend intricately on the complete crystal structure, i.e. the unit cell, space group and atomic positions, which cannot be predicted solely using such rules. Therefore, the development of computational methods for crystal structure prediction (CSP) from first principles has been a goal of computational chemistry that could significantly accelerate the design of new materials. It is only recently that the necessary advances in the modelling of intermolecular interactions and developments in algorithms for identifying all relevant crystal structures have come together to provide predictive methods that are becoming reliable and affordable on a timescale that could usefully complement an experimental research programme. The principle aim of the proposed work is to establish the use of state-of-the-art crystal structure prediction methods as a means of guiding the discovery and design of novel molecular materials. This research proposal both continues the development of the computational methods for CSP and, by developing a computational framework for screening of potential molecules, develops the application of these methods for materials design. The areas on which we will focus are organic molecular semiconductors with high charge carrier mobilities and, building on our recently published results in Nature [1], the development of porous organic molecular materials. The project will both deliver novel materials, as well as improvements in the reliability of computational methods that will find widespread applications in materials chemistry. [1] Nature 2011, 474, 367-371.

1 499 906 €

Start date: 2012-10-01, End date: 2017-09-30

Back pain affects eight out of ten adults during their lifetime. It a huge economic burden on society, estimated to cost as much as 1-2% of gross national product in several European countries. Treatments for back pain have lower levels of success and are not as technologically mature as those for other musculoskeletal disorders such as hip and knee replacement. This application proposes to tackle one of the major barriers to the development of better surgical treatments for back pain. At present, new spinal devices are commonly assessed in isolation in the laboratory under standardised conditions that do not represent the variation across the patient population. Consequently many interventions have failed during clinical trials or have proved to have poor long term success rates. Using a combination of computational and experimental models, a new testing methodology will be developed that will enable the variation between patients to be simulated for the first time. This will enable spinal implants and therapies to be more robustly evaluated across a virtual patient population prior to clinical trial. The tools developed will be used in collaboration with clinicians and basic scientists to develop and, crucially, optimise new treatments that reduce back pain whilst preserving the unique functions of the spine. If successful, this approach could be translated to evaluate and optimise emerging minimally invasive treatments in other joints such as the hip and knee. Research in the spine could then, for the first time, lead rather than follow that undertaken in other branches of orthopaedics.

1 498 777 €

Start date: 2012-12-01, End date: 2018-11-30

Cooling is essential for food and drinks, medicine, electronics and thermal comfort. Thermal changes due to pressure-driven phase transitions in fluids have long been used in vapour compression systems to achieve continuous refrigeration and air conditioning, but their energy efficiency is relatively low, and the working fluids that are employed harm the environment when released to the atmosphere. More recently, the discovery of large thermal changes due to pressure-driven phase transitions in magnetic solids has led to suggestions for environmentally friendly solid-state cooling applications. However, for this new cooling technology to succeed, it is still necessary to find suitable barocaloric (BC) materials that satisfy the demanding requirements set by applications, namely very large thermal changes in inexpensive materials that occur near room temperature in response to small applied pressures. I aim to develop new BC materials by exploiting phase transitions in non-magnetic solids whose structural and thermal properties are strongly coupled, namely ferroelectric salts, molecular crystals and hybrid materials. These materials are normally made from cheap abundant elements, and display very large latent heats and volume changes at structural phase transitions, which make them ideal candidates to exhibit extremely large BC effects that outperform those observed in state-of-the-art BC magnetic materials, and that match applications. My unique approach combines: i) materials science to identify materials with outstanding BC performance, ii) advanced experimental techniques to explore and exploit these novel materials, iii) materials engineering to create new composite materials with enhanced BC properties, and iv) fabrication of BC devices, using insight gained from modelling of materials and device parameters. If successful, my ambitious strategy will culminate in revolutionary solid-state cooling devices that are environmentally friendly and energy efficient.

