Digital Twins of the Ocean can foster a sustainable blue economy in a protected marine environment

Digital twin technology, originally developed for industrial applications, is gaining increased attention in ocean governance and multilateral negotiations. While existing research gives insights into the scope of digital ocean twin applications for environmental governance and associated technical challenges, they do not sufficiently explore how their development and use takes place across politically contentious spaces in which various public and private actors operate. To address this gap, our paper pursues two research questions: ‘Who develops and uses digital twins of the oceans and for which purposes?’ and ‘Which promises and risks are associated with digital ocean twins in the context of multilateral negotiations?’. The paper is based on empirical bibliometric research into academic literature, patents, and policy documents to identify political, scientific, and corporate actors involved in developing and applying digital ocean twins and to map their discourses. By offering a holistic view of how digital twins are, or could be, applied in ocean governance, this paper aims to contribute to the development of effective, technology-driven, but also politically sensible approaches environmental governance.

The Iliad Digital Twins of the Ocean project [1] is a large (55 partners) European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EU DTO) initative including interoperability with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The approach of Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline.The four digital twin pipeline steps are: Digital Twin Data Acquisition/Collection, Digital Twin Data Representation, Digital Twin Hybrid and Cognitive/AI Analytics Models and Digital Twin Visualisation and Control. The Iliad project has idenified these four steps as main architectural pipeline areas from an interoperability perspective, as described in the following. The architecture is system of systems based and the figure also shows the existence of potential multiple digital twins interactions.The first Digital Twin step focuses on Data acquisition and collection from various sources including collection of realtime see\nsor data, for input to the Digital Twin. This is supported by various Data Spaces and also through a direct Stream Handler. This includes both streaming data and data extraction from relevant external data sources and sensors. It includes support for handling all relevant data types and also relevant data protection handling for this step. In the Digital Twin sensor context this includes the full Observation Pyramid from remote sensing through airborne sensors to surface and subsea sensors and in-situ and IoT sensors.The second Digital Twin step focuses on Digital Twin Data Representation. The data availability for the digital twins is supported by various Digital Twin Data Lakes – connected to Data Spaces and also potentially directly to streaming observations from the previous step.The third Digital Twin step focuses on Digital Twin Hybrid and Cognitive/AI Analytics Models. The processing execution for the models is supported by various Digital Twin Engines. The fourth Digital Twin step focuses on Digital Twin Visualisation and Control. This is being supported by various types of 2D/3D/4D visualisations, and immersive visualisations and further evolutions towards the GeoVerse perspective on MetaVerse.The Iliad project is providing a framework with tools and services for these four digital twin pipeline steps aiming at technical and semantic interoperability with, and portability to, the EU DTO ecosystem of digital twins of the ocean.[1] Iliad – Digital Twins of the Ocean project, https://ocean-twin.eu/

Autonomously driving vehicles and robots that drive in public environments need to be safe and reliable under all weather conditions, including arctic winter conditions. Digital twins provide an opportunity to test autonomous vehicles in a safer, faster, and less expensive environment than carrying out tests in real-life conditions. We developed the data connection via ROS (Robot Operating System) between a mobile robot and its digital twin. This allows for almost real-time exchange of commands, information, and sensor data between the twins.The digital twins of the robo t and the testing ground are constructed in a CARLA-based autonomous driving simulator, which simulates realistic arctic winter weather conditions.The digital twin design was informed by the intended future use cases: Testing, optimizing, controlling, and monitoring autonomous driving and snow cleaning functions first with the digital twin, then in hybrid approaches.In our test setup we tested the hybrid case, where both robot twins were moving in the simulation and the real-world test area at the same time. We verified our digital twin, assessed delays, and the applicability in the intended use cases. Our results show that the digital testing ground would profit from inbuilt reference points to examine the alignment with its real-world counterpart. The communication via ROS was occurring in almost real-time , therefore, the digital twin setup was found to be applicable in hybrid digital twin testing. In the future, we will introduce an autonomous car into this digital twin setup and equip the testing ground with a 5G network.

