The ability to reduce risk and uncertainty in the operation and performance of complex adaptive systems is highly desirable across a range of applications and sectors, especially when the correct operation of those systems is critical. Recently, digital twins have delivered this capability for cyber-physical systems by combining modelling with real/relevant-time information flows. Increasingly, however, critical national infrastructure which were previously isolated networks (such as transport and energy networks) are accelerating their levels of integration, forming cyber-physical ecosystems (CPES) and creating new design challenges for digital twins. One fundamental challenge lies in synthesizing information from multiple sources in order to understand the ecosystem as a whole. In this paper, we propose a novel mapping framework to meet this need: the ‘domain relationship diagram’, which balances flexibility and interpretability with sufficient rigour to support model-based design. After establishing the method, we illustrate its usage through two use cases, both motivated by priority research in critical CPES: (i) flooding resilience in northern England and (ii) energy infrastructure at the Port of Dover. We also tie this approach into other crucial topics in digital twins for CPES, including ownership, ontologies and communication, and trust and resilience.

Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to materialise. In this paper, we discuss the key issues associated with policy digital twins, and the ways in which they differ from, and are similar to, their counterparts in other areas. We describe how multi-level agent based modelling can be used within policy digital twins to include the effects of human behaviours on outcomes; an aspect that is often largely overlooked. We also describe how digital twins can be designed for policy use cases, and present as a case study the design of a policy digital twin incorporating multi-level agent based modelling to aid a UK city council (local authority) in delivering energy transition policy. After describing both the design method used and the resultant digital twin, we discuss the effectiveness of both, as well as how the ways in which different contexts might shape the future architecture of the digital twin.

In this chapter we consider how to build a structured knowledge graph (KG) for a geometric and behavioral digital twin in the context of modal testing. The concept is based on combining geometric information from computer-aided design (CAD) model(s) and dynamic properties extracted from modal testing data and a finite element analysis (FEA) to create a digital twin. The material properties of the structure are defined in a separate bill of materials that is uploaded to the digital twin. The functionality of the digital twin is to provide a continuous "digital thread" of events during the modal test and gather the information that at a later stage could be used for validation and updating the finite element model of the structure (although that is not discussed in detail here). The modal testing data is taken from a small-scale three-story structure that is used to demonstrate the concept. The structured KG is built using the Neo4j interface, operated by the py2neo Python package. The KG is defined in an entity-event format that can be dynamically updated as new information is received in the digital twin. The CAD information is integrated into the KG using an STL file format. The KG is seeded with the STL file data and the bill of materials. Then as the modal test proceeds, data segments obtained from the sensors are added to a database and simultaneously added to the KG. As the KG evolves, it creates a digital thread of the test that can be interrogated as required to provide information to the user(s) and enable more effective asset management. The KG can also be integrated into a wider digital twin functionality of the structure.

Assembled systems typically contain mechanical joints that are in physical contact and heavily influenced by friction and vibration. Friction is affected by contact stress, temperature, material, and roughness of contacting parts, from geometrical features at the macro- to nanoscale. Understanding and predicting the friction of contact helps to create designs that reduce wear, crack propagation, damage, and energy consumption. Recently, digital twins have been used in different mechanical engineering mechanisms and systems to predict crack, damage, and frequency response functions. Digital twins, with their system-level thinking, have promoted the idea of cross-industry development and ideology. The aim of the current study is to develop the digital twin-enabling technology for a simple dry contact under reciprocating motion. This enabling technology (digital twins) is the development of a grey-box model using conventional tribometer experimental data under cyclic loading and advanced multi-scale (contact mechanics to macro-scale dynamics) finite element analysis to provide an accurate estimation in a realistic time scale for digital twins. To demonstrate this, a ball and a flat plate made of steel (304) were used to create a physical twin. The test was run using a Universal Mechanical Tester (Broker UMT-3 tribometer) under speed and load sweep conditions to determine the coefficient of friction at different operating conditions. The experimental data for friction were collected and used for machine learning along with an FEA model using Abaqus which makes the digital twin. The machine learning part of the digital twin was used to predict the coefficient of kinetic friction under different operating conditions and can interoperate with other models to greatly expand the digital twin functionality. The predicted coefficient of friction was fed to FEA model to predict the mechanical behaviour of the system such as Frequency Response Function.

