Search Results (1,420)

In cold and humid environments in high-latitude, high-altitude, and offshore regions, ice accretion sometimes occurs on airfoil blade surfaces, such as those of wind turbines. Therefore, a potential hazard exists for the equipment. For this reason, the aerodynamic characteristics of the airfoil blade degrade and power generation decreases. In the present study, the icing characteristics of airfoils in cold and foggy environments were investigated. A novel icing wind tunnel with a low LWC of 0.3 g/m³ and a small MVD of 10 μm was designed and built. An airfoil sample with the aerodynamic profile of NACA 0018 was selected, and the effects of the airfoil material and the temperature on the icing area, the thickness of ice, and the coverage scope of ice were tested and analyzed. The experimental results showed that the temperature had a more significant effect on the icing characteristics in comparison with the airfoil material. At the medium temperature, −7 °C in the present study, the icing area, the thickness of ice, and coverage scope all reached their maximum value. Specifically, the maximum cross-sectional icing areas (CIAs) on the aluminum airfoil at −4 °C, −7 °C, and −10 °C for 60 min were 35.088 mm², 66.357 mm^2, and 51.538 mm², respectively, and those on the FRP airfoil were 36.204 mm², 70.352 mm², and 47.814 mm², respectively. The FRP airfoil had a larger icing area and thickness of ice. In contrast, the aluminum airfoil had a larger coverage scope of ice, which was −0.10~0.15. In addition, the aerodynamic performance of the iced airfoil, including C_d and C_m, was also obtained through CFD. The research findings provided a foundation for further exploring the atmospheric icing of wind turbines and other structures with airfoil profiles. Full article

This study investigates the atmospheric corrosion behavior of high-lead bronze alloys within a simulated aggressive environment characterized by high humidity, acidity, chlorides, oxygen, and CO₂. The evolution of surface morphology and corrosion products was systematically monitored using colorimetric analysis and micro-Raman spectroscopy. Results indicate that the initial patina primarily comprised cuprite (Cu₂O) and cassiterite (SnO₂), which are predominantly benign phases. During the early corrosion stage, lead oxidation and carbonation prevailed, generating abundant bright-white cerussite. Subsequently, as copper-driven corrosion became dominant, these white deposits diminished and were progressively replaced by characteristic green “bronze disease”, identified as atacamite (Cu₂(OH)₃Cl). The findings reveal that preferential lead corrosion is likely to induce localized pitting, thereby accelerating degradation of the copper substrate. Consequently, higher lead content may reduce the overall corrosion resistance of bronze artifacts under these specific conditions. These results offer experimental insights into atmospheric corrosion mechanisms and inform the development of evidence-based conservation strategies for bronze cultural heritage. Full article

Anyone who has watched a distant scene shimmer above hot pavement has seen atmospheric turbulence destroy image detail. In long-range imaging, turbulence produces spatially varying blur, geometric warping, scintillation, and temporal instability. Recovering the underlying scene is therefore an ill-posed inverse problem, and learned priors must compensate for distortions that simplified optical models capture only partially. We use turbulence mitigation as the umbrella term for all countermeasures and turbulence restoration for its computational core, the estimation of a clean image from degraded observations. From a cognitive-computing perspective, mitigation is not merely image enhancement. It is an uncertainty-constrained visual inference problem in which an intelligent system must reconstruct, interpret, and act on observations relayed through a stochastic physical channel. This critical review examines how the deep learning era has reshaped turbulence mitigation, with physics as the foundation for understanding degradation and designing inductive biases. It traces the architectural progression from convolutional and adversarial networks to Transformers, denoising diffusion models, Mamba and other state-space architectures, and physics-informed frameworks. For each family, we ask a common question: How does it treat the aleatoric uncertainty intrinsic to a random optical channel and the epistemic uncertainty introduced by scarce and simulator-dominated training data? The review also analyzes datasets, simulation strategies, loss functions, and evaluation metrics, and it separates the small body of shared-protocol benchmark evidence from the far larger body of self-reported results that cannot be compared across studies. Persistent obstacles include the synthetic-to-real domain gap, the scarcity of paired real turbulence data, the mismatch between fidelity metrics and downstream task performance, and the computational cost that limits operational deployment. We close with an AI-centered agenda in which uncertainty quantification stands alongside domain adaptation as a first-order priority. Full article

The mechanical behavior of S135 and V150 (Mo-V-Nb microalloyed) drill pipe steels was systematically investigated at room temperature (RT) and elevated temperatures (100–300 °C), alongside the microstructural evolution after long-term thermal exposure at 310 °C (200–500 h). V150 steel exhibits a superior RT yield strength (1099 vs. 1012 MPa) relative to S135, attributed to grain refinement and precipitation strengthening from nanoscale MC precipitates. However, at 200–300 °C, S135 steel displays strength recovery due to dynamic strain aging (DSA) facilitated by the formation of Cottrell atmospheres. Conversely, in V150 steel, V and Nb pin free interstitial atoms, suppressing Cottrell atmosphere formation and DSA. Consequently, V150 cannot gain DSA-induced strengthening, resulting in a steeper yield strength decline (a 17.3% drop at 300 °C versus 11.5% for S135). Long-term thermal exposure further reveals divergent microstructural evolution: S135 steel achieves synchronous improvements in strength and ductility via the transformation of coarse M₃C into stable alloy carbides and the precipitation of nanoscale Mo-enriched carbides. In contrast, V150 steel undergoes Ostwald ripening and coherency loss of high-volume-fraction nano-MC precipitates, weakening dislocation pinning and accelerating dislocation annihilation, ultimately leading to the simultaneous degradation of strength and ductility. This study elucidates that while Mo-V-Nb microalloying enhances RT strength, it compromises high-temperature mechanical stability. Full article

Optical MOS solid-state relays provide electrical isolation and favorable switching performance, making them suitable as isolated switching interfaces for optoelectronic sensors used in radiation monitoring systems, high-energy physics accelerator monitoring systems, and aerospace electronic equipment. However, to the best of our knowledge, the reliability of this device under atmospheric-like neutron irradiation has not been systematically investigated. In this study, the neutron-induced degradation characteristics and potential physical mechanisms of a commercial optical MOS solid-state relay were systematically investigated through electrical characterization, Sentaurus TCAD mixed-mode simulation, and post-irradiation annealing experiments. Particular attention was paid to the possible internal coupling relationship between degradation of the internal photoelectric conversion unit and the increase in the off-state leakage current of the output-side power MOSFET. Full article

Accurately predicting high-energy laser propagation toward fast-moving aerial targets is not straightforward, since the slant range, elevation angle, atmospheric path, accumulated turbulence, and thermal blooming all change along with target motion. Because of this coupled evolution, fixed-path or quasi-static models can hardly describe, in a complete way, the time-varying loss of beam transmission and the weakening of energy concentration. To address this problem, this paper builds on established wave-optics propagation methods to develop a dynamic multi-physics framework for high-speed moving-target engagements. The central idea is to rebuild the propagation environment at every time step according to the instantaneous target position so that the optical path can be linked with height-dependent extinction, refractive-index structure, and absorption profiles; meanwhile, relative motion is represented as an effective transverse sweeping velocity, which helps reduce the accumulation effect of thermal blooming. Based on a unified split-step Fourier method, the model combines Beer–Lambert path attenuation, Kolmogorov-spectrum multilayer phase screens, and thermal-blooming phase modulation, yielding time-resolved indicators such as transmittance, received power, peak irradiance, Strehl ratio, scintillation index, pointing error, and beam-quality factor. Simulation results show that, in a 500 m static horizontal path, the attenuation module reduces to the Beer–Lambert solution, with the maximum relative error kept below ${10}^{-14}$; the turbulence phase-screen implementation is validated against the absolute Kolmogorov prediction. Over the resolved inertial interval, the ensemble-averaged structure function yields a fitted exponent $p_{\mathrm{f}\mathrm{i}\mathrm{t}}=1.671$ (theoretical: $5/3=1.667$) and a relative normalization error of $2.5\mathrm{\%}$, confirming that the generated screens reproduce both the Kolmogorov scaling and the prescribed turbulence strength. For a 1.064 μm, 2 kW Gaussian beam propagating over a 50 s trajectory of about 8 km, when the target speed increases from 0 to 300 m/s, the thermal-blooming-related beam-quality factor decreases from roughly 1.8 to 1.2, which indicates that dynamic geometry, turbulence, attenuation, and thermal blooming need to be considered together when evaluating laser propagation performance. Full article

The degradation formed during the gas-phase reaction of amyl acetate, CH₃COO(CH₂)₄CH₃, initiated by chlorine atoms (^●Cl), was investigated under atmospheric conditions using gas chromatography–mass spectrometry. The main products identified were acetic acid, formaldehyde, acetaldehyde, butyraldehyde, and propionaldehyde. Calibration curves were established for each identified product at different concentrations to enable their quantification by gas chromatography coupled with flame ionization detection. Product yields were subsequently determined from the calibration data, allowing a quantitative evaluation of the formation of the major oxidation products. The results obtained contribute to a better understanding of the atmospheric degradation pathways of amyl acetate and related ester compounds, providing useful information for assessing the atmospheric processing of ester-containing emissions, including those associated with biofuel applications. Full article

In this work, the effect of mechanical activation (MA) on the double complex salt (DCS) [Co(NH₃)₆][Fe(C₂O₄)₃]·3H₂O and its thermolysis is investigated. Mechanical activation is a promising “green chemistry” method that allows improving the physicochemical properties of the DCS [Co(NH₃)₆][Fe(C₂O₄)₃]·3H₂O and the products of its thermal decomposition in an argon atmosphere. The conditions of the MA process and the effect of MA and passivation on the process and kinetics of DCS thermal degradation were investigated. It was shown that due to the removal of outer-sphere coordinated water and carbonation of the DCS during MA, the number of thermal degradation stages changes. It was established that passivation at 450 °C for MA times of 0 and 10 min prevents spontaneous “combustion” of the thermal degradation products of the DCS. Optimal conditions for DCS processing were determined to be 5 min of MA and heat treatment at 450 °C without passivation. Under these conditions, the yield of the CoFe intermetallic compound reaches 82.6 wt%. This optimal combination of sample production conditions allows for a reduction in MA time and the elimination of the passivation process, making the process more cost-effective and creating conditions for optimizing the production of functional materials. Full article

Near-surface temperature inversions are prevalent during the cold months in Ulaanbaatar city, Mongolia, and significantly degrade urban air quality by trapping hazardous pollutants within a shallow atmospheric boundary layer. This study investigates spatiotemporal variability, physical mechanisms, and long-term evolution of near-surface temperature inversions over Ulaanbaatar by integrating 25 years (2000–2024) of ground-based meteorological and radiosonde observations, with high-resolution Weather Research and Forecasting (WRF) model simulations for 2012–2023. Our results demonstrate the four-dimensional data assimilation (FDDA) grid nudging effectively captures localized topographic influences in the WRF simulations, showing a strong agreement with radiosonde observations (R² = 0.783, p < 0.000). Near-surface temperature inversions are strongly controlled by the Siberian High, with the highest frequency occurring from December to February, when up to 67% of morning observations exhibit inversion conditions. A pronounced diurnal cycle was identified, with inversion intensity peaking at 5.6–6.8 °C during the early morning hours (02:00–08:00 LST) before reaching a minimum around 14:00 LST. Spatially, the strongest inversions occur along the low-lying Tuul River valley, where the planetary boundary layer is compressed to below 350 m and wind speeds decrease to less than 2.4 m·s⁻¹, creating persistent atmospheric stagnation. Despite these favorable conditions for inversion formation, long-term observations indicate that regional warming (+2.0 °C) and the urban heat island effects have reduced inversion frequency by 31%, inversion thickness by 170 m, and inversion intensity by 0.9 °C over the past 25 years. These findings demonstrate the strong coupling between regional complex terrain, and boundary layer thermodynamics, highlighting the need to incorporate urban ventilation corridors and topography-informed planning into climate adaptation and winter air-quality management strategies. Full article

Microplastics (MPs), defined as plastic particles smaller than 5 mm in diameter, are an emerging class of environmental contaminants of global concern. As the “Water Tower of Asia” and a critical global ecological barrier, the environmental condition of the Qinghai–Tibet Plateau has a direct influence on the ecological security of major river systems and the well-being of populations downstream. MPs have now been detected across multiple environmental compartments on the plateau, including soils, water bodies, and glaciers. Given the fragility and ecological uniqueness of the region, systematic investigation of plastic pollution here is essential for safeguarding its ecological security. Based on current research, existing data on MP pollution across the Qinghai–Tibet Plateau are reviewed and synthesized. Evidence suggests that the abundance of MP varies significantly across different environmental media in various regions and is influenced by multiple factors. Two major potential sources are identified: local anthropogenic activities and transboundary inputs via atmospheric transport and other pathways. The unique environmental conditions of the region, such as intense ultraviolet radiation, large day–night temperature variation, and frequent high-wind events, provide a distinctive setting for the migration, dispersion, transformation, and degradation of MPs across environmental matrices. Understanding the distribution, sources, and migration patterns of microplastics on the Qinghai–Tibet Plateau will help facilitate sustainable environmental management, ecosystem conservation, and pollution control in these fragile high-altitude regions. Full article

Small lakes are widespread, yet their biogeochemistry, particularly regarding greenhouse gases, remains insufficiently studied. This article presents the findings from an investigation of six small lakes in the Southern European part of Russia, conducted from September to October 2024. The primary aim was to examine the coupled distribution of methane (CH₄) and sulfide sulfur (a key metabolite of H₂S) in the lake sediments, in relation to geochemical parameters such as pH, Eh, sediment density, moisture, and the contents of sulfate ions (SO₄²⁻), organic matter, and granulometric composition. The studied sediment layers, reaching depths of up to 110 cm, consist primarily of silty clay. The lakes studied represent both freshwater (0.2–0.7 g/L) and brackish (1.3–24.2 g/L) systems. During the study period, the water column exhibited temperatures of 10.4–22.1 °C, pH values of 7.36–8.53, and dissolved O₂ concentrations ranging from 3.16 mg/L (34% saturation) to 11.79 mg/L (125% saturation). Methane concentrations in the water varied widely, from 1.6 µL/L to 37,380 µL/L. The lowest values were found in the highly mineralized Lake Bolshoy Tambukan (1.6–2.0 µL/L), while exceptionally high concentrations were detected in the bottom waters of the thermally stratified freshwater Lake Staroe. In the shallow, productive freshwater lakes, a significant portion of the organic matter undergoes limited mineralization in the water column and settles to the sediments as partially decomposed remains of sand- and coarse-silt-sized organisms. The subsequent degradation of this labile organic matter reduces bottom-water oxygen, triggering intense anaerobic processes in the upper sediment layer. In these freshwater sediments, where sulfate concentrations are relatively low, sulfate reduction is typically suppressed. Combined with an abundance of labile substrates, this condition fosters intensive methanogenesis, resulting in maximum CH₄ concentrations (33–179 µg/g). Under stable thermal stratification, such high CH₄ concentrations can also accumulate in the bottom water (e.g., up to 37. 4 mL/L in Lake Staroe), posing a risk of significant pulse emissions during autumn mixing. Conversely, the brackish Lake Bolshoy Tambukan exemplifies the crucial role of sulfate reduction, which is stimulated by sulfate-dependent anaerobic oxidation of CH₄. This process acts as a powerful natural biogeochemical barrier that curtails the emission of a major greenhouse gas. The sediments of this lake exhibited minimal CH₄ content (0.14–0.57 µg/g) alongside maximal sulfide sulfur concentrations (1.06–8.57 mg/g). Overall, this theoretical and experimental analysis demonstrates that sulfate reduction is a key determinant of redox potential, acid–base conditions, and the vertical distribution of CH₄ in the anaerobic sediments of small lakes in Southern Russia. Given the projected salinization of lakes in steppe and arid landscapes under climate change, a reduction in CH₄ emissions to the atmosphere is likely due to the enhanced sulfate-dependent anaerobic CH₄ oxidation associated with sulfate reduction in the sediments. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Haze conditions degrade image quality via atmospheric scattering and absorption, posing challenges for optical imaging applications. In recent years, deep learning has emerged as an effective method for dehazing images. However, data-driven deep learning dehazing methods typically require large amounts of labeled training data and offer limited interpretability. In this paper, we propose a physics-guided dehazing method based on polarization imaging. By integrating polarization imaging with the physical model, the proposed method enables neural network training using only a set of hazy images captured at different polarization angles, thereby reducing the reliance on labeled training data. Experimental results show that the proposed method significantly outperforms commonly used deep learning dehazing methods in terms of contrast and mean gradient, while exhibiting strong generalization and physical interpretability. By integrating physical model and polarization imaging into deep learning, this method overcomes the limitations of traditional deep learning dehazing methods and paves the way for optical imaging in haze conditions. Full article

