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Verification through Accelerated testing Leading to Improved wave energy Designs

Periodic Reporting for period 1 - VALID (Verification through Accelerated testing Leading to Improved wave energy Designs)

Okres sprawozdawczy: 2020-12-01 do 2022-05-31

Ocean energy technologies are not mature enough to overcome the challenges related to cost, performance and reliability to realise its full potential, and the development of a wave (or tidal) energy converter is surrounded by high uncertainties. Testing is a very valuable activity, and it improves confidence in design, but lengthy physical testing in one phase can delay the product development process, because testing and design processes are closely intertwined. Often faults in component and subsystems are detected through extensive and costly sea testing in late stages of the development, which can add significant cost and delays to initial schedules, and eventually leading to reconstruction or bankruptcy. Sound testing methods are needed to reduce uncertainties, increase confidence in results, support and guide the design, and thus largely assist in the decision-making process. Even if a proven technology/component/subsystem is deployed in a new context, it needs to be tested under these new conditions. This “newness” introduces uncertainties to the system and proves to be challenging for both the developer as well as for the component supplier. It is found that the supply chain is often over-confident with their own products even in a new context and environment, thus resulting in low commitment to adapt to the new requirements or to see the benefits to pursue new products. The overarching objective of VALID is to de-risk the WEC design process in terms of components reliability and survivability by developing an integrated and open platform for the testing of critical components and subsystems. VALID’s vision for wave energy is to make possible the coupling between a virtual reality and an in-house physical reality, which will enable the reduction of uncertainties in WEC design, problems associated to scale testing, testing and development times, and high expenditures associated with real-sea testing.
WP1, led by Yavin4Consultants, was initiated with a study providing an overview of the main types of Wave Energy Converters (WECs) under development, critical components and sub-systems, related design parameters, and associated requirements. A high-level definition of accelerated testing requirements was established, with a high-level review of standards and guidelines for application in the context of component design/testing for WEC technologies. Critical sub-systems and components in WEC designs were identified for Failure Mode, Effects and Criticalities Analysis worksheets. A review of theoretical models related to reliability and survivability was conducted with description of numerical and experimental modelling principles, analysis of limitations and conceptualisation of a hybrid testing framework with a focus on environmental characterisation with degradation and failure models, and overview of uncertainty quantification principles. This analysis led to the classification and description of the models, test rigs and testing platforms. Three workshops for each user case addressed the robustness of the results obtained in a hybrid testing environment, evaluating the measurement uncertainty in both virtual and physical setups.

WP2, led by AVL, has gathered requirements needed for the wave energy sector by means of workshops, meetings, and questionnaire. AVL developed an interface for the simulation tool OrcaFlex and successfully tested it (Fig 1). Based on the basic concept for model integration with test beds (Fig 2), first prototypes namely for “feeding live data to the test bed” were implemented (Fig 3 and 4).

WP3, led by Corpower Ocean (CPO), has defined a testing program according to the methodology proposed in WP1, which focuses on four of the CPO dynamic sealing systems. AVL completed a demo of co-simulation with Simulink and Orcaflex, providing input on how to handle the hydrodynamic block of CPO’s W2W model (Fig 5). A study on use of machine learning for predictive maintenance of dynamic sealing systems is ongoing with a third party. CPO have been progressing with design and commissioning of the seal rig (Fig 6) such that the electrical connections and software overhaul have been completed.

WP4, led by Tecnalia, has defined generator thermal fatigue testing according to the methodology proposed in WP1. It was identified that critical failure modes of electrical generators will focus on generator stator stresses, which cause winding failure. Upgrades for the OWC device are in progress and the generator working conditions have been quantified and power peaks characterized for accelerated tests. A fully coupled numerical model was created together with AVL (Fig 7).

WP5, led by Wavepiston (WPN), has defined a testing program according to the methodology proposed in WP1, which supports the development of a damage model and probabilistic models for mechanical seal performance. WPN has performed a detailed FMEA of the Wavepiston design (Fig 8). The user case specific W2W model and configuration to the hybrid testing platform, as well as activities related to High fidelity CFD are progressing. A new test rig is now under development.

WP6, led by RINA-C, has focused on the preparation of standardization and regulatory inventory, which identifies the areas of improvement in existing technical specifications, to liaise with IEC technical committees.

WP7, led by Aquatera Atlántico, has developed: Knowledge Exchange & Dissemination Strategy, Plan for the Exploitation and Dissemination of Results, Data Management Plan, Communications Plan and Training Program Plan.
VALID’s vision is to develop a new testing methodology enabling accelerated hybrid testing to be a common testing procedure for the wave energy sector. To allow for a three-fold interaction (physical test rigs, real environment and virtual enhanced environment), a VALID Hybrid Test Platform will be developed and used as the interface. Hybrid testing opens possibilities for a new type of testing enabling stepwise validation in the virtual world, all while lessening the need for costly, lengthy and sometimes dangerous tests. Enhanced test rigs in three user cases will allow equipment and component suppliers to test their products in a test bench that simulates real operating conditions. VALID is generating a set of assessment parameters and reliability metrics for all user cases, whereby scale effect has been identified as one major uncertainty for testing. One user case is upgrading their test rig from 1:10 to full scale to further investigate any dependencies. The electrical generator user case has quantified the operating conditions in terms of power peaks for accelerated tests, despite scale effects. Two of the user cases will integrate saltwater tanks for their testing to further reduce uncertainties related to lubrication and cooling. The integration of Orcaflex models into a co-simulation platform has been proven in VALID to improve simulation performance by 50% in terms of simulation times. To further reduce R&D time, VALID is exploring data sharing with the supply chain in two user cases, where models are run and controlled in different locations. VALID has also initiated a study with third party Trelleborg Sealing Systems to use machine learning for predictive maintenance on dynamic sealing systems. Ocean environment and its underlying effects are also being studied in the CFD process, which will enable LCOE estimates to better consider the uncertainties.
Fig 5. Photo of the dynamic seal test rig at CPO
Fig 8. Wavepiston energy collector module
Fig 7. Fully coupled MWPP numerical model built up on Model.CONNECT
Fig 1. Integration of the Wavepiston WEC model with pump and controller models in Model.CONNECT
Fig 3. Basic concept for feeding live data from measurement to test bed
Fig 4. Concept for feeding live data from measurement to test bed, Model.CONNECT and MQTT protocol
Fig 6. Example of connection between Orcina Orcaflex and Matlab Simulink using Model.CONNECT
Fig 2. Basic concept of the hybrid testing platform