| 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 |
| 13-12-2022 |
Experimentos sobre factores que afectan la comprensión de modelos de procesos
La IPRE se enmarca dentro del proyecto Fondecyt 3210147 “Understandability of process models by domain experts – theory and applications" que tiene por objetivo profundizar sobre cómo los modelos de procesos son comprendidos por personas.
Prerequisitos:
IIC2733
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 22-12-2023 |
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |
| 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 |
| 13-12-2022 |
Prerequisites:
IIC2733
Evaluation method: Nota 1-7, with 1/2 available vacants |
Mentor(s): Open in the plataform |
| 19-07-2021 |
Generation of multi-colour 3d printable models of bacterial pumps for antibiotic resistance
This is a remote research project to generate good 3d printable models from protein structures in biological databases of the cell-membrane pumps that cause antibiotic resistances in bacteria. In addition to improving the 3d printability of the protein complexes, the quaternary structure of the proteins should also be used to segment the model into up to 4 materials (different colours for 3d prints), which help to understand the function of the pumps. Basic experience in 3d printing and interest to find solutions to problems independently is a requirement.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 15-07-2021 |
Keywords:
Modelos de proceso
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |
| 08-07-2020 |
Understandability of process models by domain experts – theory and applications
The project “Understandability of process models by domain experts – theory and applications” aims to deepen about how the process models are understood by individuals who, without necessarily being experts in BPMN, are experts in the field of the modeled process (domain experts). For example, healthcare staff with respect to the procedure of placement of a venous catheter. The project aims to define a conceptual framework on the factors that influence how domain experts understand BPMN models. Then, seeks to apply the above as (1) a set of modeling guidelines then applied to model-based training, and (2) process mining algorithm(s) that allow the repair models according to these guidelines. The project has, therefore, the following topics in which interested students are welcome: literature review, design and execution of experiments, and programming.
Keywords:
Minería de Procesos
Modelos de proceso
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 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 |