La₂Si₂O₇ is a candidate environmental barrier coating (EBC) material for silicon carbide ceramic matrix composites in next-generation gas turbine engines, yet its degradation behavior under the simultaneous attack of molten CMAS and water vapor remains insufficiently understood. This study systematically examines atmospheric plasma-sprayed La₂Si₂O₇ coatings exposed to four environments (air, CMAS alone, H₂O/O₂ alone, and combined CMAS + H₂O/O₂) at 1400 °C for 8 h. Under dry air, the coating recrystallizes to La₂Si₂O₇ with negligible corrosion. CMAS attack triggers a dissolution–reprecipitation mechanism forming needle-like CaLa₄(SiO₄)₃O apatite within a denser reaction zone, which partially impedes further infiltration. Water vapor accelerated the growth of the thermally grown oxide at the bond coat interface. The combined CMAS + H₂O/O₂ environment produced a pronounced synergistic acceleration: water vapor reduced CMAS melt viscosity, enabling deeper CMAS penetration, while concurrent silica volatilization disrupted the protective apatite barrier, generating extensive porosity and through-coating cracking. These findings reveal that the inherent CMAS resistance of La₂Si₂O₇ via apatite formation is critically compromised by simultaneous water vapor, highlighting a key challenge for its application in realistic engine environments. Full article

Rainwater harvesting is increasingly recognized as a sustainable supplement to conventional water supplies and a strategy to improve water resilience. However, harvested rainwater may contain organic and inorganic contaminants introduced by atmospheric deposition and contact with collection surfaces, requiring adequate treatment before safe use. This review critically evaluates the application of adsorbent materials for harvested rainwater treatment, namely carbon-based materials, surface-modified mineral matrices, and biosorbents. The available literature was analyzed considering adsorbent characteristics, treatment conditions, removal performance, and the mechanisms potentially governing contaminant removal. The reviewed studies demonstrate that activated carbon is the most investigated adsorbent class, while surface-modified minerals and biosorbents remain less explored. Direct comparison between studies is limited by differences in experimental conditions and incomplete characterization of adsorbent properties. Adsorption performance depends on both adsorbent characteristics and rainwater chemistry, particularly pH, which affects surface charge and the aqueous speciation of many contaminants. Reported removal efficiencies do not always reflect adsorption alone, as treatment configuration and operating conditions may also promote precipitation, physical retention, and in some cases, biologically mediated degradation. Future research should prioritize standardized material characterization, mechanistic evaluation, regeneration strategies, and long-term validation to support the development of more predictable and reproducible rainwater treatment technologies. Full article