applications


the financing community, according to Graham Gow, head of product development at renewable energy consultancy Natural Power. Gow added: ‘In the past five years or so, we


have seen both of these issues addressed. Add in the fact that turbines are getting bigger and moving offshore, both of which means that associated met masts get ever more expensive – thus making remote sensing devices even more attractive.’ Lidar provides remote wind measurements


from the ground level to plan for, enhance, or even control wind turbines so they can ‘see’ the wind before it arrives. Te technology removes the need for tall mast structures and their associated cup anemometers for measuring wind speed. Masts can be expensive and difficult to install in remote locations, oſten require planning permission and can be tricky to install and maintain. Lidar represents quite a step change to the incumbent met mast technology. It is cheaper and quicker to install, easily movable aſter or even during projects and dodges the planning or safety considerations associated with traditional masts. Lidar uses an eye-safe, non-visible, continuous


wave laser beam at a range of user-defined heights to intersect the particles naturally found


IN FIVE TO 10 YEARS, REMOTE CLOUD RESOURCES WILL BE INCREASINGLY UTILISED WITHIN THE WIND POWER INDUSTRY


in the atmosphere, such as pollen and dust. Te returning, changed signal is evaluated for its Doppler shiſt to calculate the wind speed. Tis can be deployed autonomously for years at a time, gathering this vital wind data to help plan for a successful wind farm, or optimise the performance of an operational wind turbine. Future-proofing the soſtware behind these


systems is a complex task as the sensors are expected to work in remote locations for long periods of time without anyone checking on them. Jon Cage, head of soſtware at remote wind measurement systems company ZephIR Lidar, told Scientific Computing World: ‘As a result, testing, simulation, and increasingly, test driven development takes focus. It is not enough to put some code down, check it works in the lab and ship it, as might have happened in the earlier days of computing on smaller scale projects or systems.’ Increases in computing power have enabled


modular object-oriented code to take this strain and become more of a standard approach to enable, for example, unit testing to allow developers to run tests aimed at new features


www.scientific-computing.com l


ZephIR DM wind lidar deployed with Centrica Energy to optimise one of the UK’s largest offshore wind farms


or bug fixes on their workstations. Cage added: ‘Whilst cloud computing and larger systems are available at increasingly competitive prices, freely available continuous integration soſtware can be run on modest servers or workstations so that each and every change made to our algorithms is run against all of the tests and simulations that were written before, ensuring no issues have been introduced to the wider system whilst enabling new features and enhancements.’ Cloud computing is another tool that is helping


the wind power sector up its game, as David Standingford, lead technologist at the Centre for Modelling and Simulation (CFMS), said: ‘We are seeing movement from most engineering companies to cloud computing as the economics are better and the security of the cloud has improved. Additionally, massive amounts of resources are available cheaply and bandwidth has increased to match the needs of the wind power simulation space.’ ‘In five to 10 years, remote cloud resources will


be increasingly utilised within the wind power industry as the technology matures and the cost savings and security of cloud systems become more apparent. Cloud resources allow engineers remote access, as well as greater agility and the ability to partner with other engineers on a variety of projects,’ Standingford added. Tis hunger for more computational clout is


taking hold across the wind power technology space and one of the biggest challenges is optimising the modelling and simulation soſtware to match these increasingly computer intensive methods. Tis has led to two possible methodologies, according to Gow: one where progressively more complex engineering principles guide the simulation; and one where you give the system all of your data in one go and see what pops out at the other end. For the first ‘computer intensive approach’, Gow added: ‘A good example would be our gradual


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move from static flow modelling to dynamic flow modelling to numerical weather models to combined computational fluid dynamics and numerical weather prediction models.’ Te other ‘big data approach’ can be completely


blind, like an aeroplane black box system, where you chuck all the operational data at a network and see what it discovers. Gow added: ‘Te biggest issue generally is concerned with our ability to record more and more data, but being less and less able to make genuine use of it. Te solution lies in our ability to bring engineering expertise together with our mathematical modelling abilities to bring genuine value added to our processes.’


Hardy hardware and software Whether a computer intensive or big data approach is used, computational power needs to increase to match the engineers’ increasingly demanding simulation and modelling requirements. Tis can be achieved with modern day workstations, as Cage explained: ‘As the power of desktop machines reach the levels of what would have previously been considered ‘high-performance’ in yesteryear, and the tools we use evolve in orders of magnitude of efficiency, the option to run more testing and analysis on our ‘workstations’ becomes an increasingly viable option.’ It’s not just the hardware that has made this


possible, the soſtware has also upped its game, according to Cage: ‘Ever-improving efficiencies in higher level languages such as Python and accompanying scientific libraries are bringing the power of what was once the domain of complicated (and oſten expensive) mathematical modelling tools such as Matlab closer to the hands of engineers and scientists.’ ‘Te increased platform support for those


languages means that algorithms can be developed faster in more human-readable code


AUGUST/SEPTEMBER 2015 29 ➤


ZephIR


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