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

Many parts of Graph Theory have witnessed a huge growth over the last years, partly because of their relation to Theoretical Computer Science and Statistical Physics. These connections arise because graphs can be used to model many diverse structures. The focus of this proposal is on asymptotic results, i.e. the graphs under consideration are large. This often unveils patterns and connections which remain obscure when considering only small graphs. It also allows for the use of powerful techniques such as probabilistic arguments, which have led to spectacular new developments. In particular, my aim is to make decisive progress on central problems in the following 4 areas: (1) Factorizations: Factorizations of graphs can be viewed as partitions of the edges of a graph into simple regular structures. They have a rich history and arise in many different settings, such as edge-colouring problems, decomposition problems and in information theory. They also have applications to finding good tours for the famous Travelling salesman problem. (2) Hamilton cycles: A Hamilton cycle is a cycle which contains all the vertices of the graph. One of the most fundamental problems in Graph Theory/Theoretical Computer Science is to find conditions which guarantee the existence of a Hamilton cycle in a graph. (3) Embeddings of graphs: This is a natural (but difficult) continuation of the previous question where the aim is to embed more general structures than Hamilton cycles - there has been exciting progress here in recent years which has opened up new avenues. (4) Resilience of graphs: In many cases, it is important to know whether a graph `strongly’ possesses some property, i.e. one cannot destroy the property by changing a few edges. The systematic study of this notion is a new and rapidly growing area. I have developed new methods for deep and long-standing problems in these areas which will certainly lead to further applications elsewhere.

818 414 €

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

"Nanomaterials such as carbon nanotubes or graphene sheets represent the future of material science, due to their potentially exceptional mechanical properties. One great drawback of all artificial materials, however, is the decrease of strength with increasing toughness, and viceversa. This problem is not encountered in many biological nanomaterials (e.g. spider silk, bone, nacre). Other biological materials display exceptional adhesion or damping properties, and can be self-cleaning or self-healing. The “secret” of biomaterials seems to lie in “hierarchy”: several levels can often be identified (2 in nacre, up to 7 in bone and dentine), from nano- to micro-scale. The idea of this project is to combine Nature and Nanotechnology to design hierarchical composites with tailor made characteristics, optimized with respect to both strength and toughness, as well as materials with strong adhesion/easy detachment, smart damping, self-healing/-cleaning properties or controlled energy dissipation. For example, one possible objective is to design the “world’s toughest composite material”. The potential impact and importance of these goals on materials science, the high-tech industry and ultimately the quality of human life could be considerable. In order to tackle such a challenging design process, the PI proposes to adopt ultimate nanomechanics theoretical tools corroborated by continuum or atomistic simulations, multi-scale numerical parametric simulations and Finite Element optimization procedures, starting from characterization experiments on biological- or nano-materials, from the macroscale to the nanoscale. Results from theoretical, numerical and experimental work packages will be applied to a specific case study in an engineering field of particular interest to demonstrate importance and feasibility, e.g. an airplane wing with a considerably enhanced fatigue resistance and reduced ice-layer adhesion, leading to a 10 fold reduction in wasted fuel."

1 004 400 €

Start date: 2012-01-01, End date: 2016-12-31

More than 30 million people are living with diabetes in the EU, with a prevalence expected to grow to over 10% of the adult population by the year 2030. Type 2 diabetes is a major cause of cardiovascular disease related death and disability, substantially increasing the risk of myocardial infarction, stroke and peripheral arterial disease. Recent landmark trials, showing that intensive glucose control does not improve cardiovascular outcomes and may increase mortality in some circumstances, provide a compelling rationale for intense research aimed at developing novel therapeutic strategies. Type 2 diabetes is underpinned by resistance to the effects of insulin, which I have shown in endothelial cells causes reduced bioavailability of the anti-atherosclerotic molecule nitric oxide and leads to accelerated atherosclerosis. The cellular effects of insulin are mirrored by insulin-like growth factor factor-1, the bioavailability of which at its receptor is in turn is regulated by a family of high affinity binding proteins (IGFBP). Epidemiological studies demonstrate and inverse association between one of these binding proteins, IGFBP1, and diabetes-related cardiovascular risk. I have recently demonstrated that IGFBP1 when expressed in mice can ameliorate insulin resistance, obesity and atherosclerosis. In endothelial cells, I showed that IGFBP1 upregulates the production of nitric oxide indepenedently of IGF. These findings suggest that IGFBP1 may be a ‘protective’ endogenous protein and that increasing circulating levels may be a therapeutic strategy to prevent development of diabetes and cardiovascular disease. In this proposal I will address this hypothesis by employing state of the art studies in cells and novel gene modified mice to unravel the molecular basis of the protective effects of IGFBP1 and to investigate the possibility of exploiting the IGF-IGFBP axis to prevent cardiovascular disease in the setting of diabetes and obesity.

1 493 543 €

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