Multi-Objective Optimization of Mooring Systems for Offshore Renewable Energy

This is the author accepted manuscript. The final version is available from EWTEC via the link in this record.This paper presents a method for the optimization of mooring systems in offshore renewable energy systems. This methodology considers the location of anchors as well as the length, material, and diameter of the mooring lines in order to simultaneously minimize the tension in the lines, the cost of the mooring system, and the fatigue damage in the system. By considering these three objectives using a multi-objective approach rather than reduction to a single objective optimization problem allows a Pareto hull of solutions to be obtained representing a range of solutions which balance the three objectives. From this, a system designer can select the design which appropriately balances the trade-off between the competing objectives. In this work, a set of mooring designs that represent efficient solutions for the constraints are found and presented considering the OC4 DeepCWind semi-submersible at Wave Hub. This reliability-based design optimization approach will be applicable to other offshore technology subsystems allowing reliability to be considered in a multi-objective optimization from the design phase.This work is funded by the EPSRC (UK) grant for the United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]

A BEMT model for a high solidity, hubless and ducted tidal stream turbine

5th Oxford Tidal Energy Workshop (OTE 2016), 21-22 March 2016, Oxford, UKA Blade Element Momentum Theory (BEMT) model for ‘conventional’ 3 bladed designs of Tidal Stream Turbine (TST) is presented, with validations from scale model experiments carried out in a cavitation tunnel. Assumptions and limitations of the model are discussed in order to gauge potential use in assessing a high solidity, hubless and ducted TST design, which has been developed by OpenHydro. A number of adjustments to the model are considered, which are to be validated with fully blade resolved CFD studies and field data from a full scale device deployed at Paimpol-Bréhat, Brittany at the start of 2016 in collaboration with EDF.The Industrial Doctoral Centre for Offshore Renewable Energy (IDCORE) is funded by the Energy Technology partnership and the RCUK Energy Programme (Grant number EP/J500847/1)

A coupled Monte Carlo - Evolutionary Algorithm approach to optimise offshore renewables O&M

This is the author accepted manuscript. The final version is available from EWTEC via the link in this record.Improving the reliability and survivability of wave and tidal energy converters, whilst minimising the perceived risks and reducing the deployment costs, are recognised as key priorities to further develop the marine energy market. Computational decision-making models for offshore renewables have demonstrated to be valuable tools in order to provide support in these strategic areas. In this paper, the authors propose an integrated approach of Monte Carlo simulation and Evolutionary Algorithms to address these challenges. A time-domain method based on the Monte Carlo technique, with specific consideration of marine renewable energy requirements, is used for the assessment of the devices and the characterization of the offshore farms. This permits the obtainment of energy predictions and indications on the reliability, availability, maintainability and profitability of the farm. A multi-objective search, by means of a specifically designed Genetic Algorithm, is then used to determine the ideal variation of inputs set for the improvement of the results. Suitable objective functions aiming at the minimization of the maintenance costs and the maximization of the reliability are considered for this purpose. The outcomes obtainable for the assessment of an offshore farm, as well as suggested practices for the optimisation of the Operation and Maintenance (O&M) procedures, are introduced and discussed. Results on the ideal trade-off solutions between conflicting objectives are presented.The work in this paper has been conducted within the multinational Initial Training Network (ITN) OceaNET, funded under the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n° 607656. Mojo Maritime (JFMS) have provided access to Mermaid to support, and for integration with, this research

A Bayesian Updating Framework for Simulating Marine Energy Converter Drive Train Reliability

This is the author accepted manuscript. The final version is available from METSINTRODUCTION: Accurately quantifying and assessing the reliability of Marine Energy Converters (MEC’s) is critical for the successful commercialization of the industry. Without improvements in reliability and hence reductions in operation & maintenance (O&M) costs, the industry will struggle to reach competitive Levelised Cost of Energy (LCoE). At present, due to the nascent stage of the industry and commercial sensitivities there is very little reliability field data available. This presents an issue: how can the reliability of MEC devices be accurately assessed and predicted with a lack of specific reliability data? [...]The support of the ETI and RCUK Energy Program funding for IDCORE (EP/J500847/1) is gratefully acknowledged

Reliability verification of mooring components for floating marine energy converters

PublishedThis paper was presented at SHF – conference on MRE – Brest (F), October 2013.Safety factors are critical to device reliability and are applied during device development to protect against early failures. At each stage of a development a designer may apply their own safety factor in relation to the criticality of the component or subassembly for which they are responsible. This paper seeks to understand how different assessment techniques can assist the design process by refining safety factors, with the aim of reducing device costs and improving economic viability. To achieve this, a methodology is presented to assess and verify the fatigue performance of mooring components. The paper draws on field data and introduces a combined approach of modelling, service simulation and field tests to validate the reliability of components. A shackle is used as a case study to demonstrate the methodology. Results from finite element analysis (FEA) and accelerated service simulation testing on the Dynamic Marine Component test facility (DMaC) are presented and discussed, including fatigue damage and failures. FEA is found to accurately predict areas of weakness within a component, however it underestimates component strength due to unrealistic stress concentrations at applied boundary conditions. Static and fatigue tests demonstrate the complex nature of reliability estimation, with static component safety factors of 8.6 being reduced to less than 3.7 under a fatigue loading regime. Service simulation testing is found to be important in refining initial reliability estimations from S-N curves and FEA models. The effect of mean stress on fatigue failure is also found to be significant.The authors would like to acknowledge the support of the UK Centre for Marine Energy Research (UKCMER) through the SuperGen programme funded by the Engineering and Physical Sciences Research Council

