Junho Lee, Sanghyun Park, Younghun Lee, Namsu Kim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083185 - 27 July 2026

Abstract Monitoring the conditions of electric motors in industrial applications is an essential step for ensuring safety and reducing maintenance costs. This paper deals with one of the most frequent winding failures—the inter-turn short fault of an interior permanent magnet synchronous motor. A novel high-fidelity co-simulation framework toward a digital twin-based approach combining Maxwell simulation in finite element method (FEM) for the motor and control system in system software for the inverter is presented. An analysis of the motor based on a 2D FEM model is performed considering the motor topology and non-linear properties, and inductance… More >

Nada Alzaben¹, Muhammad I. Khan², Hafeez Ur Rehman Siddiqui³, Abeer Rashad Mirdad⁴, Saeed Ali Bahaj^5,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083649 - 23 July 2026

Abstract Industrial predictive maintenance is a critical challenge in modern manufacturing, where unexpected equipment failures cause significant economic losses through downtime, repair costs, and disrupted production. Conventional maintenance approaches, whether reactive or schedule-based, are becoming inadequate to manage the high-dimensional sensor information of the IoT-enabled machineries. The paper presents a novel hybrid neuro-symbolic digital twin that builds upon Remaining Useful Life (RUL) estimation by combining temporal transformers, physics-informed constraints, and counterfactual reasoning. The model integrates complementary approaches into a single and interpretable predictive system. A temporal transformer backbone is a model of long-range dependencies in multivariate… More >

Gábor Hasulyó^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.081099 - 12 July 2026

Abstract The current global energy situation is very fragile. Much more stable and predictable energy security is needed. Due to global climate conditions, it is advisable to prioritize fuels that are high in energy content and relatively easy to produce, such as hydrogen. However, the widespread deployment of hydrogen refueling stations is hampered by significant safety challenges, including hydrogen’s high flammability, its tendency to leak, and high-pressure storage requirements. This study examines how digital twin technology can be implemented to improve the safety and operational efficiency of hydrogen facilities. A dynamic digital twin model was developed… More >

Antonio Gimeno, Emilio Larrodé^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081630 - 30 June 2026

Abstract This paper presents the development of a digital twin of a heavy-duty electric truck powered by a hybrid energy system based on a hydrogen fuel cell and a battery pack. The objective of the model is to analyze different energy management strategies and to determine how the power demand of a real route can be shared between both energy sources, while keeping the fuel cell within safe operating limits to preserve its service life. The digital twin simulates vehicle dynamics, traction, and regenerative braking, and the main operational constraints of the fuel cell, including minimum… More >

Jalpa Thakkar¹, Siddharth Shankar Mishra², V. Shanmugapriya³, Mohan Kolhe^4,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078267 - 18 June 2026

Abstract The big challenge in developing wind energy over the past century, which has focused on environmentally friendly production methods to meet the requirements of modern power systems, is the need for holistic architectures that can cope with variability, connection issues, and sector coupling far beyond conventional electricity-only models. This review offers a critically synthesized, process-level overview of progressive wind–hydrogen hybrids, offering a collective view of advancements in electrical layouts, hydrogen-driven conversion routes, and AI-driven control schemes. In contrast to previous surveys that consider these areas in isolation, we provide an explicit examination of the technical… More >

DongHwan Ku, Sun Park^*, JongWon Kim

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079582 - 15 June 2026

Abstract In future smart cities, ensuring urban safety requires data-driven decision-making through real-time monitoring tailored to dynamic, complex environments. Such surveillance relies on diverse mobile sensor devices, including drones, robots, patrol vehicles, and portable sensors. However, scaling and validating these systems directly in the real world is constrained by high costs, safety risks, and limited reproducibility across operating conditions. A scalable Digital Twin (DT) model can overcome these constraints by reproducing real-world mobile surveillance in a virtual environment, enabling large-scale simulations of sensor deployment, communication scenarios, and high-density visual data processing. Nevertheless, digital twins still face… More >

Bernard P. Zeigler^*

Digital Engineering and Digital Twin, Vol.4, pp. 1-26, 2026, DOI:10.32604/dedt.2026.082492 - 02 June 2026

Abstract As Digital Twin (DT) applications expand into complex, dynamic environments, a formal methodology is lacking to ensure that the embedded digital models remain adequate for specific stakeholder goals over time. This article introduces the Experimental Frame–System Under Test (EFSUT) methodology, providing a principled foundation for linking high-level stakeholder questions to the specific models capable of answering them. EFSUT organizes the digital engineering process around three core constructs: stakeholder questions, experimental frames that formalize observational requirements, and models related through morphisms. This structure allows developers to reason about model choice, reduction, and adequacy with technical rigor… More >

Xiaoming Yu¹, Peng Wang¹, Jun Wang¹, Zhijun Chen¹, Ke Zhang¹, Duicheng Zhao², Jiapeng Shen², Chuyang Wang^2,*, Li Zhang²

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.073475 - 27 May 2026

Abstract Electrolytic capacitors in Modular Multilevel Converter-based Unified Power Flow Controllers (MMC-UPFC) are prone to parameter degradation, significantly affecting system reliability. Accurate identification of their degradation parameters—capacitance (C) and equivalent series resistance (ESR)—is essential for equipment health management. This study proposes a non-intrusive degradation parameter identification method for electrolytic capacitors in MMC-UPFC full-bridge submodules based on digital twin technology and an improved intelligent optimization algorithm. First, the degradation mechanism of electrolytic capacitors under long-term operational conditions is systematically analyzed. Second, a high-fidelity digital twin model integrating the main circuit, sampling circuit, and modulation circuit of the… More >

Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079991 - 08 May 2026

Abstract Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover More >

Tian Niu, Kaizhao Zhang, Zhongxue Gan, Wenchao Ding^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079324 - 27 April 2026

Abstract Ensuring operational safety for autonomous vehicles is a critical challenge in modern engineering, particularly due to the intricate interactions among diverse traffic participants. Traditional approaches often treat planning and prediction as unidirectional processes, failing to capture the dynamic, game-theoretic nature of real-world traffic. In the context of Digital Twins, there is an urgent need for high-fidelity virtual representations that can model the continuous, bidirectional evolution of the ego vehicle and surrounding agents to support robust decision-making under uncertainty. To address these limitations, a novel framework named Planning by Simulation with mutual influence prediction is proposed,… More >