Looking back at 30 years of research into holonic manufacturing systems, these explorations made a lasting scientific contribution to the overall architecture of intelligent manufacturing systems. Most notably, holonic architectures are defined in terms of their world-of-interest (Van Brussel et al., 1998). They do not have an information layer, a communication layer, etc. Instead, they have components that relate to real-world assets (e.g. machine tools) and activities (e.g. assembly). And, they mirror and track the structure of their world-of-interest, which allows them to scale and adapt accordingly.This research has wandered around, at times learning from its mistakes, and progressively carved out an invariant structure while it translated and applied scientific insights from complex-adaptive systems theory (e.g. autocatalytic sets) and from bounded rationality (e.g. holons). This paper presents and discusses the outcome of these research efforts.At the top level, the holonic structure distinguishes intelligent beings (or digital twins) from intelligent agents. These digital twins inherit the consistency from reality, which they mirror. They are intelligent beings when they reflect what exists in the world without imposing artificial limitations in this reality. Consequently, a conflict with a digital twin is a conflict with reality.In contrast, intelligent agents typically transform NP-hard challenges into computations with low-polynomial complexity. Unavoidably, this involves arbitrariness (e.g. don’t care choices). Likewise, relying on case-specific properties, to ensure an outcome in polynomial time, usually renders the validity of an agent’s choices both short-lived and situation-dependent. Here, intelligent agents create conflicts by imposing limitations of their own making in their world-of-interest.Real-world smart systems are aggregates comprising both intelligent beings and intelligent agents. They are performers. Inside these performers, digital twins may constitute the foundations, supporting walls, support beams and pillars because these intelligent beings are protected by their real-world counterpart. Further refining the top-level of this architecture, a holonic structure enables these digital twins to shadow their real-world counterpart whenever it changes, adapts and evolves.In contrast, the artificial limitations, imposed by the intelligent agents, cannot be allowed to build up inertia, which would hamper the undoing of arbitrary or case-specific limitations. To this end, performers explicitly manage the rights over their assets. Revoking such rights from a limitation-imposing agent will free the assets. This will be at the cost of reduced services from the agent. When other service providers rely on this agent, their services may be affected as well; that’s how the inertia builds up and how harmful legacy is created. Thus, the services of digital twins are to be preferred over the services of an intelligent agent by developers of holonic manufacturing systems.Finally, digital twins corresponding to the decision making in the world-of-interest (a non-physical asset) allow to mirror the world-of-interest in a predictive mode (in addition to track and trace). It allows to generate short-term forecasts while preserving the benefits of intelligent beings. These twins are the intentions of the decision-making intelligent agents. Evidently, when intentions change, the forecasts needs to be regenerated (i.e. tracking the corresponding reality by the twin). This advanced feature can be deployed in a number of configurations (cf. annex).

Recent advancements in health care digitalization opened the collection and availability of big data, whose analysis requires artificial intelligence-based technologies to facilitate the development of predictive tools supporting decision making in clinical practice. In this context, the idea of constructing 'digital worlds' to evaluate the performance of such novel tools becomes more attractive. Digital twins (DTs) are 'digital objects' characterized by a bi-directional interaction with their 'real-world counterparts'. DTs aim to enhance predictions further by leveraging both the predictive capabilities of digital simulations and the continuous updating of real-life data-ideally incorporating clinical records, multiomics data, and patient-reported outcomes. DTs can potentially integrate these diverse data into virtual models applicable across pre-clinical to clinical studies. Running simulations in silico on cancer cells or cancer patients' DTs can provide valuable insights into cancer biology, clinical practice, and health care education, with the added value of reducing costs and overcoming many common limitations of current studies (limited number of variables, challenges in recruiting patients with rare tumors, lack of real-life feedback). Despite their significant potential, DTs are still in their infancy, facing numerous unsolved technical and ethical challenges that hinder their application in clinical practice.

