MODELLING AND SIMULATION g This is biomimicry at its best and

‘several prototypes have already been successfully tested in a North Sea flume tank,’ according to Bailey. ‘With a collaborative cloud environment implemented into their design process, EEL energy has been able to evaluate the materials used to develop the undulating membrane, as well as the effectiveness of the product, before trialling it in a real- world environment.’ Batteries and electrolysers are another

growing area. Both of these energy storage systems provide a better match between the supply and demand of renewable energy on the grid. ‘They can, for example, be used to avoid curtailment and to deliver electric energy when there is a demand and capacity in the grid,’ Fontes explained. However, there are challenges ‘related

to the wide range of operating conditions and intermittent use these devices have to operate at,’ according to Fontes. ‘If we look at batteries, they are available

in many different chemistries, designs and for many different applications. For example, a large-flow battery system may be used for grid energy storage at a central facility, which is basically a small chemical plant. The Tesla Megapack project uses Tesla batteries for large-scale storage.’ In contrast, electrolyzers for hydrogen

generation are available more or less off the shelf for one use only - water electrolysis for generation of hydrogen at steady state. But these cutting-edge systems also demand a multiphysics approach. Fontes added: ‘Such applications require new development of the electrochemical cells and the whole system with electrolyzer and fuel cell.’ Fontes said: ‘We are constantly

developing tools for modelling of batteries, fuel cells, electrolyzers and solar cells. Also, the functionality for modelling of electric generators is improved for every release. The strategy is to make modelling more easily accessible for non- experts in mathematical modelling.’ In addition, Comsol is expanding its

“The greatest limitation is the hardware and its computational capacity. If things are done right, simulation tools allow us to analyse these big phenomena almost with unlimited precision”

28 Scientific Computing World Winter 2021  Pressure distribution around a prism with a circular section within Altair AcuSolve

functionality for generating light-weight models. ‘This is important for including accurate models in systems models and grid models,’ Fontes added. ‘The Application Builder is another important tool that we continuously develop. This allows our customers to develop software for their customers. In this respect, we are allowing many of our customers to become software developers, who can deliver multiphysics models to a larger community of scientists and engineers than we would be able to.’

Intelligent renewables Machine learning and other data-driven technologies are expected to play an increasingly important role in the simulation for both established and new types of renewables.

Xiaobing Hu, head of design and engineering applied solutions at Hexagon’s manufacturing intelligence division, said: ‘We are just scratching the surface of what we can do with AI and machine learning in the CAE industry. By applying these technologies with the rigorous engineering and CAE experience the industry has accumulated, it’s clear we can achieve greater productivity and more sustainable and innovative renewable energy technology development and operations.’ Schramm added: ‘We see the

convergence of simulation and AI, where data-driven in addition to model- based simulation will become a critical application that will be used daily by every forward-thinking energy company.’ AI-aided simulation has the potential to help across every stage of the simulation process. Hu explained: ‘In R&D, engineers can

explore the design space thoroughly and optimise systems and even the choice of materials and manufacturing processes. This front-loading of development can reduce design cycles and allow for

innovation. During validation and test phases, these AI approaches can help focus effort on meaningful and relevant tests and fill gaps between physical validation from sensors and metrology with reliable virtual data points, enabling robust design. ‘Finally, creating a smart digital reality

where virtual and physical data can be used interchangeably to make decisions requires high-quality data – virtual simulations or effective use of sensors and metrology – but AI and machine learning, combined with cloud computing, is key to making it efficient and scalable, stitching virtual and real together and analysing cumulative data patterns to predict outcomes or prescribe action.’ Machine learning can also help when dealing with multiphysics simulations, as Fontes explained: ‘The ability to package high-fidelity models into light- weight models using machine learning and other methods that can be trained with multiphysics models is kind of an emerging technology for off-the-shelf software. This could be a great help in predicting performance and operation of processes in the renewable energy sector.’ Going forward, collaboration will be the main barrier within the renewables sector, as Hu concluded: ‘Much of the data and physics-based simulation required to achieve these goals exists today, as do the data management tools and processes for simulation, materials and IoT. Our approach as part of Hexagon is to build scalable and open platforms that apply cloud and machine learning technologies with simulation and reality capture at each part of the asset lifecycle, with the goal of automating these processes. ‘Simply sending a data scientist into a lab doesn’t achieve this – it requires a deep understanding of the physics types, the manufacturing and measurement processes to connect these data and apply these techniques effectively.’

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