Proyecto de construcción en la N-340 acceso este a Tarifa

El objeto del presente proyecto es la construcción de un nuevo acceso a Tarifa en la N-340. La zona de actuación será en el p.k 84+600 donde se encontraba un nudo de intersecciones. Todo ello dará paso a un glorieta, de manera que se pueda regular el flujo de vehículos adecuadamente.Universidad de Sevilla. Grado en Ingeniería Civi

Proyecto de Construcción. Acondicionamiento de la Carretera A-379. Tramo: La Guijarrosa – Variante de La Carlota (Córdoba)

En el presente Proyecto de clave: 2-CO-1486.2-0.0-0.0-PC, consideramos que se han recogido y definido la totalidad de las unidades de obras necesarias para ejecutar el proyecto “ACONDICIONAMIENTO DE LA CARRETERA A-379. TRAMO: LA GUIJARROSA – VARIANTE DE LA CARLOTA (CÓRDOBA)”. De conformidad con el artículo 127.2 del Reglamento general de la Ley de Contratos de las Administraciones Públicas, se manifiesta que el proyecto comprende una obra completa según lo exigido por el artículo 125 del citado Reglamento. El proyecto por tanto es completo y cumple además con los requisitos de legislación vigente, por lo que se eleva a la superioridad, por si estima oportuno apruebe y permita su ejecuciónUniversidad de Sevilla. Máster en Ingeniería de Caminos, Canales y Puerto

La experiencia de la UAM-AZC: con modelos estructurales de experimentación como material didáctico

Ponencia presentada a Session 3: Educación y arquitectura en las universidades / Architectural education in the universitie

Generative adversarial networks for data-scarce spectral applications

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization

The Quasi-Passive Quadruped Robot walking: PASIQUAD

The design of the four legged walking robot "PASIQUAD" is presented in this article. It was designed in the university Carlos III of Madrid. It is a quadruped quasi-passive robot (with only one motor/actuator). The manuscript is focused on how the PASIQUAD walks and the kinematics and dynamics of the movement. In the manuscript the position, velocity and acceleration of each of its parts, as well as all the forces and torques on each of them, motor torque included, will be explain. The PASIQUAD robot copy the movement of animals and it is almost passive. That is a big advantage in energy cost

Analysis of the influence of crack location for diagnosis in rotating shafts based on 3 x energy

The aim of condition monitoring is to detect faults before a catastrophic failure occurs. Cracks in rotating shafts are especially critical. The present work studies vibration signals obtained from a rotating shaft under different crack depths and locations. Tests were performed in a rig called Rotokit at a steady state at different rotation speeds. Signals obtained are analyzed by means of energy using the Wavelet theory, specifically the Wavelet Packets Transform. Nine crack depths in the shafts were tested, from 4% to 50% of the shaft diameter. Previous related work showed good reliability for crack diagnosis using 3 x energy for cracks in the middle section. In the present work, previous results are compared to the obtained for a crack in a change of section at one side. In both crack locations, large changes in energy are observed at 3 x at high speeds. Energy levels at this harmonic were used for the inverse process of crack detection, and probability of detection curves were calculated by thresholding. Cracks with depths above 12% can be detected with reliability in the locations tested using this method.The authors would like to thankthe Spanish Government for financing through the projects RANKINE21 IDI-20101560 and MAQ-STATUS DPI2015-69325-C2-1-R

Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.This research was funded by the Spanish Government through the project MAQSTATUS with grantnumber DPI2015-69325-C2-1-R

Capacidad del rotor de un acumulador cinético (FES: Flywheel Energy Storage) para diferentes materiales utilizando el cálculo analítico tensional

Nowadays, the energy storage is a fundamental aspect and there exists so many ways to do it. One of these ways, is the energy storage associated to rotational kinetic energy, better known like Flywheel Energy Storage (FES). FES is a complex system formed by different subsystems, and the most important of these subsystems is the rotor because it´s the element that enabled performs the function of storing energy. Rotor’s can be classified into two types: the rotors made with metal and the rotors made with composites. This article exposes a comparison between different settings of rotors to demonstrate the advantage of the use of composites comparing with the use of metals, especially in terms of energy density.La acumulación de energía es un aspecto fundamental en la época que vivimos y existen numerosas formas de llevarla a cabo. Una de estas formas es la acumulación en forma de energía cinética de rotación, en lo que se conoce como acumuladores cinéticos: Flywheel Energy Storage (FES). Los acumuladores cinéticos (FES) son sistemas complejos que aúnan una serie de subsistemas, pero el más importante de todos estos es el relativo al rotor, que es el elemento que permite desempeñar la función principal de acumular energía. Los rotores se pueden clasificar en dos: los que hacen uso de materiales metálicos y los que utilizan materiales compuestos. En este artículo se expone una comparativa llevada a cabo entre diferentes configuraciones de rotores que permite corroborar la ventaja que presentan los rotores de materiales compuestos con respecto a los metálicos, en términos de capacidad energética específica

Methodology for the navigation optimization of a terrain-adaptive unmanned ground vehicle

The goal of this article is to design a navigation algorithm to improve the capabilities of an all-terrain unmanned ground vehicle by optimizing its configuration (the angles between its legs and its body) for a given track profile function. The track profile function can be defined either by numerical equations or by points. The angles between the body and the legs can be varied in order to improve the adaptation to the ground profiles. A new dynamic model of an all-terrain vehicle for unstructured environments has been presented. The model is based on a half-vehicle and a quasi-static approach and relates the dynamic variables of interest for navigation with the topology of the mechanism. The algorithm has been created using a simple equation system. This is an advantage over other algorithms with more complex equations which need more time to be calculated. Additionally, it is possible to optimize to any ground-track-profile of any terrain. In order to prove the soundness of the algorithm developed, some results of different applications have been presented.The authors wish to thank the Spanish Government for financing provided through the MCYT project "RETOS2015: sistema de monitorización integral de conjuntos mecánicos críticos para la mejora del mantenimiento en el transporte-maqstatus" and also thank the anonymous reviewers for their insightful comments and suggestions on an earlier draft of this article

Deep learning for the modeling and inverse design of radiative heat transfer

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines, including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training data sets, we demonstrate this approach in the context of three very different problems, namely (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural-network architectures trained with data sets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio