Leistungen
This report will contain all related to the further valorisation of results including further research ie link to innovation management potential strategies for commercialisation and standardisation success indicators 6 meetings with relevant stakeholders over 100 contacts made in events and 8 seminars successfully held in the end of the project Task 84 The report will be updated in M24 M36 and M48
Final report on WRF-LES and Park couplingThe model Meso scale model developed at AWST will be coupled with Park model developed at SINTEF. A coupling procedure will be described in the report. This deliverable is associated with Task 2.5 (Interface between WRF-LES and turbine CFD model) and Task 2.6 (Implementation in HPC environment)
Unified fatigue damage model and software implementation in SAMCEFThe development of a unified fatigue damage model, including an interaction criterion between intra- and inter-laminar damage, are completed. The unified model is implemented in SAMCEF and a special purpose solution strategy is developed and benchmarked. This delivery is an outcome of Task 5.3 (Unified fatigue damage model (inter- and intra-laminar) including interaction criteria between damage modes )
Numerical database of wind farm benchmarkThe numerical data base will be mainly consisting of public accessible data from previous studies as those from the Fafe High Land TerrasAltas de Fafe wind farm in the north of Portugal Waterloo Wind Farm and analysis data of a small wind farm in the Netherland This delivery is associated with Task 44 Wind farm noiseannoyance benchmark in which a benchmark study will be defined and analysed with the tools developed in Tasks 413
Key features determined with machine learningMachine Learning algorithms are implemented and based on data from system simulations of the virtual Virtual Prototype delivering highly sampled data streams are analysed with respect to the considered issues This delivery refers to Task 73 Adapt machine learning to determine the key features relevant for technical and social objectives
3D progressive fatigue delamination modelThe 3D delamination model developed will include a crack tracking algorithm and 3D J-integral of cohesive zone. The model is implemented in SAMCEF and ready to use. Its predictive 3D capabilities are demonstrated on a delamination demonstrator by comparing to experimental data. This delivery is an outcome of Task 5.1 (3D progressive delamination method based on cohesive zone modelling for fatigue loading)
Innovation Management PlanAn innovation Management Plan will be prepared and reported. This delivery will describe the structure and the regularly practiced ways of running the related organisational activities will be released and updated. The task is associated with Task 1.6 (Innovation and IP Management). The Innovation Management Plan will be updated M02, M14, M26 and M38.
Application of the coupled atmospheric-aeroacoustic simulation platform to a wind farm benchmarkDeliverable 44 was focused on a single turbine and this deliverable is mainly focused on an entire windfarm with multiple wind turbines A coupled atmospheric boundary layer ABLnearfieldraytracingBoundary Element Method model of a selected windfarm with multiple wind turbines will be developed The model will be benchmarked against available wind farm measurement data This deliverable is one of results from Task 43 Wind turbine noise mapping
Project Management PlanA report on Project Management Plan will be prepared. The project management plan will mainly consist of minutes of meeting and other information. The project management plan will be updated every year.
Results from study cases applying integrated simulation frameworkA case study was carried out The following cases have been studied 1 The effect of layout of turbines in offshore and onshore parks on wake and turbulence and how it influence productivity and turbine fatigue 2 The effect of size and position of onshore turbines on noise levels in neighborhood 3 The influence of slender design of large blades 15 MW on wind structure interaction nonlinear bending buckling and complex structural stresses promoting structural damageThis delivery is an outcome from Task 66 Study cases applying integrated simulation framework
Final technical and financial reportA Final technical and financial report will be prepared after project completion
Inverse parameter identification tool for intra-laminar fatigue damage modelThe identification tool for intra-laminar fatigue damage is implemented in SAMCEF and ready to use. The accuracy and performance of the tool are documented based on benchmark test cases for which experimental data are generated. This delivery is an outcome of Task 5.2 (Intra-laminar progressive damage model for fatigue loading and inverse parameter identification tool)
Report on AL enhancement and turbulence modelThis deliverable related to the Task 2.3 in which LES park and turbulence model will be developed. State of the art models will be reviewed. The report will discuss on the implementation of improved turbulence and actuator line models
Kick-off meeting reportA Kick-off meeting report with all partners will take place in month of May/June 2018. The location of the meeting is not decided yet. The minutes of meeting will be prepared. A report describing the agenda, highlight and minutes of meeting will be prepared.
Dissemination and communication planThe dissemination and communication plan will be the basis of the awareness creation strategy of UPWARDS. Updated during the project, it will contain a tailor-made strategy, key messages for selected audiences and procedures for publication, assistance to events, etc. (success indicators: see Table 7 in Part B for a list of quantified indicators that will need to be fulfilled to consider Task 8.1 successful in the end of the project). The plan will be updated in M06, M24 and M48
Data Management PlanGeneral guideline on the data management and handling will be defined in the Data Management Plan. It will take into account the requirements for data integration and processing of UPWARDS virtual wind turbine. The data management plan will be updated month 12, 18, 30 and 42.