1 467 521 €

Start date: 2016-04-01, End date: 2021-03-31

The agile and efficient flight of birds shows what flight performance is physically possible, and in theory could be achieved by unmanned air vehicles (UAVs) of the same size. The overall aim of this project is to enhance the performance of small scale UAVs by developing novel technologies inspired by understanding how birds are adapted to interact with airflows. Small UAVs have the potential to dramatically change current practices in many areas such as, search and rescue, surveillance, and environmental monitoring. Currently the utility of these systems is limited by their operational endurance and their inability to operate in strong turbulent winds, especially those that often occur in urban environments. Birds are adapted to be able to fly in these conditions and actually use them to their advantage to minimise their energy output. This project is composed of three tracks which contain elements of technology development, as well as scientific investigation looking at bird flight behaviour and aerodynamics. The first track looks at developing path planning algorithms for UAVs in urban environments based on how birds fly in these areas, by using GPS tracking and computational fluid dynamics alongside trajectory optimization. The second track aims to develop artificial wings with improved gust tolerance inspired by the features of feathered wings. Here, high speed video measurements of birds flying through gusts will be used alongside wind tunnel testing of artificial wings to discover what features of a bird’s wing help to alleviate gusts. The third track develops novel force and flow sensor arrays for autonomous flight control based on the sensor arrays found in flying animals. These arrays will be used to make UAVs with increased agility and robustness. This unique bird inspired approach uses biology to show what is possible, and engineering to find the features that enable this performance and develop them into functional technologies.

1 998 546 €

Start date: 2016-04-01, End date: 2021-03-31

Recent intensive research on the laser spectroscopy of neutral gas-phase biomolecules has yielded a detailed picture of their structures and conformational preferences away from the complications of the bulk environment. In contrast, work on ionic systems has been sparse despite the fact that many important molecular groups are charged under physiological conditions. To address this probelm, we have developed a custom-built laser spectrometer, which incorporates a distincitive electrospray ionisation (ESI) cluster ion source, dedicated to producing biological anions (ATP,oligonucleotides) and their microsolvated clusters for structural characterization. Many previous laser spectrometers with ESI sources have suffered from producing "hot" congested spectra as the ions were produced at ambient temperatures. This is a particularly serious limitation for spectroscopic studies of biomolecules, since these systems can possess high internal energies due tothe presence of numerous low frequency modes. Our spectrometer overcomes this problem by exploiting the newly developed physics technique of "buffer gas cooling" to produce cold ESI molecular ions. In this proposal, we now seek to exploit the new laser-spectrometer to perform detailed spectroscopic interrogations of ESI generated biomolecular anions and clusters. In addition to traditional ion-dissociation spectroscopies, we propose to develop two new laser spectroscopy techniques (Two-color tuneable IR spectroscopy and Dipole-bound excited state spectroscopy) to give the broadest possible structural characterizations of the systems of interest. Studies will focus on ATP/GTP-anions, olignonucleotides, and sulphated and carboxylated sugars. These methodologies will provide a general approach for performing temperature-controlled spectroscopic characterizations of isolated biological ions, with measurements on the corresponding micro-solvated clusters providing details of how the molecules are perturbed by solvent.

1 250 000 €

Start date: 2008-10-01, End date: 2015-06-30

Enzymes created by Nature are still more selective and can be orders of magnitude more efficient than man-made catalysts, in spite of recent advances in the design of de novo catalysts and in enzyme redesign. The optimal engineering of either small molecular or of complex biological catalysts requires both (i) accurate quantitative computational methods capable of a priori assessing catalytic efficiency, and (ii) molecular design principles and corresponding algorithms to achieve, understand and control biomolecular catalytic function and mechanisms. Presently, the computational design of biocatalysts is challenging due to the need for accurate yet computationally-intensive quantum mechanical calculations of bond formation and cleavage, as well as to the requirement for proper statistical sampling over very many degrees of freedom. Pioneering enhanced sampling and analysis methods have been developed to address crucial challenges bridging the gap between the available simulation length and the biologically relevant timescales. However, biased simulations do not generally permit the direct calculation of kinetic information. Recently, I and others pioneered simulation tools that can enable not only accurate calculations of free energies, but also of the intrinsic molecular kinetics and the underlying reaction mechanisms as well. I propose to develop more robust, automatic, and system-tailored sampling algorithms that are optimal in each case. I will use our kinetics-based methods to develop a novel theoretical framework to address catalytic efficiency and to establish molecular design principles to key design problems for new bio-inspired nanocatalysts, and to identify and characterize small molecule modulators of enzyme activity. This is a highly interdisciplinary project that will enable fundamental advances in molecular simulations and will unveil the physical principles that will lead to design and control of catalysis with Nature-like efficiency.

1 499 999 €

Start date: 2018-02-01, End date: 2023-01-31

In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g., text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning. In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g., facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g., log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning.

1 457 185 €

Start date: 2016-05-01, End date: 2021-04-30