Architecting Digital Twins

The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.

Biopharma Is Going Digital … Bit by Bit

The Internet of Things-based systems and software allow computations anywhere at any time by interconnecting individuals, networks, services, computers and artefacts that allow autonomous systems to form digitized communities. As the blueprint for software-intensive applications, and software architecture that precise the complexity of a network’s planning, development, and changing phases to effectively and efficiently build complex IoT-driven applications. In any case, there exists no comprehensive analysis in the state of the research on the adoption of MSA for IoT systems. This study effort is needed to explore architectural concepts and practices for designing and developing IoT software to excel state-of-the-art for IoTs along with suggestions and recommendations for IoT software to the adoption of MSA to fulfil the identified gaps. A systematic analysis was coordinated, covering up the literature on existing IoT solutions by studying 140 qualitatively selected articles performed between 2005 and Jan 2020. One hundred forty articles were comprised in this SLR. The findings of this study demonstrated different research topics including software architectural styles, patterns, and models to build IoT software. This research presents cloud-based computing environments, autonomous, software-defined networking, and responsive applications, and IoT-driven agent-based systems, (1) thirteen MSA architectural and design patterns for IoTs and classification of patterns, (2) classification of software architectures for IoTs into nine main categories and their sub-categories, (3) twenty-three most investigated IoT challenges, and (4) mapping of IoT challenges with software architectural solutions. The study revealed the innovative work on IoT software architecture and trends that help in the creation and dynamic adaptation of IoT software for reusability, automation and human decision-making. The outputs of this SLR are useful in revealing many recommendations to the software industry, software engineering community, and computer sciences community with over the past 15 years of research into the adoption of MSA. This study reflects a distilled awareness of architectural practices and principles to assist researchers and practitioners in promoting information sharing software architectural roles and responsibilities for the Internet of Things software.

Since its first mentioning in the literature, the concept of Digital Twin has gained traction in both industry and academia. However, there are still many open challenges when applying Digital Twins to industry-scale use cases. Applying Model-Driven Engineering techniques to the creation and maintenance of Digital Twins (also referred to as Model-Driven Digital Twin Engineering) promises automation and consistency throughout the life cycle of a Digital Twin. The exemplar provided in this paper can be used to identify open challenges when it comes to Model-Driven Digital Twin Engineering, and to demonstrate how approaches can solve them. This exemplar applies Digital Twins to an indoor air quality management use case, where CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , temperature, and humidity values of rooms within a building are measured. These values can be used to derive actions to improve work productivity and reduce the risk for virus infections. We describe three applications that make use of this Digital Twin (i.e., runtime visualization, physical simulation, and ML-based predictions), and provide an online repository with the artefacts of this exemplar.

The digital twin, adapted to the needs of urban planning and monitoring systems, is the subject of research and development in the academic, commercial and public sectors. The article provides international context in conceptual and practical implementation through their maturity level, scope, purpose, structure, input, processing features and output. The City of Zagreb is presented as an example of the implementation of GIS and 3D city models in urban planning, with activities towards a digital twin city. Based on research and local specifics, the concept of a digital city twin was proposed for the needs of urban planning, and future challenges were identified.

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development.

Digital Twins can be considered as a new phase in smart and data-driven greenhouse horticulture. A Digital Twin is a digital equivalent to a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Research indicates that they can substantially enhance productivity and sustainability, and are able to deal with the increasing scarcity of green labour in greenhouse horticulture. This paper presents the results of a systematic literature review on Digital Twin applications in greenhouse horticulture. The review identifies 8 articles that explicitly address Digital Twins in greenhouse horticulture and 115 studies that implicitly apply the Digital Twin concept in smart IoT-based systems. Findings indicate that the concept of the Digital Twin is in a seminal phase in greenhouse horticulture, but there are existing applications that are not yet framed as Digital Twins. In the reviewed papers, there is a dominant focus on the cultivation process at the greenhouse level, among others for climate control, energy management and lighting. About 9% of the articles are virtualizing plants themselves, which indicates that the granularity level addressed is still rather limited. Only 7 % of the articles look beyond plants or single greenhouses. None of the reviewed articles consider the company level. Furthermore, most applications address monitoring and control of the state and behaviour of real-life objects. More advanced applications, including predictive and prescriptive capabilities across the complete lifecycle, are still in an early stage of development, although predictive Digital Twins are gaining prominence.

