Turbulent convection flows in nature display prominent patterns in the mesoscale range whose characteristic length in the horizontal directions exceeds the system scale height. Known as the turbulent superstructure of convection, they are absent on both larger and smaller scales and evolve in ways not yet understood; but they are an essential link in the heat and momentum transport to larger scales, an important driver of intermittent fluid motion at sub-mesoscales, and one major source of uncertainty in the prognosis of climate change and space weather. In MesoComp, I will investigate the formation of superstructures in massively parallel simulations of compressible turbulent convection in horizontally extended domains, aiming for a deeper understanding of their dynamical origin and role in the transport of heat and momentum. I will then use these high-fidelity simulations to build recurrent machine learning models to predict the evolution and statistics of the superstructure and thus quantify the transport fluxes beyond the mesoscale. I will also analyse the impact of the mesoscale structures on the highly intermittent statistics at the small-scale of the flow and reveal the resulting feedback in the form of improved subgrid parametrizations by means of generative machine learning. MesoComp opens additional doors to the application of quantum algorithms in machine learning which significantly improve the statistical sampling and data compression properties compared to their classical counterparts. From a longer-term perspective, my research results in a quantum advantage for the numerical analysis of classical turbulence, which accelerates the parametrizations of mesoscale convection and increases their fidelity. This work will finally lead to more precise predictions of the on-going climate change and global warming. The results will also improve solar activity models and thus solar storm prognoses with impacts on satellite communication and electrical grids.

The growing market appeal of rechargeable lithium ion batteries (LIBs) for electric vehicles and portable electronics as well as the high cost and scarcity of lithium are driving research towards developing alternatives to LIBs. Sodium ion batteries (SIBs) have attracted considerable scientific and industrial attention as a potential alternative to LIBs with great economic benefits, which mainly attributes to the low cost and natural abundance of sodium. Moreover, SIBs share many similar characteristics with LIBs, from charge storage mechanism to cell structure, thus facilitating the production of SIBs with the existing LIB production technique and equipment. Currently, the key challenge of commercializing SIBs is to improve their performance to be comparable to LIBs. During the ERC ThreeDSurface project, we have performed both the material designing and 3D electrode designing for largely enhancing the SIB performance. A prototype of rechargeable SIB coin cells with high energy density and supercapacitor-like power density has been achieved, with performance indices that are comparable with the commercial LIBs. In particular, its supercapacitor-like high power density and superior rate capability allow ultrafast charge and discharge without deteriorating the energy density. In this PoC project, we will upscale the SIB coin cells into SIB pouch cells with low cost (< US$ 200 per kWh) and high energy capacity (≥ 30 Ah). Compared to the coin cell with only 1 Ah of a maximum energy capacity, the proposed pouch cell shall be capable of delivering much higher energy capacity in the range of 30-50 Ah, thus realizing battery system with large-scale commercial applications. Meanwhile, we will establish a production-scalable process for mass production of the SIB pouch cells, and hence paving the way towards further developing full SIB battery system for electric vehicles and portable electronics.

Wind storms, hurricanes, and heat waves, are atmospheric extreme events with a huge societal impact and significant economic costs. Thus, their correct identification is important, e.g. for off-shore wind power generation. This project is a fundamental study on hydrodynamics turbulence, whose results will provide a methodological basis for innovation in wind energy technology. Extreme atmospheric convection events are characterized by large local amplitudes of the rate at which turbulent kinetic energy is dissipated, a central quantity that cannot be predicted from the highly nonlinear mathematical equations of fluid motion. This project aims at understanding the formation and predicting such extreme events of energy dissipation in Rayleigh-Bénard convection (RBC), a paradigm for atmospheric motion. Advanced high resolution measurements of the small-scale velocity field and its gradients will therefore be performed in a pressurized convection chamber at TU Ilmenau which allows to downscale turbulence and to use Particle Image Velocimetry for flows at Rayleigh numbers up to a million or higher. By combination of measured kinetic energy dissipation rate in the bulk and wall shear stresses in the boundary layer, we will identify the advection patterns that generate the extreme dissipation events. The present experimental analysis will be complemented by existing training data records of high-resolution direct numerical simulations of the same flows. They serve to develop data-driven methods and algorithms, such as recurrent neural networks, to predict such extreme events in experimental analyses. The goal of this project is to advance our understanding of the dynamic evolution of such extreme events in a RBC flow and to develop reliable tools to predict the events. This research objective will be reached in a multidisciplinary way by a combination of high resolution optical flow measurements with the data-driven modeling and data analytics by machine learning.