Concept Of Digital Twin Research Articles

Malzeme Mühendisliğinde Gelişmiş Malzeme Davranışı Tahmini ve Üretim Süreci Optimizasyonu için Dijital İkizler ve Uygulamaları

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes.

Leveraging epoch‐era analysis and digital twin for effective system concept and execution: A CO2$_2$ storage salt‐cave project

AbstractWith the demand to reduce the release of CO‐rich gases into the atmosphere, the offshore industry is turning to systems for gas capture and storage. The construction of a cave for that purpose is planned in the salt layer of the Santos Basin, Brazil; however, numerous factors bring uncertainties to the project. We propose combining systems engineering methods and digital twin technology to enable system concept and execution aiming at value robustness. The system is modeled in mission, subsystems, components, and design attributes. System utilities and costs are estimated for a range of viable solutions based on the relevance of each attribute to the stakeholders. The alternatives are evaluated using the Epoch‐Era Analysis framework for analyzing the system's performance over time in changing future scenarios. The system model also outlines a digital twin concept and identifies how it might support salt cave design and operations. Finally, the potential of tuning and improving system evaluation based on gathered data is examined, and measures toward further digital twin development are recommended.

From framework to industrial implementation: the digital twin in process planning

AbstractIn today’s fast-paced market, companies are challenged to meet increasing customer demands and shorter product life cycles. To successfully respond to these demands, companies must produce a wide variety of different products. This requires the determination of necessary processes and resources for each product, which can be difficult for process engineers due to the high manual effort and expertise involved. The current state of research has not yet provided explicit definitions of the necessary knowledge and has not fully achieved complete process planning automation. To address this challenge, a digital twin is a valuable tool for automating and understanding process planning. This paper presents a digital twin concept for process planning. It automatically analyzes the product, determines production processes, and selects appropriate resources by linking information about products, resources, and processes. The effectiveness of the digital twin concept is demonstrated through verified and validated use cases, including the production of a compressor element.

A comprehensive digital twin framework for building environment monitoring with emphasis on real-time data connectivity and predictability

The concept of digital twins, initially developed in manufacturing, has found applications in various industries. However, its adoption in the construction field is still nascent, with a limited understanding of its benefits and implications. Existing examples of digital twin implementation in construction mainly provide general frameworks or possibilities for performance. To effectively harness the potential of digital twins in construction, establishing connectivity and predictability for data exchange between the physical and virtual realms is crucial. This research defines the essential requirements for real-time connectivity and predictability in digital twin implementation and proposes a framework and architecture based on these principles. The proposed method is evaluated through the implementation of a digital twin for monitoring the environmental performance of existing buildings, revealing its effectiveness and challenges. This study serves as a valuable exemplar for the development of a digital twin platform dedicated to construction monitoring.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics

Over the next decades, solar energy power generation is anticipated to gain popularity because of the current energy and climate problems and ultimately become a crucial part of urban infrastructure. Furthermore, in order to facilitate the rapid deployment of solar production in sustainable cities, solar technologies must be digitalized and smart. This will increase their reliability and ensure their optimal performance. This work fits into this perspective and focuses on the effectiveness of processing data using the Digital Twin (DT) framework as a state-of-art information technology for urban distributed solar PV systems. Accordingly, the present paper deals with the DT of five Building-added Photovoltaic (BAPV) systems installed at the solar village in Benguerir city, Morocco. The 3D models were generated in the Rhinoceros platform, the solar AC electrical production of each photovoltaic system is calculated by the ladybug-plugin-integrated statistical model, within the grasshopper environment. Additionally, data assessment and visualization are processed through Python. Then, a hybrid model-based and data-driven fault detection and diagnosis (FDD) approach is proposed to identify and isolate anomalies for decentralized solar PV systems at the urban scale using monitoring and inspection techniques, namely Remote Sensors (RS) and real-time solar production monitoring system. The application of the DT concept for complex dynamic systems has shown its effectiveness in ensuring optimal operating conditions for the energy systems by measuring the spatiotemporal energy performance of distributed solar PV systems at an urban scale, evolving with the changing environment, and identifying degradation based on anomalies occurrence; Only then when the physical models need to be amended. Therefore, the main goal behind this work is to advance knowledge regarding research on condition monitoring of solar systems in cities while considering issues related to anomalies like the degradation phenomenon in PV modules over time. The findings of this study have proved that the state of health and monitoring of PV systems could be successfully detected and efficiently monitored using the combination of real time monitoring and remote aerial sensing.

Direct Digital Manufacturing (DDM) Workflow for Printed Circuit Structures (PCS)

Digitally based manufacturing has been around for years and has been the basis of turnkey lights out manufacturing processes and systems. Moving forward is the concept of Direct Digital Manufacturing (DDM) which brings together conceptualizing the electronic device, developing a digital twin of the product, and digitally merging workflow with the manufacturing process. This would further extend through to physical product. This paper will illustrate DDM is the basis of developing the capability of conceptualizing, designing, and digitally manufacturing electronic devices and more specifically printed circuit structures or PCS and printed circuit cylinders, PCC. Unlike tradition PCB’s or multichip module technology, the goal is fully printed electronic devices that can merge structure with electronic functionality. This paper will explore the current state of practice in manufacturing workflow and extend to the emerging factory in a Box concept of multi-material printing of next generation electronic controller and packaging technology. This will also include exploring design tools that provide the capability of co-design mechanical function with electronic function and modeling the manufacturing process and electronic device using digital twin concepts and workflow enabling smart manufacturing of printed electronic devices and precision of DDM technology.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Digital twin modeling method of the temperature field of thermo-compression bonding blade based on generative adversarial networks

In this paper, a novel method of using deep learning to construct a digital twin of the thermos-compression bonding blade`s temperature field is presented. Usually, a heat transfer model based on numerical analysis can calculate the heat distribution in the welding process, but its complex modeling process and heavy computational effort are not appropriate for the deployment of the digital twin concept. The approach proposed in this paper is distinctive because it directly converts the collected process sensor data into the bonding blade's temperature field images and the bonding surface's temperature distribution curves for monitoring the welding process. This is achieved by training the generator and discriminator of the conditional depth convolutional generative adversarial network separately. The network is then capable of predicting the bonding blade's heat distribution within a certain range of welding parameters. Compared to methods based on numerical simulation, this approach is far more effective. Training and testing datasets were created to assess the suggested method's efficacy. Multi-scale structural similarity index, structural similarity index, peak signal-to-noise ratio, difference hash and mean absolute error were used to evaluate the simulations' quality. The results show that the simulation's results and the actual data agree quite well. The temperature changes that occur during the welding process may be quickly and accurately simulated using the proposed technology.

Application of digital twin technology in Russia: Development opportunities and constraints

Aim. To identify trends in the development of the digital twin concept in Russia.Objectives. To analyze the experience of applying such an innovative tool as a digital twin at Russian enterprises and consider the benefits of its implementation; to characterize the aspects of regulation and state support in the field of digital twin application in Russia; to reveal the main problems hindering the development of digital twin technology, as well as to propose posible solutions to the identified problems.Methods. The authors used empirical methods, in particular, the analysis and generalization of data from scientific and methodological literature; methods of observation and comparison. The article also reflects the results of scientific research of Russian and foreign scientists.Results. It is established that today the method of digital twins in the economy is becoming more and more popular. Thanks to the introduction of digital twin technology, companies manage to speed up production processes, increase their transparency, reduce costs, improve the quality of manufactured products and, in general, achieve greater efficiency, competitiveness and investment attractiveness. The main constraints to development include insufficient funds required to implement digital transformation, lack of necessary experience and highly qualified specialists, low level of legal framework in the field of digital twins and high level of risks associated with data security. For successful implementation of digital transformation, it is advisable to study and generalize successful examples of digital twins implementation both in foreign and domestic companies, to create a unified management system for this area, to develop a motivation system for companies applying digital twin technology, to increase the level of information security.Conclusions. In Russia, innovative technologies are a priority for ensuring technological leadership and entry of domestic companies into international markets. Despite lagging behind some foreign countries, Russia has great potential for the formation of a national market and the introduction of digital twin technology, especially among large corporations and high-tech enterprises. But for this purpose it is necessary to eliminate the factors that hinder the development of the digital twin method, such as high risks associated with cybersecurity, insufficient funding in the use of technology, the lack of specificity in some cases in the field of legal regulation and a number of others.

Digital Inheritance of Traditional Mongolian Robes of the Nayman Tribe

Abstract The Mongolian robe is the crystallisation of the wisdom of Mongolian people after a long period of social practice and one of the intangible cultural heritages of humanity. With the gradual disappearance of the nomadic lifestyle in the steppe, it is not easy to protect and pass on traditional Mongolian dress culture and traditional handicraft technology. This study used the digital twin concept to construct a digital twin to try on clothes for a model in a virtual environment, to realise a mirror image presentation of the target physical entity, and to use effective modern technology to protect and pass on the traditional clothing. The experiment used an AlphaM4 3D body scanner to obtain a 3D human body model, as well as reverse engineering technology to process the initial data, such as noise reduction, streamlining, hole patching and smoothing to build a personalised human body model. The traditional black cotton robe of the Nayman tribe researched in the field was mapped by the planar structure design method, and its structure and 2D samples were recovered. A 3D model of the traditional cotton robe of the Nayman tribe was established based on 3D simulation technology to achieve a 3D virtual display effect, enable better dissemination and inheritance, and give a new vitality to non-heritage clothing in the digital era. The research results can provide fresh ideas and technical support for the digital estate and development of other traditional costumes, as well as contribute to the construction of digital resources for intangible cultural heritage.

Research progress and prospect of digital twin in bridge engineering

The concept of digital twins in bridge engineering is still vague and even confused with the Bridge Information Model (BrIM). Therefore, this study provides a detailed review of 42 papers related to digital twins in bridge engineering, focusing on a proper definition, key features and creation techniques for bridge digital twin (BDT). The paper also compares BDT and BrIM from the perspectives of their elements, features, fidelity, services provided, and degree of development. The applications of BDT at different life cycle stages are identified, and the related technologies are analyzed in detail. The results show that the research clusters of BDT are divided into geometric model generation, finite element model updating, and management and are focused on the operation and maintenance phase while lacking attention in the design and construction phase. Besides, a reference framework of BDT based on the life cycle of bridges is proposed, and directions for future research are suggested.

Dual Digital Twin: Cloud–edge collaboration with Lyapunov-based incremental learning in EV batteries

The soaring potential of edge computing leads to the emergence of cloud–edge collaboration. This hierarchy enables the deployment of artificial intelligence models in the cyber–physical venue. This paper presents Dual Digital Twin, the next level of digital twin, in the presence of two levels of communication availability, for battery system real-time monitoring and control in electric vehicles. To implement the dual digital twin concept, an online adaptive model reduction problem is formulated with time scale differences induced by the time sensitivity property of industrial applications and limitations of infrastructure. To minimize the model reduction error and battery system control penalty, the online adaptive battery reduced order model framework is proposed, consisting of the gated recurrent unit neural network to construct battery internal states given Internet of things sensor measurements, and incremental learning techniques to facilitate the update of the reduced-order model given data stream. Moreover, we design the physics-informed update of the neural network using the Lyapunov stability theorem to enhance the synchronization with the physical battery behavior. A Li-ion battery and single particle digital twin model with electrolyte and thermal dynamics are utilized in the simulation to justify the effectiveness of the proposed framework. Numerical results demonstrate 1.70% average reduced-order model prediction error and 43.3% accuracy improvement with the novel physics-informed online adaptive framework. The method is also robust concerning varying environmental factors and noise.

A digital twin framework for civil engineering structures

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

Temperature monitoring system of friction stir welding based on digital twin

ABSTRACT Temperature field distribution of friction stir welding (FSW) influences weld quality directly, so real-time monitoring of the welding temperature is significant. However, it is difficult to monitor the temperature in the core zone due to mechanical obstructions, material plastic deformation and complex thermal-mechanical coupling. To tackle this issue, this study develops a digital twin-based temperature monitoring system of FSW. Initially, a five-dimensional integrated framework based on the digital twin concept is proposed, outlining the process of building a digital twin-based temperature monitoring system of FSW. Subsequently, a motion simulation model of FSW is established, and synchronous motion simulation of the welding process is achieved. Real-time temperature readings from the FSW workpiece surface are gathered via an infrared thermal imager and synchronously transmitted to the FSW temperature monitoring system using socket communication. Additionally, a predictive model utilizing Support Vector Regression (SVR) is incorporated, enabling real-time and precise prediction of extremum temperatures in the core zone and over-limit alarm functionality. Finally, the temperature monitoring system of FSW, grounded in the digital twin concept and integrating the outlined models and features, is developed and experimentally validated. The system enables operators to exercise timely control over the welding process to ensure weld quality.

No longer hype, not yet mainstream? Recalibrating city digital twins' expectations and reality: a case study perspective.

While the concept of digital twin has already consolidated in industry, its spinoff in the urban environment-in the form of a City Digital Twin (CDT)-is more recent. A CDT is a dynamic digital model of the physical city whereby the physical and the digital are integrated in both directions, thus mutually affecting each other in real time. Replicating the path of smart cities, literature remarks that agendas and discourses around CDTs remain (1) tech-centered, that is, focused on overcoming technical limitations and lacking a proper sociotechnical contextualization of digital twin technologies; (2) practice-first, entailing hands-on applications without a long-term strategic governance for the management of these same technologies. Building on that, the goal of this article is to move beyond high-level conceptualizations of CDT to (a) get a cognizant understanding of what a CDT can do, how, and for whom; (b) map the current state of development and implementation of CDTs in Europe. This will be done by looking at three case studies-Dublin, Helsinki, and Rotterdam-often considered as successful examples of CDTs in Europe. Through exiting literature and official documents, as well as by relying on primary interviews with tech experts and local officials, the article explores the maturity of these CDTs, along the Gartner's hype-mainstream curve of technological innovations. Findings show that, while all three municipalities have long-term plans to deliver an integrated, cyber-physical real-time modeling of the city, currently their CDTs are still at an early stage of development. The focus remains on technical barriers-e.g., integration of different data sources-overlooking the societal dimension, such as the systematic involvement of citizens. As for the governance, all cases embrace a multistakeholder approach; yet CDTs are still not used for policymaking and it remains to see how the power across stakeholders will be distributed in terms of access to, control of, and decisions about CDTs.

ReplayBG: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative therapies.

Design and assessment of new therapies for type 1 diabetes (T1D) management can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology here proposed allows "replaying" the scenario behind data already collected by simulating the glucose concentration obtained in response to alternative insulin/carbohydrate therapies and evaluate their efficacy. ReplayBG is based on the concept of digital twin and works in two steps. First, a personalized model of glucose-insulin dynamics is identified using data of insulin, carbohydrate, and continuous glucose monitoring (CGM). Then, this model is used to simulate the glucose concentration that would have been obtained by "replaying" the same portion of data using a different therapy. The validity of the methodology was evaluated using data from 100 virtual subjects generated with the UVa/Padova T1D Simulator (T1DS). In particular, the glucose concentration traces simulated by ReplayBG are compared with those provided by T1DS in five different scenarios of meal intake and insulin amount modifications. To further evaluate the methodology, we compared ReplayBG with a state-of-the-art methodology for the scope. Two case studies using real data are also presented to provide actual examples of ReplayBG use. ReplayBG simulates with high accuracy the effect of the considered insulin and carbohydrate treatment alterations, performing significantly better than state-of-art method in almost all considered situations. The good performance of ReplayBG in the two case studies using real data supports simulation results. ReplayBG proved to be a reliable and robust tool to retrospectively explore the effect of new treatments for T1D on the glucose dynamics. It is freely available as open source software at https://github.com/gcappon/replay-bg. ReplayBG offers a new approach to preliminary evaluate new therapies for T1D management before clinical trials.

The Next Frontier in Sarcoma Care: Digital Health, AI, and the Quest for Precision Medicine.

The landscape of sarcoma care is on the cusp of a transformative era, spurred by the convergence of digital health and artificial intelligence (AI). This perspectives article explores the multifaceted opportunities and challenges in leveraging these technologies for value-based, precision sarcoma care. We delineate the current state-of-the-art methodologies and technologies in sarcoma care and outline their practical implications for healthcare providers, administrators, and policymakers. The article also addresses the limitations of AI and digital health platforms, emphasizing the need for high-quality data and ethical considerations. We delineate the promise held by the synergy of digital health platforms and AI algorithms in enhancing data-driven decision-making, outcome analytics, and personalized treatment planning. The concept of a sarcoma digital twin serves as an illustrative paradigm for this integration, offering a comprehensive, patient-centric view of the healthcare journey. The paper concludes with proposals for future research aimed at advancing the field, including the need for randomized controlled trials or target trial emulations and studies focusing on ethical and economic aspects. While the road to this transformative care is laden with ethical, regulatory, and practical challenges, we believe that the potential benefits far outweigh the obstacles. We conclude with a call to action for multidisciplinary collaboration and systemic adoption of these technologies, underscoring the urgency to act now for the future betterment of sarcoma care and healthcare at large.

Research and application of deep learning in Metaverse

The Metaverse, an immersive and interactive virtual environment, has garnered significant attention and presents numerous opportunities for technological advancements. The incorporation of artificial intelligence (AI) within the Metaverse can lead to transformative changes across various industries and applications. This paper delves into AIs role in the Metaverse, specifically focusing on deep learning and reinforcement learning techniques to address challenges faced by different applications with in- sufficient data. An overview of AI within the Metaverse is provided, along with an exploration of the connection between the Metaverse and the Digital Twin concept. The establishment of the Metaverse environment and AI- driven activities are examined, emphasizing their applications in virtual environments and interactions. Moreover, innovative deep learning training directions leveraging the Metaverse for various applications are proposed, including autonomous vehicles, surgical medical robots, nuclear decommissioning robots, and industrial design and experimentation. Finally, the paper summarizes and looks forward to the full text.

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.