Search Results (1,409)

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article

This paper presents a data-driven surrogate model for predicting cabin airflow and its integration into system-level electric vehicle simulations for energy management analysis. The model employs a graph-based neural network with a mirror-symmetric predictor–corrector architecture and is trained on a dataset generated using computational fluid dynamics (CFD) covering a defined range of inlet velocities and temperatures. The surrogate appropriately reconstructs temperature fields and captures the dominant airflow structures at significantly lower computational cost than CFD. Quantitative evaluation shows high accuracy in passenger-relevant regions, while localized discrepancies remain confined mainly to shear-layer zones. The model enables near-real-time inference and is coupled with a system-level modeling framework for control-oriented simulations that are impractical with CFD. The study is tailored to a specific geometry and operating range, showing that targeted training strategies and physics-based extensions improve robustness, particularly under limited data conditions. Full article

The manuscript details the influence of high-temperature and high-shear processing, as well as radiation sterilization, on properties of bioresorbable and osteoconductive, patient-tailored alloplastic scaffolds for guided bone regeneration. Functionalized poly(l-lactide-co-d,l-lactide) copolymer filled with hydroxyapatite was used to produce two personalized implants for upper and lower jaw reconstruction via 3D printing. Morphology analysis (SEM, µCT), gel permeation chromatography, and thermal analysis quantified the effects of melt processing and sterilization on chain structure. Physical properties of sterilized parts, such as hardness and density, proved suitable for bone implants. Removal of the upper jaw implant after 4 months and of the lower jaw substitute after 18 months enabled monitoring of bioresorption and tissue regrowth over time. Gradual overgrowth of the implants with human tissue, initiated by the osteoconductive filler, was observed, along with time-dependent polylactide degradation, showing up to 92% molar mass reduction. The medical procedures confirmed safety, nontoxicity, non-allergenicity, and, most importantly, the tissue-forming properties of the polylactide-based formulation. Full article

Active and cost-effective catalysts are essential for obtaining high hydrogen evolution activity. In this paper, we report ZnO/ZnLDH/ZnLDO as a novel ternary Zn²⁺-based heterostructure obtained via the partial structural reconstruction of Zn-based layered double hydroxides (LDH) in a Zn²⁺-containing solution and evaluate its catalytic performance in hydrogen evolution under solar light. The reconstruction strategy induces the formation of a ZnLDH/ZnLDO platform decorated with small ZnO nanoparticles (~3 nm) that are generated in situ during the reconstruction process. The catalysts were extensively characterized for their structure and morphology by XRD, SEM, and TEM/HRTEM, while XPS analysis reveals electronic modulation across the interfaced units. For photocatalytic H₂ evolution under simulated solar light, the optimized ZnO/ZnLDH/ZnLDO (x = 15%) achieves the highest performance with a hydrogen evolution rate of 1.8 mmol h⁻¹ gcat⁻¹, outperforming the individual (ZnLDH, ZnLDO) and binary (ZnO/ZnLDH, ZnO/ZnLDO) catalysts. The enhanced activity is attributed to cascade-type charge transfer across the ternary Zn²⁺-based structures, which promotes efficient charge separation. However, excessive reconstruction (high x%) or high-temperature calcination alter the LDH/LDO balance, resulting in decreased photocatalytic activity. This work provides a new idea for designing active multiple interfaced heterostructured photocatalysts by using the partial reconstruction of LDH as a facile and cost-effective approach. Full article

Despite stringent national clean air policies, severe PM_2.5 and ozone (O₃) pollution persists in some parts of China, notably the Sichuan Basin—a key economic zone in the southwest. High-resolution assessment of the health and crop impacts of these pollutants remains limited in this region. In this study, we developed a multi-source data fusion framework based on a machine learning model to reconstruct daily PM_2.5 and O₃ concentrations at 1 km resolution during 2015–2021. The model integrates ground observations, meteorological data, chemical transport model outputs, and satellite retrievals. The model performed robustly, achieving R² values of 0.91 for PM_2.5 and 0.64 for O₃. PM_2.5 exhibited a decreasing tendency after 2017, while O₃ showed interannual variability, with peaks in 2016 and 2018. Spatially, PM_2.5 was more concentrated in urban centers, whereas O₃ showed higher levels in western Sichuan and a banded pattern in the east. Seasonal patterns were also evident: PM_2.5 increased in autumn and winter due to meteorological and emission factors, while O₃ peaked in spring and summer, driven by photochemistry and high temperatures. Topography and emissions further shaped these distributions, with mountains in the west trapping O₃ and urban clusters exacerbating PM_2.5. Based on the reconstructed dataset, we further explored the potential impacts of pollutant exposure on human health and crop yields. The results provide a high-resolution dataset for understanding pollutant variability. Full article

Monolithic ceramic catalysts are a key technology for the industrial treatment of coal mine ventilation air methane (VAM). The preparation of straight-channel NiO/CeO₂ monolithic ceramic catalysts via phase inversion addresses critical bottlenecks for industrial VAM abatement. However, high-temperature sintering leads to irreversible NiO agglomeration and coarsening, severely reducing catalytic activity. In this study, an in situ reduction–oxidation reconstruction method is developed to regenerate sinter-degraded NiO. The reconstructed catalyst increases methane conversion from below 70% after sintering to over 95% at 550 °C and achieves full conversion at 600 °C. The catalyst maintains near 100% conversion during 400 h of continuous operation at 600 °C and shows no performance degradation over 15 thermal cycles. Moreover, the reconstructed catalyst exhibits excellent steam tolerance with fully reversible deactivation. The reconstructed catalyst presents a refined porous structure with BET surface area rising from 4.5 to 11.4 m² g⁻¹, an elevated Ni³⁺/Ni²⁺ ratio (1.47 to 1.97), a higher surface adsorbed oxygen proportion (36.8% to 48.7%) and significantly strengthened NiO-CeO₂ interfacial interaction. This work provides a facile and efficient in situ regeneration strategy, greatly enhancing the VAM oxidation activity and stability of sinter-degraded monolithic ceramic catalysts. Full article

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

To address the inverse identification of contact-related thermal faults in gas-insulated switchgear (GIS), this study proposes a method for contact resistance inversion and internal temperature field reconstruction. The proposed method enables the estimation of faulty internal contact resistance using external enclosure temperature data, while simultaneously reconstructing the internal temperature field. First, a forward numerical model of GIS is established, and a POD-Kriging surrogate model is developed to achieve second-level rapid prediction of the forward problem. Based on this surrogate model, the thermal fault inversion problem is formulated as an optimization problem of fault parameters and solved using the Grey Wolf Optimizer. GIS temperature-rise experiments are performed to validate the numerical model, and a real GIS contact fault case is further analyzed. The results indicate that the proposed method yields an average inversion error of 9.5% for degraded contact resistance, with the maximum error at internal temperature monitoring points remaining below 8%. The total inversion time is approximately 30 s. These findings demonstrate that the proposed method is capable of effective online inversion and diagnosis of contact-related thermal faults in GIS equipment. Full article

Urban sprawl constantly reconfigures the land use pattern, and such transformations may significantly modify regional carbon stocks. Utilizing Tai’an City as the study site, this research established a comprehensive integrated Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Optimal Parameters-based Geographical Detector (OPGD) system to reconstruct carbon storage shifts from 2000 to 2020, project its reaction to four diverse development trajectories in 2030, and investigate the drivers underlying spatial disparities. The results indicate a persistent decline in carbon storage throughout the past two decades, with peak concentrations primarily gathered in mountain regions dominated by forest and grassland, whereas lesser amounts were grouped in urban and suburban areas defined by built-up land. Compared to 2020, the projected carbon stock in 2030 drops by 1,803,966 t under the natural growth trajectory and by 2,417,778 t under the high-quality economic growth pathway, whereas it rises by 47,326 t under cultivated land conservation and by 7679 t under ecological conservation. Elevation represents the most crucial driver among the selected variables in clarifying the spatial fluctuation of carbon storage (q = 0.3985), followed by slope (0.3323), mean annual temperature (0.2382), and the Normalized Difference Vegetation Index (NDVI) (0.1219). The synergy between elevation and NDVI produces the highest integrated explanatory power (q = 0.4906). These outcomes imply that constraining construction land growth while protecting agricultural and ecological land is vital for preserving and enhancing regional carbon sink potential. Full article

The rational design of supported Lewis acid catalysts is frequently impeded by an incomplete understanding of how the support’s synthetic history governs its intrinsic acidity and catalytic efficacy. Herein, we elucidate the structure–property–performance relationship linking the aging dynamics of a boehmite precursor to the activity of the resultant chlorinated alumina (Al₂O₃–Cl) catalyst in n-butane isomerization. Using n-butane as the probe feedstock, we investigated how alumina supports with distinct physicochemical properties regulate the performance of Al₂O₃–Cl catalysts for n-butane isomerization. By systematically adjusting the aging parameters (stirring rate, temperature, and time), we reveal that the structural evolution of the alumina support transitions from initial particle aggregation to Ostwald ripening and surface reconstruction. A decisive structure–performance correlation is identified: precursor synthesis conditions govern both the population and accessibility of specific surface hydroxyls (notably Type II terminal OH groups), which act as anchoring sites for the generation of active Lewis acid centers upon chlorination. Optimal aging parameters (300 rpm, 90 °C, 6 h) promote the formation of a hierarchical pore architecture with a maximized density of accessible hydroxyls, thereby affording enhanced Lewis acidity and superior isomerization activity. This work provides a fundamental framework for tailoring solid acid catalysts by precisely engineering the precursor architecture. Full article

Accurately predicting the temperature field of turbine blades is of great significance for evaluating the thermal reliability and service life of high-temperature components in aero-engines. However, due to the high computational cost of numerical simulations and the limitations imposed by complex geometric structures and harsh operating environments, experimental measurements can usually only obtain sparse sensor data, making the acquisition of complete temperature distributions still challenging. Therefore, reconstructing the complete temperature field under sparse measurement conditions has become a key research issue in turbine thermal analysis. To address this problem, this paper proposes an attention-enhanced CNN–LSTM framework for reconstructing transient turbine blade temperature fields from sparse data. The model combines the spatial feature extraction capability of Convolutional Neural Networks (CNNs) with the time-series modeling capability of Long Short-Term Memory networks (LSTM). An SE channel attention module is introduced in the CNN feature extraction stage to achieve adaptive recalibration of channel features, and a temporal attention mechanism is incorporated after the LSTM layer to highlight key transient thermal features. A multi-condition temperature field dataset was constructed by conducting Computational Fluid Dynamics (CFD) simulations on low-pressure turbine guide vanes, and the model was experimentally validated through thermal shock tests. The results show that the proposed model can accurately reconstruct the spatial distribution and transient evolution of the turbine blade temperature field under sparse measurement conditions. Under different operating conditions, the predicted temperature fields are highly consistent with the CFD results, with the maximum Reconstruction error remaining below 19 °C. Error distribution analysis indicates that the model has stable Reconstruction performance and good generalization ability. Full article

Sub-sag 21 in the eastern Yangjiang Sag, Pearl River Mouth Basin, South China, contains a thick lacustrine source-rock interval within the lower Wenchang Formation and is a major exploration target on the northern margin of the South China Sea. However, the timing of deposition during the early to middle Eocene remains poorly constrained, and the applicability of quantitative palaeoclimate reconstruction methods in low-latitude lacustrine basins requires further evaluation. In this study, we analyzed mudstones from the lower Wenchang Formation in Well E1. Using cyclostratigraphic constraints, we applied AstroGeoFit to construct an astronomically tuned age model, and combined palynological coexistence analysis with geochemical weathering proxies and linear–regression calibration to quantitatively reconstruct and cross-validate mean annual temperature and mean annual precipitation. Within this time-calibrated framework, we further quantified organic-carbon burial to evaluate the relationship between palaeoclimate evolution and organic-matter enrichment. The AstroGeoFit results indicate that the top of the lower Wenchang Formation in Well E1 is constrained to 44.563 Ma, and that the studied succession spans 50.249–44.563 Ma. Palynological coexistence analysis identifies three palaeoclimate phases within this interval. Method evaluation shows that the temperature reconstruction based on major-element geochemistry agrees well with the pollen-based temperature record, whereas one precipitation reconstruction based on weathering proxies shows the most robust agreement and stability relative to the pollen-based precipitation record. Reconstructed mean annual temperature ranges from 10.77 to 22.20 °C, and reconstructed mean annual precipitation ranges from 1188.27 to 1871.89 mm. Correlation analyses on the tuned timescale show that precipitation is more strongly associated than temperature with organic-matter accumulation parameters, including total organic carbon and organic carbon accumulation rate, indicating that organic carbon burial in the eastern Yangjiang Sag lake basin was mainly controlled by hydrological forcing. During the Early Eocene Climatic Optimum, carbon burial in low-latitude lakes was, therefore, not a simple response to elevated temperature, but instead reflected the integrated effects of precipitation, runoff, stratification, material supply, transport, and preservation. The evolutionary sequence further suggests that early high productivity was diluted by rapid sedimentation, reducing total organic carbon; subsequent cooling, lake deepening, and strengthened stratification enhanced organic matter preservation; and finally, tectonic subsidence together with regional humidification promoted the development and long-term preservation of high-quality lacustrine source rocks. Full article

Reliable temperature distribution measurement in ultra-low temperature (ULT) chest freezers is crucial for preserving biospecimen integrity in cryopreservation, but dense sensor arrays required for accuracy are often impractical due to space constraints and cost limitations. To address this critical challenge, this work presents a systematic data-driven framework for optimal sensor placement in large-scale (3 m³) ULT chest freezers under stable operating conditions. To our knowledge, it is the first realization of high-fidelity cryogenic temperature field reconstruction coupled with sparse sensor layout optimization tailored to large-volume ULT chest freezers. First, high-resolution reference temperature fields were constructed via universal kriging interpolation, validated with leave-one-out cross-validation (LOOCV) to achieve mean absolute error (MAE) $\le 0.67 °\mathrm{C}$ and coefficient of determination $R^2>0.92$. Principal component analysis (PCA) was then applied to training data to extract a tailored proper orthogonal decomposition (POD) basis. The first three principal components captured 99.8% of cumulative energy. Optimal sensor locations were determined via QR-column pivoting on the rank-3 POD basis, converging to a minimal configuration of 3 sensors (a 94% reduction from the 48-sensor full-scale setup). This sparse sensor network achieved exceptional reconstruction performance: grid-level MAE $\le 0.079 °\mathrm{C}$ and root mean squared error (RMSE) $\le 0.093 °\mathrm{C}$ against reference fields ($R^2\ge 0.999$), while point-level validation against experimental measurements yielded MAE $\le 0.502 °\mathrm{C}$ and RMSE $\le 0.842 °\mathrm{C}$ ($R^2\ge 0.971$). The results demonstrate that, for large-scale ULT chest freezers, the proposed data-driven approach is capable of automatically determining an optimal sparse sensor subset and enabling reliable 3D cryogenic temperature field reconstruction for efficient thermal monitoring. By resolving the trade-off between monitoring accuracy, space efficiency, and cost-effectiveness, this framework provides a scientifically rigorous alternative to empirical sensor deployment standards, offering practical scalability for cryogenic biobanking applications. Full article

Time-series machine learning models represented by long short-term memory (LSTM) networks provide an effective way to obtain high-precision sound speed profiles (SSPs) quickly and at low cost, which can meet the practical application requirements of underwater sonar systems. However, in sea areas with frequent strong internal solitary waves, the large-amplitude sound speed anomalies caused by them will seriously interfere with model learning in the form of strong outlier features, resulting in a sharp drop in SSP prediction accuracy and significant degradation of the generalization stability and robustness of the model. To address this problem, this paper proposes a time-series SSP prediction method based on a bidirectional long short-term memory (Bi-LSTM) network. First, Empirical Orthogonal Function (EOF) decomposition is used to realize the low-dimensional feature representation of SSPs, and then the bidirectional time-series feature capture capability of Bi-LSTM is used to predict the SSP sequence with large disturbances caused by strong internal solitary waves. Multiple groups of comparative experiments based on the measured temperature chain data in the continental slope area of the South China Sea show that the Bi-LSTM model has a significant improvement in prediction accuracy and robustness compared with the classical LSTM model. Among them, the Bi-LSTM model with EOF decomposition achieves a correlation coefficient of 0.995 and an average Root Mean Square Error (RMSE) as low as 0.387 m/s. Under the condition of internal solitary wave disturbance, the classical LSTM is difficult to effectively capture the large abrupt change in sound speed, while the proposed Bi-LSTM model can still achieve accurate prediction of the SSP in the disturbance section, and has both the feature recognition and evolution prediction capabilities for the strongly nonlinear internal solitary wave process. This method provides effective technical support for the rapid and large-scale reconstruction of the sound speed field under the disturbance of strong internal solitary waves. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article