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  • 2018-2022
  • European Commission
  • OA Publications Mandate: Yes
  • 2018

  • Funder: EC Project Code: 713632
    Overall Budget: 150,000 EURFunder Contribution: 150,000 EUR

    The goal of this project is to extend the Sniper architectural simulator, previously developed at Ghent University, towards ARM-based systems. Sniper is shown to be fast, accurate and low-complexity for x86 multicore systems; and it is widely used in academia, as well as in industry for high-performance computing processor systems. This project will enhance Sniper to support ARM-based systems and will explore innovation opportunities in the embedded and mobile markets.

    more_vert
  • Funder: EC Project Code: 637045
    Overall Budget: 4,201,510 EURFunder Contribution: 3,764,640 EUR

    Miniaturization, advanced high performance materials and functional surface structures are all drivers behind key enabling technologies in high added value production. It is in such areas that ultrashort pulse lasers have enabled completely new machining concepts, where the big advantages of laser machining are combined with a quasi non-thermal and therefore mild process, which can be used to machine any material with high precision. An important obstacle however that hinders the full exploitation of the unique process characteristics, is the lack of a smart / adaptive machining technology. The laser process in principle is very accurate, but small deviations, e.g. in the materials to be processed, can compromise the accuracy to a very large extend. Therefore feedback systems are needed to keep the process accurate. Within this project the goal is to develop an adaptive laser micromachining system, based on ultrashort pulsed laser ablation and a novel depth measurement sensor, together with advanced data analysis software and automated system calibration routines. The sensor can be used inline with the laser ablation process, enabling adaptive processes by fast and accurate 3D surface measurements. The integrated sensor can be used to: • measure the surface topography while machining a part, in order to adapt the micromachining process, leading to highly increased machining accuracies and no defects, • measure the surface topography before machining, to scan for existing surface defects that can be removed in an automatically generated machining process, • measure complex shaped objects prior to machining, to precisely align the machining pattern to the workpiece, • quickly validate results after machining. Therefore, the main objective of this project is to develop a sensor based adaptive micro machining system using ultra short pulsed lasers for zero failure manufacturing.

    more_vert
  • Funder: EC Project Code: 816846
    Overall Budget: 71,429 EURFunder Contribution: 50,000 EUR

    Excess sugar consumption is a major contributor to the alarming rates of obesity, diabetes, and dental disease that exist in many developed countries. According to the Credit Suisse Research Institute, close to 400M people worldwide are affected by Type II Diabetes. 4.8M people die of chronic disease every year, a number that is quickly rising. Costs to the global healthcare system are estimated at a staggering $470B per year, representing 10% of all the healthcare costs. Artificial sweeteners offer a non-caloric alternative to sugar but have health concerns of their own, and are not always effective in maintenance of a healthy diet. However their use is not totally safe. Saccharine, for example, previously used on a large scale, was abandoned as soon as it was linked with development of bladder cancer. Artificial sweetener consumption modulates the gut microbiota and raises the risk of glucose intolerance. Sweet proteins that occur naturally in some tropical plants can have a sweetness a thousand times that of sucrose. This indicates that proteins potentially represent novel lowcalorie, nutritious sweeteners – superior to either sugar or artificial sweeteners. However, producing naturally occurring sweet proteins on a large scale is challenging and limitations of their physicochemical properties will constrain their application in the food industry. Milis Bio developed “Milis”, a novel protein which will be 200 - 500 times sweeter than sugar per gram. Milis presents low caloric content, no aftertaste, and no unhealthy chemical components. Milis allows the consumer to think only about how great their food tastes, rather than worrying about what’s in it. Using a protein as a flavouring guarantees that the ingredient will be low-calorie, easily digestible, and suitable for diabetics.

    more_vert
  • Funder: EC Project Code: 642294
    Overall Budget: 3,785,870 EURFunder Contribution: 3,785,870 EUR

    Theoretical Chemistry and Computational Modelling (TCCM) is emerging as a powerful tool to help in the rational design of new products and materials for pharmaceutical, chemical, energy, computer, and new-materials industries. To achieve this goal, it is necessary to go beyond the traditional electronic structure studies, and merge complementary techniques that are normally not available at a single research group. The research programme of the TCCM-EJD aims at applying computational modelling to problems demanded by the industry and with high societal relevance, namely Materials with special properties, Biomolecules for new therapies and Energy storage. The objective of the Joint Doctorate is to prepare future research leaders, able to develop and use multidisciplinary computational techniques (methods and software), with solid communication skills, with many contacts established through the intensive relationship with worldwide leading researchers of 12 European universities and 14 additional partners, including 7 industrial and spin-off companies. A Joint Doctorate in TCCM is already operative since 2011, based on a fully participative scientific discussion and assessment of all research projects with a clear interdisciplinary character and the direct participation of the non-academic sector. The training programme puts the emphasis in common training, including 3 annual International Workshops, 3 schools on High Performance Computing and 3 tutorials in new computer codes. Career development opportunities are enhanced with regular inter-sectoral activities, transferable skill education and career coaching.

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  • Funder: EC Project Code: 643087
    Overall Budget: 3,348,720 EURFunder Contribution: 3,348,720 EUR

    The REMEDIATE ETN comprises 10 beneficiaries (from 5 EU Member States – the UK, Ireland, Germany, Denmark, and Italy) and 15 partner organisations. The beneficiaries and partner organisations are committed to the provision of innovative research and training for more cost effective and sustainable remediation of contaminated land. It is a multidisciplinary collaboration between internationally renowned research teams (from both the academic and non-academic sectors) each with complementary expertise in a wide range of site investigation and risk assessment technologies. 14 Early Stage Researchers (ESRs) will be recruited into the network and will participate in a structured and integrated research and training programme that will provide them with a highly specific blend of personalised technical and transferable skills. Each research project is designed to benefit the contaminated land sector through development of techniques and tools across a range of disciplines relating to site investigation and risk assessment, to provide better informed solutions for remediation. The unique provision of joint supervision (from both the academic and non-academic sectors) and non-academic mentoring will significantly enhance the fellows’ career prospects within the contaminated land sector. The REMEDIATE ETN will strengthen and enhance existing collaborations between the participating beneficiaries and partner organisations resulting in a cohesive and dynamic network. The output will be a new generation of highly mobile, creative and innovative entrepreneurs with the skills sets necessary to address the technical, economic and social challenges facing the contaminated land sector in Europe, both now and in the future.

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  • Funder: EC Project Code: 739778
    Overall Budget: 72,500 EURFunder Contribution: 72,500 EUR

    The overall proposal objective is to overcome the barriers to the recruitment of highly qualified PHD or equivalent specialists in gene editing techniques for agriculture applications, in particular CRISPR-Cas 9 Iden Biotechnology S.L. is a private agribiotech company founded in 2005 with the mission to serve the needs of the agri-food industry. We aim at exploiting CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats), a newly developed technology for targeted genome modification, to produce new cereal varieties with resistance to abiotic stress (resistance to low temperatures, draught and/or salinity). CRISPR/Cas9, gives scientists the ability to more precisely modify DNA by turning genes on or off or editing DNA. They can pinpoint and remove weakness or insert desired traits already found elsewhere in the species. There are three main advantages of managing to use this technique for crop improvement: Technical, legislative and competitive: • The results obtained are similar to what could be obtained through natural mutations and conventional breeding, though it is directed, more precise and quick. • Besides the technical advantages mentioned above, crops modified with this technique may not fall under the GMOs legislation, opening up new market opportunities, including the European market • Competitive advantage. The crop biotech sector in Europe could have a new innovation tool to overcome the situation defined by the European Academies Science Advisory Council as: “The E.U. falling behind international competitors in agricultural innovation and this has implications for E.U. goals for science and innovation.” With the help of the associate recruitment we aim at applying this technology to carry out targeted genome modification in cereals, in particular maize or wheat which will help us the development of new improved crop varieties, opening up new market opportunities.

    more_vert
  • Funder: EC Project Code: 668128
    Overall Budget: 2,419,740 EURFunder Contribution: 1,209,520 EUR

