MEchanics GAthering -MEGA- Seminar: Talk1 - Hydropower research and testing at EPFL; Talk2 - Machine learning potentials for molecular liquids
Event details
| Date | 26.09.2019 |
| Hour | 16:15 › 17:30 |
| Speaker | Andres Mueller (LMH, EPFL) & Max Veit (COSMO, EPFL) |
| Location | |
| Category | Conferences - Seminars |
Hydropower research and testing at EPFL by Andres Mueller (LMH, EPFL)
Hydropower Research and Testing at EPFL. The stability of our electrical grid is challenged daily by the massive integration of inherently intermittent renewable energy sources (RES), such as solar or wind power. However, in order to reach the ambitious goals for the decarbonization of our electricity supply, the share of these RES is yet to be significantly increased. In addition to being a highly reliable and clean source of electricity, hydropower also plays a crucial role in providing the grid with the necessary flexibility in terms of load balancing and frequency regulation. This requires an extension of the hydropower plants' operating range, occasionally giving rise to cavitation induced system critical flow instabilities inside the machines. The research aiming at understanding, simulating and predicting these limiting factors, in order to ultimately improve the power system's capacity to accommodate larger shares of RES, is outlined in this talk. Finally, EPFL's role as an independent provider of reduced scale model testing services in large hydropower projects around the globe is briefly introduced.
Machine learning potentials for molecular liquids by Max Veit (COSMO, EPFL)
Machine learning potentials for molecular liquids. The reliable prediction of the macroscopic properties of molecular liquids requires potential energy surface (PES) models that are not only accurate, but computationally efficient enough to handle large systems and reach long time scales typically inaccessible to explicit quantum-mechanical methods. First, I will introduce a new approach to the systematic approximation of the first-principles PES of a molecular liquid using the GAP machine learning method [A. Bartók, M. Payne, R. Kondor, and G. Csányi, Phys. Rev. Lett. 104, 136403 (2010)]. By applying machine learning to separately approximate each physical component of the interaction energy in a full many-body framework and with high and controllable accuracy, we can simulate the liquid accurately across a wide range of temperatures and pressures (with the inclusion of quantum nuclear effects) while gaining physical insight into the inner workings of the fluid. Following the recent success of this approach on predicting the equation of state of compressed fluid methane [M. Veit, S. K. Jain, S. Bonakala, I. Rudra, D. Hohl, and G. Csányi, arXiv:1810.10475], I will discuss how recent improvements in the efficiency of calculating representations of the atomic structures, and from there, the potential energy and force necessary to run simulations, will foster further development of this type of PES for other molecular liquids, as well as more demanding applications of these PESs to problems previously considered inaccessible to simulations with quantum-mechanical accuracy.
Hydropower Research and Testing at EPFL. The stability of our electrical grid is challenged daily by the massive integration of inherently intermittent renewable energy sources (RES), such as solar or wind power. However, in order to reach the ambitious goals for the decarbonization of our electricity supply, the share of these RES is yet to be significantly increased. In addition to being a highly reliable and clean source of electricity, hydropower also plays a crucial role in providing the grid with the necessary flexibility in terms of load balancing and frequency regulation. This requires an extension of the hydropower plants' operating range, occasionally giving rise to cavitation induced system critical flow instabilities inside the machines. The research aiming at understanding, simulating and predicting these limiting factors, in order to ultimately improve the power system's capacity to accommodate larger shares of RES, is outlined in this talk. Finally, EPFL's role as an independent provider of reduced scale model testing services in large hydropower projects around the globe is briefly introduced.
Machine learning potentials for molecular liquids by Max Veit (COSMO, EPFL)
Machine learning potentials for molecular liquids. The reliable prediction of the macroscopic properties of molecular liquids requires potential energy surface (PES) models that are not only accurate, but computationally efficient enough to handle large systems and reach long time scales typically inaccessible to explicit quantum-mechanical methods. First, I will introduce a new approach to the systematic approximation of the first-principles PES of a molecular liquid using the GAP machine learning method [A. Bartók, M. Payne, R. Kondor, and G. Csányi, Phys. Rev. Lett. 104, 136403 (2010)]. By applying machine learning to separately approximate each physical component of the interaction energy in a full many-body framework and with high and controllable accuracy, we can simulate the liquid accurately across a wide range of temperatures and pressures (with the inclusion of quantum nuclear effects) while gaining physical insight into the inner workings of the fluid. Following the recent success of this approach on predicting the equation of state of compressed fluid methane [M. Veit, S. K. Jain, S. Bonakala, I. Rudra, D. Hohl, and G. Csányi, arXiv:1810.10475], I will discuss how recent improvements in the efficiency of calculating representations of the atomic structures, and from there, the potential energy and force necessary to run simulations, will foster further development of this type of PES for other molecular liquids, as well as more demanding applications of these PESs to problems previously considered inaccessible to simulations with quantum-mechanical accuracy.
Practical information
- General public
- Free
Organizer
- MEGA.Seminar Organizing Committee