On peak mooring loads and the influence of environmental conditions for marine energy converters

Mooring systems are among the most critical sub-systems for floating marine energy converters (MEC). In particular, the occurrence of peak mooring loads on MEC mooring systems must be carefully evaluated in order to ensure a robust and efficient mooring design. This understanding can be gained through long-term field test measurement campaigns, providing mooring and environmental data for a wide range of conditions. This paper draws on mooring tensions and environmental conditions that have been recorded (1) for several months during the demonstration of an MEC device and (2) over a period of 18 months at a mooring test facility. Both systems were installed in a shallow water depth (45 m and 30 m, respectively) using compliant multi-leg catenary mooring systems. A methodology has been developed to detect peak mooring loads and to relate them to the associated sea states for further investigation. Results indicate that peak mooring loads did not occur for the sea states on the external contour line of the measured sea states, but for the sea states inside the scatter diagram. This result is attributed to the short-term variability associated with the maximum mooring load for the given sea state parameters. During the identified sea states, MEC devices may not be in survival mode, and thus, the power take-off (PTO) and ancillary systems may be prone to damage. In addition, repeated high peak loads will significantly contribute to mooring line fatigue. Consequently, considering sea states inside the scatter diagram during the MEC mooring design potentially yields a more cost-effective mooring system. As such, the presented methodology contributes to the continuous development of specific MEC mooring systems.The work described in this publication has received funding from the Technology Strategy Board (TSB), Project Number 100855. The authors would like to acknowledge the support of the South West Regional Development Agency for its support through the Partnership for Research in Marine Renewable Energy (PRIMaRE) institution. They also gratefully acknowledge Fred Olsen for supplying the measurements of mooring loads

Mooring System Design Optimization Using a Surrogate Assisted Multi-Objective Genetic Algorithm

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.This article presents a novel framework for the multi-objective optimization of o shore re- newable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a oating o shore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the o shore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally e cient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of op- timal solutions characterizing how design changes impact the trade-o between these two competing objectives.This work is funded by the EPSRC (UK) grant for the SuperGen Marine United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]. The authors would also like to thank Jason Jonkman at NREL who provided the hydrodynamic data for the OC4 semi-submersible and Orcina Ltd. for providing OrcaFlex

Component test facilities for marine renewable energy converters

This paper describes how the PRIMaRE group at University Exeter is engaging in the establishment of appropriate reliability methods suitable for application to marine renewable devices with a key area being the production of suitable failure rate data for the marine renewable energy industry. This activity seeks to mitigate uncertainties and cost implications associated with the reliability assessment of marine energy converters (MECs) due to an omnipresent lack of applicable failure rate data. The capability of two facilities, namely i) the South Western Mooring Test Facility (SWMTF) and ii) the Dynamic Marine Component Test facility (DMaC), to perform specimen and accelerated component testing is discussed. A case study, using data from wave tank tests and numerical simulations performed for the SWMTF, serves to illustrate how evidence of component reliability under operational conditions could be provided.The authors would like to acknowledge the support of the South West Regional Development Agency through the PRIMaRE institution. They would also like to acknowledge the European Community's Sixth Framework Programme HYDRALAB III, Contract no. 022441 (RII3). The second author would like to acknowledge the funding support from the Engineering and Physical Sciences Research Council (EPSRC) under the SUPERGEN Marine Doctoral Programme. Thanks also to Orcina for provision of their Orcaflex software

Reliability assessment of tidal stream energy: significance for large-scale deployment in the UK

This is the author accepted manuscript. The final version is available from CRC PressThe UK has ambitious plans to harness its available tidal stream resource, estimated at 95TWh/year by The Crown Estate (2013). The economic viability of large-scale deployments will be largely governed by aspects of plant availability, including reliability. Using available information on environmental parameters of (pre-) consented sites across the UK, this paper explores subassembly target reliability levels for tidal stream devices. Reliability models of devices are investigated to establish the influence of environmental site conditions with regard to underlying subassembly failure rates and target reliability levels. Hence, a relia-bility-focussed perspective on the planned deployments is presented

Towards automated and integrated data collection - standardising workflow processes for the offshore wind industry

Conference paper abstractA significant amount of operation and maintenance (O&M) data are being generated daily from offshore wind farms. Most of them are coming from a variety of monitoring systems, maintenance reports and environmental sources. The challenge with having a wide diversity of data in inhomogeneous types and formats, is the considerable human effort involved in the initial extraction, transformation and loading (ETL) stages for these data to be processed and analysed. Although several commercial solutions are available, aiming to improve data management to support O&M decision making, the initial ETL phase is still a work-intensive process. One of the main reasons is that the organization and structure of the data flow does not allow easy access to the data. Due to the rapid growth of the offshore wind industry, there is a need to automate and integrate some of these processes in order to reduce the human effort and the associated costs. The aim is to facilitate a responsive, data driven decision making for O&M. This paper and presentation show the results of re-structuring and automation of the daily maintenance procedures that achieve a more efficient data analysis. These early results also indicate that less man-hours and a smaller number of people need to work on data collection. The framework and the steps followed will be of interest to offshore wind farm developers and operators to automate their data collection workflow