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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.

  • Energy Research
  • 2014

  • Funder: National Science Foundation Project Code: 1006190
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  • Funder: European Commission Project Code: 256769
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  • Funder: UK Research and Innovation Project Code: 700401
    Funder Contribution: 23,122 GBP

    Small scale horizontal axis wind turbines (HAWT) and vertical access wind turbines (VAWT) are unable to handle high winds or turbulent conditions. At very high wind speeds wind turbines shut down. Existing designs focus on external mechanical and electrical systems to reduce the output rather than exploit the attributes of low and high wind conditions. Turbines with static blades cannot effectively capture the direct wind energy for all the blades. Existing designs rely on a small proportion of the total blade area and typically feature a symmetrical profile (equal profile each side). Existing vertical axis machines have an inherent inefficiency because while one blade is working well with the wind, other blades are effectively pulling in the wrong direction – causing them to behave as a brake. Vertogen Ltd have identified a gap in the market for a variable pitch VAWT.

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  • Funder: National Science Foundation Project Code: 1346122
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  • Funder: National Science Foundation Project Code: 1066447
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  • Funder: Science Foundation Ireland Project Code: 13/TIDA/I2732
    Funder Contribution: 124,659 EUR
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  • Funder: UK Research and Innovation Project Code: 751217
    Funder Contribution: 5,000 GBP

    Reliability is vital in the growth of the wind industry, especially offshore, and to realise the UK's targets for 2020. The global wind industry with 285,000 MW of wind power installation, of which 4,600 MW is offshore, spent over £3.3 billion for the maintenance and repair of wind turbines in 2011. A recent study by EPRI shows that a cost reduction of 47%, equivalent to £385,000 over a 20-year lifetime of a 3 MW onshore turbine, can be achieved by using predictive maintenance practices based on condition monitoring systems (CMS). Reliawind has developed a unique CMS technology with substantially enhanced precision and wider range of fault prediction. Its unique feature is the use of electrical data, already available in the wind turbine controller, analysed by an advanced model-based self-learning algorithm to detect mechanical and electrical faults in the wind turbine drivetrain.

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  • Funder: National Science Foundation Project Code: 1236239
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  • Funder: UK Research and Innovation Project Code: EP/J50015X/1
    Funder Contribution: 390,995 GBP

    Over the last 20 years, photovoltaic solar cells (PV) for power generation have spawned an industry that focuses on continuously improving materials to use the solar energy that is available more efficiently. With efficiencies approaching 12%, huge effort has been focused onto improving efficiency by using multi-layer materials, profiled surfaces, thin film technologies etc due to the huge positive impact they have on the carbon economy. On a recently completed TSB programme, CONVERT (TSB ref. AE100D), two of the partners of the present consortium (the Wolfson Centre at Brunel University and Intrinsiq Materials Ltd) developed long-life down-converting phosphors that, once proven by scaling up in demonstrators, will be used worldwide in coatings and on cells to transfer more of the sun's energy into preferred PV frequencies. In this project we will scale up DSSCs to show the effect of augmenting the natural energy available and hence enhance energy output. In addition, we will harvest the light from a third of G24i's current cell's surface that is wasted and redirect the light to the active regions of the cell, thereby aiming to achieve performance gains of ca. 25%.

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  • Funder: Swiss National Science Foundation Project Code: PBSKP2_145827
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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.
110 Projects
  • Funder: National Science Foundation Project Code: 1006190
    more_vert
  • Funder: European Commission Project Code: 256769
    more_vert
  • Funder: UK Research and Innovation Project Code: 700401
    Funder Contribution: 23,122 GBP

    Small scale horizontal axis wind turbines (HAWT) and vertical access wind turbines (VAWT) are unable to handle high winds or turbulent conditions. At very high wind speeds wind turbines shut down. Existing designs focus on external mechanical and electrical systems to reduce the output rather than exploit the attributes of low and high wind conditions. Turbines with static blades cannot effectively capture the direct wind energy for all the blades. Existing designs rely on a small proportion of the total blade area and typically feature a symmetrical profile (equal profile each side). Existing vertical axis machines have an inherent inefficiency because while one blade is working well with the wind, other blades are effectively pulling in the wrong direction – causing them to behave as a brake. Vertogen Ltd have identified a gap in the market for a variable pitch VAWT.

    more_vert
  • Funder: National Science Foundation Project Code: 1346122
    more_vert
  • Funder: National Science Foundation Project Code: 1066447
    more_vert
  • Funder: Science Foundation Ireland Project Code: 13/TIDA/I2732
    Funder Contribution: 124,659 EUR
    more_vert
  • Funder: UK Research and Innovation Project Code: 751217
    Funder Contribution: 5,000 GBP

    Reliability is vital in the growth of the wind industry, especially offshore, and to realise the UK's targets for 2020. The global wind industry with 285,000 MW of wind power installation, of which 4,600 MW is offshore, spent over £3.3 billion for the maintenance and repair of wind turbines in 2011. A recent study by EPRI shows that a cost reduction of 47%, equivalent to £385,000 over a 20-year lifetime of a 3 MW onshore turbine, can be achieved by using predictive maintenance practices based on condition monitoring systems (CMS). Reliawind has developed a unique CMS technology with substantially enhanced precision and wider range of fault prediction. Its unique feature is the use of electrical data, already available in the wind turbine controller, analysed by an advanced model-based self-learning algorithm to detect mechanical and electrical faults in the wind turbine drivetrain.

    more_vert
  • Funder: National Science Foundation Project Code: 1236239
    more_vert
  • Funder: UK Research and Innovation Project Code: EP/J50015X/1
    Funder Contribution: 390,995 GBP

    Over the last 20 years, photovoltaic solar cells (PV) for power generation have spawned an industry that focuses on continuously improving materials to use the solar energy that is available more efficiently. With efficiencies approaching 12%, huge effort has been focused onto improving efficiency by using multi-layer materials, profiled surfaces, thin film technologies etc due to the huge positive impact they have on the carbon economy. On a recently completed TSB programme, CONVERT (TSB ref. AE100D), two of the partners of the present consortium (the Wolfson Centre at Brunel University and Intrinsiq Materials Ltd) developed long-life down-converting phosphors that, once proven by scaling up in demonstrators, will be used worldwide in coatings and on cells to transfer more of the sun's energy into preferred PV frequencies. In this project we will scale up DSSCs to show the effect of augmenting the natural energy available and hence enhance energy output. In addition, we will harvest the light from a third of G24i's current cell's surface that is wasted and redirect the light to the active regions of the cell, thereby aiming to achieve performance gains of ca. 25%.

    more_vert
  • Funder: Swiss National Science Foundation Project Code: PBSKP2_145827
    more_vert