Search Results (213)

To reveal the controlling mechanism of cooling rate on the continuous cooling transformation, microstructure evolution and mechanical performances of Q580R low-temperature pressure vessel steel, this study took industrial-scale Q580R steel as the research object. The JMatPro thermodynamic software was utilized for simulating and calculating its equilibrium phase diagram, TTT diagram, CCT diagram and mechanical property evolution. Continuous cooling experiments with a wide range of cooling rates between 0.1 and 50 °C/s were executed on a Gleeble-3500 thermal simulator. Combined with optical microscopy, scanning electron microscopy and Vickers hardness tester for microstructure characterization and property testing, the measured CCT diagram was constructed and contrasted with the simulation results for verification. Experimentally, the phase composition of Q580R steel evolves at regular intervals with cooling rate. As the cooling rate rises, the ferrite content constantly decreases, the bainite content first increases and subsequently decreases, and the martensite content constantly increases. When the cooling rate reaches 30 °C/s, the martensite proportion can exceed 90%, and the microstructure is significantly refined. The hardness of the material first increases rapidly and subsequently trends to be steady as the cooling rate rises, reaching 308 HV₁₀ at 50 °C/s. The measured transformation law, microstructure evolution and hardness change exceedingly corresponds to the JMatPro simulation results. This validates the credibility of the simulation prediction. This study clarifies the quantitative relationship among “cooling rate-microstructure-properties” of Q580R steel, which can provide theoretical basis and data support for the precise design of heat treatment process and the optimization of strength and toughness. The established relationship can directly guide the formulation of controlled cooling parameters during hot rolling and off-line quenching and tempering production of Q580R pressure vessel plates, helping manufacturers optimize industrial heat-treatment procedures to satisfy low-temperature toughness requirements for petrochemical and cryogenic pressure vessel service. Full article

Accurate remaining useful life (RUL) prediction of rolling bearings was essential for condition-based maintenance because bearing service degradation was primarily governed by progressive rolling-contact fatigue at the rollingelement–raceway interface, whereas vibration signals provided measurable responses to this degradation rather than being its physical cause. However, reliable RUL prediction remained challenging because vibration measurements were noisy, nonlinear, stage-dependent, and sensitive to operating-condition shifts. In this study, a health-indicator-guided temporal-attention framework was developed for bearing RUL prediction using public run-to-failure vibration datasets. The novelty of this work lay in integrating degradation-consistent health indicator construction, sliding-window life-cycle representation, and HI-guided temporal attention into a unified and interpretable prediction framework. First, degradation-sensitive vibration features were extracted and fused into a compact health indicator (HI) to represent the progressive deterioration trend. Then, sliding-window sequences were generated and processed by a Transformer-based temporal-attention network, through which long-range temporal dependencies were captured and higher weights were assigned to informative degradation segments near stage transitions and late-life acceleration. Experiments on the XJTU-SY and IMS datasets showed that the proposed method improved prediction stability, reduced late-life error amplification, and achieved better performance than baseline variants without HI or temporal attention. Ablation analysis confirmed that HI construction mitigated cross-stage drift, whereas temporal attention enhanced transition sensitivity during accelerated degradation. Robustness and cross-domain tests further indicated that the method maintained acceptable degradation-following behavior under noise perturbations and operating-condition changes, although explicit domain-adaptation mechanisms were still required for strongly shifted target domains. Full article

Prestressed steel strands transfer structural loads through complex anchorage systems. During through-anchorage ultrasonic guided-wave inspection, strong reflections generated in the anchorage segment may obscure defect-related echoes and create blind zones in the received signals. This study investigates the generation mechanisms of these anchorage-induced reflections and evaluates the relative roles of stress-induced acoustoelastic impedance variation and load-dependent interfacial contact evolution. An acoustoelastic finite element model is first used to estimate the reflection contribution caused by stress concentration alone. The results show that the stress-induced reflection remains weak, with the reflection coefficient remaining below 0.0125 even at 80% of the ultimate tensile strength. A sensitivity-based equivalent spring-contact model is then employed to examine whether effective strand–wedge and wedge–anchorage interfacial stiffness variations can generate anchorage reflections with comparable order of magnitude and load-dependent trends. The contact-based model produces much stronger reflections, and roughness-sensitivity analysis indicates that the load-dependent trend is not governed by a single nominal roughness assumption. Multi-specimen stepwise tensioning experiments show repeatable load-dependent reflection trends at both 80 kHz and 240 kHz. The results therefore suggest that, within the investigated geometry and loading range, interfacial contact evolution is a more plausible dominant contributor to anchorage-induced guided-wave reflections than stress-induced acoustoelastic impedance variation. This work focuses on the physical origin of anchorage reflections and provides a mechanistic basis for interpreting anchorage-induced interference in future through-anchorage defect detection. Full article

This study examines the effects of jet Reynolds number (Re) and jet hole diameter (d) on flow and heat transfer in the leading-edge full-impingement cooling channel of a gas turbine nozzle guide vanes (NGV). Experiments via transient liquid crystal and numerical simulations were conducted. Results reveal that the peak Nusselt number (Nu) initially increases and then reaches a fixed value from root to tip in the spanwise direction. The area-averaged Nu presents the descending trend of the shower-head surface, pressure surface, and suction surface. In addition, the bleeding from film holes causes significant local flow acceleration and Turbulence Kinetic Energy (TKE) enhancement of 10.69%, resulting in local heat transfer elevation. The heat transfer enhancement region on both pressure and suction surfaces is inclined towards the shower-head at a 5% span region. Increasing the jet hole diameter (d) results in a decrease in both averaged Nu and TKE on the target surface. Simultaneously, the Nu gradient increases. When d = 1.6 mm, there is a recirculation zone near the hub on the suction surface and a strong crossflow near the hub on the pressure surface. The jet flow on the target surface is bending towards the shower-head. When d = 0.8 mm, the overall heat transfer is highest. However, considering heat transfer uniformity, a jet hole diameter of d = 1.2 mm offers better application. Full article

Traffic flow forecasting remains challenging because raw traffic flow observations often contain mixed temporal patterns, including slowly varying trends and fast local fluctuations. To address this issue, this paper proposes a Multivariate Empirical Mode Decomposition (MEMD)-guided dual-branch recurrent framework for multistep point forecasting. Specifically, MEMD is used as an alignment-preserving multivariate decomposition mechanism to obtain frequency-aligned components, which are then reconstructed into low-frequency trend and high-frequency residual components. The trend component is modeled by a Long Short-Term Memory (LSTM) branch to capture smooth long-term evolution, while the residual component is learned by a Bidirectional Gated Recurrent Unit (Bi-GRU) branch to characterize short-term oscillatory dynamics. A lightweight fusion head is then used to integrate the two branch-specific representations for final prediction. Experiments on PeMS04 and PeMS08, two traffic datasets derived from the California Department of Transportation Performance Measurement System, show that the proposed method achieves competitive performance across mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), reaching 19.67/31.59/12.95% on PeMS04 and 15.51/24.43/9.86% on PeMS08. Compared with representative recent baselines, the proposed method achieves competitive results, with relative gains reaching 5.89% on PeMS04 and 5.35% on PeMS08 in selected metric-wise comparisons. These results indicate that MEMD-guided trend–residual representation learning can improve multistep traffic flow forecasting. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article

