Search Results (8,124)

This study proposes a comprehensive methodology for the experimental characterization and non-linear dynamic modelling of Polycrystalline Diamond (PCD) bearings, establishing a high-fidelity digital twin approach for the condition monitoring of rotating machinery. The research addresses complex rotor–stator interactions through the development of a multibody numerical framework. A structural 1D Finite Element (FE) model of the stator assembly was first calibrated via experimental modal analysis, achieving a high correlation with the first four bending modes and a maximum frequency discrepancy of only 1.4%. This validated structure was integrated into a non-linear multibody environment to simulate transient rub-impact events at rotational speeds up to 5500 rpm across varying clearance configurations. The model successfully captures the transition from stable periodic orbital motion to the stochastic and chaotic regimes observed in high-clearance setups. Frequency-domain validation further confirms the model’s accuracy in identifying supersynchronous harmonics and energy distribution patterns. Quantitative analysis shows that high-clearance configurations generate impact forces exceeding 6000 N, providing critical data for structural health assessment. These results demonstrate that the proposed digital twin serves as a robust physical foundation for diagnostic systems, enabling the identification of contact-induced vibrational signatures that are essential for training prognostic algorithms. This approach facilitates the autonomous monitoring of critical rotating machinery in demanding industrial and subsea applications, supporting the transition toward active balancing and model-based vibration control strategies. Full article

To address the poor adaptability and rigid initiation modes of the loitering munition fuze in complex environments and the inadequacy of single fuzzy control against strong interference, this paper proposes a dual-loop adaptive reconfiguration control method. The architecture integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with fuzzy logic. The inner loop uses TD3 to dynamically optimize fuzzy scaling factors based on real-time interference and state deviations. Concurrently, the outer loop utilizes a Fuze Readiness Index (FRI) and a finite state machine to manage real-time multi-modal mission switching (e.g., proximity, delay, and airburst) and reverse safety-state conversions. Co-simulations under non-stationary composite interference show that the proposed method reduces the burst height RMSE by 82.4% and 61.6% compared with the fixed-threshold and standard fuzzy baselines under the considered non-stationary composite interference setting, respectively. The false alarm rate (FAR) is reduced to 0.15%, and the reconfiguration response time under sudden interference is shortened to 12 ms. Even under extreme conditions, such as 400 ms sensor signal loss, the relative error remains within 5%. These simulation results demonstrate the potential of the proposed architecture to improve precision, responsiveness, and robustness under dynamic interference conditions and show good robustness to intermittent observation loss within the simulated operating envelope. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Three-dimensional integrated circuit (3D IC) packaging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced stresses, and interconnect aging demands monitoring and control capabilities that surpass traditional offline metrology. Although Digital Twin (DT) technology provides a principled route to real-time reliability management, the existing literature remains fragmented and frequently blurs the distinction between static multi-physics simulation workflows and truly dynamic, closed-loop twins. This critical review addresses these deficiencies through three main contributions. First, we clarify the Digital Twin hierarchy to resolve terminological ambiguity between digital models, shadows, and twins. Second, we synthesize three foundational enabling technologies. We examine physics-based modeling, emphasizing the shift from finite-element analysis (FEA) to real-time surrogates. We analyze data-driven paradigms, highlighting virtual metrology (VM) for inferring latent metrics. Finally, we explore in situ sensing, which serves as the “nervous system” coupling the physical stack to its virtual counterpart. Third, beyond a descriptive survey, we outline a possible hybrid DT architecture that leverages physics-informed machine learning (e.g., PINNs) to help reconcile data scarcity with latency constraints. Finally, we outline a standards-aligned roadmap incorporating IEEE 1451 and UCIe protocols to support the transition from passive digital shadows toward more adaptive and fully coupled Digital Twin frameworks for 3D IC manufacturing and field operation. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

Combining Response Surface Methodology (RSM) with Central Composite Design (CCD) is a powerful statistical approach to optimizing materials in energy storage systems. This study presents an open-source Python (v3.8+) framework that replicates and validates the RSM-based optimization of NiCo₂S₄–graphene supercapattery materials. We validated the framework by replicating a 20-experiment CCD analyzing graphene/NCS ratios, hydrothermal time, and S/Ni molar ratios. Advanced optimization using the Differential Evolution algorithm was integrated to efficiently solve the high-dimensional response surface space. The model explained 97.16% of the variance, and comprehensive diagnostic tests confirmed the assumptions of normality and residual independence. This approach provides an open-source methodology that supports reproducible and scalable data-driven material design and facilitates transparent computational materials science studies. Full article

