Zobrazeno 1 - 10
of 1 725
pro vyhledávání: '"Baldan, P"'
Autor:
Baldan, Selena, Ferronato, Massimiliano, Franceschini, Andrea, Janna, Carlo, Zoccarato, Claudia, Frigo, Matteo, Isotton, Giovanni, Collettini, Cristiano, Deangeli, Chiara, Rocca, Vera, Verga, Francesca, Teatini, Pietro
Underground gas storage is a versatile tool for managing energy resources and addressing pressing environmental concerns. While natural gas is stored in geological formations since the beginning of the 20th century, hydrogen has recently been conside
Externí odkaz:
http://arxiv.org/abs/2408.01049
When can two sequential steps performed by a computing device be considered (causally) independent? This is a relevant question for concurrent and distributed systems, since independence means that they could be executed in any order, and potentially
Externí odkaz:
http://arxiv.org/abs/2407.06181
Autor:
Baldan, Giacomo, Guardone, Alberto
This study investigates the flow evolution past a pitching NACA0012 airfoil undergoing deep dynamic stall using a wall-resolved large eddy simulation approach. Numerical results are validated against experimental data from Lee and Gerontakos at Reyno
Externí odkaz:
http://arxiv.org/abs/2405.12036
A numerical investigation of the flow evolution over a pitching NACA 0012 airfoil incurring in deep dynamic stall phenomena is presented. The experimental data at Reynolds number Re = 135 000 and reduced frequency k = 0.1, provided by Lee and Geronta
Externí odkaz:
http://arxiv.org/abs/2404.14172
Autor:
Baldan, Marco, Di Barba, Paolo
Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residua
Externí odkaz:
http://arxiv.org/abs/2402.06261
Autor:
Baldan, Giacomo, Guardone, Alberto
A physics-based machine learning framework is developed to compute the aerodynamic forces and moment for a pitching NACA0012 airfoil incurring in light and deep dynamic stall. Three deep neural network frameworks of increasing complexity are investig
Externí odkaz:
http://arxiv.org/abs/2401.14728
Autor:
Marco Baldan, Paolo Di Barba
Publikováno v:
IET Science, Measurement & Technology, Vol 18, Iss 9, Pp 514-523 (2024)
Abstract Physics‐informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the lo
Externí odkaz:
https://doaj.org/article/d0e0cd888b80442eb7dede2982d4cbb1
Autor:
Baldan, Marco, Blauth, Sebastian, Bošković, Dušan, Leithäuser, Christian, Mendl, Alexander, Radulescu, Ligia, Schwarzer, Maud, Wegner, Heinrich, Bortz, Michael
Publikováno v:
Chemie Ingenieur Technik 96(5) 2024
Diazo compounds are gathering interest for their potential in promoting greener synthesis routes. We investigate, at a lab-scale, the continuous synthesis of diazo acetonitrile (DAN) using a micro-structured flow reactor and a flow reaction calorimet
Externí odkaz:
http://arxiv.org/abs/2310.09315
Autor:
Blauth, Sebastian, Baldan, Marco, Osterroth, Sebastian, Leithäuser, Christian, Apfel, Ulf-Peter, Kleinhaus, Julian, Pellumbi, Kevinjeorkios, Siegmund, Daniel, Steiner, Konrad, Bortz, Michael
Publikováno v:
Chemie Ingenieur Technik 96(5) 2024
We consider the shape optimization of flow fields for electrochemical cells. Our goal is to improve the cell by modifying the shape of its flow field. To do so, we introduce simulation models of the flow field with and without the porous transport la
Externí odkaz:
http://arxiv.org/abs/2309.13958
Autor:
Franceschini, Andrea, Zoccarato, Claudia, Baldan, Selena, Frigo, Matteo, Ferronato, Massimiliano, Janna, Carlo, Isotton, Giovanni, Teatini, Pietro
Underground gas storage (UGS) is a worldwide well-established technology that is becoming even more important to cope with seasonal peaks of gas consumption due to the growing uncertainties of the energy market. Safety issues concerning the reactivat
Externí odkaz:
http://arxiv.org/abs/2308.02198