Seeing the Problem: How Data Visualization Enhances Corrosion Management for Aging Assets

The dynamic nature of manufacturing and evolving customer demands require agile adaptation within Manufacturing Ecosystems—interconnected networks of enterprises and institutions collaborating to develop market—oriented solutions. To support this adaptation, it is crucial to evaluate large volumes of data and assess alternative scenarios electively. Digital Twins (DTs) enable the replication of physical systems into virtual models, facilitating the exploration of such scenarios. In most applications, Decision Support (DS) is essential and can be considered intrinsic to DTs. By integrating DS within DTs, the loop can be closed—transforming simulation information into actionable decisions. This study investigates recent advances and trends in the use of DTs for DS in production processes, with a focus on applications in Manufacturing Ecosystems. A systematic review is conducted to examine how DTs contribute to complex and holistic decision‐making, including tasks such as production planning, maintenance scheduling, and defect management. Special attention is given to how decisions are made within DT‐based applications and the extent of their autonomy and complexity. The review contributes to the identification of current research directions and gaps regarding the integration of DTs and DS, with the aim of supporting more effective and adaptive manufacturing strategies.

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

Corrosion management and mitigation from steam cycle contaminants are top priorities for power plant operators. As such, accurate estimation of corrosion rate dependencies on steam cycle chemistry contamination levels are of critical importance. While electrochemical methods can detect corrosion rate increases due to known corrosion agents, like chloride or sulfates, they often underrepresent the danger they present to power plant operations due to their inability to quantify localized corrosion values and the extent of under-deposit corrosion (UDC). High resolution X-ray computed tomography (CT) scans can bridge this gap and provide a means of measuring of localized corrosion rates to better inform the risks associated with specific steam cycle contaminants. If combined with conventional electrochemical monitoring, these methods provide a more comprehensive picture of the corrosion risk associated with particular steam cycle chemistry environments. Herein, we compare corrosion rates obtained from these methods for carbon steel samples exposed to steam cycle chemistries at 300 °C and 124 bar for 60 hours in a laboratory setting. Both linear sweep voltammetry (LSV) and linear polarization resistance (LPR) were used to estimate corrosion rate throughout the exposure test. After the exposure test, X-Ray CT scans were done at a resolution of 710 nm over a sample region of 1.4 mm to determine general and localized corrosion rates. The highest rates calculated for the localized corrosion were all an order of magnitude higher than the general corrosion values calculated from LSV and LPR. The largest discrepancy between the general and localized corrosion rate was observed for chloride contamination. For this case, general corrosion rates were between 0.21 and 0.57 mm y-1, whereas localized corrosion rates were as large as 5.55 mm y-1.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Abstract. A series of panoramic images are usually used to generate a 720° panorama image. Although panoramic images are typically used for establishing tour guiding systems, in this research, we demonstrate the potential of using panoramic images acquired from multiple sites to create not only 720° panorama, but also three-dimensional (3D) point clouds and 3D indoor models. Since 3D modeling is one of the goals of this research, the location of the panoramic sites needed to be carefully planned in order to maintain a robust result for close-range photogrammetry. After the images are acquired, panoramic images are processed into 720° panoramas, and these panoramas which can be used directly as panorama guiding systems or other applications. In addition to these straightforward applications, interior orientation parameters can also be estimated while generating 720° panorama. These parameters are focal length, principle point, and lens radial distortion. The panoramic images can then be processed with closerange photogrammetry procedures to extract the exterior orientation parameters and generate 3D point clouds. In this research, VisaulSFM, a structure from motion software is used to estimate the exterior orientation, and CMVS toolkit is used to generate 3D point clouds. Next, the 3D point clouds are used as references to create building interior models. In this research, Trimble Sketchup was used to build the model, and the 3D point cloud was added to the determining of locations of building objects using plane finding procedure. In the texturing process, the panorama images are used as the data source for creating model textures. This 3D indoor model was used as an Augmented Reality model replacing a guide map or a floor plan commonly used in an on-line touring guide system. The 3D indoor model generating procedure has been utilized in two research projects: a cultural heritage site at Kinmen, and Taipei Main Station pedestrian zone guidance and navigation system. The results presented in this paper demonstrate the potential of using panoramic images to generate 3D point clouds and 3D models. However, it is currently a manual and labor-intensive process. A research is being carried out to Increase the degree of automation of these procedures.

3D point cloud data captured from Oil and Gas (O&G) facilities are becoming increasingly abundant. This data supports the interactive visualization of complex plants, which is central to numerous industrial advancements. As industries seek to innovate and optimize their processes, the versatile use of 3D point cloud data is proving invaluable beyond traditional O&G applications. The precision and comprehensiveness of 3D point cloud data facilitate efficient design, construction, maintenance and operational monitoring across various industrial sectors. Digital Twin (DT) solutions enable this by maintaining 3D representations, such as 3D CAD models, that accurately reflect the current state of facilities. As installations change, the process of 3D scanning is often repeated to capture the latest updates. Engineers and designers then use modelling software to visualize the resulting point cloud and update the facility's 3D representation, incorporating any new modifications. However, this involves interpreting the visual point cloud data and structuring it into meaningful objects (such as valves, tanks, etc.), a process that remains manual, requiring labor-intensive tasks that are prone to errors and incur significant costs due to the need for specialized labor. We propose an Artificial Intelligence approach, that uses a deep learning-based algorithm to automatically segment 3D objects within a point cloud. Our workflow recognizes instances of usual components of production systems, such as control valves, pipes, and transmitters. A state-of-the-art 3D instance segmentation approach is adapted to the O&G domain. Given the lack of publicly available instance segmentation datasets in this domain, we manually annotate 54 scenes from an O&G offshore plant, each of them around 20 m2 in footprint. To further enhance model training, we also incorporated synthetic point clouds. Practical results on real point clouds, acquired from operational facilities and pre-processed as required by commercial DT solutions, indicated a performance of approximately 70% success (f1-score) in segmenting components of O&G production systems, reaching up to 85% for components such as control valves. Accordingly, a 52.4% reduction in costs and efforts associated with manually interpreting 3D point clouds is estimated. Point cloud instance segmentation has attracted much attention in the computer vision research community, and many approaches have been proposed. However, results in the literature are heavily biased toward a few benchmarking datasets, which were acquired in indoor environments like offices and rooms. Few studies have addressed the O&G domain, creating a gap in the literature for achieving more competitive results. This lack of comprehensive research limits the potential for innovation and optimization in the industry. This paper contributes by (i) assessing the performance of state-of-the-art algorithms in the domain of O&G facilities, (ii) proposing modifications to better generalize to components of O&G production systems, (iii) objectively measuring the impact of this algorithm in reducing manual effort for engineers, and (iv) advancing towards a new DT functionality that derives an asset 3D representation, that is faithful to physical reality, through the automatic interpretation of point cloud scans.

Based on the current applications in the oil and gas industry, this paper approaches the main challenges in the development of a Digital Twin for subsea production system assets. Bibliographical research was carried out to select relevant digital twin solutions developed in the different sectors of the oil and gas industry, in order to identify the main challenges, risks and prospects for implementing this type of technology. Furthermore, this manuscript also provides solutions developed by the authors based on the experiences in research, development and innovation of national projects for digital transformation. These solutions contemplate data analysis, connections with databases, definitions of recurrent degradation models in the oil and gas industry. A digital twin can be defined as the digital representation of a system or asset, which calculates states and provides relevant information through integrated models and data, to support decisions throughout the life cycle. In the upstream, whose main activities involve the exploration and production of oil and gas, digital twin can be used as a tool for asset integrity management in the production system and associated with integrity management methodologies such as Risk Based Inspection (RBI). The implementation of a digital twin is a complex task and has several challenges, since it is composed of many models and data that need to be interconnected. However, it is common for data from different sources to be stored on different systems. Another relevant challenge concerns the models associated with digital twins, which are sources of uncertainties. The lack of consistent models and coordination between current physical and virtual models, complex data validation, collection and processing challenges are obstacles in implementing a digital twin. If well implemented, the digital twin can help in the decision-making process related to asset integrity management by following the asset throughout its life cycle. In an RBI approach, it can reduce the OPEX cost of subsea systems. This paper will approach the stages of the development of a digital twin for subsea production systems assets and the main challenges based on recent studies carried out in the oil and gas industry such as the requirement for transparency in data exchange while maintaining security and confidentiality, data-model assimilation, uncertainty and validation of models. In addition, it will be presented the experience acquired by the authors in the development of solutions for integrity management.

Autonomous Digital Process Twins (ADPTs) represent an advanced paradigm in intelligent computational systems that extends traditional digital twin frameworks by incorporating real-time analytics, artificial intelligence, and autonomous decisionmaking capabilities. Conventional digital twins are primarily designed for monitoring, visualization, and offline simulation; however, emerging complex environments require systems capable of adaptive reasoning and real-time optimization. This paper proposes a comprehensive software-centric architecture for an Autonomous Digital Process Twin that enables continuous synchronization between dynamic process models and intelligent decision engines. The proposed architecture integrates virtual process modeling, distributed real-time analytics, reinforcement learning-based decision intelligence, and simulation- driven control evaluation within a scalable computational framework. A mathematical formulation based on dynamic state-space modeling and Markov decision processes is introduced to support predictive reasoning and adaptive optimization. Extensive simulation experiments demonstrate improvements in decision accuracy, latency reduction, and scalability under high computational workloads. The results indicate that the architecture provides a robust foundation for next-generation intelligent systems capable of autonomous adaptation in complex digital environments. This research contributes to the advancement of intelligent digital twin ecosystems by emphasizing algorithmic autonomy, scalable architecture design, and realtime decision intelligence.

This paper elaborates on the large number of Uncertainty Quantification (UQ) techniques that have been proposed to enhance the reliability and the fidelity of Digital Twins that are increasingly finding applications in domains like Robotics and Smart Manufacturing. Digital twins are virtual duplicates or virtual models of a physical asset; they use advanced techniques such as data analytics and simulation-driven methods. However, the development and use of these advanced systems are plagued by a host of uncertainties, which are mainly introduced from sensor noise, intermittent connectivity, biases from data processing, and model abstractions and simulation stochasticity. Such uncertainties can be quantified by methods such as frequentist statistics, interval analysis, Bayesian inference, and random sampling. The mapping is important in gaining insights into these UQ methods and their associated advantages and limitations and the mitigation guidelines are to be used throughout the Digital Twin pipeline. UQ at its core involves real-time adaptive control in dynamically changing environments that leverage state awareness towards responsive action within predictive control models and feedback systems. In addition, machine learning algorithms support the ability to make better decisions from the identification of patterns in historical data to make plans for responsive trajectories of robots. UQ further allows the collaboration of human and machine, giving early warnings on anomalies and risks that enhance visibility which further fosters coordination and communication during disruptive situations. Robust development of digital twins for robotics and manufacturing relies on integrated UQ practices. The current review provides best practices, insights, and guidelines on the application of UQ across modeling, control strategies, and collaborative workflows aimed at delivering actionable and reliable insights from digital twin simulations, analytics, and decision support.

Environmental uncertainties and hyperconnectivity force technosocio-economic systems to introspect and adapt to succeed and survive. Current practices in decision-making are predominantly intuition-driven with attendant challenges for precision and rigor. We propose to use the concept of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free 'in silico' experimentation aid to help: (i) understand why a system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have new potential for impact. Our approach contributes an original meta model for simulatable digital twin of industry scale techno-socioeconomic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss the rigor of our validation of the proposed approach and associated technology infrastructure through a representative sample of industry-scale real-world use cases.

Digital twins, dynamic, patient-specific virtual replicas of physical systems are gaining momentum across various medical fields. Their capacity to simulate and predict biological behavior offers unprecedented opportunities for personalized care. While their utility has been demonstrated in oncology, their application in urology remains nascent, and there is still a significant gap in literature findings. This review explores the theoretical framework, current evidence, and future potential of digital twins technology in managing urological conditions such as urolithiasis, benign prostatic hyperplasia (BPH), and nonmuscle invasive bladder cancer. A narrative literature review, guided by SANRA criteria, was conducted using PubMed and Scopus databases to identify relevant studies on the application of digital twins in healthcare, with a focus on urology and related fields. Additional sources from AI, imaging, and computational modelling were incorporated to support technological and theoretical concepts. Real-world use cases and clinical scenarios were constructed to illustrate the conceptual application of digital twins in endourology. Although limited in number, preliminary studies have shown promising results in uro-oncology, particularly for prostate and renal cancers. No models have yet been developed for benign urological diseases. Hypothetical applications in endourology include personalized procedural planning, dynamic follow-up, prediction of obstruction or recurrence, and real-time intraoperative guidance. Integration of imaging, functional data, and AI algorithms could create continuously adaptive simulations to support decision-making and improve treatment outcomes. However, barriers such as data complexity, cost, lack of validation frameworks, and clinician trust persist. Digital twins hold transformative potential for the future of urology, promising to personalize care across diagnosis, treatment, and surveillance. While the path to clinical integration is challenged by technical, ethical, and infrastructural hurdles, collaborative efforts and real-world validation studies are critical to realizing their full potential in clinical practice.

Recent research studies and surveys have shown that air pockets in wastewater transmission systems cause extensive damage and hindrance. Air pockets decrease flow capacity, they induce significant head losses and energy losses, they prompt serious corrosion, and they can trigger and/or enhance pressure surges. In the advent of concern for energy conservation, more and more research is invested in studying the effect of air pockets on flow efficiency and energy consumption. Conclusions are that air pockets have a very significant impact on these, especially in wastewater force mains. Much has been written in the past about Hydrogen Sulfide corrosion in wastewater pipelines. The EPA published a number of papers, pamphlets, and reports to Congress on this subject. More and more papers and studies today, attribute Hydrogen Sulfide corrosion to air pockets, including a number of papers presented at the ASCE Pipelines 2009 Conference in San Diego. Most of these papers refer to detection, monitoring, evaluation, assessment and management of corrosion, but not of prevention. They point out the contribution of air pockets to Hydrogen Sulfide corrosion, but they do not discuss the means and ways of preventing air pocket accumulation. Though, for many years, it was commonly accepted by surge experts that air pockets dampen surges, it was determined in recent research studies that air pockets of certain sizes and in certain locations along a pipeline, can induce and/or enhance surges. These surges, especially in force mains weakened by corrosion, can cause intensive and severe damage to pipelines, resulting in soil and water source polluting leakage, and health endangering pathogen and contaminant intrusion to drinking water transmission systems.

In modern medicine, novel technologies play a key role in improving the diagnosis, treatment, and management of patient health. One of the most promising innovations is Digital Twin technology, which enables the creation of virtual models of real-world objects, including human organs. This technology opens new possibilities for personalised medicine, disease prediction, and the optimisation of medical processes. Digital Twins are already actively used in various industries, such as manufacturing, aviation, and urban planning, but their potential in medicine is only beginning to unfold. They allow for creating detailed models of an individual patient’s body, contributing to more accurate diagnostics, simulation of treatment responses, and enhanced efficiency of medical interventions. This article explores the key aspects of Digital Twin medical applications, their advantages, challenges, and development prospects. Special attention is given to the technical foundations of this technology, its use in personalised medicine, health monitoring, disease prediction, and the optimisation of healthcare facilities. For the practical part, the study shows how to create a Digital Twin of a heart based on real patient data. Additionally, the study investigates a model of the heart’s Digital Twin, demonstrates its use, and illustrates how to replicate heart functionality using the Digital Twin. The study describes how a part of a Digital Twin was created using the suggested architecture and outlines how to use and extend this functionality. More than that, the study shows how to integrate artificial intelligence into the Digital Twin architecture and how artificial intelligence algorithms help create Digital Twins. The research shows how to build the Digital Twin using machine learning and artificial intelligence technologies, and how to choose the mathematical model for the Digital Twin. To conclude, the study defines further work, problems, and difficulties in creating the Digital Twin of the heart. The study shows the perspectives on using Digital Twins, their practicality, and real-life cases where Digital Twins can save lives. Also, the study defines the next steps for the Digital Twin creation, testing, implementation, and usage in the real-life healthcare industry.