    Biowaste valorisation is an attractive approach in the framework of the EU Waste Management policies and the development of a circular economy. Waste from biostreams and different biobased sources are being under-utilised as potential resource of valuable compounds. Fertilisers play an important role as suppliers of nutrients relying on their production heavily on fossil mineral resources. European Fertiliser industry is besides very dependent on imports of these raw materials, being vulnerable to supply and pricing policies. Main objective of the proposal is to build up a breakthrough concept of Fertiliser Industry, strengthening European competitiveness and boosting the biobased economy potential, through the development of a new value chain, which will achieve turning solid and liquid residues, specifically ashes of different origins and livestock effluents, into high quality valuable products, a new generation of fertilisers. NEWFERT will focus on a viable and cost-effective industrial nutrient recycling scheme, developing new biorefining technologies aimed at increasing nutrient recovery ratios and mitigating environmental and socio-economical impact of the current fertilisers by replacing non renewable and fossil nutrients with biobased materials in their composition. Projected benefits also include substantial energy savings and CO2 emissions reduction. NEWFERT aims to decrease raw material dependency, prevent resource depletion and reduce the environmental impact increasing significantly the Fertiliser industry sustainability. The work organisation has been designed to link and pursue a successful industrial integration supported by a solid life-cycle cost analysis. The strategy of the work plan is based on 8 workpackages. NEWFERT consortium is lead by FERTIBERIA and composed by a balanced set of 6 partners from 4 European Union member countries: biobased industries, SMEs, RTOs and academic institutions covering nutrients recovery from biobased waste field.

    more_vert
  • Funder: EC Project Code: 816728
    Overall Budget: 71,429 EURFunder Contribution: 50,000 EUR

    Electrical energy storage (EES) is a fundamental enabler to the deployment of renewable energy and provides cost-savings in other markets. The market is projected to grow from 1.1 GW in 2016 to 21.6 GW in 2025. Pumped hydroelectric storage (PHS) accounts for 98% of global energy storage, however they are geographically limited, environmentally impactful and require huge upfront costs. Other state-of-the-art solutions available in the market i.e. batteries for EES cannot scale-up to meet the demands on the electrical grids and networks. This results in an underutilisation or ineffective use of renewable energy sources. Teraloop has created a highly scalable, kinetic energy storage system, which draws upon proven technologies (flywheel energy storage, magnetic levitation and brushless motors.), innovatively configured for grid-scale storage with minimal visual and environmental footprint. The scalability of the product results in applicability from voltage support to load following. The development roadmap comprises three major phases: Phase1: Market & technical feasibility: Utilise SME instrument phase 1 funding to complete market analysis – define requirements and favourable market conditions. Find a demonstrator partner and explore engineering requirements. Phase2: An industrial demonstrator of 10MW Teraloop: Utilise SME instrument phase2 funding to find suitable stakeholders and subcontractors. Build, run and test Teraloop. Expand IP, communicate and disseminate phase 2 activities. Phase3: Commercialise 10MW Teraloop and develop 100MW Teraloop: Teraloop recognises their ambitious vision and mission will only be delivered through strategic partnerships with investors, technology companies, the energy storage industry and the public sector.

    more_vert
  • Funder: EC Project Code: 699221
    Overall Budget: 597,500 EURFunder Contribution: 597,500 EUR