Digital Twins represent a major step towards integrated engineering analysis of data available for a physical system. A digital twin is a virtual replica of an asset and aims to provide relevant information for decision-makers, enabling risk mitigation and optimization of its real-world counterpart. Physical simulations, optimization, and data analytics, combined, enhance efficiency and safety during lifecycle. The operational insights provided to operators by plotting precise and contextualized data contained in the digital twin ecosystem improve company's workforce productivity, avoid mistakes and contribute to better and safer asset operation. Data flowing from the physical object to its digital representation ensures the update of the Digital Twin state in an automated or supervised manner. Data generated by the Digital Twin can provide valuable information for operators and decision makers, assisting in production optimization and risk mitigation. Controlling the physical object can then be done manually or automatically in order to attend the aspired goals. The so-called 4.0 Industry tends to gradually incorporate Digital Twins into its processes, leading to a blurred boundary between the physical and the digital worlds. In the Oil & Gas industry, digital twins are becoming a reality across many stages of the production process. This article shows the progress and challenges of a digital twin solution focused on upstream, or, more precisely speaking, on the Flow Assurance and Artificial Lift aspects of oil and gas production of Brazilian offshore fields.

Digital Twins have recently emerged as a major new area of innovation. Digital Twins are often found at the core of “smart” solutions that have also emerged as major areas of innovation. Modeling and Simulation (M&S) approaches create a model of a real-world system that is linked to data sources and is used to simulate and predict the behavior of its real-world counterpart. On the face of it Digital Twins and M&S appear to be similar, if not the same. Is this actually the case? Are the two fields really separate or is Digital Twin research re-inventing the “M&S wheel”? To investigate these relationships, in this panel we will explore some contemporary innovations with Digital Twins and discuss whether or not Digital Twins is a contemporary “refresh” or “rebranding” of M&S or if there are exciting new synergies.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

PurposeThe implementation of digital twin in e-government services will become the future of public service delivery. It has a great promise for significantly optimizing e-government service delivery in public services because digital twin can be leveraged to achieve value co-creation, which can be turned for innovation and new knowledge creation. The purpose of this study is to fill a knowledge gap in the domain of e-government with digital twin enabled.Design/methodology/approachThis study examined the concept of digital twins in the context of e-government for innovation management. This research applied exploratory research discussing a dynamic and interpretive model that examines the main factors to consider when developing digital twins for the Fourth Industrial Revolution’s integration of e-government services. This study begins with a thorough assessment and then evaluates the results to propose a model that would be used as a benchmark for future research. Secondary data was gathered from a variety of previously published primary research sources, including peer-reviewed journals, case studies, periodicals, newspapers and books.FindingsE-government with digital twin platform will become increasingly integral to business or public value creation and can be managed individually as people and organizations expect much greater value for their well-being that is linked to a number of better outcomes. E-government with digital twin will no longer to be seen as a static web service but the next enabling platform to offer a comprehensive digital advisory for each and every user. The digital twin’s goal is to extract all of a user’s digital activity processes and thoroughly analyze them across all of e-services. When there are crucial issues or problems that need to be alerted to the (physical) user, the digital twin will present options, solutions and recommendations based on the entire gathered data continuum.Research limitations/implicationsThis study is conducted to provide a better understanding of the digital twin’s impact on public service delivery in the future. When it comes to e-government, a digital twin is a digital representation of an individual with the ability to integrate e-government services (such as e-citizenship, e-employment, e-participation, e-business, e-commerce, e-health, e-learning, e-regulation, e-entertainment and so on) with nearly real-time data and advanced analytics. Individuals will be able to improve, discover, foresee and make better and faster decisions as a result of the digital twin. The proposed model shows a future scenario for e-government services, in which the key principle of Industrial Revolution 4.0, Cyber Physical Systems, is accommodated by digital twins.Originality/valueThis study provides academics, policymakers and practitioners in the fields of technology, public and/or private service delivery and public policy, with the opportunity to define priorities, processes and outcomes of e-government services and thereby benefit more directly from the findings of the study. This study presents some novel insights into e-government services the use of digital twins to optimize public service delivery.

