First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

A new technique for process-oriented rain area classification using Meteosat Second Generation SEVIRI nighttime data is introduced. It is based on a combination of the Advective Convective Technique (ACT) which focuses on precipitation areas connected to convective processes and the Rain Area Delineation Scheme during Nighttime (RADS-N) a new technique for the improved detection of stratiform precipitation areas (e.g. in connection with mid-latitude frontal systems). The ACT which uses positive brightness temperature differences between the water vapour (WV) and the infrared (IR) channels (ΔT<sub>WV-IR</sub>) for the detection of convective clouds and connected precipitating clouds has been transferred from Meteosat First Generation (MFG) Metesoat Visible and Infra-Red Imager radiometer (MVIRI) to Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). RADS-N is based on the new conceptual model that precipitating cloud areas are characterised by a large cloud water path (<i>cwp</i>) and the presence of ice particles in the upper part of the cloud. The technique considers information about both parameters inherent in the channel differences ΔT<sub>3.9-10.8</sub>, ΔT<sub>3.9-7.3</sub>, ΔT<sub>8.7-10.8</sub>, and ΔT<sub>10.8-12.1</sub>, to detect potentially precipitating cloud areas. All four channel differences are used to gain implicit knowledge about the <i>cwp</i>. ΔT<sub>8.7-10.8</sub> and ΔT<sub>10.8-12.1</sub> are additionally considered to gain information about the cloud phase. First results of a comparison study between the classified rain areas and corresponding ground based radar data for precipitation events in connection with a cold front occlusion show encouraging performance of the new proposed process-oriented rain area classification scheme

Discriminating raining from non-raining clouds at mid-latitudes using Meteosat Second Generation daytime data

International audienceA new method for the delineation of precipitation during daytime using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid-latitude frontal systems). The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension (both represented by the cloud water path (cwp)), and the existence of ice particles in the upper part of the cloud. The technique considers the VIS0.6 and the NIR1.6 channel to gain information about the cloud water path. Additionally, the channel differences ?T8.7-10.8 and ?T10.8-12.1 are considered to supply information about the cloud phase. Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the channel differences, the value combination of the four variables is compared to ground based radar data. The retrieval is validated against independent radar data not used for deriving the transfer function and shows an encouraging performance as well as clear improvements compared to existing optical retrieval techniques using only IR thresholds for cloud top temperature

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

Offshore Wind Turbine Fault Alarm Prediction

This is the final version. Available on open access from Wiley via the DOI in this recordOffshore wind operation and maintenance (O&M) costs could reach up to 1/3 of the overall project costs. In order to accelerate the deployment of offshore wind farms, costs need to come down. A key contributor to the O&M costs are the component failures and the downtime caused by them. Thus, an understanding is needed on the root cause of these failures. Previous research has indicated the relationship between wind turbine failures and environmental conditions. These studies are using work order data from onshore and offshore assets. A limitation of using work orders is that the time of the failure is not known and consequently the exact environmental conditions cannot be identified. However, if turbine alarms are used to make this correlation, more accurate results can be derived. This paper quantifies this relationship and proposes a novel tool for predicting wind turbine 1 fault alarms for a range of subassemblies, using wind speed statistics. A large variation of the failures between the different subassemblies against the wind speed is shown. The tool uses five years of operational data from an offshore wind farm to create a data-driven predictive model. It is tested under low and high wind conditions, showing very promising results of more than 86% accuracy on seven different scenarios. This study is of interest to wind farm operators seeking to utilize the operational data of their assets to predict future faults, which will allow them to better plan their maintenance activities and have a more efficient spare part management system.Energy Technology InstituteRCUK Energy ProgrammeEDF Energ

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

Data insights from an offshore wind turbine gearbox replacement

This is the final version. Available from IOP Publishing via the DOI in this record.Gearboxes are a complex, yet vital assembly for non-direct-drive offshore wind turbines, which are designed to last for the lifetime of the asset. However, recent studies indicate that they may have to be replaced as early as 6.5 years. Moreover, their contribution to offshore wind farm failures and downtime has been shown to be amongst the three most critical assemblies with the highest material cost required. An improved understanding of these premature failures and the ability to predict them in advance could reduce inspection and maintenance costs, as well as to help overcome many logistical and planning challenges. The objective of this paper is to present the lessons learnt from a gearbox exchange performed in one of the offshore wind turbines at Teesside offshore wind farm, comprising 27 2.3MW wind turbines. The paper takes a condition monitoring perspective and uses the identified spalling at the inner part of the planetary bearing as the governing failure mode. A data management system has been setup, incorporating all the operational data received, including maintenance log information and sensor data. A period of up to 2.5 years, prior to the the gearbox exchange, is examined for this study. SCADA and CMS data of the faulty turbine are compared against the wind farm, using statistical methods and machine learning techniques. Supervised learning models are built, which will help predict similar failures in the future. Results show how different data sources can contribute in gearbox failure diagnosis and help to expedite failure detection for Teesside offshore wind farm and similar wind turbine and gearbox types. This paper will be of interest to wind farm developers and operators to build predictive models from monitoring data that can forecast potential gearbox failures.Energy Technology InstituteResearch Council Energy ProgrammeEuropean Unions Horizon 202

