ERC FUNDED PROJECTS

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

ALUFIX proposes an original strategy for the development of aluminium-based materials involving damage mitigation and extrinsic self-healing concepts exploiting the new opportunities of the solid-state friction stir process. Friction stir processing locally extrudes and drags material from the front to the back and around the tool pin. It involves short duration at moderate temperatures (typically 80% of the melting temperature), fast cooling rates and large plastic deformations leading to far out-of-equilibrium microstructures. The idea is that commercial aluminium alloys can be locally improved and healed in regions of stress concentration where damage is likely to occur. Self-healing in metal-based materials is still in its infancy and existing strategies can hardly be extended to applications. Friction stir processing can enhance the damage and fatigue resistance of aluminium alloys by microstructure homogenisation and refinement. In parallel, friction stir processing can be used to integrate secondary phases in an aluminium matrix. In the ALUFIX project, healing phases will thus be integrated in aluminium in addition to refining and homogenising the microstructure. The “local stress management strategy” favours crack closure and crack deviation at the sub-millimetre scale thanks to a controlled residual stress field. The “transient liquid healing agent” strategy involves the in-situ generation of an out-of-equilibrium compositionally graded microstructure at the aluminium/healing agent interface capable of liquid-phase healing after a thermal treatment. Along the road, a variety of new scientific questions concerning the damage mechanisms will have to be addressed.

1 497 447 €

Start date: 2017-01-01, End date: 2021-12-31

"Summary: the quest for a general functional tensor framework for blind source separation Our overall objective is the development of a general functional framework for solving tensor based blind source separation (BSS) problems in biomedical data fusion, using tensor decompositions (TDs) as basic core. We claim that TDs will allow the extraction of fairly complicated sources of biomedical activity from fairly complicated sets of uni- and multimodal data. The power of the new techniques will be demonstrated for three well-chosen representative biomedical applications for which extensive expertise and fully validated datasets are available in the PI’s team, namely: • Metabolite quantification and brain tumour tissue typing using Magnetic Resonance Spectroscopic Imaging, • Functional monitoring including seizure detection and polysomnography, • Cognitive brain functioning and seizure zone localization using simultaneous Electroencephalography-functional MR Imaging integration. Solving these challenging problems requires that algorithmic progress is made in several directions: • Algorithms need to be based on multilinear extensions of numerical linear algebra. • New grounds for separation, such as representability in a given function class, need to be explored. • Prior knowledge needs to be exploited via appropriate health relevant constraints. • Biomedical data fusion requires the combination of TDs, coupled via relevant constraints. • Algorithms for TD updating are important for continuous long-term patient monitoring. The algorithms are eventually integrated in an easy-to-use open source software platform that is general enough for use in other BSS applications. Having been involved in biomedical signal processing over a period of 20 years, the PI has a good overview of the field and the opportunities. By working directly at the forefront in close collaboration with the clinical scientists who actually use our software, we can have a huge impact."

2 500 000 €

Start date: 2014-04-01, End date: 2019-03-31

"Tissue engineering (TE), the interdisciplinary field combining biomedical and engineering sciences in the search for functional man-made organ replacements, has key issues with the quantity and quality of the generated products. Protocols followed in the lab are mainly trial and error based, requiring a huge amount of manual interventions and lacking clear early time-point quality criteria to guide the process. As a result, these processes are very hard to scale up to industrial production levels. BRIDGE aims to fortify the engineering aspects of the TE field by adding a higher level of understanding and control to the manufacturing process (MP) through the use of in silico models. BRIDGE will focus on the bone TE field to provide proof of concept for its in silico approach. The combination of the applicant's well-received published and ongoing work on a wide range of modelling tools in the bone field combined with the state-of-the-art experimental techniques present in the TE lab of the additional participant allows envisaging following innovation and impact: 1. proof-of-concept of the use of an in silico blue-print for the design and control of a robust modular TE MP; 2. model-derived optimised culture conditions for patient derived cell populations increasing modular robustness of in vitro chondrogenesis/endochondral ossification; 3. in silico identification of a limited set of in vitro biomarkers that is predictive of the in vivo outcome; 4. model-derived optimised culture conditions increasing quantity and quality of the in vivo outcome of the TE MP; 5. incorporation of congenital defects in the in silico MP design, constituting a further validation of BRIDGE’s in silico approach and a necessary step towards personalised medical care. We believe that the systematic – and unprecedented – integration of (bone) TE and mathematical modelling, as proposed in BRIDGE, is required to come to a rationalized, engineering approach to design and control bone TE MPs."

1 191 440 €

Start date: 2011-12-01, End date: 2016-11-30

Summary: Comprehensive set of circuits and design methods to create next generation electronic circuits with strong built-in trust and security. Electronics are integrating/invading into the human environment at an amazing speed, called the Internet-of-Things and next the Internet-of-Everything. This creates huge security problems. Distributed (e.g. body) sensors, pick up often very private data, which is sent digitally into the cloud, over wireless and wired links. Protection of this data relies on high-quality cryptographic algorithms and protocols. The nodes need to be cheap and lightweight, making them very vulnerable to eavesdropping and abuse. Moreover, post-Snowden, society realizes that the attack capabilities of intelligence agencies, and probably following soon of organized crime and other hackers, are orders of magnitude stronger than imagined. Thus there is a strong demand to re-establish trust in ICT systems. In this proposal we focus on the root of trust: the digital hardware. The overall objective is to provide fundamental enabling technologies for secure trustworthy digital circuits which can be applied in a wide range of applications. To master complexity, digital hardware design is traditionally split into different abstraction layers. We revisit these abstraction layers from a security viewpoint: we look at process variations to the benefit of security, standard cell compatible digital design flow with security as design objective, hardware IP blocks for next generation cryptographic algorithms and protocols (e.g. authenticated encryption schemes, post-quantum public key schemes), integration into embedded HW/SW platforms, and methods to provide trust evidence to higher levels of abstraction. To strengthen the security we investigate the links between the layers. Finally an embedded application is selected as design driver, the security evaluation of which will be fed back to the individual layers.

