| Feb. 13, 2024, 4:20 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 64018, "fields": {"project": 12226, "organisation": 11092, "amount": 1377723, "start_date": "2022-02-01", "end_date": "2026-01-31", "raw_data": 180381}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 56855, "fields": {"project": 12226, "organisation": 11092, "amount": 1377723, "start_date": "2022-02-01", "end_date": "2026-01-31", "raw_data": 157611}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
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[{"model": "core.projectfund", "pk": 49672, "fields": {"project": 12226, "organisation": 11092, "amount": 1377723, "start_date": "2022-02-01", "end_date": "2026-01-31", "raw_data": 135353}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 42424, "fields": {"project": 12226, "organisation": 11092, "amount": 1377723, "start_date": "2022-02-01", "end_date": "2026-01-31", "raw_data": 103501}}]
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| Nov. 27, 2023, 2:15 p.m. |
Added
35
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{"external_links": []}
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| Nov. 21, 2023, 4:39 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 35132, "fields": {"project": 12226, "organisation": 11092, "amount": 1377723, "start_date": "2022-02-01", "end_date": "2026-01-31", "raw_data": 64283}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 102789, "fields": {"project": 12226, "organisation": 12101, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 102788, "fields": {"project": 12226, "organisation": 15281, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 102787, "fields": {"project": 12226, "organisation": 12798, "role": "FELLOW_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 102786, "fields": {"project": 12226, "organisation": 11467, "role": "COLLAB_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 102785, "fields": {"project": 12226, "organisation": 12515, "role": "COLLAB_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 102784, "fields": {"project": 12226, "organisation": 12798, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 64675, "fields": {"project": 12226, "person": 15368, "role": "FELLOW_PER"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 64674, "fields": {"project": 12226, "person": 15368, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:05 p.m. |
Updated
35
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{"title": ["", "Extremes, Performance and Longevity for Offshore Renewable Energy Resilience"], "description": ["", "\nThis project is aimed at understanding the complex fluid dynamics of wind farms and how this impacts on energy yield predictions. Offshore wind energy is developing rapidly in the UK - reaching the net-zero emissions targets may require growth of the sector from 10GW today to over 90GW by 2050. Turbines are rapidly increasing in size, with today's 8 MW turbines dwarfing the average 3-4MW turbines being installed in 2015, but will themselves be dwarfed by the 10-12MW turbines, almost 200m in diameter, currently being certified and selected for upcoming projects. Wind farms have increasingly large numbers of turbines as well, with eight UK wind farms now exceeding 100 turbines and the largest, Walney Wind Farms, with 189.\n\nLifetime assessment of wind farm performance relies heavily on accurate modelling of the wind around and through the farm. The current generation of engineering models used to design wind farm layouts and estimate turbine loads are based on over-simplified wake interactions and empirical representations of much smaller turbines than those now being deployed. These models are being used beyond their validated envelopes resulting in high uncertainty in model predictions. It has been shown through comparison to high-fidelity simulations and field data that these models do not capture large-farm emergent wake and atmospheric interaction physics and are therefore not suitable for the large and closely-spaced wind farms currently in development. New technologies such as floating wind turbines have significant potential for the UK but require a different modelling approach due to the coupled aero-hydrodynamics of turbine and platform and resultant wake dynamics. Furthermore, turbine performance changes over time, for example as a result of blade erosion, which also must be considered when evaluating lifetime performance and the scale of deployment required to meet the 2050 targets. This project will develop the next generation of modelling that is required to predict how wind turbine performance changes over time and enable a transition from the current approach of minimising interactions between wind turbines to managing and exploiting these interactions to improve whole-farm lifetime performance.\n\nAs wind turbines and farms have grown larger, facilitated in particular by the move offshore, the aerodynamic effects of energy extraction are felt over much larger distances and interactions with atmospheric winds have become increasingly significant. Simplifications to existing engineering models to improve computational efficiency have meant that these large-scale effects are neglected, leading to uncertainties that impact on energy production estimates and thus cost of energy. Furthermore, as wind energy comes to form a larger share of overall UK energy production, it is increasingly important to understand the impacts of wind variability on the resilience of energy supply and future energy storage requirements, particularly during extreme weather events. Fully resolving all of the many spatial and temporal scales of the underlying aerodynamic phenomena involved in wind energy is a challenge that will remain beyond the scope of engineering applications for many years to come.\n\nThe focus of this project is to reduce the complexity of this challenge by systematically identifying, scale-separating and modelling the key physics that drives wind turbine performance, wake development and interaction with the wind resource. This will be achieved through the development of novel multi-scale models, supported by state-of-the-art experiments and high-fidelity simulation, which couple momentum transfer across these disparate scales. This approach will enable new wind farm control and design strategies to be explored, as well as providing more accurate predictions of energy production and resilience, reducing uncertainty and enhancing the value of wind energy to the UK.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Active"]}
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| Nov. 20, 2023, 2:05 p.m. |
Added
35
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{"external_links": [49046]}
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| Nov. 20, 2023, 2:05 p.m. |
Created
35
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[{"model": "core.project", "pk": 12226, "fields": {"owner": null, "is_locked": false, "coped_id": "fe933005-41a4-444f-ad07-1330a38906f1", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 64266, "created": "2023-11-20T13:45:30.338Z", "modified": "2023-11-20T13:45:30.338Z", "external_links": []}}]
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