Empowering Citizens with Digital Twins: A Blueprint

The exponentially growing amount of digital information and data analysis increase the ability to perceive the holistic situation of people. This article applies the digital twin paradigm to strengthen a person's ability to utilize information about themselves by creating a digital representation of their situation to support their well-being. More specifically, we propose a blueprint to empower individuals by improving their self-determination regarding their personal data. The blueprint will help service and data providers, both public and private, to develop a common understanding of the role and possibilities of a citizen's controlled personal digital twin of themselves-a citizen digital twin (CDT)-for creating people-centric solutions. The blueprint also provides a rational framework for service development based on CDTs and serves as a basis for strategic guidance of service development. We demonstrate this with a case study of confirmation class students.Peer reviewe

Digital Twins in Industry

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

OpenTwins: An open-source framework for the development of next-gen compositional digital twins

Funding for open access charge: Universidad de Málaga / CBU

Further Developing a Procedural Digital Twin for Road Tunnels

A Digital Twin is a close to as possible replica of a real world application, in the digital world. The Digital Twin aims to simulate a process, in real time so the user can generate information and value from the process. It is a representation of a physical asset, object or service. Often the Digital Twin takes data from real sensor to mimic the live process in a digital representation. In this thesis, the focus is on Digital Twin representations of road tunnels in Norway. This thesis builds further on previously designed implementation and thesis written at University of Stavanger. The project creates tunnel twins for any given Norwegian tunnel, by using Satens Vegvesen public API for tunnel information. Extending work made previously that connects static models with generated sensor data to display sensors in a tunnel made in Unity. This thesis furthers develops this project, by aiming to find ways to improve the architecture made in the current implementation while also finding new features to add based on what is seen in other Digital Twins in the academic world. Implementing these new features and changes to see how they are helpful to the continuous work of this Digital Twin project

Developing and Testing Digital Twins for Vehicle Collision Prediction: A Machine Learning and Genetic Search Algorithm Approach

This thesis focuses on developing a digital twin which can predict and avoid collisions. The digital twin does this by using different machine learning models that are trained on data from the SVL Simulator. By harnessing the power of machine learning, the digital twin demonstrates promising abilities in collision prediction and prevention. Additionally, a genetic search algorithm is developed to generate specialized testing data, enabling comprehensive evaluation of the digital twin's performance. The central contribution of this research lies in exploring the viability of utilizing test data that is generated by a genetic search algorithm to evaluate the performance of the digital twin. By employing the genetic search algorithm to generate data resembling real collision scenarios, classified as collisions, an interesting evaluation framework is established. Through the evaluation process, which involves analyzing the number of accurately classified collisions by the digital twin, insights are gained into the model's effectiveness in predicting collisions. This contributes to the ongoing efforts in enhancing the accuracy of collision prediction systems, ultimately leading to improved safety measures in autonomous driving and intelligent transportation systems

BIM integrated digital twin framework for improving data visualization

The use of Building Information Modeling (BIM) methodology in Facility Management (FM) has become increasingly important due to its potential to improve data visualization and decision-making processes. Digital twins, a virtual replica of a physical building, can be integrated with BIM to provide FM with more detailed and easy-to-analyze information. This study presents a BIM integrated digital twin framework for improving data visualization, and a case study implementation of a digital twin using Autodesk Forge to showcase indoor air quality data provided by a sensor in a room. Results show that the implementation of a digital twin through sensor integration has a great potential to improve the efficiency and effectiveness of facility managers in their daily activities. The continuous information provided by the digital twin can help facility managers make informed and proactive decisions to ensure occupant safety, well-being, and energy efficiency of the building