Collision Risk Model for High-Density Airspaces

This chapter describes a collision risk model (CRM) of airspace scenarios to describe their safety levels when populated by given air traffic. The model requires the use of representative data, containing a description of the flown aircraft trajectories. It is a combination of deterministic and probabilistic mathematical tools able to estimate the level of safety. Furthermore, the model captures the frequency and spatial distribution of the encounters and conflicts, the time in advance the conflict is identified and the overall reaction time of the Air Traffic Control ATC system, and finally, the effectiveness of the ATC as safety layer. The model considers that the risk of an air miss depends on two different factors: on the one hand, the frequency of exposure to risks and, on the other, the chance of collision associated to this exposure. The exposure to risk is captured following a deterministic data-driven approach, whereas the associated chance of collision is derived from a statistical mathematical model, fed by the kinematics of the encounter and the statistics associated to the accuracy of the aircraft state vector when following a planned trajectory

Analysis of the geometric altimetry to support aircraft optimal profiles within future 4D trajectory management

The use of barometric altimetry is to some extent a limiting factor on safety, predictability and efficiency of aircraft operations, and reduces the potential of the trajectory based operations capabilities. However, geometric altimetry could be used to improve all of these aspects. Nowadays aircraft altitude is estimated by applying the International Standard Atmosphere which differs from real altitude. At different temperatures for an assigned barometric altitude, aerodynamic forces are different and this has a direct relationship with time, fuel consumption and range of the flight. The study explores the feasibility of using sensors providing geometric reference altitude, in particular, to supply capabilities for the optimization of vertical profiles and also, their impact on the vertical Air Traffic Management separation assurance processes. One of the aims of the thesis is to assess if geometric altitude fulfils the aeronautical requirements through existing sensors. Also the thesis will elaborate on the advantages of geometric altitude over the barometric altitude in terms of efficiency for vertical navigation. The evidence that geometric altitude is the best choice to improve the efficiency in vertical profile and aircraft capacity by reducing vertical uncertainties will also be shown. In this paper, an atmospheric study is presented, as well as the impact of temperature deviation from International Standard Atmosphere model is analyzed in order to obtain relationship between geometric and barometric altitude. Furthermore, an aircraft model to study aircraft vertical profile is provided to analyse trajectories based on geometric altitudes

Predicting the electricity demand response via data-driven inverse optimization

A method to predict the aggregate demand of a cluster of price-responsive consumers of electricity is discussed in this presentation. The price-response of the aggregation is modeled by an optimization problem whose defining parameters represent a series of marginal utility curves, and minimum and maximum consumption limits. These parameters are, in turn, estimated from observational data using an approach inspired from duality theory. The resulting estimation problem is nonconvex, which makes it very hard to solve. In order to obtain good parameter estimates in a reasonable amount of time, we divide the estimation problem into a feasibility problem and an optimality problem. Furthermore, the feasibility problem includes a penalty term that is statistically adjusted by cross validation. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The estimated price-response model is used to forecast the power load of a group of heating, ventilation and air conditioning systems, with positive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. European Research Council: This research work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 755705)

A novel machine learning‐based methodology for tool wear prediction using acoustic emission signals

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%

RosneT: a block tensor algebra library for out-of-core quantum computing simulation

With recent Quantum Devices showing increasing capabilities to perform controlled operations, further development on Quantum Algorithms may benefit from Quantum Simulations on classical hardware. Among important applications one finds verification and debugging of Quantum Algorithms, and elucidating the frontier for real Quantum Advantage of new devices [1]. Tensor Networks are regarded as an efficient numerical representation of a Quantum Circuit, but exponential growth forces tensors to be distributed among computing nodes. A number of methods and libraries have appeared recently to implement Quantum Simulators with Tensor Networks [2], [3] intended for HPC clusters. In this work we develop a Python library called RosneT using a task-based programming model able to extend all tensor operations into distributed systems, and prepared for existing and upcoming Exascale supercomputers. It is compatible with the Python ecosystem, and offers a simple programming interface for developers

Criteria for Positioning Active Multilateration Stations Located Close to Distance Measuring Equipment

The need for the use of another surveillance system when radar cannot be used is the reason for the development of the Multilateration (MLT) Systems. However, there are many systems that operate in the L-Band (960-1215MHz) that could produce interference between systems. At airports, some interference has been detected between transmissions of MLT systems (1030MHz and 1090MHz) and Distance Measuring Equipment (DME) (960-1215MHz)

GPC mediante descomposición en valores singulares (SVD). Análisis de componentes principales (PCA) y criterios de selección

El control predictivo basado en modelos o Model Predictive Control (MPC), no hace referencia al diseño concreto de un controlador sino más bien a un conjunto de ideas o características para el desarrollo de estrategias de control que, aplicadas en un mayor o menor grado, dan lugar a diferentes tipos de controladores con estructuras similares. El MPC es una de las técnicas de control que más se ha desarrollado en los ámbitos académico e industrial en las últimas décadas debido sobre todo a su simplicidad y eficiencia. Sin embargo, no es fácil relacionar los parámetros de ajuste del controlador y las prestaciones del bucle cerrado. En este sentido, es importante diseñar algoritmos de control predictivo que garanticen la estabilidad nominal del bucle cerrado, con tiempos de cálculo pequeños y con un significado claro de sus parámetros sobre las prestaciones del sistema o sobre el esfuerzo de control. La aportación fundamental de esta tesis está relacionada con la definición de un nuevo tipo de controlador predictivo, el PC-GPC, versión modificada de un GPC estándar. En este controlador se ha sustituido el factor de ponderación de la acción de control por un nuevo parámetro denominado número de componentes principales (NPC). La relación entre el nuevo parámetro (NPC) y algunos indicadores numéricos, como la norma del vector de acciones de control o el número de condición de la matriz dinámica G, hacen que su elección esté basada en criterios menos subjetivos que la ponderación de las acciones de control. Además, se ha analizado este tipo de controlador tanto en el ámbito de procesos SISO como MIMO, así como sus características de robustez y estabilidad. Por otro lado, se ha deducido un método de cálculo de un controlador PC-GPC para garantizar la estabilidad nominal de bucle cerrado, cuando el modelo conocido es exacto.Sanchís Saez, J. (2002). GPC mediante descomposición en valores singulares (SVD). Análisis de componentes principales (PCA) y criterios de selección [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4924Palanci

Sistema de almacenamiento de energía eléctrica en central de ciclo combinado

El presente Proyecto Final de Carrera estudia la implantación de un sistema de almacenamiento de energía eléctrica, adecuado para la instalación en una Central Eléctrica de Ciclo Combinado. Así mismo, su estructura podrá ser aplicable a otros tipos de centrales eléctricas, industrias, o instalaciones con mínimas modificaciones. El objeto incluye el estudio, cálculo y diseño del sistema de almacenamiento de energía, evaluando diferentes tipos de tecnologías existentes en la actualidad, y seleccionando la más adecuada en cuanto capacidad de almacenamiento, impacto medioambiental, desarrollo de la tecnología, tamaño, costes, viabilidad económica, etc. Estudiándose a continuación la tecnología de almacenamiento a utilizar, así como su integración dentro del Ciclo Combinado, elementos auxiliares y aparamenta eléctrica para su interconexión con la instalación existente. Finalmente se realizará un estudio de viabilidad económica de la solución proyectada.Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de Cartagen