Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance

The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron

El soft power de China en Perú en los años 2005-2015

El presente trabajo aborda el estudio del soft power de China en Perú en la década 2005-2015.El objetivo principal de la investigación es la identificación de la estrategia y de las herramientas de proyección del poder blando – y su relación con la diplomacia pública – de la RPCh en Perú en el periodo de interés. La investigación analiza cuatro ejes identificados: la diplomacia tradicional (representación diplomática y acuerdos bilaterales, cooperación internacional, elementos de integración de la paradiplomacia), la diplomacia pública (actividades y comunicación de la Embajada de la RPCh en Lima, el “nation branding”, la e-diplomacy, becas estatales), la diplomacia cultural (asociaciones culturales, cooperación entre universidades y presencia de Institutos Confucio) así como la recepción del poder blando (encuestas, régimen visado y turismo). Este trabajo de investigación se desarrolla en seis capítulos. El capítulo I se orienta a definir el soft power y se proponen los indicadores para medirlo. El capítulo II aborda investigaciones y antecedentes del poder blando chino en general, relacionados a América Latina y al Perú. Los capítulos III, IV, V y VI analizan las herramientas del poder blando relacionados – respectivamente – a la diplomacia tradicional, pública y cultural, considerando también la orientación de la opinión publica peruana. Por último, se exponen las conclusiones y reflexiones producto del proceso de investigaciónTesi

Digital Image Correlation in Assessing Structured-Light 3d Scanner's Gantry Stability: Performing David's (michelangelo) High-Accuracy 3d Survey

Abstract. The paper presents results from applying Digital Image Correlation (DIC) technique to determine deformations and verify stability on a gantry during surveying operations on the Michelangelo's David at the Galleria dell'Accademia di Firenze museum in Florence. An advanced hi-resolution Structured-light 3D scanner has been used to create a hi-detailed digital twin of the masterpiece. Considering the high scanner sensitivity, a contactless, remote and passive monitoring system of the gantry stability has been chosen to guarantee maximum freedom of movement around the David and avoid any interference during scanning operations. Due to the remarkable elevation of the statue, which reaches almost 7 meters on his pedestal, and considering the cramped operating area around the statue, an ad-hoc gantry has been designed and deployed. The sophisticated scanner's technique and the extreme hi-resolution required for the survey needed firm gantry stability during scanning operations from one side. The complex geometries and the considerable extension of the statue surface impose extended flexibility and a nimble elevation platform from the other side. Thanks to the DIC technique the gantry stability has been constantly monitored with an accuracy of 0.03 ÷ 0,04 pixels, optimising scanning scheduling and, consequently, operations efficiency. A comparison of scans with post-processed deformation patterns allowed to optimise the scanning schedule, minimising downtime, and maintaining the needed platform stability threshold for effective scanning

Vibration Analyses of a Gantry Structure by Mobile Phone Digital Image Correlation and Interferometric Radar

The study presents results from applying the Real Aperture Radar interferometry technique and Digital Image Correlation through a mobile phone camera to identify static and dynamic deformations of a gantry during surveying operations on the Michelangelo’s David at the Galleria dell’Accademia di Firenze Museum in Florence. The statue has considerable size and reaches an elevation of more than seven meters on its pedestal. An ad-hoc gantry was designed and deployed, given the cramped operating area around the statue. The scanner had a stability control system that forbid surveying in instrument movements. However, considering the unicity of the survey and its rare occurrence, the previous survey had been carried out in the year 2000; verifying stability and recording deformations is a crucial task, and necessary for validation. As the gantry does not have an on-board stability sensor, and considering the hi-survey accuracy requested, a redundant, contactless, remote monitoring system of the gantry and the statue stability was chosen to guarantee the maximum freedom of movement around the David to avoid any interference during scanning operations. Thanks to the TInRAR technique, the gantry and the statue were monitored with an accuracy of 0.01 mm. At the same time, a Digital Image Correlation analysis was performed on the gantry, which can be considered a Multi-Degree-Of-Freedom (MDOF) system, to accurately calculate the vibration frequency and amplitude. A comparison between TInRAR and DIC results reported substantial accordance in detecting gantry’s oscillating frequencies; a predominant oscillation frequency of 1.33 Hz was identified on the gantry structure by TinSAR and DIC analysis

Clustering techniques applied to a high-speed train’s pantograph–catenary subsystem for electric arc detection and classification

Assessment of the current collection properties of a pantograph–catenary system mounted on a train is of great importance. Excessive electric arcing can lead to wear of the system’s components, and, at the same time, it can be an index of wear status. In this paper we investigate the possibility of detecting arcing events in the pantograph–catenary collection system without the need of additional equipment installed on-board the train. Data that is currently measured and recorded for modern high-speed trains (i.e. voltage and current) are analysed in order to detect and quantify electric arcs and shed light on the current collection quality of the pantograph–catenary system. This work was performed in cooperation with Trenitalia s.p.a. who provided the data it collects on-board high-speed trains in regular passenger service

Reducing CO2 Emissions and Improving Water Resource Circularity by Optimizing Energy Efficiency in Buildings

Climate neutrality by 2050 is a priority objective and reducing greenhouse gas (GHG) emissions, increasing energy efficiency, and improving the circularity processes of resources are the imperatives of regulatory and economic instruments. Starting from the central themes of the mitigation of the causes of climate change and the interdependence represented by the water–energy nexus, this research focuses, through the application of the principles of the circular and green economy, on deep energy zero-emission renovation through the improvement of circularity processes of water resources in their integration with energetic ones on the optimization of their management within urban districts, to measure their capacity to contribute towards reducing energy consumption and CO2 emissions during water use and distribution in buildings. After defining the key strategies and the replicable intervention solutions for the circularity of water resources, the investigation focuses on the definition of the research and calculation method set up to define, in parallel, the water consumption of an urban district and the energy consumption necessary to satisfy water requirements and CO2 emissions. Starting from the application of the calculation method in an existing urban district in Rome, 10 indicators of quantities have been developed to define water and energy consumption and their related CO2 emissions, focusing on the obtained results to also define some interventions to reduce water and energy consumption and CO2 emissions in territories that suffer a medium-risk impact from contemporary climatic conditions

Cytokine Overproduction, T-Cell Activation, and Defective T-Regulatory Functions Promote Nephritis in Systemic Lupus Erythematosus

Lupus nephritis (LN) occurs in more than one-third of patients with systemic lupus erythematosus. Its pathogenesis is mostly attributable to the glomerular deposition of immune complexes and overproduction of T helper- (Th-) 1 cytokines. In this context, the high glomerular expression of IL-12 and IL-18 exerts a major pathogenetic role. These cytokines are locally produced by both macrophages and dendritic cells (DCs) which attract other inflammatory cells leading to maintenance of the kidney inflammation. However, other populations including T-cells and B-cells are integral for the development and worsening of renal damage. T-cells include many pathogenetic subsets, and the activation of Th-17 in keeping with defective T-regulatory (Treg) cell function regards as further event contributing to the glomerular damage. These populations also activate B-cells to produce nephritogenic auto-antibodies. Thus, LN includes a complex pathogenetic mechanism that involves different players and the evaluation of their activity may provide an effective tool for monitoring the onset of the disease