Smart cities in the time of climate change and Covid‐19 need digital twins

The internet of things, digital twins of smart connected products, and thereby enabled smart services are topics of great interest and have been gaining traction for many years. However, many questions concerning the application-oriented usage of digital twins still need to be scrutinized. Therefore, this paper examines the question of an application-oriented framework for value creation with digital twins using design science research approaches. A conceptual reference framework is presented based on earlier research and iteratively developed within workshops with three companies. The framework incorporates primary dimensions of external and internal value creation and data resources. Further, it discusses the product life cycle, the real-world counterpart, value creation in the ecosystem, and the generational aspect of the digital twins. Furthermore, applying the framework to a use case with an industrial research partner helps to show the contributions to the industrial sector. The framework provides utility to practitioners as a means of creating a common sense in interdisciplinary teams, communicating digital twin projects to internal and external stakeholders, and as a toolbox for specific challenges concerning digital twins. In addition, the framework distinguishes itself from existing approaches by including the service ecosystem and its actors while considering the principles of product life cycle management. Therefore, using the framework in other use cases will test the approach on different industries and products. Furthermore, there is a need to develop approaches for implementing and developing an existing case.

Emergencies such as storms, wildfires, floods, or earthquakes can affect various interconnected environmental and critical infrastructure systems, potentially causing failures that may cascade from one system to another. For instance, such events can contaminate water sources, disrupt the operation of water treatment, supply, disinfection, and distribution, cause overflows in sewerage and drainage systems, and disrupt services in power grids, telecommunication networks, and transportation networks. Especially in cases of contamination or disruption of disinfection, such events can result in severe risks to public health, the economy, and the environment.Addressing these challenges requires a unified Cyber-Physical-Socio-Environmental System (CPSES) approach that models the interactions and dependencies among the various components. We propose an Integrated Digital Twin architecture as a holistic framework that incorporates and coordinates different Digital Twins modelling the different Cyber, Physical, Social and Environmental systems, to capture the propagation of contaminants and estimate their impact.The CPSES framework incorporates real-time sensor data, geographical information systems (GIS), computational models, and state-estimation algorithms to dynamically model events and enable proactive planning and real-time decision support for local authorities, first responders, utility operators, and public health officials.For example, a sudden storm can increase water levels in a reservoir, causing an overflow that significantly raises the water level in a downstream river. This, in turn, can lead to sewage overflow from a nearby manhole, potentially affecting first responder operations, and flooding a power substation, which disrupts its operation and, in turn, disconnects a pump supplying water to a central tank.A core technology for implementing this framework are the Data Spaces, which serve as secure, standardized environments for ingesting and sharing data among multiple stakeholders and infrastructure operators. Moreover, State Estimation is critical for producing realistic assessments of the current and near-future states of the system. State Estimation can be extended by combining physics-based models with machine learning, to estimate unobserved system states and continuously update parameter values. As a result, data spaces, integrated with GIS, computational models, state estimation, and machine learning, provide a Digital Twin that serves as a single point of reference. This allows risk analysts to assess vulnerabilities, estimate the spread of events, and model cascading effects on other systems.This integration, facilitates rapid and precise interventions, such as rerouting water supplies, isolating at-risk sewer lines, or reconfiguring power distribution. The HPC-based urgent-computing paradigm can also be considered to ensure stakeholders receive risk assessments, contamination maps, and infrastructure failure forecasts within the strict timeframes required for crisis response.To demonstrate real-world applicability, we discuss the Cyprus Digital Twin, an innovative platform where a simulated emergency triggered a contamination/overflow event in the Yermasogia Reservoir. This event threatened the aquifer and the extraction of potable water from boreholes. By integrating contamination propagation models, public health models, flood hazard models, geospatial data, power network fragility curves, and real-time sensor measurements, the Digital Twin and its tools were able to provide comprehensive situational awareness, assess the potential impact of the event, and support the rapid decision-making process.