    The PNOWWA project will produce methods for the probabilistic short-term forecasting of winter weather and enable the assessment of the uncertainty in the ground part of 4D trajectories. 4D trajectory management is a necessary concept to meet future growth in air traffic; probabilistic forecasts will be used in ATM applications to support operational planning in surface management and ATM decision making, thereby increasing airport capacity, shortening delays and promoting safety. PNOWWA will demonstrate very short-term (0-3h, "nowcast") probabilistic winter weather forecasts in 15min time resolution based on an extrapolation of movement of weather radar echoes and improve predictability of changes in snowfall intensity caused by underlying terrain (such as mountains and seas). Research demonstrations are conducted both offline and online at the Operative User Environment (OUE) site representing influence of the underlying terrain to forecast accuracy. An extensive user consultation will analyze needs to ensure products are suitable to be integrated in various applications on the ATM side. The adjustment to user needs will cover the most relevant parameters (visibility, intensity and snow depth) and operationally important thresholds of the selected parameters (e.g. heavy snowfall). The PNOWWA project has linkages to completed work in ongoing EU SESAR1 program, where FMI developed Step 1 (Time Based Operations) winter weather solutions based on deterministic forecasts to local OUE. The initial concept of short-range snowfall forecasts improvement with usage of weather radar has been validated in that context, and the second phase solutions (Step 2: Trajectory-based Operations) will be developed in EU SESAR 2020 program. The proposed PNOWWA project will develop the methods for deducing probability forecasts of winter weather required by SESAR 2020. PNOWWA project will also deliver a roadmap towards implementation with connection points in future SESAR projects.

    more_vert
  • Funder: EC Project Code: 699303
    Overall Budget: 998,125 EURFunder Contribution: 998,125 EUR

    ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions. Trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide KPIs. The goal of INTUIT is to explore the potential of visual analytics, machine learning and systems modelling techniques to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are: 1. to conduct a systematic characterisation of the ATM performance datasets available at different spatial and temporal scales and evaluate their potential to inform the development of new indicators and modelling approaches; 2. to propose new metrics and indicators providing new angles of analysis of ATM performance; 3. to develop a set of visual analytics and machine learning algorithms for the extraction of relevant and understandable patterns from ATM performance data; 4. to investigate new data-driven modelling techniques and evaluate their potential to provide new insights about cause-effect relationships between performance drivers and performance indicators; 5. to integrate the newly developed analytical and visualisation functionalities into an interactive dashboard supporting multi-dimensional performance assessment and decision making for both monitoring and management purposes.

    visibility270
    visibilityviews270
    downloaddownloads1,767
    Powered by Usage counts
    more_vert
Advanced search in
Projects
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
  • Funder: EC Project Code: 713632
    Overall Budget: 150,000 EURFunder Contribution: 150,000 EUR

    The goal of this project is to extend the Sniper architectural simulator, previously developed at Ghent University, towards ARM-based systems. Sniper is shown to be fast, accurate and low-complexity for x86 multicore systems; and it is widely used in academia, as well as in industry for high-performance computing processor systems. This project will enhance Sniper to support ARM-based systems and will explore innovation opportunities in the embedded and mobile markets.

    more_vert
  • Funder: EC Project Code: 637045
    Overall Budget: 4,201,510 EURFunder Contribution: 3,764,640 EUR

    Miniaturization, advanced high performance materials and functional surface structures are all drivers behind key enabling technologies in high added value production. It is in such areas that ultrashort pulse lasers have enabled completely new machining concepts, where the big advantages of laser machining are combined with a quasi non-thermal and therefore mild process, which can be used to machine any material with high precision. An important obstacle however that hinders the full exploitation of the unique process characteristics, is the lack of a smart / adaptive machining technology. The laser process in principle is very accurate, but small deviations, e.g. in the materials to be processed, can compromise the accuracy to a very large extend. Therefore feedback systems are needed to keep the process accurate. Within this project the goal is to develop an adaptive laser micromachining system, based on ultrashort pulsed laser ablation and a novel depth measurement sensor, together with advanced data analysis software and automated system calibration routines. The sensor can be used inline with the laser ablation process, enabling adaptive processes by fast and accurate 3D surface measurements. The integrated sensor can be used to: • measure the surface topography while machining a part, in order to adapt the micromachining process, leading to highly increased machining accuracies and no defects, • measure the surface topography before machining, to scan for existing surface defects that can be removed in an automatically generated machining process, • measure complex shaped objects prior to machining, to precisely align the machining pattern to the workpiece, • quickly validate results after machining. Therefore, the main objective of this project is to develop a sensor based adaptive micro machining system using ultra short pulsed lasers for zero failure manufacturing.

    more_vert
  • Funder: EC Project Code: 816846
    Overall Budget: 71,429 EURFunder Contribution: 50,000 EUR

    Excess sugar consumption is a major contributor to the alarming rates of obesity, diabetes, and dental disease that exist in many developed countries. According to the Credit Suisse Research Institute, close to 400M people worldwide are affected by Type II Diabetes. 4.8M people die of chronic disease every year, a number that is quickly rising. Costs to the global healthcare system are estimated at a staggering $470B per year, representing 10% of all the healthcare costs. Artificial sweeteners offer a non-caloric alternative to sugar but have health concerns of their own, and are not always effective in maintenance of a healthy diet. However their use is not totally safe. Saccharine, for example, previously used on a large scale, was abandoned as soon as it was linked with development of bladder cancer. Artificial sweetener consumption modulates the gut microbiota and raises the risk of glucose intolerance. Sweet proteins that occur naturally in some tropical plants can have a sweetness a thousand times that of sucrose. This indicates that proteins potentially represent novel lowcalorie, nutritious sweeteners – superior to either sugar or artificial sweeteners. However, producing naturally occurring sweet proteins on a large scale is challenging and limitations of their physicochemical properties will constrain their application in the food industry. Milis Bio developed “Milis”, a novel protein which will be 200 - 500 times sweeter than sugar per gram. Milis presents low caloric content, no aftertaste, and no unhealthy chemical components. Milis allows the consumer to think only about how great their food tastes, rather than worrying about what’s in it. Using a protein as a flavouring guarantees that the ingredient will be low-calorie, easily digestible, and suitable for diabetics.

    more_vert
  • Funder: EC Project Code: 642294
    Overall Budget: 3,785,870 EURFunder Contribution: 3,785,870 EUR

    Theoretical Chemistry and Computational Modelling (TCCM) is emerging as a powerful tool to help in the rational design of new products and materials for pharmaceutical, chemical, energy, computer, and new-materials industries. To achieve this goal, it is necessary to go beyond the traditional electronic structure studies, and merge complementary techniques that are normally not available at a single research group. The research programme of the TCCM-EJD aims at applying computational modelling to problems demanded by the industry and with high societal relevance, namely Materials with special properties, Biomolecules for new therapies and Energy storage. The objective of the Joint Doctorate is to prepare future research leaders, able to develop and use multidisciplinary computational techniques (methods and software), with solid communication skills, with many contacts established through the intensive relationship with worldwide leading researchers of 12 European universities and 14 additional partners, including 7 industrial and spin-off companies. A Joint Doctorate in TCCM is already operative since 2011, based on a fully participative scientific discussion and assessment of all research projects with a clear interdisciplinary character and the direct participation of the non-academic sector. The training programme puts the emphasis in common training, including 3 annual International Workshops, 3 schools on High Performance Computing and 3 tutorials in new computer codes. Career development opportunities are enhanced with regular inter-sectoral activities, transferable skill education and career coaching.

    visibility282
    visibilityviews282
    downloaddownloads623
    Powered by Usage counts
    more_vert
  • Funder: EC Project Code: 643087
    Overall Budget: 3,348,720 EURFunder Contribution: 3,348,720 EUR

    The REMEDIATE ETN comprises 10 beneficiaries (from 5 EU Member States – the UK, Ireland, Germany, Denmark, and Italy) and 15 partner organisations. The beneficiaries and partner organisations are committed to the provision of innovative research and training for more cost effective and sustainable remediation of contaminated land. It is a multidisciplinary collaboration between internationally renowned research teams (from both the academic and non-academic sectors) each with complementary expertise in a wide range of site investigation and risk assessment technologies. 14 Early Stage Researchers (ESRs) will be recruited into the network and will participate in a structured and integrated research and training programme that will provide them with a highly specific blend of personalised technical and transferable skills. Each research project is designed to benefit the contaminated land sector through development of techniques and tools across a range of disciplines relating to site investigation and risk assessment, to provide better informed solutions for remediation. The unique provision of joint supervision (from both the academic and non-academic sectors) and non-academic mentoring will significantly enhance the fellows’ career prospects within the contaminated land sector. The REMEDIATE ETN will strengthen and enhance existing collaborations between the participating beneficiaries and partner organisations resulting in a cohesive and dynamic network. The output will be a new generation of highly mobile, creative and innovative entrepreneurs with the skills sets necessary to address the technical, economic and social challenges facing the contaminated land sector in Europe, both now and in the future.

