The Role of Computational Science in Wind and Solar Energy: A Critical Review

Abstract

This paper concerns technology challenges for the wind and solar sectors and the role of computational science in addressing the above. Wind energy challenges include understanding the atmospheric flow physics, complex wakes and their interaction with wind turbines, aeroelastic effects and the associated impact on materials, and optimisation of wind farms. Concentrated solar power technologies require an optimal configuration of solar dish technology and porous absorber in the volumetric solar receiver for efficiency and durability and to minimise the convective heat losses in the receiver. Computational fluid dynamics and heat transfer have advanced in terms of numerical methods and physics-based models and their implementation in high-performance computing facilities. Despite this progress, computational science requires further advancement to address the technological challenges of designing complex systems accurately and efficiently, as well as forecasting the system’s performance. Machine Learning models and optimisation techniques can maximise the performance of simulations and quantify uncertainties in the wind and solar energy technologies. However, in a similar vein, these methods require further development to reduce their computational uncertainties. The need to address the global energy challenges requires further investment in developing and validating computational science methods and physics-based models for accurate and numerically efficient predictions at different scales.

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

“…Furthermore, heat transfer applications, concerning the addition of nanoparticles in simple fluids [97][98][99][100][101][102] or the investigation of flows in microfluidic channels [103][104][105][106][107][108] play a central role. Of equal significance, though with fewer instances, several recent reviews refer to aerodynamics [109][110][111][112][113], geosciences, geoengineering and environmental subjects [114][115][116][117][118][119][120][121][122][123][124][125][126], energy applications [127][128][129][130], biological processes [131][132][133][134], control processes [135][136][137], multiscale simulations [138][139][140][141], and general ML [2,…”

A Computational Methodology for Assessing Wind Potential

“…The computational methodology can be extended to identify the optimal location of the installation of wind farms regarding both wind energy harnessing and noise minimisation. Coupling the present computational framework with acoustic models [19,84,85] can provide a simulation framework to achieve the above.…”

“…Assessing the suitability of a location regarding its wind energy potential requires a systematic process that encompasses various stages ranging from meteorological analysis and site characterisation to the numerical simulations using sophisticated modelling techniques [10][11][12][13][14][15]. Computational Fluid Dynamics (CFDs) have been pivotal in simulating airflow around wind turbines and farms and contributing to the development of wind turbine technologies by optimising blade designs, enhancing aerodynamic efficiency, and reducing structural loads [16][17][18][19]. Moreover, real-time monitoring and predictive maintenance systems showcase the application of smart technologies and machine learning (ML) algorithms in wind farm management, thus enabling early issue detection and minimising downtime [19,20].…”

“…Computational Fluid Dynamics (CFDs) have been pivotal in simulating airflow around wind turbines and farms and contributing to the development of wind turbine technologies by optimising blade designs, enhancing aerodynamic efficiency, and reducing structural loads [16][17][18][19]. Moreover, real-time monitoring and predictive maintenance systems showcase the application of smart technologies and machine learning (ML) algorithms in wind farm management, thus enabling early issue detection and minimising downtime [19,20].…”