Search Results (168)

Deep marine carbonate formations are affected by multi-stage tectonism and dissolution, causing frequent wellbore instability and complex drilling events. This study integrates electrical imaging logs (FMI), core observations, and environmental scanning electron microscopy (SEM) to characterize the multi-scale structural features of the target formation. These features include macroscopic fractures 1~5 mm wide, mesoscopic dissolution vugs 3–10 mm in diameter, and microscopic loose grain boundaries. Mechanical parameters were obtained via a multi-field coupled rock testing platform, identifying a pronounced confining-pressure strengthening effect. A continuous well-log inversion model with correlation coefficients > 0.89 was established. By incorporating a weak-plane slip criterion and leakage mechanism, a four-pressure profile prediction model was developed. Field application in Well PT-101 demonstrated that natural fractures and vugs drastically reduced the local leakage pressure equivalent density from a theoretical matrix baseline of 2.10~2.20 g/cm³ down to 1.22~1.35 g/cm³. This degradation narrows the safe mud-weight window to near zero in localized anomaly zones. The predicted pressure profiles matched precisely with field records, including multiple gas invasions, a lost-circulation event at 5784 m, and a severe loss-kick coexistence at 5775 m. These results provide a quantitative basis for wellbore structure optimization and precise mud-weight design in deep fractured-vuggy carbonates. Full article

Composite micromechanics is commonly divided between rapid mean-field estimates and computationally intensive full-field representative-volume-element (RVE) simulations. When these routes use different files, conventions and post-processing procedures, discrepancies can reflect bookkeeping rather than mechanics. This paper introduces HARVEST (Homogenisation and Representative Volume Element Simulation Tool; version 0.7.0.dev0), a general-purpose platform that coordinates mean-field homogenisation, three-dimensional RVE generation, finite-element model preparation, solver execution, homogenisation, parameter studies and post-processing through common project, service and provenance boundaries. The numerical framework is material-agnostic, whereas verification and validation are demonstrated using polymer-based nanocomposites. The Mori–Tanaka bulk response for spherical inclusions reproduces the Hashin composite-sphere result to machine precision, independent orientation procedures agree to a relative difference of $1.6\times {10}^{-14}$, and a sequential coated-particle approximation differs from an analytical composite-sphere reference by at most $0.417$% over 24 polymer-relevant configurations. An archived full-field epoxy/silica-type campaign using kinematic uniform boundary conditions and ${20}^3$ structured cells remains within the Hashin–Shtrikman interval at five inclusion fractions, with realisation scatter below $0.4$%. For published epoxy nanocomposites, aligned halloysite-nanotube predictions differ from measured flexural moduli by $-0.89$ and $+0.48$%, while spherical carboxyl-terminated butadiene–acrylonitrile-rubber predictions differ by $-7.03$ and $-2.25$%. The main conclusion is that a shared, traceable description of constituents, morphology, loading and outputs supports rapid mean-field screening followed by selective full-field analysis using the same material definition. The principal advantage over single-route or loosely coupled workflows is cross-route consistency and reproducibility. HARVEST is applicable to formulation screening, sensitivity studies and local-field assessment in particulate, tubular, rubber-modified, porous and mixed-matrix polymer systems, and can be extended through validated constitutive, geometry, solver and result adapters. Full article

To address the challenges of GNSS signal blockages and severe multipath effects in complex urban environments, this paper proposes a tightly coupled precise point positioning (PPP)/inertial navigation system (INS) integration method based on an adaptive robust Kalman filter (ARKF) for smartphones. The proposed method integrates a robust estimation module based on the IGG-III weight function and an adaptive factor derived from vehicle dynamic intensity and geometric precision indicators, to mitigate observation outliers and dynamic model errors. To evaluate the positioning performance of this algorithm, two typical vehicle experiments based on the GNSS and inertial measurement unit (IMU) chipsets of the Xiaomi Mi 8, as well as an external H30 IMU, were conducted. Experimental results show that in the urban expressway environment, the horizontal root mean square (RMS) error of the loosely coupled PPP/INS solution was reduced by 74.17% compared with the conventional PPP solution, while the maximum horizontal positioning error of the tightly coupled PPP/INS solution was reduced by 44.82% compared with the loosely coupled PPP/INS solution. In the complex urban road and tunnel environments, the proposed ARKF-based tightly coupled PPP/INS method achieved a 36.79% reduction in horizontal RMS error compared with the tightly coupled PPP/INS solution based on the standard extended Kalman filter (EKF) and demonstrated more robust positioning performance in the tunnel. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Traditional Digital Twins (DTs) in energy sectors lack cyber-threat awareness, while cybersecurity DTs overlook downstream physical impacts. Loosely coupled co-simulations attempt to bridge this gap but introduce computational lags that mask critical cross-domain vulnerabilities. To address these limitations, this paper proposes a unified, tightly coupled Virtual Digital Twin (VDT) framework that integrates energy systems and cybersecurity domains into a single environment. The methodology models the precise mathematical, thermal, and electrical constraints of key assets to capture cross-domain feedback loops. Specifically, a power transformer and a microgrid-connected inverter serve as case studies to map cyberattack vectors directly onto physical definitions. Numerical simulation evaluates multiple threat scenarios, including supervisory, measurement, and physical-level (harmonic) attacks on the transformer, alongside short-circuit and hybrid phase-harmonic attacks on the inverter. Results show how subtle digital disruptions propagate past communication layers to induce physical degradation and operational stress. By explicitly detailing the governing equations and providing sensitivity analyses, this work delivers a transparent, high-fidelity methodology for protecting critical cyber–physical infrastructures from asset-destructive manipulations. Full article

Industrial digital twins integrate physical objects, dynamic models, control systems, data analysis tools, 2D and 3D visualization, and existing software systems. Much research has focused on the functional composition of the digital twin, modeling, and application scenarios, while the organization of the digital twin as an evolving software system composed of technologically heterogeneous and autonomous subsystems remains insufficiently formalized. The goal of this study is to develop a conceptual composite architecture for an industrial digital twin, in which complex subsystems are viewed as highly interconnected and loosely coupled service components of a higher-order system. The research method is based on analogy, transfer, and adaptation of proven principles of distributed and microservice systems to the constraints of industrial digital twins. An architectural model is proposed that includes a physical object, a process model, SCADA subsystems, a unified data exchange subsystem, 2D and 3D representations, VR/AR components, and automated model building modules. The practical feasibility of the approach is demonstrated using a proof-of-concept digital twin of a methanol–ammonia co-production plant, integrating Honeywell UniSim Design R460.1, web-based SCADA, Unity, and specialized 2D and 3D representation generation modules. This demonstration confirms the feasibility of integrating independently developed subsystems and the technological heterogeneity of the solution, but does not constitute a production test of performance, scalability, or cost effectiveness. Requirements for contract stability, a consistent interaction environment, assigned data responsibility, version compatibility, and complete documentation are defined. Research limitations and areas for subsequent quantitative architecture validation are identified. Full article

The integration of Concentrated Solar Power (CSP) and Photovoltaic (PV) technologies represents a promising strategy to enhance the reliability, flexibility, and dispatchability of solar-based electricity generation. The novelty of this work lies in the development and assessment of an integrated PV–CSP hybrid power plant in a series configuration, where the two technologies are energetically coupled and coordinated with thermal energy storage and an electrolyzer under a grid-minimization operating strategy. Unlike most previous studies, which investigate PV and CSP systems as standalone or loosely coupled technologies, the proposed approach simultaneously optimizes renewable electricity utilization, dispatchable operation, and green hydrogen production. A comprehensive simulation framework was developed using site-specific solar irradiance data, component performance models, thermal energy storage characteristics, and electrolyzer operating constraints. A seasonal operating strategy was adopted, with the CSP plant and the electrolyzer operating from 15 April to 15 October, while the PV system generated electricity throughout the entire year. Under these conditions, the electrolyzer operated for 4416 h·year⁻¹, producing 1000 t·year⁻¹ of green hydrogen and requiring an annual electricity demand of 52.4 GWh. The hybrid renewable system supplied 37.2 GWh of this demand, corresponding to a renewable penetration of approximately 71%, while the remaining 29% was covered by grid electricity purchases. Results show that the series hybridization of CSP and PV technologies improves overall plant performance compared with standalone solar systems. In particular, the integration of thermal energy storage within the CSP subsystem enabled dispatchable generation and more stable electrolyzer operation. All the electricity generated by the CSP plant was directly utilized by the electrolyzer, and approximately 17% of the renewable electricity supplied to the electrolyzer was delivered during periods when PV production was unavailable, corresponding to 12.2% of the total annual electricity demand of the electrolyzer. Furthermore, of the total annual PV generation of 33.6 GWh, 15.1 GWh were directly used for hydrogen production, while 18.5 GWh were exported to the electrical grid, resulting in a positive annual electricity balance. The analysis provides design and operational guidelines for optimizing integrated PV–CSP plants coupled with hydrogen production systems under a grid-minimization strategy. The findings confirm that hybrid solar systems integrating dispatchable CSP generation, thermal energy storage, and PV technologies can significantly increase renewable penetration, support stable, low-carbon power generation, and enable large-scale green hydrogen production with reduced dependence on grid-supplied electricity. Full article

Tank vibrations of a power transformer, originating primarily from winding vibration and core vibration through mechanical coupling and fluid–structure interaction, are regarded as essential carrier signals for assessing the integrity of the winding. To improve the diagnostic accuracy of winding condition, this paper presents a vibration centroid-based diagnostic model that integrates feature fusion from vibration signals. According to the frequency spectrum of vibration signals obtained using Zoom-FFT, a set of new spatial vibration feature vectors—namely vibration centroid coordinates and Boyce-Clark shape index—were defined. This approach converts spatially distributed vibration signals into compact and discriminate indicators. A diagnostic model was subsequently constructed by integrating the grey wolf optimization (GWO) algorithm with the least squares support vector machine (LSSVM), ensuring that optimal classification performance was achieved. No-load, short-circuit, and load tests were made on a 35 kV-rated oil-immersed transformer. During the experiments, the transformer winding was divided into four categories: healthy condition, winding looseness, axial deformation, and radial deformation. The proposed GWO-LSSVM-based classifier was trained and tested using the defined vibration feature vectors. The results indicate that the proposed method achieves superior performance, with a recognition rate of 98.44%, and offers high efficiency. Full article

The growing reliance on cyber deception as a defensive mechanism has revealed persistent limitations in existing deception infrastructures, particularly in their ability to scale, adapt, and provide continuous observability under realistic adversarial workloads. Conventional honeypot deployments are predominantly monolithic and statically configured, which constrains their responsiveness to dynamic attack conditions and limits their applicability in contemporary distributed environments. This work presents a microservice-oriented cyber deception platform that reconceptualizes deception infrastructure as a composition of loosely coupled, independently deployable services. The platform integrates containerized honeypots, a lightweight API-driven orchestration layer, and a centralized telemetry pipeline to enable rapid instantiation, dynamic reconfiguration, and high-resolution monitoring of attacker interactions. Unlike prior approaches that treat deployment, orchestration, and monitoring as separate concerns, the proposed design explicitly unifies these components within a single, measurable system architecture. To support principled reasoning about system behaviour, the paper introduces first-order analytical models that characterize deployment latency, resource utilisation, telemetry throughput, and operational cost as functions of attacker concurrency. These models are not intended as exact predictors, but as tractable abstractions that enable interpretation of system performance and guide capacity planning. Model parameters are empirically derived and validated through controlled experimentation. Evaluation is conducted within a reproducible cyber-range environment using scripted adversarial workloads that emulate reconnaissance, authentication attempts, and sustained interactive sessions. The results indicate that containerised deployment reduces instantiation latency to approximately 1.2 s under warm-start conditions, compared to tens of seconds for virtual machine-based baselines. Resource utilisation exhibits approximately linear scaling under moderate concurrency, while the telemetry pipeline sustains ingestion rates exceeding 18,000 events per minute without observable loss. Stress testing further reveals that telemetry processing, rather than orchestration, constitutes the primary scalability bottleneck. These findings suggest that microservice-based architectures can provide a viable and extensible infrastructure substrate for cyber deception, supporting both operational deployment and integration with higher-level adaptive and learning-based defence mechanisms. The contribution of this work lies not in introducing new deception strategies, but in enabling their practical realisation through a scalable and observable system design. Full article

To address the issues of temperature rise accumulation, structural vibration, and air gap degradation that occur during the long-term operation of Anode Saturable Reactors used in high-voltage direct-current (HVDC) converter valves, electromagnetic-structural and electromagnetic-thermal multi-physics coupling analysis models were established using COMSOL Multiphysics software. The monitorable quantities capable of characterizing defects in Anode Saturable Reactors were systematically investigated from three aspects: vibration signals, thermal signals, and electrical signals. First, a one-way electromagnetic-structural coupling vibration model was established to analyze the vibration characteristics under normal operation, loose core conditions, and polyurethane hardening conditions. Second, an electromagnetic-thermal coupling model was established to compare the core loss and temperature rise distribution between the defect-free condition and the condition with reduced air gap defects. Finally, the effects of air gap reduction on electrical parameters such as unsaturated inductance and frequency sweep impedance were analyzed. The results indicate that the dual-peak characteristics and residual vibration of vibration signals can reflect the looseness of the iron core, while thermal aging of the polyurethane filling material further weakens the system’s damping capacity, intensifying vibration impact on the core and structural components. A reduction in air gap leads to an increase in local core loss of approximately 47.9%, giving rise to local hot spots. For the shell-type ASR investigated in this study, the temperature rise of the reactor casing remains almost unchanged. The unsaturated inductance and the impedance value near 10 kHz are highly sensitive to air gap variations and can serve as effective feature quantities for online monitoring. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity ($\epsilon *={\epsilon }^{\prime }-j{\epsilon }^{″}$) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ${\epsilon }^{″}$ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ${\epsilon }^{\prime }$ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