    visibility71
    visibilityviews71
    downloaddownloads806
    Powered by Usage counts
    more_vert
  • Funder: EC Project Code: 739778
    Overall Budget: 72,500 EURFunder Contribution: 72,500 EUR

    The overall proposal objective is to overcome the barriers to the recruitment of highly qualified PHD or equivalent specialists in gene editing techniques for agriculture applications, in particular CRISPR-Cas 9 Iden Biotechnology S.L. is a private agribiotech company founded in 2005 with the mission to serve the needs of the agri-food industry. We aim at exploiting CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats), a newly developed technology for targeted genome modification, to produce new cereal varieties with resistance to abiotic stress (resistance to low temperatures, draught and/or salinity). CRISPR/Cas9, gives scientists the ability to more precisely modify DNA by turning genes on or off or editing DNA. They can pinpoint and remove weakness or insert desired traits already found elsewhere in the species. There are three main advantages of managing to use this technique for crop improvement: Technical, legislative and competitive: • The results obtained are similar to what could be obtained through natural mutations and conventional breeding, though it is directed, more precise and quick. • Besides the technical advantages mentioned above, crops modified with this technique may not fall under the GMOs legislation, opening up new market opportunities, including the European market • Competitive advantage. The crop biotech sector in Europe could have a new innovation tool to overcome the situation defined by the European Academies Science Advisory Council as: “The E.U. falling behind international competitors in agricultural innovation and this has implications for E.U. goals for science and innovation.” With the help of the associate recruitment we aim at applying this technology to carry out targeted genome modification in cereals, in particular maize or wheat which will help us the development of new improved crop varieties, opening up new market opportunities.

    more_vert
  • Funder: EC Project Code: 668128
    Overall Budget: 2,419,740 EURFunder Contribution: 1,209,520 EUR

    Biowaste valorisation is an attractive approach in the framework of the EU Waste Management policies and the development of a circular economy. Waste from biostreams and different biobased sources are being under-utilised as potential resource of valuable compounds. Fertilisers play an important role as suppliers of nutrients relying on their production heavily on fossil mineral resources. European Fertiliser industry is besides very dependent on imports of these raw materials, being vulnerable to supply and pricing policies. Main objective of the proposal is to build up a breakthrough concept of Fertiliser Industry, strengthening European competitiveness and boosting the biobased economy potential, through the development of a new value chain, which will achieve turning solid and liquid residues, specifically ashes of different origins and livestock effluents, into high quality valuable products, a new generation of fertilisers. NEWFERT will focus on a viable and cost-effective industrial nutrient recycling scheme, developing new biorefining technologies aimed at increasing nutrient recovery ratios and mitigating environmental and socio-economical impact of the current fertilisers by replacing non renewable and fossil nutrients with biobased materials in their composition. Projected benefits also include substantial energy savings and CO2 emissions reduction. NEWFERT aims to decrease raw material dependency, prevent resource depletion and reduce the environmental impact increasing significantly the Fertiliser industry sustainability. The work organisation has been designed to link and pursue a successful industrial integration supported by a solid life-cycle cost analysis. The strategy of the work plan is based on 8 workpackages. NEWFERT consortium is lead by FERTIBERIA and composed by a balanced set of 6 partners from 4 European Union member countries: biobased industries, SMEs, RTOs and academic institutions covering nutrients recovery from biobased waste field.

    more_vert
  • Funder: EC Project Code: 816728
    Overall Budget: 71,429 EURFunder Contribution: 50,000 EUR

