Periodic Reporting for period 1 - Smart-TURB (A Physics-Informed Machine-Learning Platform for Smart Lagrangian Harness and Control of TURBulence)
Okres sprawozdawczy: 2021-05-01 do 2022-10-31
The project is attacking important issues connected to Lagrangian and Eulerian data-driven problems for complex flows, with particular interests to applications to geophysical and laboratory set-ups. We have started to attach single-probe self-propelled objects with specific point-to-point navigation targets in complex flows, and multi-probe cooperative or adversarial games in stochastic and/or turbulent flow Filed-taxis based on optimal and heuristic search for odor emissions in stochastic models and in realistic numerical simulations of turbulence with a mean wind have also been attacked. Among the various techniques developed we cite the ones based on Reinforcement learning, Adversarial Reinforcement Learning and optimal control.
Data assimilation problems for 2d Eulerian rotating turbulent flows have been also developed by using both equations-based and data-driven tools, including linear methods based on principal orthogonal decomposition and non-linear ones, based on convolutional neural networks. Comparison against nudging is also systematically performed. Explorative studies of complex multi-component and multi-phase flows, using Lattice Boltzmann Techniques, and development of new algorithm to study nucleation with mesoscopic approaches are also under investigation. Among the most important impact for the society we cite the possibility to better assimilate data for numerical weather forecast, develop augmented reality approaches for bio-fluids and medicine, developing of optimal strategies for odor and contaminant sources in the presence of strong flow fluctuations. The overall objectives are to make a quantitative jump to data-driven approaches for fluid dynamics, offering open data suites to help the community for developing benchmarks and grand challenges,
Data assimilation problems for 2d Eulerian rotating turbulent flows have been also developed by using both equations-based and data-driven tools, including linear methods based on principal orthogonal decomposition and non-linear ones, based on convolutional neural networks. Comparison against nudging is also systematically performed. Explorative studies of complex multi-component and multi-phase flows, using Lattice Boltzmann Techniques, and development of new algorithm to study nucleation with mesoscopic approaches are also under investigation. Among the most important impact for the society we cite the possibility to better assimilate data for numerical weather forecast, develop augmented reality approaches for bio-fluids and medicine, developing of optimal strategies for odor and contaminant sources in the presence of strong flow fluctuations. The overall objectives are to make a quantitative jump to data-driven approaches for fluid dynamics, offering open data suites to help the community for developing benchmarks and grand challenges,
We have developed:
(i) numerical algorithms for direct numerical simulations HPC applications of odor sources emissions in turbulent flows with mean wind.
(ii) developed Lattice Boltzmann Approaches for Nudging in Rayleigh Benard
(iii) Algorithm using Reinforcement Learning for single and multile Lagrangian probes in complex flows
(iv) algortihms using Generative Adversarial Networks (GAN) for Data Assimilation of geophysical flows
(v) algorithms using POD and EPOD to benchmark GAN approaches
(vi) Algorithms based on Lattice Boltzmann for multi-component micro and nano fluids
Main results:
(i) first application of Nudging to Rayleigh Benard at high turbulent intensity
(ii) first application of GAN to reconstruct fully developed turbulent rotating flows
(iii) first application of adversarial reinforcement learning to study prey-preadator dynamics at low Reynolds
(iv) development of theoretical definition of Tolmann length for nucleation problems at nano-scale using mesoscopic algorithms
(v) studyng of non-newtonian effects in complex emulsions
(i) numerical algorithms for direct numerical simulations HPC applications of odor sources emissions in turbulent flows with mean wind.
(ii) developed Lattice Boltzmann Approaches for Nudging in Rayleigh Benard
(iii) Algorithm using Reinforcement Learning for single and multile Lagrangian probes in complex flows
(iv) algortihms using Generative Adversarial Networks (GAN) for Data Assimilation of geophysical flows
(v) algorithms using POD and EPOD to benchmark GAN approaches
(vi) Algorithms based on Lattice Boltzmann for multi-component micro and nano fluids
Main results:
(i) first application of Nudging to Rayleigh Benard at high turbulent intensity
(ii) first application of GAN to reconstruct fully developed turbulent rotating flows
(iii) first application of adversarial reinforcement learning to study prey-preadator dynamics at low Reynolds
(iv) development of theoretical definition of Tolmann length for nucleation problems at nano-scale using mesoscopic algorithms
(v) studyng of non-newtonian effects in complex emulsions
We expect to define a set of benchmarks and validation steps for the community working on Lagrangian and Eulerian data-driven tools for complex flows. Improving data assimilation, data augmentation, data generation abilities in many geophysical and applied situations where complex turbulent flows are acting. We expect to develop Physisc-Informed Data-Driven new algorithms and applications to turbulent flows, and to develop novel forcing mechanisms to control turbulent transitions and properties.