125 research outputs found

    Digital-Twins towards Cyber-Physical Systems: A Brief Survey

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    Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

    Flexible Views for View-based Model-driven Development

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    Modern software development faces the problem of fragmentation of information across heterogeneous artefacts in different modelling and programming languages. In this dissertation, the Vitruvius approach for view-based engineering is presented. Flexible views offer a compact definition of user-specific views on software systems, and can be defined the novel ModelJoin language. The process is supported by a change metamodel for metamodel evolution and change impact analysis

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

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    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

    Digital Twins in Industry

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    Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies

    Production Engineering and Management

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    The annual International Conference on Production Engineering and Management takes place for the sixth time his year, and can therefore be considered a well - established event that is the result of the joint effort of the OWL University of Applied Sciences and the University of Trieste. The conference has been established as an annual meeting under the Double Degree Master Program ‘Production Engineering and Management’ by the two partner universities. The main goal of the conference is to provide an opportunity for students, researchers and professionals from Germany, Italy and abroad, to meet and exchange information, discuss experiences, specific practices and technical solutions used in planning, design and management of production and service systems. In addition, the conference is a platform aimed at presenting research projects, introducing young academics to the tradition of Symposiums and promoting the exchange of ideas between the industry and the academy. Especially the contributions of successful graduates of the Double Degree Master Program ‘Production Engineering and Management’ and those of other postgraduate researchers from several European countries have been enforced. This year’s special focus is on Direct Digital Manufacturing in the context of Industry 4.0, a topic of great interest for the global industry. The concept is spreading, but the actual solutions must be presented in order to highlight the practical benefits to industry and customers. Indeed, as Henning Banthien, Secretary General of the German ‘Plattform Industrie 4.0’ project office, has recently remarked, “Industry 4.0 requires a close alliance amongst the private sector, academia, politics and trade unions” in order to be “translated into practice and be implemented now”. PEM 2016 takes place between September 29 and 30, 2016 at the OWL University of Applied Sciences in Lemgo. The program is defined by the Organizing and Scientific Committees and clustered into scientific sessions covering topics of main interest and importance to the participants of the conference. The scientific sessions deal with technical and engineering issues, as well as management topics, and include contributions by researchers from academia and industry. The extended abstracts and full papers of the contributions underwent a double - blind review process. The 24 accepted presentations are assigned, according to their subject, to one of the following sessions: ‘Direct Digital Manufacturing in the Context of Industry 4.0’, ‘Industrial Engineering and Lean Management’, ‘Management Techniques and Methodologies’, ‘Wood Processing Technologies and Furniture Production’ and ‘Innovation Techniques and Methodologies

    Actes des SixiÚmes journées nationales du Groupement De Recherche CNRS du Génie de la Programmation et du Logiciel

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    National audienceCe document contient les actes des SixiÚmes journées nationales du Groupement De Recherche CNRS du Génie de la Programmation et du Logiciel (GDR GPL) s'étant déroulées au CNAM à Paris du 11 au 13 juin 2014. Les contributions présentées dans ce document ont été sélectionnées par les différents groupes de travail du GDR. Il s'agit de résumés, de nouvelles versions, de posters et de démonstrations qui correspondent à des travaux qui ont déjà été validés par les comités de programmes d'autres conférences et revues et dont les droits appartiennent exclusivement à leurs auteurs

    New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

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    Premio Extraordinario de Doctorado de la UAH en 2013Lozano RodrĂ­guez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

    New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

    Get PDF
    Premio Extraordinario de Doctorado de la UAH en 2013Lozano RodrĂ­guez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study
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