Persona:
Rodríguez Prieto, Álvaro

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0000-0002-0712-7472
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Rodríguez Prieto
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    Analytical and numerical study for selecting polymeric matrix composites intended to nuclear applications
    (SAGE, 2019-12) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    This study describes a methodological proposal to select composite materials which are suitable to be employed to manufacture pipes that can properly withstand environments subjected to gamma and neutronic radiation. The methodology is used to select, among many others, the optimal composite material whose properties are used afterwards to simulate several pipe sections by finite element analysis, comparing the results with a well-known nuclear-grade steel, WWER 15Kh2MFAA. The most suitable composite material according to the defined criteria is composed of a phenolic resin matrix reinforced with long boron fibres and exhibit great properties to be used in a nuclear reactor environment: good radiation resistance and mechanical properties with a very low density at low cost. It can be concluded that, in some cases, composite material pipes can be a better option than steel ones. Extending the method to be employed in other industries or with other components could be seen as future works.
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    Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks
    (IEEE, 2020-01-10) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively.