Small jet engine reservoir computing digital twin

Machine learning was applied to create a digital twin of a numerical simulation of a single-scroll jet engine. A similar model based on the insights gained from this numerical study was used to create a digital twin of a JetCat P100-RX jet engine using only experimental data. Engine data was collected from a custom sensor system measuring parameters such as thrust, exhaust gas temperature, shaft speed, weather conditions, etc. Data was gathered while the engine was placed under different test conditions by controlling shaft speed. The machine learning model was generated (trained) using a next-generation reservoir computer, a best-in-class machine learning algorithm for dynamical systems. Once the model was trained, it was used to predict behavior it had never seen with an accuracy of better than 1.8% when compared to the testing data

A Simulation Based Approach to Digital Twin’s Interoperability Verification & Validation

The digital twins of production systems are one of the pillars of the Indus-try of the Future. Despite numerous on-going research and development initiatives the verification and validation of the digital twin remains a major scientific obstacle. This work proposes a simulation-based approach to achieve this goal: support Digital Twin verification and validation through the definition of a dedicated framework. A simulation model is used in place of the real-world system for ensuring the digital twin behaves as expected and for assessing its proper interoperability with the system to be twinned with. Then the simulation model is replaced by the real-world sys-tem, to interoperate with the verified and validated digital twin. With such an approach, the interoperability middleware, i.e. the IoT between the sys-tem and its digital twin can also be modeled, simulated, verified and vali-dated. Consequently, an optimized solution can be built for an entire value chain, from the system to its digital twin and conversely. © 2022 CEUR-WS. All rights reserved

Digital twins in cyber effects modelling of IoT/CPS points of low resilience

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port

Digital twins in cyber effects modelling of IoT/CPS points of low resilience

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port

A review of digital twin technologies for enhanced sustainability in the construction industry

Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.This work was supported by the Royal Academy of Engineering Industrial Fellowship [grant IF2223B-110

Diseño e implementación de un prototipo de " Digital Twin" aplicado a una planta PSA generadora de oxígeno, con el uso de internet de las cosas

El presente trabajo de investigación trata sobre el diseño e implementación de un “Digital Twin”.Empezando con el diseño del activo físico, desde la integración de sensores previamente instaladosen el lugar, pasando por la programación usada en todos los controladores. Posteriormente se diseña el activo digital, escogiendo el procesador teniendo en cuenta su mínimocosto y consumo de energía, pasando por la selección de servicios y la arquitectura que estos usaron, para finalmente interconectarlos. Luego se generaron dashboards para poder observar con claridad la información recogida por lossensores y enviadas previamente, como muestra de esto también se implementa un sistema SCADA, reforzando la facilidad de integración que trae el “Digital Twin”. También como parte de la investigación se desarrolla un modelo de Machine Learning como retroalimentación del “Digital Twin” hacia el activo físico, mejorando con esto la automatizacióndel proceso de generación de oxígeno, regulando el flujo para poder mantener la pureza de oxígen