Surface Observations Research Articles

Wafer chamfering grinding wheels dressing via dynamic deflection laser beam

Wafer chamfering forming grinding wheels (WCF) is a kind of V-shaped circular groove forming wheel with a large diameter and small grooves. In this study, a dynamic deflection laser beam method is presented to improve the dressing quality of WCF. The compensation effect of deflected laser on the surface laser energy density of materials was analyzed, and the blocking effect caused by oversized deflection angle of laser beam was analyzed. A C-W model was proposed for laser dressing of WCF. Based on C-W model, trajectory planning for laser dressing was carried out, and dressing experiments were completed. The results show that the contour transition of the grinding wheel is more smooth. Compared to single direct laser beam dressing method and static deflection laser dressing method, the contour exhibits better roundness and the PV values reduced to 5.1μm. Surface observation revealed strip-shaped patterns and some flocculent metamorphic layer. The SEM result shows that there are no obvious crack defects on the surface.

CO2 variability over a tropical coastal station in India: Synergy of observation and model

Carbon dioxide (CO2), being the prime greenhouse gas, has largest contribution in radiative forcing and global warming due to increased anthropogenic emissions leading to regional and global climate change. We performed CO2 measurements using a continuous monitoring instrument at a tropical coastal station at the southern tip of India (Thumba; 8.54° N, 76.86° E) during 2017–2021. Combining measurements with model simulations and satellite observations, present study aims to understand CO2 variabilities, contribution of various sources and role of dynamics. The observed trend of 2.19 ppm y−1 is well simulated (2.47 ppm y−1) by the chemistry-transport model (MIROC4-ACTM), which is contributed by trends in fossil fuel (4.59 ppm y−1), biosphere (−1.46 ppm y−1) and ocean (−0.66 ppm y−1) tracers of CO2 flux. Mean seasonal cycle with amplitude of about 8 ppm is observed, with a maximum during premonsoon (April) and minimum at the end of the monsoon season (September). The model reproduced the observed seasonal cycle well (r2 = 0.89) and showed that the seasonal peak is predominantly contributed by the land biospheric flux seasonality. Biospheric sink during postmonsoon is counterbalanced by emissions from fossil fuel burning. Biosphere and fossil fuel are major contributors in shaping the observed seasonal pattern with negligible seasonality in oceanic tracer due to its uniform flux patterns in the Indian Ocean. Mean diurnal pattern of CO2 exhibits lower values during daytime (cleaner marine airmass) and higher values during nighttime (continental airmass) with deeper diurnal amplitude in the winter (~50 ppm) than in the summer (~31 ppm). Seasonal pattern in total column CO2 from OCO-2 (Orbiting Carbon Observatory-2) is similar to the surface observations but shows nearly half of the observed seasonal amplitude. Long-term continuous measurements over distinct environments are desirable for further deepening the understanding of CO2 variability, estimation of regional carbon budget and for climate mitigation policies.

Prediction of atmospheric temperature in Sacramento area based on ARIMA and ETS models

Abstract. As global temperatures increased by 1.1 Celsius degrees, there has unprecedented shifts in climate systems. With the rising impact of global warming, exploring the warming trend helps to better understand and maintain the local environment and economy. This study focuses on predicting atmospheric temperature in the Sacramento area using ARIMA and ETS models. The research uses the temperature data from Sacramento Airport's Automated Surface Observing System (ASOS) and explores the prediction performance of ARIMA, ETS, and ARIMAX models in predicting daily average temperatures. The results indicate the ARIMAX (1,1,1) model is the most suitable for forecasting this temperature data with the lowest AIC and RMSE values. However, there are still challenges, in particular the dependence of the ARIMAX model on future exogenous variables, which leads to the forecast outcomes less accurate. To enable this ARIMAX models have better prediction performance, incorporating more exogenous variables is a potential solution. Therefore, the methods discussed in this paper provides ideas for the atmospheric temperature forecasting and points out the direction for further research.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