Rapid urbanization and the growth of data-driven planning have increased the need for tools that support integrated, transparent, and accountable urban governance. While Building Information Modeling (BIM) is well established in project delivery, its potential role in city-scale governance remains underexplored. This study conducts a structured qualitative evidence synthesis informed by PRISMA reporting principles and comparative case analysis to investigate how BIM, in combination with GIS, IoT, and AI, intersects with emerging digital governance practices. Through a synthesis of peer-reviewed research and documented case studies, the review evaluates how BIM supports data integration, interoperability, decision-making, regulatory compliance, collaborative governance, and sustainability. The findings suggest that BIM functions as a governance-support infrastructure when embedded within coordinated institutional frameworks, standardized data environments, and interoperable digital ecosystems. Based on these insights, the paper proposes a conceptual framework that organizes BIM governance into technical, institutional, social, and ethical–regulatory dimensions. The review suggests that BIM’s governance potential depends on institutional alignment, regulatory clarity, and sustained organizational capacity, rather than technological capability alone. Full article

Electric flight accelerates innovation and demands digitalization. DIREKT develops digital twins across the lifecycle of (hybrid) electric propulsion systems to fuse data, cut costs, and shorten time-to-market. In this context we present a production-to-design feedback approach. A system is developed which compares the scanned manufactured part with the design to visualize manufacturing deviations to improve upcoming designs. The system is tested with three different additive manufacturing technologies and two parts from an urban air mobility electric propulsion system. Furthermore, the comparison data is stored in a knowledge base for machine-learning-driven deviation prediction later on. Full article

The increasing complexity of Cyber-Physical Energy Systems, driven by the high penetration of power electronics, advanced control, and digitalization, demands scalable, flexible real-time simulation platforms beyond traditional laboratory-based solutions. This paper investigates the feasibility of deploying open-source real-time power system simulation frameworks on cloud-based infrastructures while meeting real-time computational constraints. An open-source architecture based on DPsim and the VILLAS framework is implemented and evaluated across five computing environments using open-source tools: bare-metal, non-cloud virtual machines, private cloud Kubernetes clusters, public cloud virtual machines, and public cloud Kubernetes clusters. Each environment is carefully configured and tuned using real-time operating systems, CPU isolation, and affinity mechanisms to improve deterministic behavior. Performance and scalability are assessed through a benchmark based on replicated IEEE 9-bus systems, progressively increasing system size, and measuring simulation-timestep execution time. The results show that cloud and cloud-like infrastructures can support soft and, under controlled conditions, firm real-time simulation tasks, although achievable system scale decreases as additional abstraction layers are introduced. The study identifies practical performance limits for each infrastructure and discusses their suitability for different real-time simulation and co-simulation applications. These findings demonstrate that cloud-based real-time simulation can complement traditional digital real-time simulators, enabling scalable and cost-effective CPES experimentation. Full article

In urban subway construction, shield tunneling inevitably passes in close proximity to existing pile foundations, inducing adverse effects on their internal forces and deformations. Taking the twin shield tunnels with small clearance adjacent to the bridge piles as the engineering background, this study establishes a three-dimensional finite element numerical model to investigate the deformation and internal force responses of the adjacent pile foundations under different pile lengths, twin-tunnel construction sequences, and tunnel face pressure conditions. The findings indicate that the primary influence zone affected by twin-tunnel excavation extends approximately twice the tunnel diameter (2D) before and after the pile foundation location. Compared with short piles, longer piles exhibit smaller vertical displacements. Meanwhile, the lateral displacements, additional axial forces and bending moments of medium and long piles increase, with their maximum values occurring near the tunnel centerline. For the near pile, when the right tunnel is excavated first, compared with the condition of the left-tunnel-first excavation, the lateral and vertical displacements slightly increase. In addition, the maximum additional axial force increases by 38.8%, while the maximum additional bending moment decreases by approximately 21%. Tunnel face pressure exerts a moderate influence on the vertical displacement of both the surrounding soil and pile foundation, while its effect on lateral displacement and internal forces is relatively insignificant. The tunnel face pressure within the range of 200 kPa to 300 kPa provides optimal control over pile foundation deformation. Full article

Background/Objectives: Twin pregnancies carry substantially elevated perinatal risks, yet tools for first-trimester risk stratification remain limited. This retrospective cohort study evaluated the predictive value of crown–rump length (CRL) and nuchal translucency (NT) discordance for adverse perinatal outcomes in 184 twin pregnancies at Ankara Etlik City Hospital, Turkey (October 2022–January 2024). Methods: CRL discordance ≥ 10% and NT discordance ≥ 20% were assessed for a birth-weight-based proxy of fetal growth restriction (FGR), preeclampsia, and neonatal outcomes using multivariable logistic regression adjusted for chorionicity, body mass index (BMI), and conception mode. Results: CRL discordance ≥ 10% was independently associated with the birth-weight-based FGR proxy (adjusted odds ratio [OR] 7.79, 95% confidence interval [CI] 3.95–20.12, p < 0.001; area under the curve [AUC] 0.736). NT discordance ≥ 20% was also independently associated with the birth-weight-based FGR proxy (OR 3.74, 95% CI 1.91–8.39, p < 0.001; AUC 0.612). Both parameters were associated with lower Apgar scores. IVF conception was independently associated with preeclampsia in an exploratory analysis (OR 5.31, 95% CI 1.41–28.66, p = 0.016). Continuous modelling confirmed a dose–response relationship for CRL discordance (OR per 1% increase = 1.20, 95% CI 1.13–1.32). Conclusions: These findings suggest that first-trimester CRL discordance may provide useful early prognostic information for birth-weight-based adverse growth outcome in twin pregnancies, pending prospective validation in cohorts with Doppler-based FGR ascertainment. Full article