Wireless power transfer (WPT) is increasingly used in smart manufacturing, unmanned platforms, and contactless power-supply applications. However, weak coupling, load-dependent impedance drift, and spatial misalignment can shift the resonant condition, leading to unstable output voltage and reduced transfer efficiency. This paper proposes a constant-voltage WPT method that combines a non-uniform winding coupler, parasitic coils, and dynamic capacitor compensation. A composite magnetic coupler with dense outer windings, loose inner windings, and parasitic coils is first developed, and a region-based electromagnetic model is established to characterise self-inductance, mutual inductance, and coupling coefficients. An improved LCC-S compensation network with a dynamic capacitor compensation matrix is then derived to keep the system close to resonant operation at the nominal 85 kHz operating point under load variation and coil-displacement-induced coupling changes. A zero-voltage-switching-angle tracking method with mutual-inductance correction is further introduced to compensate for phase deviation and maintain soft-switching operation through limited switching-frequency adjustment. Experimental validation demonstrates that the system maintains a stable constant-voltage output across a load range of 20–50 Ω and under 5 cm lateral and longitudinal offsets. The measured efficiency remains above 89% and reaches 93.7% under the optimal coupling and load-matching condition. Full article

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

Synthetic aperture radar (SAR) can acquire navigation data to correct inertial navigation system (INS) errors even under global navigation satellite system (GNSS)-denied conditions. However, when unmanned aerial vehicles (UAVs) may deactivate the SAR system to maintain radio silence, or the SAR sensor may be subjected to transient interference, the INS/SAR integrated navigation system transitions to degraded scenarios without SAR navigation data. Furthermore, the irregular sampling characteristics of SAR navigation data pose significant challenges to the localization performance of the INS/SAR integrated navigation system. In order to address the above challenges faced by UAVs, we propose a liquid time-constant (LTC) network-enhanced INS/SAR integrated localization method. The method adopts a loosely coupled integration strategy with training and prediction modes. During training, an LTC-assisted localization prediction network (LTC-ALPN) is designed to model input–output relationships using prior flight data while explicitly accounting for the non-uniform temporal sampling characteristics of SAR measurements. In prediction mode, the trained LTC-ALPN forecasts missing SAR navigation information, which is subsequently fused with INS outputs via a Kalman filter to maintain high-precision positioning during SAR outages. Experimental results demonstrate that, compared to pure INS localization in degraded scenarios, the proposed method reduces northward error MAE and RMSE by approximately 92.8% and 93.9% and eastward error MAE and RMSE by 54.1% and 67.1%. Against suboptimal network baselines, further improvements of 50.8%/38.1% (north) and 17.1%/16.7% (east) in MAE/RMSE were achieved. Full article

Deploying Large Language Models (LLMs) in collaborative multi-agent settings represents a promising frontier for complex AI problem-solving, yet the field lacks systematic mechanisms to manage the inherent coordination overhead and resource contention that arise at scale. Existing LLM-based Multi-Agent System (MAS) frameworks predominantly adopt sequential or loosely coupled execution models, which fail to exploit the parallelism potential of modern computing environments and limit overall system throughput. To bridge this gap, this paper presents DynTaskMAS, a framework that redefines task orchestration in LLM-based MASs through a dynamic task graph abstraction. Rather than treating tasks as static pipelines, DynTaskMAS continuously models task interdependencies at runtime, enabling opportunistic parallel execution while preserving logical correctness. The architecture integrates four synergistic components: a runtime task decomposition module that captures evolving dependencies among subtasks; a scheduling engine that dispatches ready tasks to available agents without centralized bottlenecks; a context propagation layer that maintains shared semantic state across concurrently executing agents; and a self-tuning workflow controller that adapts execution priorities based on observed system load. Together, these components address a core tension in LLM-based MAS design, balancing agent autonomy with coordinated efficiency. Evaluations across tasks of varying complexity confirm that DynTaskMAS delivers substantial gains in execution efficiency (21.3–33.0% reduction), resource utilization (from 65% to 88%), and agent scalability (3.47× throughput with 16 concurrent agents) compared to sequential baselines. This work offers a generalizable architectural blueprint for next-generation LLM-based Multi-Agent Systems operating under real-world dynamic and resource-constrained conditions. Full article