<b><sc>Abstract.</sc></b> Cooling tomatoes after harvesting and keeping them refrigerated during their transportation is a key step toward preserving the quality attributes of tomatoes. This is a challenging task to accomplish in Nigeria, mainly because shipments of tomatoes from the tomato-producing states in northern Nigeria to the non-producing states in southern Nigeria were predominately not transported in a refrigerated ambient condition. To minimize postharvest losses of tomatoes during transportation, quantification of changes in quality would be extremely valuable. For this purpose, a digital tomato twin was developed based on mechanistic physics-based modeling. This digital twin simulated the actual thermal profile of tomatoes during transportation, based on sensed ambient environmental conditions. The impacts of the adoption of a low-cost, simple plastic packaging container compared with the traditional container (raffia baskets) on tomato's quality evolution during transportation were analyzed in-silico using the developed digital twin. Generally, the values of colour parameter (a*), firmness (N), and lycopene content (LC, mg/100ml) predicted by the digital twin model for tomatoes transported in raffia baskets were all higher than prediction for plastic container. For example, in shipment #2 the predicted colour parameter (a*) was 46.9 % more than the value predicted for the tomatoes in the plastic container at the end of the transportation. Similarly, lycopene content (LC) increased from 1.14 mg/100ml to 2.58 mg/100ml and 1.80 mg/100ml for raffia baskets and plastic container and firmness (N) decreased by 89% and 58 % in the raffia basket and plastic container respectively.

The development and progress in information and communication technologies will transform traditional products into smart products and allow to offer novel smart services [1]. Herein, the digital twin (DT) concept is regarded as a key technology to create value with smart services [2]. Although the research and applications of DTs emerge continuously many concerns are to be scrutinized [3]. The lack of a shared conceptual framework for DTs with an unambiguous terminology [4] complicates cross-functional discussions. Therefore, a systematization of the main dimensions of DTs is proposed in the form of an ontology and a conceptual framework thereof derived. The research questions addressed in this paper are a) «Which dimensions are used to classify and structure DTs in academic literature?», b) «What are the fundamental differences or specifications within these dimensions?» and c) «How do these different specifications relate to each other?» The focus of the research is on the objective to find classification systematics that are a) representing the entire spectrum of DTs, b) universally valid in all DT related domains and c) applicable in research and practice. A systematic literature review on the relevant aspects of DTs was conducted and the findings iteratively advanced within workshop sessions with academic experts. DTs are considered as integrators of physical and digital worlds as well as internal and external value creation. Further, the creation of DTs requires per definition the use of digital data. Hence, the proposed ontology and conceptual framework for DTs include the following main dimensions to consider for every DT: Data resources, external value creation and internal value creation. The main subdimensions of the data resources are the data sources to obtain the data, the data categories and the data formats. The main subdimension of the external value creation are the attributes of the services as the basis of the value propositions, the level of smartness of the connected products and the actors on the different levels of the ecosystem. The main subdimensions of the internal value creation are the lifecycle phases of products, the product management levels and the different generations of both. The proposed ontology and conceptual framework support researchers and practitioners in positioning and structuring their intended DT activities and communicating them to internal and external stakeholders. The holistic view on the data resource dimension further allows to easily deduct the needed data for certain applications or deduct possible applications from already available data.

Purpose This paper aims to fill the research gap on digital twin technology and its broad applicability during construction by shedding light on its interaction with Building Information Modeling (BIM) from a construction project management perspective. It presented the true potential of the digital twins in the construction phase of the project lifecycle. Design/methodology/approach The paper employed a two-step methodology that included a comprehensive synthesis of the literature on digital twins through the construction management lens and a questionnaire survey to assess the impact of digital twin services brought to light on parallel BIM uses. Findings The paper provides validated applications and many advantages of the digital twin on construction project management. It suggests that the industry should take advantage of 10 digital twin services introduced to eliminate the low efficiency and lack of productivity that the construction industry is still facing. Research limitations/implications The paper is one of the rare and pioneering studies that addresses the interaction between the digital twin and BIM from a construction management perspective with a quantitative approach examining the reflection of academic publications on the industry and their reception among industry professionals. Practical implications The paper provides a meaningful definition for the industry by grounding the concept of digital twin in existing technologies and their practical applications. This provides a roadmap for managers to approach the problems and BIM limitations they need to overcome in their companies or projects with tailor-made solutions. Originality/value The paper is one of the pioneering quantitative studies that fulfills an identified need to investigate digital twin technology for construction management and its extensive applicability for quickly evolving construction sites.

Objective of the paper is to describe and present results of using a "Digital Twin" in Drilling Operations (Planning and Engineering, Training and Operational Support) in the last 10 years for Operators worldwide. The concept of Digital Twin was first introduced by Michael Grieves at the University of Michigan in 2003 through Grieves’ Executive Course on Product Lifecycle Management. Winning a Formula 1 race is no longer just about building the fastest car, hiring the bravest driver and praying for luck. These days, when a McLaren technology group races in Monaco or Singapore, it beams data from hundreds of sensors wired in the car to Woking, England. There, analysts study that data and use complex computer models to relay optimal race strategies back to the driver. The McLaren race crew and the online retailers both harness data and use algorithms to make reasonable projections about the future, Parris explains. The concept is called Digital Twin [1]. A Digital Twin contains information such as a piece of equipment or asset, including its physical description, instrumentation, data and history. A Digital Twin can be created for assets ranging from a well to a piece of equipment to an entire oilfield. For example, a subsea system could have a Digital Twin via a simulation model of a subsea system's components, including the blowout preventer, tiebacks, risers, manifolds, umbilical and moorings. Drilling and extracting simulations can determine whether virtual designs can actually be built using the machines available," GE said. "Last but not least, real-time data feeds from sensors in a physical operating asset are now used to know the exact state and condition of an operating-asset product, no matter where it is in the world"[2].

This article presents the concept of the digital twin of a city. The urban economy is a complex multi-vector system. Creating a single digital twin of such a system is now a difficult task to solve. In this article the concept of a digital twin of a city is presented. The authors propose an evolutionary approach to this problem, according to which digital twins of individual elements of the urban environment are consistently built on a single hardware and software platform. These digital twins are linked in a single cooperative system that allows one digital twin to use data produced by other digital twins. The article gives the definition and architecture of such a system. It describes the classes of models that can be used to create digital twins. Special attention is paid to neural network models and models for data analysis. The information infrastructure of the digital twin of a city, including sensory networks, data cleansing mechanisms and nebulous calculations, is considered.

Software architecture involves a series of decisions based on many factors in a wide range of software development. Architects face recurring issues in different software architecture design, and to reduce huge cost and risks, software architecture decisions can rely on a set of idiomatic patterns commonly named architectural styles or patterns. Architectural pattern determines the vocabulary of components and connectors that are used in instances of the pattern together with a set of constraints to combine the two. Little contemporary data exists to document actual practices used by software professionals when selecting and incorporating architectural patterns for their projects in industry. Therefore, a comprehensive survey of software professionals was conducted to attempt to discover these practices. This exploratory survey and its quantitative results offer opportunities for further interpretation and comparison. Data from this survey are presented in this paper and include characteristics of projects, practices, organizations, and practitioners related to the usage of architectural patterns. Some of the notable findings include that architectural patterns are widely used in software projects with the Model–View–Controller being the most common. Despite reported difficulties in incorporating architectural patterns, the majority of the software professionals revealed that patterns were the most essential for completing the projects. The most difficult pattern to implement and the most expensive to adopt was the peer-to-peer, while the easiest was the client–server.

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.

As an innovative information technology, Digital Twin has greatly promoted the development of intelligent manufacturing in the industry. However, in the field of geotechnical engineering, there are still few researches on this aspect, which is still a “new territory” and “no man's land”. The concept of digital twin coincides with the needs of geotechnical engineering informatization, so the introduction of digital twin technology into the field of geotechnical engineering will help to promote the development process of geotechnical engineering informatization and digitization. This paper puts forward and defines the digital twin model of geotechnical engineering, describes the connotation of the digital twin model, and studies the architecture of the digital twin model of geotechnical engineering. On this basis, the integration and sharing mechanism of geotechnical engineering digital twin data based on BIM technology is proposed. In order to break through the defect that the geotechnical engineering information model has not fully played its role for a long time, an integrated model of geological body and structural body is constructed based on the construction and integration module of geotechnical digital twin model. Furthermore, the geotechnical engineering digital twin simulation analysis module is developed to initially form the geotechnical engineering digital twin model, so as to realize the geotechnical engineering digital design, collaborative construction, visual decision-making and transparent management.