A Rare Tropical Cyclone Associated With Southwest Monsoon Over the Northern Part of the South China Sea—Tropical Storm Maliksi

AbstractThis study examines the characteristics and development of Tropical Storm Maliksi, which is a special case of tropical cyclone developed in the northwestern part of the South China Sea during a southwest monsoon outbreak. Detailed analyses were conducted using observational data and forecast products. Surface observations, radar wind profilers, aircraft data, and satellite products were used to evaluate Maliksi's wind structure, revealing multiple circulation centers and gale force winds. Vertical wind profiles, warm core structure, wind waves, and the influence of sea temperatures and salinity on Maliksi's intensification were investigated. Regarding forecasting, AI‐based models outperformed conventional numerical weather prediction (NWP) models in predicting Maliksi's initial development, though both struggled to capture the persistence of strong winds as the system moved inland. High‐resolution NWP simulations were employed to examine terrain‐induced wind variability around Hong Kong International Airport, revealing the mountain wake effect and uneven wind and turbulence distribution. These findings provide insights into the challenges of forecasting and monitoring such tropical cyclones, and highlight the need for enhanced observational platforms and forecasting tools along coastlines vulnerable to these systems.

HALOBATES: An autonomous surface vehicle for high-resolution mapping of the sea-surface microlayer and near-surface layer on essential climate variables

Abstract Essential climate variables (ECVs) are physical, chemical, or biological parameters supporting the characterization of the climate system. The oceanic ECVs temperature and salinity have been defined for the sea surface (upper 1 mm) and bulk water (upper 2 meters). Direct observation and sampling of the sea surface from ships and buoys is limited due to the potential loss of integrity of the sea surface and contamination by the research platform. The design, development, and operation of a research vehicle with autonomous capabilities based on commercially available components are described in this study. The autonomous operation and fully automated sampling enable high-resolution mapping of the sea surface microlayer (SML), i.e., the upper < 1mm layer, and near-surface layer (< 1.2 m). Sampling the SML is based on a rotating glass disk sampler. The autonomous surface vehicle HALOBATES has been equipped with multiple conductivity, temperature, and depth sensors (CTDs) in a flow-through system to measure both sea surface and bulk temperature and salinity. The performance of the autopilot under various scenarios is demonstrated. Typical anomalies of ECVs within the SML and near-surface layer are evident and forced by both atmospheric and oceanic processes. Data analyses reveal HALOBATES' ability to resolve thermohaline structure on different spatial and temporal scales, for example, small-scale freshwater lenses and thermohaline changes across oceanic fronts. The integration of an Acoustic Doppler Current Profiler supports the interpretation of thermohaline structures. HALOBATES is a platform with cutting-edge technology for understanding climate-related processes between the ocean and atmosphere and providing high-resolution sea surface data for validating satellite products of ECVs.

Multiday Soil Moisture Persistence and Atmospheric Predictability Resulting From Sahelian Mesoscale Convective Systems

AbstractSkill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales.

Convectively Induced Secondary Circulations and Wind‐Driven Heat Fluxes in the Surface Energy Balance Over Land

AbstractIncreased resolution has enabled kilometer‐scale weather and climate models to partially resolve secondary circulations, including horizontal convective rolls (HCRs) and cold pool gust fronts. Although these circulations are ubiquitous in convective boundary layers over land, their impacts on the surface energy balance are largely unknown. Doppler lidar and surface observations were combined with DOE E3SM land model experiments, revealing increased surface winds (5 m/s) and heat fluxes (50 W/m2) in convergent branches of HCRs. Larger wind‐driven flux responses (up to 150 W/m2) were found along gust fronts. Surface energy balance shifts to accommodate wind‐driven fluxes, reducing ground heat conduction and longwave cooling. Our findings from the US Southern Great Plains are broadly relevant to modeling convective boundary layers. In particular, widely used subgrid wind gust parameterizations were found to be physically inconsistent with resolved secondary circulations and could worsen climate prediction biases at kilometer‐scales.

Fatigue Fracture Mechanism and Life Prediction of TA1 Titanium Alloy Clinched Joints

ABSTRACTThis study investigated the fatigue fracture mechanisms and life prediction of clinched joints made from titanium alloy TA1. The fatigue tests revealed that TA1 titanium alloy clinched joints exhibited failure characterized by fracture of the lower plate at three distinct fatigue load levels. Additionally, finite element analysis indicated that cold work hardening enhanced the fatigue performance of these joints. Observations of fracture surfaces using scanning electron microscopy identified the crack source and its propagation path, which correlated with the location of maximum principal stress from the finite element simulations. Fretting wear was also observed in this critical region. Furthermore, fatigue life predictions for TA1 titanium alloy clinched joints were made using Paris' law and the local strain approach. Both methods closely matched experimental results across different fatigue life intervals. Overall, the local strain approach exhibited superior predictive capability compared to Paris' law, taking into account various influencing factors.

Enhancing Algal Bloom Level Monitoring with CYGNSS and Sentinel-3 Data

Algal blooms, resulting from the overgrowth of algal plankton in water bodies, pose significant environmental problems and necessitate effective remote sensing methods for monitoring. In recent years, Global Navigation Satellite System–Reflectometry (GNSS-R) has rapidly advanced and made notable contributions to many surface observation fields, providing new means for identifying algal blooms. Additionally, meteorological parameters such as temperature and wind speed, key factors in the occurrence of algal blooms, can aid in their identification. This paper utilized Cyclone GNSS (CYGNSS) data, Sentinel-3 OLCI data, and ECMWF Re-Analysis-5 meteorological data to retrieve Chlorophyll-a values. Machine learning algorithms were then employed to classify algal blooms for early warning based on Chlorophyll-a concentration. Experiments and validations were conducted from May 2023 to September 2023 in the Hongze Lake region of China. The results indicate that classification and early warning of algal blooms based on CYGNSS data produced reliable results. The ability of CYGNSS data to accurately reflect the severity of algal blooms opens new avenues for environmental monitoring and management.

The Evaluation of Global and Regional Applications of Model for Prediction Across Scales-Atmosphere (MPAS) Against Weather Research Forecast (WRF) Model over California for a Winter (2013 DISCOVER-AQ) and Summer (2016 CABOTS) Episode

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California.

Experimental Investigation of Meter-Level Resolution Radar Measurement at Ka Band in Yellow Sea

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena.

Using a digital microscope in the mechanic’s laboratory workshop

This article provides a description of laboratory work in mechanics devoted to determining the coefficient of friction of contacting surfaces. The novelty of this work lies in the fact that the experiment is accompanied by observation of surfaces through a digital microscope. This work is a worthy addition to the park of experimental works of the Physics Department of the Technical University.

Mechanisms of deformation, damage and life behavior of inconel 617 alloy during creep-fatigue interaction at 700 °C

The continuous fatigue tests and creep-fatigue tests with the imposition of strain hold at the peak strain were conducted at 700 °C on Inconel 617 alloy. The strain amplitude of 0.25 %, 0.30 %, 0.35 %, 0.40 %, and hold time of 60 s, 600 s and 1800 s were used. The cyclic deformation behavior and dynamic strain aging (DSA) were discussed. The strain localization, dislocation substructure and precipitation behavior were carefully characterized, which provided physical information to understand the cyclic deformation behavior. The dominant damage mechanism and damage interaction, responsible for the cracking behavior were identified based on the fracture surface observation and secondary cracks morphology. The cyclic life saturation effect was comprehensively elucidated from the perspective of macroscopic mechanical response and microscopic deformation mechanism.

Calculation of key parameters of tropospheric mapping function based on random forest method

The tropospheric mapping functions (MFs) play a crucial role in estimating the delay of electromagnetic waves as they traverse the troposphere, particularly in space geodetic techniques like GNSS and VLBI, where these waves are the primary measurement tool. Currently, empirical models that rely on non-meteorological parameters are commonly used to calculate MFs, offering convenience but often yielding less accurate results in regions with volatile climates. Such inaccuracies can lead to errors in slant total delays reaching the meter-level, subsequently affecting other data products. Although authoritative bodies periodically release essential meteorological parameters for MF calculation or provide ready-made MF products, these often come with a delay of several days or require specific authorization. To address this, we introduced a method leveraging the random forest (RF) model to rapidly derive key MFs from surface observations. Experimental results indicate that the RF model significantly enhances the accuracy of MFs, with improvements exceeding 60% for the hydrostatic component and over 20% for the wet component, effectively eliminating seasonal systematic deviations; although it is somewhat inferior to the MFs from VMF3-FC, RF does not require an internet connection and can independently train and construct models, making it very suitable for independently operated systems. We further trained a series of RF models using varied sample spaces and analyzed their performance under different combinations of feature dimensions and time spans. This analysis suggests that an optimal balance exists when considering the challenges in acquiring sample data, training time, model size, computational efficiency, and accuracy.

On the corrosion resistance of the CoCrFeMnNi high entropy alloys in chloride-containing sulfuric acid solutions

The electrochemical reactivity of the high entropy Cantor alloy (CoCrFeMnNi) was investigated via potentiodynamic polarization curves, electrochemical impedance spectroscopy (EIS), and surface analysis as a function of chloride content, in sulfuric acid solutions. The results revealed that corrosion mechanism of the CoCrFeMnNi alloy at the corrosion potential, is a two-step mechanism, involving one adsorbed intermediate, similar to that of pure iron in the H2SO4, and this model provided a good description of the impedance data. X-ray photoelectron spectroscopy (XPS) surface characterization and impedance data fitting revealed that chloride ions do not alter the corrosion mechanism or the thickness of the surface oxide film. Instead, they affect the corrosion resistance by changing the composition of the oxide film. Additionally, this work demonstrated that the corrosion mechanism of the CoCrFeMnNi alloy varies with the applied potential, as evidenced by impedance measurements under potentiostatic polarization. Surface observations using scanning electron microscopy (SEM) and XPS analysis provided insights into the morphology and composition of the surface oxide film at different applied potentials, explaining the observed changes in impedance.

Gelatin Methacrylic Acid Hydrogel-Based Nerve Growth Factors Enhances Neural Stem Cell Growth and Differentiation to Promote Repair of Spinal Cord Injury.

The challenge in treating irreversible nerve tissue damage has resulted in suboptimal outcomes for spinal cord injuries (SCI), underscoring the critical need for innovative treatment strategies to offer hope to patients. In this study, gelatin methacrylic acid hydrogel scaffolds loaded with nerve growth factors (GMNF) were prepared and used to verify the performance of SCI. The physicochemical and biological properties of the GMNF were tested. The effect of GMNF on activity of neuronal progenitor cells (NPCs) was investigated in vitro. Histological staining and motor ability was carried out to assess the ability of SCI repair in SCI animal models. Achieving nerve growth factors sustained release, GMNF had good biocompatibility and could effectively penetrate into the cells with good targeting permeability. GMNF could better enhance the activity of NPCs and promote their directional differentiation into mature neuronal cells in vitro, which could exert a good neural repair function. In vivo, SCI mice treated with GMNF recovered their motor abilities more effectively and showed better wound healing by macroscopic observation of the coronal surface of their SCI area. Meanwhile, the immunohistochemistry demonstrated that the GMNF scaffolds effectively promoted SCI repair by better promoting the colonization and proliferation of neural stem cells (NSCs) in the SCI region and targeted differentiation into mature neurons. The application of GMNF composite scaffolds shows great potential in SCI treatment, which are anticipated to be a potential therapeutic bioactive material for clinical application in repairing SCI in the future.

Elucidating the impacts of aerosol radiative effects for mitigating surface O3 and PM2.5 in Delhi, India during crop residue burning period

Atmospheric aerosol radiative effects regulate surface air pollution (O3 and PM2.5) via both the aerosol–photolysis effect (APE) and the aerosol–radiation feedback (ARF) on meteorology. Here, we elucidate the roles of APE and ARF on surface O3 and PM2.5 in the heavily polluted megacity, Delhi, India by using a regional model (WRF-Chem) with constraints from limited surface observations. While APE reduces surface O3 (by 6.1%) and PM2.5 concentrations (by 2.4% via impeding the secondary aerosol formations), ARF contributes to a 2.5% and 17.5% increase in surface O3 and PM2.5, respectively. The ARF from smoke enhances PM2.5 (by 8%), black carbon (by 10%), and primary organic aerosol (by 18%) during late autumn when crop residue burning is significant. The synergistic APE and ARF have a negligible impact on the total concentrations of O3 and PM2.5. Hence, the reduction of PM2.5 may lead to O3 escalation due to weakened APE. Sensitivity experiments indicate the need and effectiveness of reducing VOC emission for the co-benefits of mitigating both O3 and PM2.5 concentrations in Delhi.

Influence of suppressed blistering by heavy ion pre-damage on deuterium retention in tungsten

Surface damage and fuel retention are one of the major threats to the performance of plasma-facing materials (PFMs) in ITER and future fusion reactors. This work aims to investigate the influence of suppressed blistering by heavy ion pre-damage on deuterium (D) retention in tungsten (W). Recrystallized W samples were irradiated with 3.5 MeV iron (Fe13+) ions at room temperature to create displacement damage with a peak damage level of 0.1 dpa. Afterwards, a series of low-energy (38 eV) D plasma exposures were performed at 500 K. Three exposure fluences below and above the blistering threshold (5 × 1024 ∼ 3 × 1025 D m−2) of the pre-damaged W are selected to decouple and couple the damage-induced defects and blistering-induced defects, respectively. Surface observations show that no blisters are formed in un-damaged W after low-fluence D exposure (5 × 1024 D m−2), whereas severe blistering (surface coverage ratio: 34.2 %) occurs in the high-fluence case (3 × 1025 D m−2). In contrast, only a small number of blisters (4.2 %) are formed in Fe-damaged W when D fluence reaches 3 × 1025 D m−2. Moreover, Fe pre-damage increases D retention by a factor of 3.33 and 1.20 at the low-fluence (5 × 1024 D m−2) and medium-fluence (1 × 1025 D m−2) D exposure, respectively. While in the high-fluence case (3 × 1025 D m−2), the enhancement effect of D retention in Fe-damaged W is significantly weakened, such that retention is lower for damaged W, probably due to the significant suppression effect on surface blistering and its accompanying defect formation. This work highlights the suppressed blistering-induced D retention by pre-existing damage in W.

Design and Experimental Research of a Non-Destructive Detection Device for High-Precision Cylindrical Roller Dynamic Unbalance

Due to their small size and light mass, small precision cylindrical rollers present challenges in dynamic unbalance detection, including difficulties in measurement and the risk of surface damage. This paper proposes a non-destructive detection device for assessing the dynamic unbalance of small precision cylindrical rollers. The device utilizes an air flotation support method combined with resonance amplification to indirectly measure the dynamic unbalance. A dynamic model of the air flotation tooling-cylindrical roller vibration system was developed to explore the relationship between the vibration parameters of the air flotation tooling and the dynamic unbalance of the cylindrical roller. Modal analysis and harmonic response analysis were performed, revealing that the amplitude of the vibration system at resonance could be detected using the sensor. Additionally, modal testing was conducted to determine the natural frequency of the system. A non-destructive detection platform was constructed for testing the dynamic unbalance of cylindrical rollers. Microscopic observation of the roller surface before and after testing confirmed that the device successfully performs non-destructive detection of dynamic unbalance.