2 369 250 €

Start date: 2016-09-01, End date: 2021-08-31

CONTEXT - Nanoporous structures are used for application in catalysis, molecular separation, fuel cells, dye sensitized solar cells etc. Given the near molecular size of the porous network, it is extremely challenging to modify the interior surface of the pores after the nanoporous material has been synthesized. THIS PROPOSAL - Atomic Layer Deposition (ALD) is envisioned as a novel technique for creating catalytically active sites and for controlling the pore size distribution in nanoporous materials. ALD is a self-limited growth method that is characterized by alternating exposure of the growing film to precursor vapours, resulting in the sequential deposition of (sub)monolayers. It provides atomic level control of thickness and composition, and is currently used in micro-electronics to grow films into structures with aspect ratios of up to 100 / 1. We aim to make the fundamental breakthroughs necessary to enable atomic layer deposition to engineer the composition, size and shape of the interior surface of nanoporous materials with aspect ratios in excess of 10,000 / 1. POTENTIAL IMPACT Achieving these objectives will enable atomic level engineering of the interior surface of any porous material. We plan to focus on three specific applications where our results will have both medium and long term impacts: - Engineering the composition of pore walls using ALD, e.g. to create catalytic sites (e.g. Al for acid sites, Ti for redox sites, or Pt, Pd or Ni) - chemical functionalization of the pore walls with atomic level control can result in breakthrough applications in the fields of catalysis and sensors. - Atomic level control of the size of nanopores through ALD controlling the pore size distribution of molecular sieves can potentially lead to breakthrough applications in molecular separation and filtration. - Nanocasting replication of a mesoporous template by means of ALD can result in the mass-scale production of nanotubes.

1 432 800 €

Start date: 2010-01-01, End date: 2014-12-31

Low-rank matrix approximation (LRA) techniques such as principal component analysis (PCA) are powerful tools for the representation and analysis of high dimensional data, and are used in a wide variety of areas such as machine learning, signal and image processing, data mining, and optimization. Without any constraints and using the least squares error, LRA can be solved via the singular value decomposition. However, in practice, this model is often not suitable mainly because (i) the data might be contaminated with outliers, missing data and non-Gaussian noise, and (ii) the low-rank factors of the decomposition might have to satisfy some specific constraints. Hence, in recent years, many variants of LRA have been introduced, using different constraints on the factors and using different objective functions to assess the quality of the approximation; e.g., sparse PCA, PCA with missing data, independent component analysis and nonnegative matrix factorization. Although these new constrained LRA models have become very popular and standard in some fields, there is still a significant gap between theory and practice. In this project, our goal is to reduce this gap by attacking the problem in an integrated way making connections between LRA variants, and by using four very different but complementary perspectives: (1) computational complexity issues, (2) provably correct algorithms, (3) heuristics for difficult instances, and (4) application-oriented aspects. This unified and multi-disciplinary approach will enable us to understand these problems better, to develop and analyze new and existing algorithms and to then use them for applications. Our ultimate goal is to provide practitioners with new tools and to allow them to decide which method to use in which situation and to know what to expect from it.

1 291 750 €

Start date: 2016-09-01, End date: 2021-08-31

In survival analysis investigators are interested in modeling and analysing the time until an event happens. It often happens that the available data are right censored, which means that only a lower bound of the time of interest is observed. This feature complicates substantially the statistical analysis of this kind of data. The aim of this project is to solve a number of open problems related to time-to-event data, that would represent a major step forward in the area of survival analysis. The project has three objectives: [1] Cure models take into account that a certain fraction of the subjects under study will never experience the event of interest. Because of the complex nature of these models, many problems are still open and rigorous theory is rather scarce in this area. Our goal is to fill this gap, which will be a challenging but important task. [2] Copulas are nowadays widespread in many areas in statistics. However, they can contribute more substantially to resolving a number of the outstanding issues in survival analysis, such as in quantile regression and dependent censoring. Finding answers to these open questions, would open up new horizons for a wide variety of problems. [3] We wish to develop new methods for doing correct inference in some of the common models in survival analysis in the presence of endogeneity or measurement errors. The present methodology has serious shortcomings, and we would like to propose, develop and validate new methods, that would be a major breakthrough if successful. The above objectives will be achieved by using mostly semiparametric models. The development of mathematical properties under these models is often a challenging task, as complex tools from the theory on empirical processes and semiparametric efficiency are required. The project will therefore require an innovative combination of highly complex mathematical skills and cutting edge results from modern theory for semiparametric models.

2 318 750 €

Start date: 2016-09-01, End date: 2021-08-31