ERC FUNDED PROJECTS

"This proposal develops a unified, underpinning technology to create novel, complex and biomimetic 3D environments for the control of tissue growth. As director of Cambridge Centre for Medical Materials, I have recently been approached by medical colleagues to help to solve important problems in the separate therapeutic areas of breast cancer, cardiac disease and blood disorders. In each case, the solution lies in complex 3D engineered environments for cell culture. These colleagues make it clear that existing 3D scaffolds fail to provide the required complex orientational and spatial anisotropy, and are limited in their ability to impart appropriate biochemical and mechanical cues. I have a strong track record in this area. A particular success has been the use of a freeze drying technology to make collagen based porous implants for the cartilage-bone interface in the knee, which has now been commercialised. The novelty of this proposal lies in the broadening of the established scientific base of this technology to enable biomacromolecular structures with: (A) controlled and complex pore orientation to mimic many normal multi-oriented tissue structures (B) compositional and positional control to match varying local biochemical environments, (C) the attachment of novel peptides designed to control cell behaviour, and (D) mechanical control at both a local and macroscopic level to provide mechanical cues for cells. These will be complemented by the development of (E) robust characterisation methodologies for the structures created. These advances will then be employed in each of the medical areas above. This approach is highly interdisciplinary. Existing working relationships with experts in each medical field will guarantee expertise and licensed facilities in the required biological disciplines. Funds for this proposal would therefore establish a rich hub of mutually beneficial research and opportunities for cross-disciplinary sharing of expertise."

2 486 267 €

Start date: 2013-04-01, End date: 2018-03-31

The rupture of an Aortic Aneurysm (AA), which is often lethal, is a mechanical phenomenon that occurs when the wall stress state exceeds the local strength of the tissue. Our current understanding of arterial rupture mechanisms is poor, and the physics taking place at the microscopic scale in these collagenous structures remains an open area of research. Understanding, modelling, and quantifying the micro-mechanisms which drive the mechanical response of such tissue and locally trigger rupture represents the most challenging and promising pathway towards predictive diagnosis and personalized care of AA. The PI's group was recently able to detect, in advance, at the macro-scale, rupture-prone areas in bulging arterial tissues. The next step is to get into the details of the arterial microstructure to elucidate the underlying mechanisms. Through the achievements of AArteMIS, the local mechanical state of the fibrous microstructure of the tissue, especially close to its rupture state, will be quantitatively analyzed from multi-photon confocal microscopy and numerically reconstructed to establish quantitative micro-scale rupture criteria. AArteMIS will also address developing micro-macro models which are based on the collected quantitative data. The entire project will be completed through collaboration with medical doctors and engineers, experts in all required fields for the success of AArteMIS. AArteMIS is expected to open longed-for pathways for research in soft tissue mechanobiology which focuses on cell environment and to enable essential clinical applications for the quantitative assessment of AA rupture risk. It will significantly contribute to understanding fatal vascular events and improving cardiovascular treatments. It will provide a tremendous source of data and inspiration for subsequent applications and research by answering the most fundamental questions on AA rupture behaviour enabling ground-breaking clinical changes to take place.

1 499 783 €

Start date: 2015-04-01, End date: 2020-03-31

With the recognition that wind energy will become an important contributor to the world’s energy portfolio, several wind farms with a capacity of over 1 gigawatt are in planning phase. In the past, engineering of wind farms focused on a bottom-up approach, in which atmospheric wind availability was considered to be fixed by climate and weather. However, farms of gigawatt size slow down the Atmospheric Boundary Layer (ABL) as a whole, reducing the availability of wind at turbine hub height. In Denmark’s large off-shore farms, this leads to underperformance of turbines which can reach levels of 40%–50% compared to the same turbine in a lone-standing case. For large wind farms, the vertical structure and turbulence physics of the flow in the ABL become crucial ingredients in their design and operation. This introduces a new set of scientific challenges related to the design and control of large wind farms. The major ambition of the present research proposal is to employ optimal control techniques to control the interaction between large wind farms and the ABL, and optimize overall farm-power extraction. Individual turbines are used as flow actuators by dynamically pitching their blades using time scales ranging between 10 to 500 seconds. The application of such control efforts on the atmospheric boundary layer has never been attempted before, and introduces flow control on a physical scale which is currently unprecedented. The PI possesses a unique combination of expertise and tools enabling these developments: efficient parallel large-eddy simulations of wind farms, multi-scale turbine modeling, and gradient-based optimization in large optimization-parameter spaces using adjoint formulations. To ensure a maximum impact on the wind-engineering field, the project aims at optimal control, experimental wind-tunnel validation, and at including multi-disciplinary aspects, related to structural mechanics, power quality, and controller design.

1 499 241 €

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

Space exploration is one of boldest and most exciting endeavors that humanity has undertaken, and it holds enormous promise for the future. Our next challenges for the spatial conquest include bringing back samples to Earth by means of robotic missions and continuing the manned exploration program, which aims at sending human beings to Mars and bring them home safely. Inaccurate prediction of the heat-flux to the surface of the spacecraft heat shield can be fatal for the crew or the success of a robotic mission. This quantity is estimated during the design phase. An accurate prediction is a particularly complex task, regarding modelling of the following phenomena that are potential “mission killers:” 1) Radiation of the plasma in the shock layer, 2) Complex surface chemistry on the thermal protection material, 3) Flow transition from laminar to turbulent. Our poor understanding of the coupled mechanisms of radiation, ablation, and transition leads to the difficulties in flux prediction. To avoid failure and ensure safety of the astronauts and payload, engineers resort to “safety factors” to determine the thickness of the heat shield, at the expense of the mass of embarked payload. Thinking out of the box and basic research are thus necessary for advancements of the models that will better define the environment and requirements for the design and safe operation of tomorrow’s space vehicles and planetary probes for the manned space exploration. The three basic ingredients for predictive science are: 1) Physico-chemical models, 2) Computational methods, 3) Experimental data. We propose to follow a complementary approach for prediction. The proposed research aims at: “Integrating new advanced physico-chemical models and computational methods, based on a multidisciplinary approach developed together with physicists, chemists, and applied mathematicians, to create a top-notch multiphysics and multiscale numerical platform for simulations of planetary atmosphere entries, crucial to the new challenges of the manned space exploration program. Experimental data will also be used for validation, following state-of-the-art uncertainty quantification methods.”

1 494 892 €

Start date: 2010-09-01, End date: 2015-08-31