    Electrical energy storage (EES) is a fundamental enabler to the deployment of renewable energy and provides cost-savings in other markets. The market is projected to grow from 1.1 GW in 2016 to 21.6 GW in 2025. Pumped hydroelectric storage (PHS) accounts for 98% of global energy storage, however they are geographically limited, environmentally impactful and require huge upfront costs. Other state-of-the-art solutions available in the market i.e. batteries for EES cannot scale-up to meet the demands on the electrical grids and networks. This results in an underutilisation or ineffective use of renewable energy sources. Teraloop has created a highly scalable, kinetic energy storage system, which draws upon proven technologies (flywheel energy storage, magnetic levitation and brushless motors.), innovatively configured for grid-scale storage with minimal visual and environmental footprint. The scalability of the product results in applicability from voltage support to load following. The development roadmap comprises three major phases: Phase1: Market & technical feasibility: Utilise SME instrument phase 1 funding to complete market analysis – define requirements and favourable market conditions. Find a demonstrator partner and explore engineering requirements. Phase2: An industrial demonstrator of 10MW Teraloop: Utilise SME instrument phase2 funding to find suitable stakeholders and subcontractors. Build, run and test Teraloop. Expand IP, communicate and disseminate phase 2 activities. Phase3: Commercialise 10MW Teraloop and develop 100MW Teraloop: Teraloop recognises their ambitious vision and mission will only be delivered through strategic partnerships with investors, technology companies, the energy storage industry and the public sector.

    more_vert
  • Funder: EC Project Code: 699221
    Overall Budget: 597,500 EURFunder Contribution: 597,500 EUR

    The PNOWWA project will produce methods for the probabilistic short-term forecasting of winter weather and enable the assessment of the uncertainty in the ground part of 4D trajectories. 4D trajectory management is a necessary concept to meet future growth in air traffic; probabilistic forecasts will be used in ATM applications to support operational planning in surface management and ATM decision making, thereby increasing airport capacity, shortening delays and promoting safety. PNOWWA will demonstrate very short-term (0-3h, "nowcast") probabilistic winter weather forecasts in 15min time resolution based on an extrapolation of movement of weather radar echoes and improve predictability of changes in snowfall intensity caused by underlying terrain (such as mountains and seas). Research demonstrations are conducted both offline and online at the Operative User Environment (OUE) site representing influence of the underlying terrain to forecast accuracy. An extensive user consultation will analyze needs to ensure products are suitable to be integrated in various applications on the ATM side. The adjustment to user needs will cover the most relevant parameters (visibility, intensity and snow depth) and operationally important thresholds of the selected parameters (e.g. heavy snowfall). The PNOWWA project has linkages to completed work in ongoing EU SESAR1 program, where FMI developed Step 1 (Time Based Operations) winter weather solutions based on deterministic forecasts to local OUE. The initial concept of short-range snowfall forecasts improvement with usage of weather radar has been validated in that context, and the second phase solutions (Step 2: Trajectory-based Operations) will be developed in EU SESAR 2020 program. The proposed PNOWWA project will develop the methods for deducing probability forecasts of winter weather required by SESAR 2020. PNOWWA project will also deliver a roadmap towards implementation with connection points in future SESAR projects.

    more_vert
  • Funder: EC Project Code: 699303
    Overall Budget: 998,125 EURFunder Contribution: 998,125 EUR

    ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions. Trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide KPIs. The goal of INTUIT is to explore the potential of visual analytics, machine learning and systems modelling techniques to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are: 1. to conduct a systematic characterisation of the ATM performance datasets available at different spatial and temporal scales and evaluate their potential to inform the development of new indicators and modelling approaches; 2. to propose new metrics and indicators providing new angles of analysis of ATM performance; 3. to develop a set of visual analytics and machine learning algorithms for the extraction of relevant and understandable patterns from ATM performance data; 4. to investigate new data-driven modelling techniques and evaluate their potential to provide new insights about cause-effect relationships between performance drivers and performance indicators; 5. to integrate the newly developed analytical and visualisation functionalities into an interactive dashboard supporting multi-dimensional performance assessment and decision making for both monitoring and management purposes.

    visibility270
    visibilityviews270
    downloaddownloads1,767
    Powered by Usage counts
    more_vert