Digital Thread and Twin for Systems Engineering: Design to Retirement

Technological advancements have led to the transition of manufacturing industries to Smart Manufacturing and Industry 4.0. Promising concepts such as Digital Twin and Digital Thread could help speed up the transition. One benefit of using digital twins is to enable continuity of lifecycle information. However, currently, most digital-twin implementations focus on modeling a particular lifecycle stage of a physical element in “silos”. This makes companies missing out on up to 65% of possible value of digital twin investments. This also results in it challenging to incorporate diverse data streams from different lifecycle stages. Digital thread has been used to represent information flow along the product lifecycle. Using information across product lifecycle stages will facilitate interoperability and reusability of digital twins. Because data from each lifecycle stage could be accessed and managed systematically, this will ensure the value and the credibility of the digital twin. A lot of confusion still remains in industry about what are digital twin and digital thread as well as their relationships. Using lifecycle data from a digital thread for digital twin implementation is complex because of the heterogeneity of standards and technologies involved. In this paper, we provide definitions of both digital twin and digital thread; we highlight the benefit of using them for interoperability and reusability of a digital twin for product lifecycle management and analysis; we propose a methodology for implementing digital twins using lifecycle data supported by a digital thread. Finally, we showcase the proposed methodology by providing an example of integrating digital twins with a digital thread.

Over the past two decades, our understanding of product development has changed fundamentally. Disciplines that were once clearly distinct from one another, such as mechanical engineering, electrical engineering, and software development, have now become increasingly intertwined. Products are no longer isolated objects, but rather nodes in a network of systems, data, and processes. In this environment, it is no longer enough to manage individual data sets – they must flow, remain consistent, and be available to the right people at the right time. The AI based lifecycle management system of Digital Threads (Digital Thread for short) has therefore established itself as the backbone of modern system engineering. Early approaches to product data management (PDM) focused on central storage of CAD data and the management of versions and approvals. However, with the advent of complex mechatronic systems, networked vehicles, and modular plants, it became clear that this approach was not sufficient. Companies began to realize that it was not enough to simply store geometries; they needed a system that mapped the entire lifecycle of a product, from the initial requirement through development and production to operation and decommissioning. Today, Digital Threads’ AI based lifecycle management system acts as a digital nervous system that connects disciplines such as mechanics, electronics, software, and simulation. While system engineering provides methodical approaches for structuring requirements and architectures, Digital Thread ensures that the associated data are findable, traceable, and up to date. This object oriented approach creates transparency and enables end to end traceability, which is a key requirement of modern systems engineering. Another feature of modern digital thread approaches is the seamless integration of models and product data. Changes in one discipline, for example, in design, can automatically detect and forward all affected areas such as requirements, tests, and functions. This promotes the continuous flow of information and prevents unnoticed consequences of changes elsewhere in the system. The close link between AI based lifecycle management systems for digital threads and system engineering goes beyond technical necessities and promotes cultural change in companies. Organizations must not only introduce technology, but also clearly define processes and responsibilities in order to fully exploit the effectiveness of this system. Clear, transparent handling of changes, systematic management of variants and integration of real time data from operations are key success factors for the implementation of digital threads. The concept of ”‘digital twin’, which enables a continuous connection between the models of real and virtual products, can only be realized through the consistency and transparency offered by the digital threads. This, in turn, promotes more sustainable and data driven decision making processes that can be taken into account throughout the entire product lifecycle, from development to maintenance. Overall, it is clear that AI based digital thread lifecycle management systems not only provide the technical basis for system engineering, but are also an integral element for the successful development and operation of complex, networked systems. They promote innovation, improve quality, and enable companies to make their product development more flexible and efficient.

We present a structural digital twin system based on component-based parametric reduced order modeling using Reduced Basis Finite Element Analysis (RB-FEA) framework. This enables fast, detailed, and holistic modeling of large-scale structures, such as floating and fixed production and drilling platforms, and crucially, the digital twin's parametric nature and fast solve time enables the system to be connected seamlessly with inspection and sensor data. We demonstrate RB-FEA based structural digital twins, and capabilities for connecting them to sensor data and data science methods to increase predictive power and engineering insight. We show how the digital twin is a key enabler for a Digital Thread – a data-driven architecture that links together information generated from across the product lifecycle. Inspection data, such as thickness measurements, can be directly mapped into the structural digital twin. Sensor data, such as motion and strain can be transformed into loading data, e.g. to estimate sea-states and calculate stress and fatigue accumulation based on actual conditions, or to "tune" model parameters to match data-based measurements, such as mode shapes and frequencies. This enables continuous tracking of the asset and assessment of its current state, based on quantitative and detailed structural analysis, at any time. Ultimately the structural digital twin provides an interpretation layer for integration of monitoring and inspection data, leading to a fully automated digital thread. We present a framework, based on a cloud-based simulation platform and Python-based scripting, to automate the workflows listed above. Digital twin provides a single source of truth about asset condition and allows data driven communication and decision taking between multiple stakeholders – operators, contractors, regulators, etc. We will show examples of real-world applications of RB-FEA digital twin for FPSO and Jacket platforms and development work for digital thread with emphasis on data driven value creation along the entire life cycle of the asset. The Reduced Basis FEA based structural digital twin is a key enabler of data driven digital thread in addition to sensor frameworks and data science systems. RB-FEA provides fast, parametric, and physics-based analysis providing a transparent interpretation layer for data obtained from other systems. It provides a key element allowing knowledge management along asset lifecycle – from concept to decommissioning. High fidelity, physics based global simulations, such as RB-FEA digital twins, are a key enabler for effective digital transformation of industrial operations. They enable real-time insights into asset performance that are otherwise unattainable, providing tools for Asset Integrity Management, Risk Based Inspection, Automation and Optimization of Operations as well as enabling full digital thread enhancing Safety and CAPEX and OPEX.