Waterborne transport accounts for approximately 25% of the European Union’s greenhouse gas (GHG) emissions, a share expected to grow with increasing global trade. To meet the EU’s goal of a 90% reduction in GHG emissions by 2050, alternative fuels and innovative technologies are essential. Ammonia (NH 3 ) has emerged as a promising zero-carbon fuel; however, it requires nearly three times the storage volume of conventional fuel oil. The NH3CRAFT project addresses this challenge by aiming to design and demonstrate safe, sustainable large-scale NH 3 storage—1,000 m 3 at 10 bar—aboard a 31,000 Deadweight tonnage (DWT) vessel. The project employs a digital platform that integrates computational models, simulations, and real-time sensor data—including AI-enabled inspections—to support the development of a Digital Twin (DT). While DTs are well-established in the automotive and aerospace industries, their use in maritime applications is evolving, especially in the areas of structural health monitoring (SHM) and predictive maintenance. This study applies a systems engineering approach from an SHM specialist’s perspective to assess the feasibility of implementing a DT specifically for the NH 3 fuelling system. Key technical gaps and system-level considerations for NH 3 -specific SHM integration into the DT were identified through collaboration with project partners. These include challenges in sensor deployment, data fusion, and the need for high-fidelity modelling of NH 3 behaviour under dynamic maritime conditions. The study outlines requirements for building a hard system capable of ensuring long-term structural integrity and safety. The development of a DT for the NH 3 fuelling system presents significant potential to enhance SHM and operational safety. A strategic decision remains pending within the consortium regarding the full-scale implementation of the DT. This study provides a foundational framework to support that decision and future DT development in maritime NH 3 applications.

Digital twins serve as comprehensive virtual replicas of real world objects and flourish in combination with modern simulation technology, resulting in a broad variety of simulation-based methods. A key aspect in the realization of digital twins is an object-oriented modeling approach, which is based upon a distinct differentiation between model structures and simulation functions, allowing both to be developed independently. Real world objects are encapsulated in structurally equivalent digital twins, enabling to model complex system-of-systems with the same semantic as their real world counterpart. Despite the great potential of digital twins, current realizations lack the capability to fuse digital twins with formalized description of their interactive behavior as e.g. required for the functional validation of systems in complex operational scenarios. The behavior of the overall system needs to be implicitly derived from the individual actions and reactions of all involved actors, resulting in a potential emergent behavior that must to be mirrored in simulation. Subsequently, the implementation of such an approach requires the integration of capabilities to the structure of digital twins as well as a novel methodology for the process and behavior modeling with digital twins. In this paper, we present such an approach and its application for the functional validation of an ADAS function by simulating and analyzing virtual test scenarios.

In an Industrie 4.0 (I4.0), rigid structures and architectures applied in manufacturing and industrial information technologies today will be replaced by highly dynamic and self-organizing networks. Today’s proprietary technical systems lead to strictly defined engineering processes and value chains. Interacting Digital Twins (DTs) are considered an enabling technology that could help increase flexibility based on semantically enriched information. Nevertheless, for interacting DTs to become a reality, their implementation should be based on open standards for information modeling and application programming interfaces like the Asset Administration Shell (AAS). Additionally, DT platforms could accelerate development and deployment of DTs and ensure their resilient operation.This chapter develops a suitable architecture for such a DT platform for I4.0 based on user stories, requirements, and a time series messaging experiment. An architecture based on microservices patterns is identified as the best fit. As an additional result, time series data should not be integrated synchronously and directly into AASs, but rather asynchronously, either via streams or time series databases. The developed DT platform for I4.0 is composed of specialized, independent, loosely coupled microservices interacting use case specifically either syn- or asynchronously. It can be structured into four layers: continuous deployment, shop-floor, data infrastructure, and business services layer. An evaluation is carried out based on the DT controlled manufacturing scenario: AAS-based DTs of products and manufacturing resources organize manufacturing by forming highly dynamic and self-organizing networks.Future work should focus on a final, complete AAS integration into the data infrastructure layer, just like it is already implemented on the shop-floor and business services layers. Since with the standardized AAS only one interface type would then be left in the DT platform for I4.0, DT interaction, adaptability, and autonomy could be improved even further. In order to become part of an I4.0 data space, the DT platform for I4.0 should support global discovery, data sovereignty, compliance, identity, and trust. For this purpose, Gaia-X Federation Services should be implemented, e.g., as cross-company connectors.