Complex weather degrades both perception reliability and tire–road adhesion, thereby reducing the safety margin and responsiveness of intelligent driving longitudinal control. This study proposes a reproducible evaluation method for adverse weather operational design domains based on parameter perturbation testing and comprehensive assessment. Snow, fog, and rain are graded using standard quantitative thresholds and are coupled with road slipperiness to construct a weather–road state set. A mechanism-oriented indicator system, a combined subjective–objective weighting strategy, and a multi-level fuzzy comprehensive evaluation model are then used to generate quantitative capability scores. The method is validated on a co-simulation framework integrating vehicle–sensor simulation, a driving simulator, and a digital-twin testing environment using representative autonomous emergency braking scenarios. Results show that increasing weather severity, decreasing road adhesion, and higher initial speed reduce the post-braking safety margin and prolong collision-response time. The proposed method differentiates performance across weather–road states and provides quantitative support for test-coverage planning and capability boundary calibration in adverse weather operational design domains. Full article

Decarbonising greenhouse food production requires improvements in thermal management, energy efficiency, and system integration. Greenhouse energy demand is shaped by coupled heat and mass transfer processes, particularly envelope performance, ventilation, and latent heat associated with humidity control. This article synthesises recent advances in greenhouse microclimate control with emphasis on heat transfer, low-carbon heating and cooling, thermal storage, renewable and waste heat integration, and advanced modelling and control approaches. The review shows that humidity control and latent load management are primary drivers of winter energy use, as moisture removal through ventilation and dehumidification directly increases the sensible heating required to maintain indoor temperature setpoints. When assessed using realistic psychrometric relationships, ventilation and dehumidification can dominate peak heating demand and seasonal consumption. The performance of heat pumps, storage systems, semi-closed greenhouse concepts, and renewable heat pathways depends on how thermal loads are defined, how system boundaries are set, and how technologies are integrated in operation. Digital twins, predictive control, and hybrid physics-data models are increasingly used to manage variability in weather, energy prices, and infrastructure constraints. Greenhouse decarbonisation cannot be treated as a simple substitution of energy sources. System performance depends on coordinated design and operation, including heat recovery, moisture removal, and integration of supply technologies. Semi-closed and heat recovery-based configurations can reduce the ventilation–heating penalty and lower primary energy demand compared with vent-to-dry approaches. Long-term market projections suggest that the commercial greenhouse sector could expand substantially by 2050 under plausible growth scenarios, reflecting increased capital investment rather than a proportional rise in global food output. Net-zero greenhouse production is achievable through combined improvements in thermal management, electrification, and renewable energy integration. However, large-scale deployment depends on consistent modelling assumptions, credible economic assessment, and alignment with heat and CO₂ supply infrastructure. The transition is therefore shaped by system integration and planning as much as by individual technologies. Full article

This study investigates the effects of vehicle air-conditioning parameters on cabin thermal environment and occupant comfort. Computational fluid dynamics and discrete particle simulations involving different inlet-vent angles, inlet relative humidity (RH) levels, and occupant counts were conducted to analyze airflow, temperature, and RH. Thermal comfort was assessed using predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), equivalent homogeneous temperature, and mean age of air (MAA). As a result, the uniform airflow at a 30° inlet angle provided the best global thermal comfort based on PMV (0.49) and PPD (10.02), whereas a 0° inlet angle improved local comfort around the chest area. Maintaining an inlet RH of 40–50% enhanced overall thermal comfort. Increasing the occupant counts raised the average cabin temperature to 301.76 K (Case 9), while also affecting local airflow patterns and MAA distributions; the addition of rear-seat occupants increased the local temperature around the driver’s left hand. These findings provide practical guidance for vehicle heating, ventilation, and air-conditioning system design, indicating that ventilation strategies should consider global comfort indices, localized airflow, thermal patterns, and particle removal performance. Overall, this parametric study highlights the association between vehicle cabin conditions and thermal comfort, providing baseline data for digital twin–based adaptive ventilation systems. Full article

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures. Full article