Smart Manufacturing advances have recently emerged, focused on the integration of Digital Thread (DTh) and Digital Twin (DT). DTh-DT has been explored in additive manufacturing, aircraft production, and weapons. However, there is still an area of opportunity to explore DTh-DT in collaborative manufacturing cells, layouts, and specifically, in the creation of a system to make an intelligent robotic cell. This work provides a novel approach of a DTh-DT developed to function within a manufacturing robotic cell. A discrete event simulation (DES) was created with open-source programming software, representing the cell. Information is collected from the physical process and sent to a cloud service. The DES request the data from the cloud is executed and the results are received by a database, which works with a Server (Ignition 8.0). The Server receives data and displays results, working as the link between the DT and the Product Lifecycle. This work presents a quick look at a DTh applied to a robotic cell, an approach to a DT, and the use of different types of communications and platforms. The potential benefits of applying this type of configuration in an Industry 4.0 environment are described and critically reviewed.

This paper presents a digital twin prototype of the PiCar-X by Sunfounder based on the middleware Robot Operating System (ROS), Docker, and the ARCHES Digital Twin Framework, which provides tools to exchange data between a physical and digital twin. The digital twin prototype can be used to explore our implementation and test all software functions without needing to use the physical PiCar-X. The actual hardware is replaced with emulators or a simulation, and the interfaces are virtualized. The embedded control system operating the physical PiCar-X and the digital twin prototype are identical. Our goal is to provide researchers and practitioners with an affordable and straightforward example to explore how the concepts physical twin, digital model, digital template, digital thread, digital shadow, digital twin, and digital twin prototype can be implemented. These concepts were originally used for development, testing, monitoring, and operating an underwater network of ocean observation systems in the project ARCHES (Autonomous Robotic Networks to help Human Societies).

The application of digital twins is increasing in several fields. Mirroring the current state of the asset and making predictions of the future state are the main purposes of digital twins. Inside the German Aerospace Center (DLR), an internal project is set up to find methods, technologies and processes for digital twins. Several institutes are contributing to the project, including institutes in the IT domain like the Institute of Software Methods for Product Virtualization or the Institute for Software Technology on one side, and the aviation engineering domain on the other side, e.g. the Institute of Flight Systems, the Institute of Composite Structures and Adaptive Systems and the Institute of Maintenance, Repair and Overhaul. In order to demonstrate the capabilities and identify new development opportunities of digital twins, three different use cases are defined. These use cases include the virtual product house, the virtual engine and the research aircraft. For the research aircraft use case, the digital twin can be seen as a research tool within the organization. The research questions of the project are addressing several information technology related issues like data formats, data sizes, data storage concepts, provenance, and security. Additionally, the project addresses the definitions of the digital twin, the digital thread, and the application layer as well as a common digital twin vision. The next steps in the project are the implementation and demonstration of first prototypes for the individual use cases. This paper gives an overview over the project results and the developments for digital twins. The aim is to digitally map aircraft and their components with all their characteristics and relevant data.

A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also be considered as one of the possible realizations of the digital thread paradigm. As part of this approach, a three-level digital thread reference architecture is suggested, which includes the following levels: (i) digital thread support level; (ii) agile architecture support level; (iii) digital shadow support level. This approach has been used in the development of a number of real systems, and has shown its effectiveness in supporting system agility at the exploitation and modernization stages. The proposed approach is focused on building digital twin-based systems. This article may be interesting for specialists engaged in research and development in the domain of IoT- and IIoT-based information systems, primarily architects.

To keep pace with growing system complexity, Digital Engineering (DE) Model Based Systems Engineering (MBSE) capabilities must be expanded. A digital thread based approach is required to holistically evaluate measures of effectiveness/performance for missions. This paper describes the approach taken to connect requirements, SysML‐based architecture, and analytical Modeling and Simulation (M&S) yielding an interdisciplinary digital thread supporting mission analysis. The analysis was conducted in support of a multi‐domain (land, air, sea, and space) multi‐platform mission to detect, track, and engage the enemy. The approach used commercially available tools, strategically connected to establish an Authoritative Source of Truth (ASOT) for each piece of data, and gain insights about the System of Systems (SoS) beyond that which the component models could provide on their own. The results of these efforts produced a multipurpose SoS MBSE toolset enabling semi‐automated trade study analysis, while more importantly serving as a basis from which to expand systems engineering activities during subsequent phases of the systems' lifecycle. This paper concludes by identifying further opportunities for MBSE research and development to expand the developed capability.

The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the supply chain. This work aims to develop a graph data model of the Digital Thread, in the context of the vision of polyglot persistence, that interconnects the different phases of the lifecycle and their corresponding data models, processes, and IT systems. This work proposes a Digital Thread Graph that integrates a Digital Model and a derived Digital Twin, using object and relation attributes for view creation and filtering while minimizing redundancy.

Digital Twin (DT) technology has rapidly matured from pilot projects to integral components of advanced asset management and process optimization in the oil and gas (O&G) industry. This review provides a structured synthesis of the current state of digital twin frameworks, with a focus on offshore and topside gas-processing systems, such as those found on Floating Production Storage and Offloading (FPSO). Emphasis is placed on high-fidelity process simulations and scalable architectures integrating real-time data with advanced analytics. Drawing on over 85 peer-reviewed sources and industrial frameworks, the paper outlines modular DT architectures, encompassing steady-state and dynamic process simulations (e.g., Aspen HYSYS), reduced-order and hybrid machine learning models, co-simulation environments, and advanced equation-of-state packages (e.g., GERG-2008). Special attention is given to compressor map integration, Equations of State (EOS) selection, ISO/IEC standard compliance, and digital thread continuity. Additionally, the review explores economic and sustainability-driven DT implementations, including flare and methane mitigation, ISO 50001-aligned energy optimization, and lifecycle/decommissioning strategies. It concludes with a technical and economic assessment of DT maturity for gas compression facilities, identifying research gaps in standardization, long-term validation, and cybersecurity integration. The insights provided are intended to support decision-makers, engineers, and researchers in deploying scalable, auditable, and high-impact DT solutions across the O&G value chain.

Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reducedorder models (ROMs) derived from high-fidelity simulations of the PT’s nominal behavior, and (ii) specialize these prototypes into DT instances by leveraging historical sensor data from the target PT. Despite the broad applicability of ML, its use in DT engineering remains largely ad hoc. Indeed, while conventional ML pipelines often train a single model for a specific task, DTs typically require multiple, task- and domain-dependent models. Thus, a more structured approach is required to design DTs. In this paper, we introduce DesCartes Builder, an opensource tool to enable the systematic engineering of ML-based pipelines for real-time DT prototypes and DT instances. The tool leverages an open and flexible visual data flow paradigm to facilitate the specification, composition, and reuse of ML models. It also integrates a library of parameterizable core operations and ML algorithms tailored for DT design. We demonstrate the effectiveness and usability of DesCartes Builder through a civil engineering use case involving the design of a real-time DT prototype to predict the plastic strain of a structure.

The concept of the digital twin can be thought of as a virtual representation of a physical product, engineering system or facility. This paper presents the role of predictive engineering analytics, alongside operating data, in the digital twin. Using case studies, the authors demonstrate how predictive approaches can be developed to provide data where it cannot be measured and predict future operating data to improve performance, life and integrity of equipment, systems and facilities. The digital twin is fundamentally based on data, larger datasets provide greater insight. Sensor and inspection data are critical. However, there are scenarios in which engineers require data where it cannot be measured or requires data that cannot be measured. Engineers require ways to extract this data, this can be done through predictive engineering analytics. Predictive engineering analytics, in the form of science-based simulations, combines multiple approaches, often based on fundamental principles of physics and engineering. This paper will demonstrate how high-fidelity approaches, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), combined with system-level simulation and reduced-order modelling can work together with field data to provide this data in real-time. Two case studies presented show how combinations of different levels of science-based modelling approaches can help. A subsea thermal digital twin demonstrates how high-fidelity simulations, undertaken during system design, can be the foundation for reduced order system models capable of capturing critical thermal performance in real time; aiding hydrate risk management during operation. The methods used to train the real-time predictive approach are demonstrated. The second case presented focuses on structural integrity of a heat exchanger showing how real-time sensor data can be translated into structural integrity data and insight through simulation. The cases presented demonstrate the value of science-based predictions to generate data that cannot be obtained from operational sensor data alone. The authors aim to show how the predictive element of the digital twin can be first generated during design and evolved into real-time predictive approaches that provide operations with data that cannot be gained from sensors; when and where it is needed. Digital twins typically use physical data, limiting operators of in-field equipment to make operational decisions based on information from sensor locations and historical data alone. This can limit assessment of operational performance and integrity of complex production and process systems. In this paper the authors aim to show how, by combining predictive approaches, at differing levels of fidelity and based on fundamental scientific principles, it is possible to generate the missing data required, in both space and time. This can inform and guide operations; filling the gaps where and when physical data is not available.

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