Investigation on Anisotropic Mechanical Behavior of Ti-6Al-4V Alloy via Schmid Factor and Kernel Average Misorientation Distribution

Anisotropic mechanical behavior of the Ti-6Al-4V alloy is essential for its forming and service. Generally, it is preferable to minimize the in-plane anisotropy of Ti-6Al-4V sheet. The present work investigates the anisotropy of Ti-6Al-4V alloy by tensile tests along the rolling direction (RD), transverse direction (TD), and diagonal direction (DD) of the sheet, evaluating the anisotropic yield and flow behaviors and exploring the causes of these anisotropic properties. The intrinsic deformation mechanism of Ti-6Al-4V alloy tensioned along different directions was studied with Schmid factor and kernel average misorientation (KAM) analysis. The samples tensioned along the RD and TD of the sheet (denoted as RD sample and TD sample) show similar yield stress, while tensile along the DD (denoted as DD sample) leads to lower yield strength. The mechanical anisotropy exhibited by the Ti-6Al-4V sheet is closely related to the crystallographic texture. The flow stresses of the RD and TD samples are higher than that of the DD sample due to the higher density of dislocations generated during the tensile deformation, in which prismatic ⟨a⟩ dislocations make a great contribution to coordinating plastic deformation

GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations

Differential code biases (DCBs) are important parameters in GNSS (Global Navigation Satellite System) applications such as positioning as well as ionosphere remote sensing. In comparison to the conventional approach, which utilizes ground-based observations and parameterizes global ionosphere maps together with DCBs, a method is presented for GPS and BeiDou system (BDS) satellite DCB estimation using onboard observations from the Chinese Fengyun-3C (FY3C) satellite. One month worth of GPS and BDS data during March 2015 was exploited and the GPS C1C-C2W and BDS C2I-C7I DCBs were explored. To improve DCB estimation precision, the dual frequency carrier phase measurements leveled by code measurements were used to form basic observation equation. Code multipath errors of the FY3C onboard GPS/BDS observations were assessed and modeled as grid maps, and their impact on DCB estimation was analyzed. By correcting code multipath errors, the stability of DCB estimates was improved by 5.0%, 3.1%, 16.2% and 13.6% for GPS, and BDS geosynchronous orbit satellites (GEOs), inclined geosynchronous satellite orbit satellites (IGSOs) and medium Earth orbit satellites (MEOs), respectively. The monthly stability of FY3C-based DCBs was at the order of 0.1 ns for GPS satellites, 0.2 ns for BDS GEOs and 0.1 ns for BDS IGSOs and MEOs. By comparison to the ground-based DCB products issued by other institutions, FY3C-based DCBs showed stability degradation for BDS C02 and C05 satellites, while, for other satellites, the stability reached a similar or even superior level. The estimated FY3C receiver DCB stability was at the order of 0.2 ns for both GPS and BDS. In addition to the DCB estimates, the obtained vertical total electron content above the FY3C satellite orbit was also investigated and its realism was examined in physical and numerical aspects

Atmospheric Density Response to a Severe Magnetic Storm Detected by the 520 km Altitude Spherical Satellite

The polar-orbiting spherical experimental satellite of China for atmospheric density detection with an altitude of ~520 km was successfully launched on 14 October 2021. Based on the dynamic inversion method for atmospheric density and the precise orbit determination data obtained by its GNSS, we inverted the orbital atmospheric density during the severe geomagnetic storm in early November 2021. In this paper, we compared the atmospheric density data obtained by the spherical satellite with the simulations of the MSISE00 and the DTM, evaluated their error distribution, and analyzed the response of the atmospheric density during the severe geomagnetic storm in the dawn–dusk orbit of 520 km altitude. The properties and the physical processes for the atmospheric density of the time evolutions in different latitudes and the global distributions during the severe geomagnetic storm were obtained. We found that the substantial disturbance enhancement and recovery of the atmospheric density of the dawn–dusk orbit have a close correlation with the geomagnetic indexes Kp and Dst. The elevation extends from the poles to the equator, and the relative variation in two hemispheres demonstrates a bimodal nearly symmetric growth structure. The maximum relative variation of the two hemispheres both occurred in the middle latitude, and, for this case, the enhancement of atmospheric density in the mid-latitude region accounted for a larger proportion. The asymmetry between the northern and southern hemispheres is demonstrated by the fact that the absolute value and absolute change in the southern hemisphere in summer are larger than those in the northern hemisphere, and the bimodal structure of the relative variation is inclined to the northern hemisphere

FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval

FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval

Atmospheric Density Response to a Severe Magnetic Storm Detected by the 520 km Altitude Spherical Satellite

The polar-orbiting spherical experimental satellite of China for atmospheric density detection with an altitude of ~520 km was successfully launched on 14 October 2021. Based on the dynamic inversion method for atmospheric density and the precise orbit determination data obtained by its GNSS, we inverted the orbital atmospheric density during the severe geomagnetic storm in early November 2021. In this paper, we compared the atmospheric density data obtained by the spherical satellite with the simulations of the MSISE00 and the DTM, evaluated their error distribution, and analyzed the response of the atmospheric density during the severe geomagnetic storm in the dawn–dusk orbit of 520 km altitude. The properties and the physical processes for the atmospheric density of the time evolutions in different latitudes and the global distributions during the severe geomagnetic storm were obtained. We found that the substantial disturbance enhancement and recovery of the atmospheric density of the dawn–dusk orbit have a close correlation with the geomagnetic indexes Kp and Dst. The elevation extends from the poles to the equator, and the relative variation in two hemispheres demonstrates a bimodal nearly symmetric growth structure. The maximum relative variation of the two hemispheres both occurred in the middle latitude, and, for this case, the enhancement of atmospheric density in the mid-latitude region accounted for a larger proportion. The asymmetry between the northern and southern hemispheres is demonstrated by the fact that the absolute value and absolute change in the southern hemisphere in summer are larger than those in the northern hemisphere, and the bimodal structure of the relative variation is inclined to the northern hemisphere

Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast

The global navigation satellite system (GNSS) radio occultation (RO) technique is an atmospheric sounding technique that originated in the 1990s. The data provided by this approach are playing a consistently significant role in atmospheric research and related applications. This paper mainly summarizes the applications of RO to numerical weather prediction (NWP) generally and specifically for tropical cyclone (TC) forecast and outlines the prospects of the RO technique. With advantages such as high precision and accuracy, high vertical resolution, full-time and all-weather, and global coverage, RO data have made a remarkable contribution to NWP and TC forecasts. While accounting for only 7% of the total observations in European Centre for Medium-Range Weather Forecasts’ (ECMWF’s) assimilation system, RO has the fourth-largest impact on NWP. The greater the amount of RO data, the better the forecast of NWP. In cases of TC forecasts, assimilating RO data from heights below 6 km and from the upper troposphere and lower stratosphere (UTLS) region contributes to the forecasting accuracy of the track and intensity of TCs in different stages. A statistical analysis showed that assimilating RO data can help restore the critical characteristics of TCs, such as the location and intensity of the eye, eyewall, and rain bands. Moreover, a non-local excess phase assimilation operator can be employed to optimize the assimilation results. With denser RO profiles expected in the future, the accuracy of TC forecast can be further improved. Finally, future trends in RO are discussed, including advanced features, such as polarimetric RO, and RO strategies to increase the number of soundings, such as the use of a cube satellite constellation

Distinct Regimes of O₃ Response to COVID-19 Lockdown in China

Restrictions on human activities remarkably reduced emissions of air pollutants in China during the COVID-19 lockdown periods. However, distinct responses of O3 concentrations were observed across China. In the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions, O3 concentrations were enhanced by 90.21 and 71.79% from pre-lockdown to lockdown periods in 2020, significantly greater than the equivalent concentrations for the same periods over 2015–2019 (69.99 and 43.62%, p 2) was the major driver of enhanced O3 in the BTH region as it is a NOx-saturated region. In the YRD region, season-shift induced changes in the temperature/shortwave radiative flux, while lockdown induced declines in NO2, attributable to the rise in O3. In the PRD region, the slight drop in O3 is attributed to the decreased intensity of radiation. The distinct regimes of the O3 response to the COVID-19 lockdown in China offer important insights into different O3 control strategies across China