Adversarial super-resolution of climatological wind and solar data.

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a [Formula: see text] resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change's Fifth Assessment Report.

Autonomic closure for turbulence simulations.

A new approach to turbulence closure is presented that eliminates the need to specify a predefined turbulence model and instead provides for fully adaptive, self-optimizing, autonomic closures. The closure is autonomic in the sense that the simulation itself determines the optimal local, instantaneous relation between any unclosed term and resolved quantities through the solution of a nonlinear, nonparametric system identification problem. This nonparametric approach allows the autonomic closure to freely adapt to varying nonlinear, nonlocal, nonequilibrium, and other turbulence characteristics in the flow. Even a simple implementation of the autonomic closure for large eddy simulations provides remarkably more accurate results in a priori tests than do dynamic versions of traditional prescribed closures.