Risk management planRisk management plan describing major risks in the project will prepared. In total five risk management plan in M6, M12, M18, M30 and M42 will be prepared.
Stakeholder engagementA relevant public and stakeholder needs and opinion will be used as input for the specification of models to developed. A report on this will be published
Literature review on social and environmental issues and acceptance of wind turbinesThe most recent literature on social acceptance of wind turbines will be reviewed, in particular in relation to noise, shadow flicker and risk. This review results in a manuscript for a peer-reviewed publication and also serves as input to develop the participatory modelling approach. This task refers to Task 7.1: Develop participatory approach to wind power modelling.
Report and data on the effect of fatigue loading history on damage developmentThe experimental campaign on the effect of fatigue loading history on damage development are completed Experimental evidence observations and data from the campaign are reported This delivery is an outcome of Task 54 Effect of fatigue loading history on damage development inter and intralaminar
A report on the modelling of non-linear wind-wave interactionA report describing a brief review of state of the art methods used for modelling the Wind-wave interaction will be presented. In addition to this Large eddy simulation of wind-wave interaction using OpenFoam will also be presented.
Reports with data from on and offline panelsData from the online surveys and panels in two European countries on social and environmental aspects of wind turbines notably noise shadow flicker and risk will be presented analysed and compiled in a report Some of this information will also be published in peerreviewed journals This task refers to Task 72 Collect stakeholder data on noise shadow flicker risk and other social and environmental issues
The machine learning algorithms from WP 7 are integrated into a stream processing framework and tested. An appropriate data exchange interface has been implemented and tested. The ML Algorithms are ready to use in the HPC framework. This deliver is an outcome of Task 6.4 ( HPC for machine learning methods )
Serial Integrated System Simulation Platform provided to partnersAn open-source simulation platform containing following form is implemented and delivered to the partners: The partners can use this platform for initialization and parametrization of models, Running and stopping simulations. The simulation tools and models from WP2-WP5 are coupled in serial form including data exchange between models. This delivery is an outcome of Task 6.1 ( Simulation Platform Interfaces and Linking of Models )
UPWARDS websiteThis deliverable is a key tool for communication. It will contain the project details, with the objectives and a description of the consortium put forward. All public deliverables of the project will be uploaded and available to all project stakeholders. (success indicator: 5000 visits at least, in the end of the project) (Task 8.1)
Model Order Reduction Toolbox implementedMOR algorithms for models in WP2 – WP5 increasing simulation speed are proposed to partners and implemented in a software toolbox and tested. This delivery is associated with Task 6.2 ( Model order reduction)
HPC- Integrated Simulation Environment implemented and testedThe individual software tools parallelized in WP2WP5 are implemented in a the integrated HPC simulation platform The HPC integrated simulation will be tested and ready to use at the cluster Beehive This delivery is a result of Task 63 HPC for Integrated Simulation Platform
UPWARDS will generate huge amount of simulation data The methodologies and major results from the project will be published in open access journals or freely accessible reports In addition an open database containing relevant result and raw data will be established Dedicated project data management system server Open data for publications and open pilot strategies will be followed Most of the data are generated using Opensource software Use of EU project such as OpenAIRE will be exploited for publishing the relevant data
Veröffentlichungen
Autoren:
Helena Solman; Mattijs Smits; Bas van Vliet; Simon R. Bush
Veröffentlicht in:
VOLUME=72;ISSN=22146296;TITLE=Energy Research and Social Science, Ausgabe 22146296, 2021, Seite(n) Volume 72, ISSN 2214-6296
Herausgeber:
Elsevier Limited
DOI:
10.1016/j.erss.2020.101876
Autoren:
Helena Solman; Julia Kirch Kirkegaard; Mattijs Smits; Bas van Vliet; Simon R. Bush
Veröffentlicht in:
urn:issn:14629011, Ausgabe 14629011, 2022, Seite(n) 272-279, ISSN 1462-9011
Herausgeber:
Elsevier BV
DOI:
10.1016/j.envsci.2021.10.027
Autoren:
L. Carreras, A. Turon, B.L.V. Bak, E. Lindgaard, J. Renart, F. Martin de la Escalera, Y. Essa
Veröffentlicht in:
Composites Part A: Applied Science and Manufacturing, Ausgabe 122, 2019, Seite(n) 107-119, ISSN 1359-835X
Herausgeber:
Pergamon Press Ltd.
DOI:
10.1016/j.compositesa.2019.04.026
Autoren:
A. Hussain Kadar, S. Le Bras, H. Bériot, W. De Roeck, W. Desmet, C. Schram
Veröffentlicht in:
AIAA Journal, 2021, ISSN 1533-385X
Herausgeber:
American Institute of Aeronautics and Astronautics, Inc
DOI:
10.2514/1.j060388
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