Due to its unique hydrogeological conditions, the Southwest Karst Area (SKA) in China experiences droughts far more frequently than non-karst regions. Exploring the distribution patterns and driving factors of different drought types is crucial for enhancing the region’s disaster prevention and mitigation capabilities and effectively addressing climate change risks. Using meteorological data from 1979 to 2023 in the SKA—including precipitation, temperature, humidity, potential evapotranspiration, and soil moisture—this study employed Copula theory to construct the Standardized Temperature Deficit Index (SDTI), the Standardized Humidity–Temperature Deficit Index (SDHTI), and the Standardized Atmosphere–Soil Index (SASI). Based on these indices and run theory, this study revealed the spatial distribution characteristics of different drought types (general, atmospheric, and composite) in terms of intensity, frequency, severity, and duration. Furthermore, the Mann–Kendall test and random forest analysis were applied to investigate drought trends and primary driving factors. The results indicate that droughts in the SKA exhibit significant regional characteristics and an overall worsening trend. Among them, droughts in karst-developed regions are generally more severe, though their manifestations vary across areas: compound droughts are particularly severe on the western Sichuan Plateau but relatively mild in Guangxi. In contrast, atmospheric droughts are more pronounced in Guangxi. Regarding trends, the rate of drought intensification was relatively moderate in Guangxi and the western Sichuan Plateau but more pronounced in other regions, with the maximum increase reaching 0.59. However, this upward trend is not statistically significant. Additionally, drought in karst areas was characterized by high frequency and intensity but shorter duration and lower severity, whereas the opposite was true in non-karst areas. Random forest analysis revealed that temperature is the primary driver of SDTI (2.60), while relative humidity and temperature have significant impacts on SDHTI (3.21 and 2.42, respectively). Soil moisture and temperature contribute most significantly to SASI (2.08 and 1.48, respectively). These findings provide important insights to guide the rational allocation of regional water resources and optimize agricultural management strategies. Full article

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential $V(x,t)$ and weighted by a time-dependent strength ${\gamma }_t$. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on ${\gamma }_t$. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article

Urban canals in rapidly growing cities often experience water quality deterioration from wastewater inputs and stormwater runoff, with impacts that vary across space and time. This study aimed to quantify five-year spatiotemporal patterns of key water quality indicators in the Mae Kha Canal, Chiang Mai, Thailand, and to identify persistent degradation hotspots to support management. Monthly longitudinal data (2020–2024) for dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, and water temperature (WT) were collected at 18 monitoring stations and analyzed using locally estimated scatterplot smoothing (LOESS) for trend exploration, repeated-measures correlation for association between parameters, and Geographic Information Systems-based spatiotemporal mapping using inverse-distance-weighted interpolation. Results showed that DO remains very low across much of the canal, while BOD was persistently high; pH was relatively stable near neutral and WT exhibited clear seasonal variability. Spatial mapping indicated that upstream sections generally had better quality, whereas the urban middle reaches repeatedly exhibited hotspots of low DO and high BOD. BOD and DO levels positively correlate with pH level (p < 0.001). In conclusion, the Mae Kha Canal has sustained impairment over 2020–2024, highlighting the need for strengthened wastewater control, stormwater management, and targeted remediation guided by hotspot-based monitoring. Full article

This study experimentally investigates the evolution of natural frequencies of premium threaded connections under varying interface contact stiffness, aiming to establish a non-destructive vibration-based method for evaluating sealing contact conditions. The sealing interface features a sphere-on-cone configuration, and Hertzian contact theory is used to derive the contact pressure distribution, which shows a nonlinear increase in peak pressure with increasing normal load. Modal experiments were conducted under free–free boundary conditions using an impact hammer on a Φ88.9 mm × 6.45 mm P110 premium threaded connection. Three make-up torque levels (4081 N·m, 4393 N·m and 4691 N·m) were applied to create distinct contact states, and the first five orders of natural frequencies were extracted from the measured acceleration responses, using frequency response function (FRF) analysis with peak-picking identification. The results demonstrate that natural frequencies increase significantly with make-up torque, following a power-law relationship f = αT^β with R² > 0.97 for the first three modes. A critical torque range of 4200–4400 N·m is identified, below which frequencies rise sharply and above which the increase slows due to contact stiffness saturation. Lower-order modes are more sensitive to contact stiffness variations than higher-order modes. The findings confirm that natural frequency can serve as an effective non-destructive indicator for assessing tightening quality and detecting loosening in premium threaded connections, offering practical guidance for torque optimisation and structural health monitoring in oilfield operations. Although only three torque levels are used, the observed trend is physically consistent with contact mechanics theory and widely reported joint stiffening behavior. Therefore, the fitted relationship should be interpreted as a physically guided empirical model rather than a purely statistical fit. Full article

Reliable tracking of multi-pollutant air-quality progress is essential for assessing policy effectiveness and health risks, yet most assessments still focus on single pollutants. We analysed population-weighted exposures to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and household air pollution (HAP) for Australia and the global average over 1990–2019, using harmonised estimates from a Global Burden of Disease–type framework. Non-parametric LOESS and semi-parametric generalised additive models were applied to characterise long-term trends, and a composite clean-air progress index (CAPI; 1990 = 1) was constructed to summarise joint changes in the three pollutants. Statistical and Monte Carlo methods were used to propagate reported exposure uncertainty into both pollutant-specific trends and the composite index. Globally, exposures to PM_2.5, NO₂ and HAP all declined, and the CAPI fell to around 0.7 by 2019, indicating substantial multi-pollutant improvement relative to 1990. In Australia, NO₂ decreased more rapidly than the global mean, but PM_2.5 showed little long-term decline and the HAP-related metric increased more than three-fold. As a result, Australia’s CAPI rose to approximately 1.6–1.7, with Monte Carlo uncertainty envelopes remaining well above 1 from the early 2000s onwards. Correlation analyses revealed that pollutants improved together at the global scale, but were partially decoupled in Australia, implying that source-specific gains have not translated into aggregate clean-air progress. These findings demonstrate that single-pollutant assessments can obscure important trade-offs and that multi-pollutant, uncertainty-aware indices such as CAPI provide a more informative basis for benchmarking national trajectories against global experience and for guiding integrated clean-air policy. Full article

With the rapid development of digital technologies, augmented reality (AR) has created new possibilities for the presentation and dissemination of cultural heritage. However, conventional digital guide systems in historic districts are typically dominated by static information delivery, lacking interactivity and user engagement, which limits their effectiveness in enhancing public understanding of historic architectural environments and related cultural knowledge. To address this limitation, this study focuses on historic architectural districts and proposes a narrative-based AR cultural exploration approach embedded in real architectural space. The Hubu Mountain historic architectural district in Xuzhou, China, was selected as the case study. First, grounded theory was employed to systematically analyze the cultural resources of the district and extract key cultural narrative elements. Based on these elements, a design framework for a narrative AR cultural exploration system was constructed. Subsequently, a mobile AR interactive system was developed using the Unity 2022.3 LTS and Vuforia Engine 10. A total of 80 participants were recruited and randomly assigned to either an experimental or a control group. Cultural knowledge tests, an immersive experience scale, and a dissemination intention scale were used to evaluate the outcomes, and the collected data were analyzed statistically. The results indicate that, compared with a conventional text–image guide condition, the narrative AR exploration condition significantly improved participants’ cultural cognition and dissemination intention. Specifically, the experimental group achieved significantly higher post-test scores in cultural knowledge than the control group, and a significant between-group difference was also observed in dissemination intention. In terms of immersive experience, although the experimental group reported higher mean scores than the control group, the difference did not reach statistical significance, showing only a possible improving trend. These findings suggest that an integrated narrative AR cultural exploration condition can enhance public understanding of historic architectural districts and strengthen the communication potential of heritage experiences in real built environments. This study provides a digital interpretation approach for historic architectural districts and offers empirical support for the use of AR-based interactive systems in architectural heritage communication and public engagement. Full article

Subgrade collapse threatens coastal infrastructure under harsh environments, where deterioration accelerates deformation and failure risk. Accurate prediction is essential, yet traditional monitoring suffers from low informatization and delayed response. Thus, this paper presents a Micro-Electro-Mechanical Systems (MEMS)-based intelligent perception-driven method for subgrade collapse deformation prediction to improve the level of intelligence in subgrade collapse monitoring and prediction. Firstly, a hierarchical prediction framework is established based on subgrade deformation monitoring scenarios, consisting of an intelligent perception layer, a collapse deformation prediction layer, and a functional application layer, with the functions of each layer systematically defined. Secondly, two key technologies involved in the proposed framework, including MEMS data cleaning and time-series feature extraction, as well as the deformation prediction model, are identified and corresponding solutions are developed. Finally, a linear sliding rail experiment and a subgrade collapse model test are conducted to validate the feasibility and effectiveness of the proposed method. The results indicated that effective MEMS data cleaning was achieved through Leave-One-Out Encoding (LOOE) encoding, missing value imputation, and normalization. Accurate time-series feature representation was obtained by combining seismic parameter extraction with a sliding window strategy. The improved the improved Long Short-Term Memory–Back Propagation (LSTM-BP) model model achieved accurate prediction of collapse displacement, with an accuracy of 95.56%. The proposed MEMS-based intelligent perception method accurately captured the evolution trend and spatial heterogeneity of subgrade collapse deformation, and the results can be used to support and guide early warning of subgrade collapse, providing technical support for the safety and durability management of coastal and offshore infrastructure under harsh environmental conditions. Full article

This study investigates the optimization of selected process parameters in the rotary friction welding (RFW) process of CuSi₃Fe₂Zn₃ silicon bronze alloys using Response Surface Methodology (RSM) with tensile strength as the primary response. The effects of rotation speed, friction time and friction pressure were evaluated, and the steepest ascent method was applied to determine the best parameters. The results indicated that rotation speed and friction time were the most influential parameters for enhancing tensile strength. A maximum tensile of 424 MPa was achieved under conditions of 3300 rpm, friction time of 25 s, friction pressure of 0.5 MPa, forging time of 16 s, and forging pressure of 8 MPa. However, confirmation experiments exhibited noticeable variability, indicating limitations in process repeatability. Tensile properties, hardness evaluation, microstructural characterization, and thermographic analysis were conducted to assess the quality of the welded joints. Microstructural analysis revealed recrystallized equiaxed grains in the welding center zone, consistent with severe plastic deformation, while microcracks and microvoids were observed and likely contributed for failure during tensile testing. Despite grain refinement, a reduction in microhardness was detected, suggesting the influence of thermal softening mechanisms. Thermographic analysis indicated that the average temperature at the welding center zone reached 564 °C. In conclusion, RSM proved to be a useful tool for identifying trends and guiding process optimization. The results highlight the importance of process stability and control in achieving consistent performance in RFW of copper-based alloys. Full article

Kissing bond damage, as an early stage of debonding in CFRP-reinforced structures, severely threatens the integrity and performance of structures. However, conventional ultrasonic guided wave methods are not sensitive to this sort of damage due to the micro scale of the damage and the complexity of the wave at the interface. To address this problem, the reciprocity of nonlinear guided waves is proposed to identify this damage and a novel reciprocity index based on the correlation coefficient of kissing bond damage-induced second harmonic waves is developed. A finite element model is established and kissing bond in CFRP-reinforced steel plate is simulated with a novel method, which is close to the actual condition of the kissing bond and can help reveal the interaction between guided waves and the interface. Experimental tests are also carried out to verify the proposed method. In addition, to prove the high efficiency of the proposed method, correlation coefficients of directly received signals are calculated to compare with the proposed reciprocity index. Both simulation and experiment results illustrate that the reciprocity index rises as the length of kissing bond increases, while correlation coefficients of directly received signals did not show a monotonic trend as the damage length changes, reflecting the validity and high sensitivity of the proposed method in identifying and quantitatively evaluating kissing bond damage in CFRP bonded steel structures. Full article