Dynamic stall phenomena carry the risk of negative damping and instability in wind turbine blades. It is crucial to model these phenomena accurately to reduce inaccuracies in predicting design driving (fatigue and extreme) loads. Some of the inaccuracies in current dynamic stall models may be due to the fact that they are not properly designed for high angles of attack and that they do not specifically describe vortex shedding behaviour. The Snel second-order dynamic stall model attempts to explicitly model unsteady vortex shedding. This model could therefore be a valuable addition to a turbine design software such as Bladed. In this paper the model has been validated with oscillating aerofoil experiments, and improvements have been proposed for reducing inaccuracies. The proposed changes led to an overall reduction in error between the model and experimental data. Furthermore the vibration frequency prediction improved significantly. The improved model has been implemented in Bladed and tested against small-scale turbine experiments at parked conditions. At high angles of attack the model looks promising for reducing mismatches between predicted and measured (fatigue and extreme) loading, leading to possible lower safety factors for design and more cost-efficient designs for future wind turbines.
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Dynamic stall phenomena carry the risk of negative damping and instability in wind turbine blades. It is crucial to model these phenomena accurately to reduce inaccuracies in predicting design driving (fatigue and extreme) loads. Some of the inaccuracies in current dynamic stall models may be due to the fact that they are not properly designed for high angles of attack and that they do not specifically describe vortex shedding behaviour. The Snel second-order dynamic stall model attempts to explicitly model unsteady vortex shedding. This model could therefore be a valuable addition to a turbine design software such as Bladed. In this paper the model has been validated with oscillating aerofoil experiments, and improvements have been proposed for reducing inaccuracies. The proposed changes led to an overall reduction in error between the model and experimental data. Furthermore the vibration frequency prediction improved significantly. The improved model has been implemented in Bladed and tested against small-scale turbine experiments at parked conditions. At high angles of attack the model looks promising for reducing mismatches between predicted and measured (fatigue and extreme) loading, leading to possible lower safety factors for design and more cost-efficient designs for future wind turbines.
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This paper discusses the potential application of procedural content generation to a game about economical crises, intended to teach a large general audience about how banks function within a market-guided economy, and the financial risks and moral dilemmas that are involved. Procedurally generating content for abstract and complex notions such as inflation, market crashes, and market flux is different from generating spatial maps or physical assets in a game, and poses several design challenges. Instead of generating these kinds of phenomena and other macro-economic effects directly, the designers aim to let them emerge from automatically generated game mechanics. The game mechanics we propose include generic business models that can be parameterized to model the behavior of companies in the game, while the player controls the actions of a bank. What makes generating these game mechanics particularly challenging is the interaction between phenomena at different levels of abstraction. Therefore, relevant economic concepts are discussed in terms of design challenges, and how they could be modeled as emergent properties using generative methods.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The main aim of KiNESIS is to create a Knowledge Alliance among academia, NGOs, communities, local authorities, businesses to develop a program of multidisciplinary activities in shrinking areas with the aim of promoting and fostering ideas, projects, workforce, productivity and attractiveness. The problems affecting peripheral territories in rural or mountain areas of the interior regions, compared to small, medium or large population centres and large European capitals, are related to complex but clear phenomena: the emigration of young generations, abandonment and loneliness of elderly people, the loss of jobs, the deterioration of buildings and land, the closing of schools and related services, the disappearance of traditions and customs, the contraction of local governments, which in absence of adequate solutions can only generate worse conditions, leading to the abandonment of areas rich in history, culture and traditions. It is important that these communities - spread all over Europe - are not abandoned since they are rich in cultural traditions, which need to be preserved with a view to new developments, intended as "intelligent" rebirth and recovery.The focus of KiNESIS is to converge the interest of different stakeholders by recalling various skills around abandoned villages to make them "smart" and "attractive".Keeping in mind the triangular objectives of cooperation and innovation of research, higher education and business of the Knowledge Alliance action, the project aims are: i) revitalising depopulated areas by stimulating entrepreneurship and entrepreneurial skills; ii) creating local living laboratories, shared at European level, in which the exchange of knowledge, best practices, experiences can help promote social inclusion and entrepreneurial development;iii) experimenting new, innovative and multidisciplinary approaches in teaching and learning; iv) facilitating the exchange, flow and co-creation of knowledge at a local and global level.
Retour richt zich voornamelijk op consumentengedrag ten opzichte van het retourneren van duurzame producten en in het bijzonder het retourneren van recyclebare karpetten. Het is geformuleerd door Arapaha BV uit Maastricht en Donkersloot Trade BV uit Bussum, in samenwerking met Fontys Expertisecentrum Circulaire Transitie, Phenomena BV en CuRe Technology BV. Daarmee is de volledige keten, van ontwerp, productie, verkoop en logistiek vertegenwoordigd. De rol van consumentengedrag bij het recyclen van duurzame producten staat centraal in dit project, zonder de technische mogelijkheden en de economische haalbaarheid uit het oog te verliezen. Binnen dit onderzoeks- en ontwikkelingsproject willen Arapaha BV en Donkersloot Trade BV een samenwerking opzetten met Fontys Hogescholen (Expertisecentrum Circulaire Transitie, de opleiding Toegepaste Psychologie en de minor Circulaire Economie). Arapaha BV verzorgt input voor het materialenpaspoort, faciliteert reparatie en hergebruik en/of past moleculaire recycling van geretourneerde goederen toe. Donkersloot BV en Phenomena BV onderzoeken het design en de marktaspecten, zoals distributie en de retourlogistiek vanaf de eindgebruiker. Arapaha BV zal recyclingproeven conform de CuRe technologie uitvoeren en Fontys neemt de vraagstelling omtrent de beïnvloeding van consumentengedrag op zich, alsmede de uitwerking van het materialenpaspoort en een businessmodel. Het eindresultaat van dit project bestaat – naast een interventie voor de beïnvloeding van het gedrag van de eindgebruiker, uit een materialen paspoort- en advies met betrekking tot een (repliceerbaar) businessmodel. De bevindingen worden tijdens een afsluitend event– via het netwerk van de betrokken partijen- gedeeld met andere MKB-bedrijven in Noord-Brabant en Limburg en gepubliceerd in een nader te bepalen vakblad.