Optimising Structural Loading and Power Production for Floating Wave Energy Converters

This is the author accepted manuscript. The final version is available from EWTEC via the link in this record.This paper investigates the design trade-off between power production and structural loading for Wave Energy Converters (WECs), based on tank test results for the Albatern 12S floating wave energy array. This work feeds into the design development process, which is currently in the concept design and testing phase. The paper focuses on two methods for reducing structural loading: limiting the power take off (PTO) torque generation capacity (for operational loads), and controlling the PTO damping (for extreme loads). The torque that can be generated by the primary PTO mechanism affects the size (and cost) of the structural components within the device. Increased torque results in a potentially greater power capture, but also greater structural loading. It is therefore important to highlight the target torque limit early in the design process. The aim of this work is to identify the optimum torque limit to refine the design towards the lowest overall Levelised Cost of Energy (LCoE). In addition, a high-level investigation of the impact of PTO damping on extreme loading has been carried out, to help to identify appropriate “operational” and “survival” sea states for the device. The paper calculates an optimum torque limit for the device at the West Harris site and quantifies the trade-off between Annual Energy Production and structural cost, using the LCoE as an optimisation criteria. The approach is in principle applicable to other technologies, if the design drivers are adjusted to the technology’s working principle.Tank testing was funded by Wave Energy Scotland (WES) as part of the Novel Wave Energy Converter Stage 1 (NWEC1) programme. This work has been carried out as part of the IDCORE programme, funded by the Energy Technology Institute and RCUK Energy programme (grant no. EP/J500847/1

Chiral Condensate and Short-Time Evolution of QCD(1+1) on the Light-Cone

Chiral condensates in the trivial light-cone vacuum emerge if defined as short-time limits of fermion propagators. In gauge theories, the necessary inclusion of a gauge string in combination with the characteristic light-cone infrared singularities contain the relevant non-perturbative ingredients responsible for formation of the condensate, as demonstrated for the 't Hooft model.Comment: 4 pages, Revtex

From non-degenerate conducting polymers to dense matter in the massive Gross-Neveu model

Using results from the theory of non-degenerate conducting polymers like cis-polyacetylene, we generalize our previous work on dense baryonic matter and the soliton crystal in the massless Gross-Neveu model to finite bare fermion mass. In the large N limit, the exact crystal ground state can be constructed analytically, in close analogy to the bipolaron lattice in polymers. These findings are contrasted to the standard scenario with homogeneous phases only and a first order phase transition at a critical chemical potential.Comment: 12 pages, 7 figures, revtex; v2: improved readability, following advice of PRD referee; accepted for publicatio

Reducing Peak & Fatigue Mooring Loads: A Validation Study for Elastomeric Moorings

This is the author accepted manuscript. The final version is available from EWTEC via the link in this record.Fibre ropes are often specified for floating wave and tidal energy device mooring systems. The relatively low axial stiffness goes some way towards mitigation of the peak and fatigue mooring loads. However, the minimum breaking load (MBL) of a fibre rope dictates its axial stiffness and hence the free selection of low axial stiffness is not possible with conventional rope. The resulting mooring stiffness is often sub-optimal, giving rise to elevated peak and fatigue loads. Elastomeric, nonlinear mooring elements solve this by partially de-coupling the axial stiffness from the MBL and offering an initial soft response with increasing stiffness for higher strains. These nonlinear elastomeric moorings have the potential to reduce the peak and fatigue mooring loads as indicated by numerical studies. This work uses a validated numerical model to quantify the load reduction achievable by substituting a novel elastomeric tether in place of a conventional fibre rope. Field data is used to validate the base case model of the highly dynamic South West Moorings Test Facility (SWMTF). The base case mooring design utilises Nylon ropes which are subsequently replaced with elastomeric tethers in the validated model. The results show that the peak mooring loads are reduced substantially upon substituting the elastomeric tethers for the conventional ropes. Subsequently this allows a downward iteration of MBL and axial stiffness towards an optimal condition, providing the lowest achievable load case. In most instances, the optimum iteration outcome also allows a reduction in catenary chain weight. The reduction in peak tension is accompanied by an increase to the buoy excursion in surge. However, the mean peak excursion increase is 21% whilst the mean peak tension reduction is 66%.This work was partly funded by the EPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]. The development of the Exeter Tether was partly funded by the Open Innovation Platform, supported by the Higher Education Council for England. The authors would also like to acknowledge the support from Lankhorst Ropes throughout the technology development