Mathematics) and is affiliated with the IPPT PAN since 1973.He has promoted the Department of Intelligent Technologies, devoted mostly to structural health monitoring problems, adaptive impact absorption, and dynamic load identification (both off-line and on-line).

One of the most common uses of digital twins is to provide information to a user to aid in the decision-making process. The process a digital twin undertakes to generate information can be considered a digital twin output function. These can involve predictive simulations, historical trends, and other types of analysis using the data gathered directly from the physical twin and the models contained within the digital twin. Because of this dependency on model simulations and gathered data, the concept of trust is highly relevant to the development of digital twins. To evaluate the trust of a digital twin, specifically the quantitative aspects, the calculation of uncertainty and performance metrics is vital. This chapter considers how performance metrics can be used to compare the output functions of a digital twin to the measured quantities of interest in the physical twin, and thus provide additional information to establish trust in the digital twin that aids in decision-making. This approach will be demonstrated using an engineering dynamics example related to vibration testing.

Chatter is one of the major issues that cause undesirable effects limiting machining productivity. Passive control devices, such as tuned mass dampers (TMDs), have been widely employed to increase machining stability by suppressing chatter. More recently, inerter-based devices have been developed for a wide variety of engineering vibration mitigation applications. However, no experimental study for the application of inerters to the machining stability problem has yet been conducted. This article presents an implementation of an inerter-based dynamic vibration absorber (IDVA) to the problem of chatter stability, for the first time. For this, it employs the IDVA with a pivoted-bar inerter developed in the study by Dogan et al. (2022, “Design, Testing and Analysis of a Pivoted-Bar Inerter Device Used as a Vibration Absorber, Mechanical Systems and Signal Processing,” 171, p. 108893) to mitigate the chatter effect under cutting forces in milling. Due to the nature of machining stability, the optimal design parameters for the IDVA are numerically obtained by considering the real part of the frequency response function (FRF), which enables the absolute stability limit in a single degree-of-freedom (SDOF) to be maximized for a milling operation. Chatter performance is experimentally validated through milling trials using the prototype IDVA and a flexible workpiece. The experimental results show that the IDVA provides more than 15% improvement in the absolute stability limit compared to a classical TMD.

The role of inerter-based devices has generated considerable interest in terms of suppressing the vibrations in machines and structures. The inerter is a mechanical device that generates force proportional to the relative acceleration between its terminals. Recently, it has been shown that inerter-based dynamic vibration absorbers (IDVAs, for the mass ratios between 0 and 0.2) can improve the chatter suppression performance compared to a traditional tuned mass damper (TMD) for the same mass ratios. This study proposes an IDVA applied to machining operations as a novel active control method to increase chatter suppression performance. Considering the TMD application as a virtual passive absorber (VPA) method in active control, IDVAs can be potentially employed in the same framework. A proof-mass actuator, which is mounted on a beam that is designed to support a flexible structure, is proposed. Once the IDVA parameters are optimised, a time-domain model is applied to explore the actuator saturation effects. The effect of an IDVA as a novel active control method on chatter stability is then evaluated. The simulated stability lobe diagram shows that the IDVA increases the absolute chatter stability by just above 20%. To validate the simulation results, an experimental setup is designed including a flexible workpiece to be machined and a proof-mass actuator assembled using a beam. In summary, it is shown that inerter-based dynamic vibration absorbers, as an active control method, can successfully be implemented to improve the chatter suppression performance and critical limiting depth of cut.