Browsing by Author "Richmond, Mark"

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    Development of a stochastic computational fluid dynamics approach for offshore wind farms
    (IOP, 2018-06-19) Richmond, Mark; Kolios, Athanasios; Pillai, V. S.; Nishino, Takafumi; Wang, L.
    In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.
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    Evaluation of an offshore wind farm computational fluid dynamics model against operational site data
    (Elsevier, 2019-10-31) Richmond, Mark; Antoniadis, Antonis F.; Wang, Lin; Kolios, Athanasios; Al-Sanad, Shaikha A.; Parol, Jafarali
    Modelling wind turbine wake effects at a range of wind speeds and directions with actuator disk (AD) models can provide insight but also be challenging. With any model it is important to quantify the level of error, but this can also present a challenge when comparing a steady-state model to measurement data with scatter. This paper models wind flow in a wind farm at a range of wind speeds and directions using an AD implementation. The results from these models are compared to data collected from the actual farm being modelled. An extensive comparison is conducted, constituted from 35 cases where two turbulence models, the standard k-ε and k-ω SST are evaluated. The steps taken in building the models as well as processes for comparing the AD computational fluid dynamics (CFD) results to real-world data using the regression models of ensemble bagging and Gaussian process are outlined. Turbine performance data and boundary conditions are determined using the site data. Modifications to an existing opensource AD code are shown so that the predetermined turbine performance can be implemented into the CFD model. Steady state solutions are obtained with the OpenFOAM CFD solver. Results are compared in terms of velocity deficit at the measurement locations. Using the standard k-ε model, a mean absolute error for all cases together of roughly 8% can be achieved, but this error changes for different directions and methods of evaluating it.
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    ItemOpen Access
    Industry survey response of criteria weights for lifting technologies in the offshore wind energy environment.
    (Cranfield University, 2018-06-12 13:10) Richmond, Mark; Balaam, Toby; douglas Causon, Paul; Cevasco, Debora; Leimeister, Mareike
    Industry response data to the survey conducted for the journal article titled 'Multi-Criteria Decision Analysis for Benchmarking Human-Free Lifting Solutions in the Offshore Wind Energy Environment'
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    Multi-criteria decision analysis for benchmarking human-free lifting solutions in the offshore wind energy environment
    (MDPI, 2018-05-07) Richmond, Mark; Balaam, Toby; Causon, Paul; Cevasco, Debora; Leimeister, Mareike; Kolios, Athanasios; Brennan, Feargal
    With single components weighing up to hundreds of tonnes and lifted to heights of approximately 100 m, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. Guidelines and standards for health and safety in lifting operations exist; however, having people directly beneath the load is still common practice in offshore wind turbine installations. Concepts for human-free offshore lifting operations in the categories of guidance and control, connections, and assembly are studied in this work. This paper documents the process of applying Multi-Criteria Decision Analysis (MCDA), using experts’ opinions for the importance of defined criteria obtained by conducting an industry survey, to benchmark the suitability of the concepts at two stages. Stage one streamlined possible options and stage two ranked the remaining suite of options after further development. The survey results showed that criteria such as ‘reduction of risk’, ‘handling improvement’ and ‘reliability of operation’ were most important. The most viable options, weighted by industry opinion, to remove personnel from areas of high risk are: Boom Lock and tag lines, a camera system with mechanical guidance, and automated bolt installation/fastening for seafastening. The decision analysis framework developed can be applied to similar problems to inform choices subject to multiple criteria.