Porous metals have low densities and novel physical, mechanical, thermal, electrical, and acoustic properties. Hence, they have attracted a large amount of interest over the last few decades. One of their applications is for thermal management in the electronics industry because of their fluid permeability and thermal conductivity. The heat transfer capability is achieved by the interaction between the internal channels within the porous metal and the coolant flowing through them. This paper studies the fluid flow and heat transfer in open-cell porous metals manufactured by space holder methods by numerical simulation using software ANSYS Fluent. A 3D geometric model of the porous structure was created based on the face-centered-cubic arrangement of spheres linked by cylinders. This model allows for different combinations of pore parameters including a wide range of porosity (50 to 80 pct), pore size (400 to 1000 lm), and metal particle size (10 to 75 lm). In this study, water was used as the coolant and copper was selected as the metal matrix. The flow rate was varied in the Darcian and Forchheimer's regimes. The permeability, form drag coefficient, and heat transfer coefficient were calculated under a range of conditions. The numerical results showed that permeability increased whereas the form drag coefficient decreased with porosity. Both permeability and form drag coefficient increased with pore size. Increasing flow rate and decreasing porosity led to better heat transfer performance.

The current study presents the thermal control design of the 3U Cubesat "Pakal" from "Misión Colibrí". As part of the design requirements, a thermal analysis was carried out to study the effects of the space environment over the spacecraft. This was achieved by making a lumped parameter approach in the critical thermal cases and studying the effects of environmental heat fluxes on each critical component. The model was verified using Thermal Desktop software. Then a Passive Thermal Control (PTC) was proposed to keep critical components at their operating temperature. The proposed PTC consists of two types of passive control: thermal coatings and single-layer insulation.

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During many years, the search for new and improved materials has been an arduous task. It has mainly focused on experimentation and in recent years on computer aided techniques (i.e. numerical simulation). These two approaches defined the way material science works. Yet, both techniques have shown cost-efficiency disadvantages. Optimization algorithms, like the ones used in machine learning, have proven to be an alternative tool when dealing with lots of data and finding a particular solution. Even though the use of machine learning is a well stablished technique in other fields, its application in material science is relatively new. Material Informatics provides a new approach to analyse materials such as porous metals by employing previous data sets. This paper studies a new technique to predict the heat transfer coefficient of an open-cell porous structure while running water passes through the material. A CFD data set was employed by a Machine Learning technique in order to establish a relationship between the input parameters (porosity, pore size, pore distribution and flow rate) and the heat transfer coefficient of the sample. The results obtained from the analyses were compared with previous findings, concluding that by utilising a Machine Leaning technique is possible to obtain a more accurate and much better fit model.