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

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.

Automated summary

This paper discusses the technology challenges in the wind and solar sectors and the role of computational science in addressing them. The challenges in wind energy include understanding atmospheric flow physics, complex wakes, aeroelastic effects, and wind farm optimization. Concentrated solar power technologies require optimal configuration for efficiency, durability, and reducing heat losses. Computational fluid dynamics and heat transfer have advanced in terms of numerical methods and physics-based models. However, further development is needed to accurately and efficiently design complex systems and forecast performance.