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In fluid dynamics, a flow control device is used to control, manage, or modify the behavior of a fluid flow. Jet actuators work by releasing high-velocity jets of fluid, usually air or gas, into the surrounding environment to control or manipulate the flow of fluids. In this study, the flow control device, which was a dual synthetic jet actuator (DSJA), acted as a lift enhancement device over an optimized NACA 0012 aerofoil with a rounded trailing edge (TE) (Coanda surface approximately 9% of the trailing edge was modified) to enhance the lift at various angles of attack (AOAs). Fluctuating pressure inlets were introduced in two slots. When the dual synthetic jets were in control, the out-of-phase jets from the upper and lower trailing edge jets helped to boost the lift coefficient. The suction stroke from the lower half of the jet made the Coanda effect stronger in the upper half. The upper trailing edge jet deflected downwards merged with the lower one and helped to deflect the flow field closer to the bottom half. An unsteady CFD analysis was performed on optimized airfoils with and without a DSJ, with a driving frequency of 40.6 and a reduced frequency of 0.025 at a Reynolds number of 25000. The results obtained at different angles indicated that the L/D ratio was improved by 13.5% at higher angles of attack in the presence of the DSJA.

Ionosphere is the upper atmosphere region that contains sufficient number of electrons which disturb the propagation of radio signal travel from navigational satellite to ground/user receiver. Ionospheric delay in range measurement is related to its Total Electron Content (TEC). Ionospheric delay results in range error and degrades the user position accuracy of navigational satellite systems such as Global Positioning System (GPS) and Indian Regional Navigational Satellite System (IRNSS). Hence a suitable TEC prediction model to correct the range delay in single frequency range measurement is necessary. In dual frequency receiver, ionospheric delay is estimated and eliminated using the two range measurements performed at the same time. This paper describes the TEC prediction methodology using Ordinary Kriging based Surrogate Model (OKSM). OKSM is evaluated using the data received and collected from the IRNSS receiver station installed at ACS College of Engineering (ACSCE), Bengaluru (12.8913 °N, 77.4658 °E), India and other International GNSS Service (IGS) network stations. IRNSS TEC data (January 2018) is calculated by using dual frequency (L5 & S) pseudo range method and TEC is smoothed by normal cubic smoothing spline method. IRNSS Vertical TEC (VTEC) is predicted from 16 January 2018 to 26 January 2018 by using previous six days of estimated VTEC values. Similarly, GPS VTEC for IGS station at IISC, Bengaluru is also predicted for same duration to validate the developed OKSM. In order to evaluate the performance of the developed forecasting model for different geographic locations, solar activity conditions and seasons, the VTEC is predicted and analyzed for different latitude regions such as low-latitude PHON station (6.9599 °N, 158.2101 °E), mid-latitude ALGO station (45.9588 °N, −78.0714 °E) and high-latitude NRIL station (69.3618 °N, 88.3597 °E) during different solar activity conditions (Low-2008, Medium-2011 and High-2013 solar activity) and during different seasons (spring, summer, rainy and winter) in the year 2017. From the analysis of OKSM prediction results, it is observed that, RMSE of predicted TEC varies from 0.79 to 3.6 TECU, MAE is 0.4 to 3 TECU and MAPE is within 40% for ionospheric quiet days. VTEC is also predicted during storm days (26 October 2003 to 31 October 2003). To study the performance of the model, VTEC prediction results of OKSM are compared with prediction results from Standard Persistence Model (SPM) and VTEC derived from International Reference Ionosphere (IRI-2016) model. The RMSE of OKSM is 1.9679 TECU, MAE is 1.245 TECU and MAPE is 9%, whereas for SPM, RMSE is 4.8372 TECU, MAE is 3.7496 TECU and MAPE is 36%. Similarly, for IRI-2016 model, RMSE is 7.9 TECU, MAE is 7.1976 TECU and MAPE is 66%. Therefore, TEC predictions by OKSM are better than SPM and IRI-2016. The results show that the OKSM is useful for applications in ionospheric studies.

Purpose This study aims to find the characteristics of supercritical airfoil in helicopter rotor blades for hovering phase using numerical analysis and the validation using experimental results. Design/methodology/approach Using numerical analysis in the forward phase of the helicopter, supercritical airfoil is compared with the conventional airfoil for the aerodynamic performance. The multiple reference frame method is used to produce the results for rotational analysis. A grid independence test was carried out, and validation was obtained using benchmark values from NASA data. Findings From the analysis results, a supercritical airfoil in hovering flight analysis proved that the NASA SC rotor produces 25% at 5°, 26% at 12° and 32% better thrust at 8° of collective pitch than the HH02 rotor. Helicopter performance parameters are also calculated based on momentum theory. Theoretical calculations prove that the NASA SC rotor is better than the HH02 rotor. The results of helicopter performance prove that the NASA SC rotor provides better aerodynamic efficiency than the HH02 rotor. Originality/value The novelty of the paper is it proved the aerodynamic performance of supercritical airfoil is performing better than the HH02 airfoil. The results are validated with the experimental values and theoretical calculations from the momentum theory.

