| 24-11-2023 |
Modelamiento de la evaporación hidrógeno verde líquido en tanques de almacenamiento con geometría arbitraria
El hidrógeno verde es una tecnología de almacenamiento de energías renovables cuyo uso o combustión no genera dióxido de carbono. Su baja densidad a condiciones estándar de presión y temperatura (ρ = 0,082 kg/m^3) dificulta su escalamiento industrial. El hidrógeno líquido tiene una densidad 860 veces mayor, pero su punto de ebullición de 23 K a presión atmosférica produce que los alrededores lo calienten y evaporen durante su almacenamiento. El objetivo del IPRE es optimizar es desarrollar un modelo 1-D para la evaporación isobárica de hidrógeno verde líquido. Esto contribuirá a mejorar la competitividad económica y la seguridad del almacenamiento criogénico de energías renovables. Actividades: • Implementar modelo 1-D no estacionario (fase vapor) y de parámetros agrupados no estacionario (líquido) para tanques con geometrías arbitrarias en Python/Julia. • Parametrizar perímetros de tanques utilizados en la industria en función de la altura • Encontrar el mejor diseño Req: IIQ2003
Keywords:
Python
métodos numéricos
modelos matemáticos
energias renovables
Modelamiento físico
Hidrógeno verde
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/1 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 24-11-2023 |
Modeling of the evaporation of liquid green hydrogen in storage tanks with arbitrary geometry
Green hydrogen is a renewable energy storage technology that does not generate carbon dioxide when used or burned. Its low density under standard pressure and temperature conditions (ρ = 0.082 kg/m³) makes industrial scaling difficult. Liquid hydrogen has a density 860 times greater, but its boiling point of 23 K at atmospheric pressure causes the surroundings to heat it and evaporate during storage. The goal of IPRE is to develop a 1-D model for the isobaric evaporation of liquid green hydrogen. This will contribute to improving the economic competitiveness and safety of cryogenic storage of renewable energies. Activities: • Implement a non-stationary 1-D model (vapor phase) and a non-stationary grouped parameters model (liquid) for tanks with arbitrary geometries in Python/Julia. • Parameterize tank perimeters used in the industry as a function of height. • Find the best design. Requirement: IIQ2003
Keywords:
Python
métodos numéricos
modelos matemáticos
energias renovables
Modelamiento físico
Hidrógeno verde
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/1 available vacants |
Mentor(s): Open in the plataform |
| 07-12-2018 |
Deconvolution and optimal transport
One of the main properties of an optical system is its resolution. This is defined as the minimum separation between two ideal point sources so that they can be distinguished from one another when observed through the system. In practice, the diffraction of light imposes a physical limit to the resolution of the system. For a linear system, this process is typically modeled by a convolution by the Point Spread Function (PSF). For this reason, a technique that improves the resolution of the system can be interpreted as a deconvolution method. The objective of this project is to study the connection between deconvolution methods and optimal transport, and how the performance of deconvolution methods based on optimal transport compare to the state of the art. Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |