Model-Based Integration of Augmented/Virtual Reality Into Digital Twin

Biopharma Is Going Digital … Bit by Bit

Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework

A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase.

Developing Digital Twins for energy efficiency in the production phase of products

ABSTRACTOrganizations continuously develop Digital Twins across a wide number of applications and industries. While this represents a testimony to the benefits and opportunities Digital Twins provide (AIAA 2022), their development, maintenance and evolution still face major challenges (Bordeleau et al. 2020). Most past and current efforts focusing on the development of Digital Twins have relied on ad‐hoc approaches, where most of the efforts start with building models without properly framing the problem (Martin 2019, Lu et al. 2020). More importantly it has led to the development of models that provide a solution to the wrong problem and consequently fail to address the core questions and needs of the stakeholders (Martin 2019)]. The development and implementation of Digital Twins also lack standardization (Shao 2021), instead relying on bespoke methods and technologies (Niederer et al. 2021), which in turns leads to a lack of consistency in their description and implementation, as well as limited interoperability across applications, tools and disciplines (Niederer et al. 2021, Piroumian 2021). The development of Digital Twins has been plagued by a lack of scalable approaches, leading to implementations that are highly specialized and require considerable resources in terms of subject matter expertise (Niederer et al. 2021).The development of standardized methodologies has been identified as a means to unleash the full potential of Digital Twins (Shao 2021, Piroumian 2021) and increase their adoption across a wider range of disciplines and applications (Niederer et al. 2021). To that end, this paper presents a domain agnostic and descriptive reference model, which builds on recognized industry practices and guidelines, to support the planning, description, and analysis of Digital Twins. It also introduces real‐world examples of aerospace Digital Twin use cases and discusses how the generic reference model supports the various use case applications. Finally, this paper briefly discusses recommendations and next steps on how to realize value from Digital Twins more broadly.

Traditionally, verification of equipment and systems has been carried out by deploying surveyors to perform physical verifications. Today, the development of digital twins, representing physical assets, provides the possibility to leverage data to enhance and replace the conventional verification and validation efforts undertaken by industry stakeholders. Digitalization, new technologies, algorithms, and artificial intelligence (AI), incorporated in digital twins, can be used to execute effective verifications. When such digital twins have proven to provide genuine and trustworthy evidence, the evidence can be used in assessments towards specified acceptance criteria, and issue, maintain, and renew certificates. Some parts of the assessment itself may even become automated through the use of algorithms and AI solutions, further increasing the importance of the rigor and intensity with which these digital twins must be qualified and assured. This paper is developed in order to support the development of trustworthy digital twins for data-driven-verification (DDV) systems and methods. Use of digital twins should be acceptable as long as they provide the same, or higher, level of assurance as the traditional methods. DDV also aims at reducing non-productive time, costs associated with surveyors attending the physical asset, redundant verification activities, and the knowledge sharing, and transfer burden imposed upon the asset organization.

A critical component of the autonomous control system is the implementation of digital twin (DT) for diagnosing the conditions and prognosing the future transients of physical components or systems. The objective is to achieve an accurate understanding and prediction of future behaviors of the physical components or systems and to guide operating decisions by an operator or an autonomous control system. With specific requirements in the functional, interface, modeling, and accuracy, DTs are developed based on operational and simulation databases. As one of the modeling methods, data-driven methods have been used for implementing DTs since they have more adaptive forms and are able to capture interdependencies that can be overlooked in model-based DTs. To demonstrate the capabilities of DTs, a case study is designed for the control of the EBR-II sodium-cooled fast reactor during a single loss of flow accident, where either a complete or a partial loss of flow in one of the two primary sodium pumps is considered. Based on the definition of DTs and the design of autonomous control system, DTs for diagnosis and prognosis are implemented by training feedforward neural networks with suggested inputs, training parameters, and knowledge base. Furthermore, inspired by the validation and uncertainty quantification scheme for scientific computing, a list of sources of uncertainty in input variables, training parameters, and knowledge base is formulated. The objective is to assess qualitative impacts of different sources of uncertainty on the DT errors. It is found that the performance of DT for diagnosis and prognosis satisfies the acceptance criteria within the training databases. Meanwhile, the accuracy of DTs for diagnosis and prognosis is highly affected by multiple sources of uncertainty.

– The thought of digital twins has gained substantial attention in recent years due to its potential to transform various industries, including renewable energy. Digital twins involve the creation of virtual models that mirror the behaviour and characteristics of real-world physical systems. In the perspective of solar plants, digital twins have emerged as a promising tool to enhance performance monitoring, predictive maintenance, and overall operational efficiency. Digital twin engineering, characterized by its dynamic data modelling of industrial assets, offers a disruptive technology capable of adapting to real-time changes in the environment and operations. This living model can predict future infrastructure behaviour and proactively identify potential issues within the physical system. The article highlights the essential components of the digital twin ecosystem, such as sensor technologies, the Industrial Internet of Things, simulation, modelling, and machine learning, underscoring their relevance in predictive maintenance applications. This review provides an in-extensive review of the development and application of digital twins for predicting and mitigating faults and defects in solar power plants. It opens with a look at current developments, underlining the rising focus on digital twins for optimizing solar farms. It begins with an overview of existing solutions in the field, highlighting the growing interest in leveraging digital twin technology to enhance solar plant operations. Additionally, the article outlines the implementation stage of a prototype digital twin for a solar power plant.

Digital engineering is the practice of creating repeatable frameworks to bring the power of automation and information technologies to complex systems. Model‐based systems engineering (MBSE) is an essential part of digital engineering by providing a roadmap for digitalization. Digital engineering and MBSE can be applied in a myriad of situations, one of which is for the development of digital twins for autonomous and remote control. Autonomous and remote operation of physical assets can provide numerous benefits to organizations and industries that deal with complex and distributed systems. The automation of the operation of a physical asset can be achieved through a digital twin, connected to the inputs and outputs of the asset and using machine learning (ML) and artificial intelligence (AI). Development of the digital twin requires understanding of systems interfaces and incorporating this understanding in digital systems. The effort described herein aims to determine the feasibility and benefit of such a process through the development and evaluation of a digital twin connected to a heat‐pipe test‐bed environment. Many challenges need to ultimately be addressed by a digital twin including data quality, infrastructure, privacy and security, and more. However, the focus of this paper will be on the application and use of digital engineering for the development of autonomous digital twins through a repeatable framework that can be applied across various domains and assets.

The concept of Digital Twins is still in an infant state of development. Digital Twins are often built as a tool to aid in better understanding of physical systems through simulation. They can be used to visualize information during operations and provide instructions during training or execution of procedures. The use of Digital Twins to test is appealing as it can be done quickly and safely. However, testing without inclusion of the physical system can lead to a reality gap. The reality gap can lead to high risks when applying concepts tested on digital Twins to the physical system directly. Sometimes interaction with the physical system is unfeasible. In this paper, we present an experimental laboratory that we built to provide a platform for the development of high quality Digital Twins through a feedback loop. The physical system is a Palfinger crane. Our replicate physical twin is a Universal collaborative industrial robot model UR16e due to its similar anatomy to the crane. The RoboDK simulation software was used to rapidly develop a digital twin of the UR16e. We demonstrate a solution to the interoperability problem in digital Twins using the monitoring adaptation loop from the Autonomic Adaptation System of the Arrowhead Framework.

Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins.

A digital twin is regarded as a potential solution to optimize positive energy districts (PED). This paper presents a compact review about digital twins for PED from aspects of concepts, working principles, tools/platforms, and applications, in order to address the issues of both how a digital PED twin is made and what tools can be used for a digital PED twin. Four key components of digital PED twin are identified, i.e., a virtual model, sensor network integration, data analytics, and a stakeholder layer. Very few available tools now have full functions for digital PED twin, while most tools either have a focus on industrial applications or are designed for data collection, communication and visualization based on building information models (BIM) or geographical information system (GIS). Several observations gained from successful application are that current digital PED twins can be categorized into three tiers: (1) an enhanced version of BIM model only, (2) semantic platforms for data flow, and (3) big data analysis and feedback operation. Further challenges and opportunities are found in areas of data analysis and semantic interoperability, business models, data security, and management. The outcome of the review is expected to provide useful information for further development of digital PED twins and optimizing its sustainability.

The digital twin technology platform has not yet achieved the expected acceptance and wider implementation in the maritime industry. So far, most of the focus of the digital twin application discussions have centred around what to learn from big data in ship operation, and to a lesser extent, has anybody extended this discussion to include the benefits such new technology can contribute to the enhancement of the upstream ship concept and basic design activities, as well as detailed engineering. This paper particularly pays attention to this latter, partly forgotten, application area. There could be many reasons behind such a reluctance to take on new technology and utilize it to its full potential. It is hypothesized and argued by this article that the development has focused on applications that are too complex, too expensive and reflect, to a little extent, real-life needs. Lack of effective data transfer and transaction interphases among relevant stakeholders is another important factor creating these inefficiencies. This paper document how and why such inefficiencies in novel digitization technology adoption and adaptation exist and hamper the progress of achieving noticeable benefits of such implementations and how such development hurdles can be eliminated. Real-life user cases and several contributions in the professional literature suggest that more effective implementation of digital twin technology requires further discussions and investigations relating to three important aspects: i) a common and accepted definition of what is a digital twin; ii) an agreed-upon scalable and systemic approach to what is the solution space for a digital twin solution and iii) which systemic method to be used for digital twin development. Digital-twin technology must combine effective ship in operation and ship design feedback and feed forwarding, including their inherent people involvement and market behaviour. This article reviews the status of digital twin technology in the maritime domain and proposes a common definition of the digital twin. The latter part of the article proposes a systemic perspective for effective digital twin development and a method for a goal-oriented digital twin development in the novel ship design domain as well for ships in operations. Real-life user-case examples are elaborated upon to support our suggestions for improvement. The paper summarizes that, in its current form, the success rate of the digital twin technology implementation is so far, limited. Thus, the short- and long-term benefits to be achieved from digital twin applications in relation to vessel operations and their designs are also limited. This paper advises ways for improvement of the present situation.

Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving complex digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization, which are rarely seamlessly aligned in open-source solutions. In this paper, we present an open-source framework for the development of compositional digital twins, i.e., advanced digital twins that link individual entities or subsystems to create a higher degree digital twin, allowing knowledge sharing and data relationships. In this open framework, digital twins can be easily developed and orchestrated with 3D-connected visualizations, IoT data streams, and real-time machine-learning predictions. To demonstrate the feasibility of the framework, a use case in the Petrochemical Industry 4.0 has been developed.

Digital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.