| 01-06-2022 |
Predicción de contactos termo-elasto-hidrodinámicos por medio de métodos de aprendizaje automático
La reducción de las pérdidas por fricción y desgaste en los tribo-contactos lubricados de elementos de máquinas o componentes mecánicos sometidos a grandes cargas es esencial para desarrollar sistemas energéticamente eficientes y fiables. En particular, la modelización de los contactos concentrados y termo-elasto-hidrodinámicamente lubricados (TEHL), en los que se superponen la deformación elástica local de las superficies de fricción y la formación de una lámina de fluido hidrodinámico, es comparativamente compleja y computacionalmente costosa. Basado en resultados previos, este proyecto estudiantil se basará en la hipótesis de que los métodos de aprendizaje automático predicen las características de los contactos con gran exactitud y más rápidamente que los complejos modelos de simulación. Los datos de las simulaciones de contacto se utilizarán para entrenar enfoques de aprendizaje automático, como Artificial Neural Networks.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 2/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 01-06-2022 |
Prediction of Thermo-elastohydrodynamic contacts by machine learning approaches
Reducing friction and wear losses in the lubricated tribo-contacts of heavily loaded machine elements or mechanical components is essential for developing energy-efficient and reliable systems. In particular, the modeling of concentrated and thermo-elasto-hydrodynamically lubricated (TEHL) contacts, in which local elastic deformation of friction surfaces and the formation of a hydrodynamic fluid film are superimposed, is comparatively complex and computationally expensive. Based on previous results, this student project will be based on the hypothesis that machine learning methods predict contact characteristics with high accuracy and faster than complex simulation models. Data from contact simulations will be used to train machine learning approaches, such as Artificial Neural Networks.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 2/2 available vacants |
Mentor(s): Open in the plataform |
| 15-12-2021 |
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |
| 27-12-2018 |
Virtual Reality Applications for Astrophysics
Numerical simulations are used to model complex astrophysical systems, from planetary systems to accretion discs around black holes. Many times, it is a challenge to analyse and even view the simulation data. Our group has already developed 360-degree animations that place the viewer at the position of the Milky Way's central black hole <https://youtu.be/YKzxmeABbkU>, or in the middle of two stars passing by each other. We are now interested to go one step further, and develop virtual reality (VR) applications, in which the user can move freely through the simulated system and manipulate it. We are looking for a student interested in actively participating in this development, using our VR hardware. While there are no formal requirements, we expect the student to have good programming skills and to be able to communicate in English. Experience and/or a strong interest in VR, Astrophysics, or digital animation will be considered a plus.
Keywords:
VirtualReality
hidrodinámica
visualización
Astronomia
astronomy
simulación numérica
astrofísica
astrophysics
virtual reality
realidad virtual
VR
black holes
agujeros negros
numerical simulation
hydrodynamics
visualisation
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |