Floorless closed-loop microdosing for rail friction: a physics-only digital twin on Kuwait city’s blue corridor

Effect of moderating friction of wheel/rail interface on vehicle/track dynamic behaviour

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

The future trajectory of manufacturing will include the profound inclusion of the internet, communication, and cognition within industrial applications, driven by ongoing advancements in science and engineering. The increasing sophistication of technological innovations, including the Internet of Things (IoT), massive data sets, and cloud computing (CC), has led to the development and utilization of a promising technology - Digital Twin (DT). Presently, investigations on DT have yielded several ideas and results that have been implemented across various domains. This study introduced a Digital Twin Technology for Real-Time Monitoring and Control in Smart Manufacturing (DTMC- SM). The DT is an important technology that allows for actualizing the potential of SM and Industry 4.0 to enhance the functioning of manufacturing processes. Fuelled by collecting and examining substantial information from linked virtual and physical environments, DTs are real-time digital representations of physical operations, procedures, or items that facilitate the assessment and enhancement of corporate performance. This research presents an innovative DT framework for continuous surveillance and assessment of extensive SM systems. An SM flow-shop implementation is shown to demonstrate the efficacy of the suggested technique.

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication-Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed.

Digital Twin Technology provides a rigorous, end-to-end treatment of how live data, executable models, and governed decision loops can deliver measurable gains in quality, uptime, safety, and sustainability. The book formalizes the digital twin as a controlled feedback system—sense → model → predict → decide → actuate → learn—distinguishing it from digital shadows and offline simulation. It synthesizes reference architectures across asset, data, model, decision, and governance layers; embeds verification, validation, and uncertainty quantification to produce defensible predictions; and specifies assurance cases suitable for regulated, safety-critical contexts. Bridging design and operations through the digital thread, the volume demonstrates repeatable value in manufacturing, robotics, energy systems, buildings, water networks, and urban mobility, with reproducible pipelines and policy-linked KPIs. Methods span physics-based and data-driven modeling, hybrid surrogates, causal inference, and twin-in-the-loop experimentation, complemented by zero-trust security and privacy-preserving collaboration. The final chapters integrate economics, ethics, and standardization into an actionable roadmap, aligning technical maturity with workforce inclusion and net-positive outcomes. Designed for researchers, practitioners, and policymakers, this book offers a common analytical language and a practical playbook to scale trustworthy twins across sectors. Keywords: digital twin, digital thread, cyber-physical systems, uncertainty quantification, verification and validation, assurance cases, hybrid modeling, prescriptive analytics, zero-trust security, privacy-preserving analytics, industrial IoT, time-sensitive networking, energy systems, smart manufacturing, human–robot collaboration, model risk management, AIOps, resilience engineering, sustainability

The rapid evolution of smart manufacturing necessitates innovative approaches for real-time monitoring, control, and optimization of production processes. Artificial Intelligence (AI) and Digital Twins (DT) have emerged as transformative technologies capable of enhancing operational efficiency, predictive maintenance, quality assurance, and adaptive process control. This paper presents a comprehensive framework for integrating AI with digital twin systems to enable real-time manufacturing control. The study reviews current methodologies, architectures, and use cases, highlights the key capabilities and benefits of AI-enabled digital twins, and discusses practical challenges and implementation considerations. Additionally, schematic representations of system architecture, data flow, and closed-loop control are provided to guide practitioners and researchers. The findings suggest that the integration of AI and digital twins significantly enhances manufacturing responsiveness, flexibility, and overall performance while offering a foundation for future manufacturing systems.

Manufacturing resilience and agility through processes digital twin: design and testing applied in the LPBF case

Smart inverters supervised through a cyber network become vulnerable to cyberattacks. In this work, a self-security method is developed and implemented using the digital twin concept for smart inverters. This paper shows a digital twin can be formed using the dynamic model for grid-interactive inverters. In the proposed self-security method, the incoming setpoints are autonomously examined using the digital twin, and only the safe setpoints are engaged to the inverter’s local controller. This paper demonstrates the details of the self-security algorithm and how the inverter’s digital twin is formed. In particular, an inverter's stable and unstable operation is experimentally verified by changing the power setpoints engaged to the local controller, using a laboratory setup including a three-phase 5-kVA SiC-MOSFET inverter. The results demonstrate that the digital twin model can potentially protect inverters from an abnormal operation, i.e., instability, by examining the incoming new setpoints before engaging the setpoints to the local controller.

Smart inverters connected to a communication network are susceptible to man-in-the-middle attacks. In this paper, a self-security approach is implemented using the digital twin concept for smart inverters. The digital twin is formed using the inverter’s normal operating region and the inverter’s dynamic model. Then, the incoming setpoints are autonomously examined using the digital twin, and only the safe setpoints are engaged to the inverter’s local controller. This paper demonstrates how the inverter’s normal operating region and dynamic model are formed. In particular, the normal operation region is experimentally verified by changing the P and Q setpoints engaged to the local controller, using a laboratory setup including a three-phase 1.5-kVA SiC-MOSFET inverter and a 12-kW NHR 9410 regenerative power grid emulator. The results demonstrate that the self-security technique can potentially protect inverters from man-in-the-middle attacks by examining the incoming commands (new setpoints) using the inverter’s digital twin before engaging the setpoints to the local controller.

Through the introduction of programmable logic controller (PLCs), Dynamic Process & Controls modeling, integrating with Multiphysics Mechatronics & 3D equipment simulation modeling, companies can work in the online real-time environment. This modeling of equipment or processes builds the foundation for digital transformation of subsea, topside, onshore and plant environments. In the design and operation of field equipment, the physics based Digital Twin is getting more and more traction to develop virtually the equipment because of recent prediction accuracy improvement and faster calculation times. Such digital twins allow to find the optimal operating conditions and predictive maintenance schedules for operation. In this timeslot we will explain, based on few industrial examples, a new set of capabilities that allow companies to get the maximum out of digital twins to be able to use them on their equipment. By applying a structured process using Digital Twins to be able to convert the existing knowledge & data at Companies into solution to be more predictive on their equipment. This will deliver substantial return on investment (ROI) for the Oil and Gas Industry. An AI based methodology to perform Model Order Reduction on the digital twin to be able to get real time response in connection to online unit information An AI based methodology to convert the reduced model into a virtual sensor for online quality predictions or predictive maintenance scheduling as well as to use it for creating an optimal controller of the unit based on the product requirements Fast edge computing hardware that can collect data from sensors and, in real time, run the Executable Digital Twin (xDT) and suggest corrective action to the operator or run in closed loop control

This paper presents a digital twin-based condition monitoring method for DC-DC power converters, which features non-invasive and without additional hardware. To demonstrate it, a buck converter is applied as a case study with theoretical analysis and experimental verification. The digital twin of the buck converter is established, which includes the power stage, sampling circuit, and close-loop controller. Particle Swarm Op-timization (PSO) algorithm is applied to minimize the difference between the digital twin and its physical counterpart. Compare to conventional methods, the proposed method is able to monitor the health indicators of the key components in the buck converter: capacitor and MOSFET, without adding extra measurement circuits. Moreover, because the digital twin is a replica of the physical buck converter, accessing to the internal buck converter is unnecessary, which is non-invasive.

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost.

Chapter 12 - Digital Twin, Cyber–Physical System, and Internet of Things

Digital twin is one of the key technology of intelligent digital manufacturing field, the related research is still in the initial stage This article in complex parts machining production line as the research object, from the geometric physical behavior rules four aspects to explore the digital twin modeling technology of production line Through the geometric model and the size of the physical model visualization expression line shape Based on the three-layer model of geometric physical behavior, the digital twin behavior model is constructed to describe the operation failure rule of the machine and unit. Driven by real-time information collection and fusion technology, closed-loop control and iterative optimization are realized Finally, the application value of digital twin technology in intelligent monitoring information feedback decision control is demonstrated through the intelligent control system of machining production line.

Reservoir management in mature waterflood fields demands accurate dynamic modeling and agile optimization under complex heterogeneity and ever-changing conditions. This paper presents a novel Reservoir Engineering Reservoir Simulation (REROSIM) approach– a data-driven interwell connectivity model augmented as a digital twin – to predict reservoir dynamics and optimize operations in the Changqing Oilfield of China. The REROSIM methodology represents the reservoir as a network of connection units linking injectors and producers, characterized by transmissibility (conductivity) and connected volume parameters instead of traditional gridblocks. History-matched with production/ injection data via automated machine-learning calibration, the model captures time-varying well connectivity and flow paths with high speed and reasonable accuracy. REROSIM integrates seamlessly into a digital twin framework: continuous field data feeds the model for real-time performance forecasting and scenario simulation, enabling proactive management decisions.We outline the theoretical underpinnings of the REROSIM/REROSIM model, including the conversion of 3D flow fields to a 1D network, Pollock streamline-inspired path tracing, and non-gradient optimization for large-scale control of injection and production schedules. A field case study in Changqing Oilfield demonstrates model calibration to historical waterflood performance and polymer flood data.Simulation experiments show close agreement with full-physics numerical simulation and significant advantages over traditional models like capacitance–resistance models (CRM) and streamline simulations in speed and adaptability. The REROSIM digital twin was applied to optimize injection allocation and infill well placement, yielding a predicted increase in sweep efficiency from 61% to 83% and a 10% improvement in injection utilization. The model also guided multi-level profile control (water shutoff) decisions by identifying dominant channeling pathways (high Lorenz coefficients) and selecting appropriate gel blocking strategies, which improved water-oil ratio and enhanced recovery.Through comprehensive validation, we compare REROSIM against conventional methods, highlighting its strength in capturing dynamic connectivity and enabling smart reservoir management. Finally, we discuss the implications for digital transformation in oilfields–emphasizing how physics-based data-driven models like REROSIM act as enablers for real-time optimization, uncertainty reduction, and closed-loop reservoir control. This work contributes a scalable, intelligent reservoir simulation paradigm blending engineering principles with advanced analytics to meet the demands of modern digital oilfields.