Advancements in space weather forecasting have become crucial for understanding and mitigating the impacts of solar activity on Earth’s ionosphere. This research focuses on the prediction of Total Electron Content (TEC) during M-class solar flare events in 2023. TEC is a vital parameter for satellite communications and navigation, making accurate forecasting imperative. Two prediction models, Long Short-Term Memory (LSTM) neural networks and Surrogate Models based on Ordinary Kriging (OKSM), are employed. LSTM, known for capturing temporal dependencies, is contrasted with OKSM, a geostatistical interpolation technique capturing spatial autocorrelation. The study utilizes TEC measurements from the Hyderabad (HYDE) GPS station for model training and evaluation along with solar and geomagnetic parameters. The performance metrics for both models across various solar flare dates are measured using Root Mean Square Error (RMSE), Normalized RMSE, Correlation Coefficient (CC), and Symmetric Mean Absolute Percentage Error(sMAPE). The research interprets the results, highlighting the strengths and limitations of each model. Notable findings include LSTM’s proficiency in capturing temporal variations and OKSM’s unique spatial perspective. Different solar flare intensities are analyzed separately, demonstrating the model’s adaptability to varying space weather conditions. The average performance metrics during M 4.65 SF events for the OKSM model, in terms of Root Mean Square Error is 5.61, Normalized RMSE is 0.14, Correlation Coefficient is 0.9813, and Symmetric Mean Absolute Percentage Error is 14.90. Similarly, for LSTM, the corresponding averages are 10.03, 0.24, 0.9313, and 28.64. The research contributes valuable insights into the predictive capabilities of LSTM and OKSM for TEC during solar flare events. The outcomes aid in understanding the applicability of machine learning and geostatistical techniques in space weather prediction. As society’s reliance on technology susceptible to space weather effects grows, this research is pivotal for enhancing space weather forecasts and ensuring the robustness of critical technological infrastructure on Earth.

Total electron content (TEC), which quantifies the quantity of free electrons in the Earth’s ionosphere, is a crucial parameter that experiences discrepancies during seismic events. This study investigates the potential of utilizing TEC prediction at the BAKO position in Indonesia during earthquakes. TEC data and solar parameters were collected for six preselected earthquakes, encompassing the earthquake event periods. Three prediction models, namely, ARMA, OKSM 1, and OKSM 2, were employed to predict TEC for a period spanning 8 days. The input parameters required for TEC prediction were obtained from the IONOLAB and OMNIWeb database. The OKSM 1 model is constructed with the input parameters like solar radio flux at 10.7 cm (F10.7), disturbance storm time index (Dst), solar wind (Sw), sunspot number (SSN), and TEC values, while the OKSM 2 model is developed with the parameters like geomagnetic indices (Kp and Ap) and solar indices SSN and F10.7 along with TEC data. The ARMA model is constructed with TEC data. The primary objective of this research is to assess the utility of TEC prediction based on the influence on input parameters for the kriging models and to identify the most effective model for predicting TEC variations associated with seismic events. Four evaluation metrics were systematically utilized to gauge the performance of each model. This rigorous evaluation aims to deliver perceptions into the predictive accuracy, reliability, and potential practical implications of TEC predicting during earthquakes. Upon comparison, the OKSM 2 model demonstrated superior predictive accuracy, exhibiting a notable agreement with the true TEC. The results suggest that OKSM 2 holds promise as a reliable model for earthquake‐related TEC prediction. The average RMSE values range from 4.06 to 8.06, indicating the models’ ability to predict seismic events with a reasonable magnitude of error. Similarly, the average MAE values, ranging from 3.32 to 6.71, underscore the models’ overall accuracy in predicting the absolute differences between actual and predicted TEC. The CC values, averaging between 0.97 and 0.99, highlight a strong relationship between predicted and actual TEC values. Additionally, the average sMAPE values, ranging from 0.11 to 0.21, demonstrate the models’ effectiveness in minimizing percentage‐based errors. While variations exist across different earthquakes, these average metrics collectively suggest promising predicting capabilities.

The effects of solar, geomagnetic, and ionospheric anomalies on satellite communication are inextricable. Range Error (RE) is the most common fault that is faced by the navigational receivers during solar flares. Since RE always depends on the Total Electron Content (TEC) available across the satellite ray path, a prediction model capable of predicting the TEC in advance will be an excellent deterrent during adverse space weather conditions. In this research, Cokriging based Surrogate Model (COKSM) is constructed for predicting the TEC variations that occurred during the month of January 2022 to April 2022 over Hyderabad region. The input data used in the construction of the model includes F 10.7 radio flux, Sunspot number (SSN), Geomagnetic index Kp and Ap along with Vertical TEC (VTEC) data collected from Hyderabad station located in 17.31° N latitude and 78.55° E longitude. The data is collected in hourly averaged resolution for a period of 120 days covering January to April 2022. The variations in Ionospheric TEC due to solar flares and geomagnetic anomalies that occurred during the selected observation dates are principally analyzed in order to evaluate the prediction capability of the COKSM program during adverse conditions. The performance of the model is evaluated using metrics like Root Mean Square Percentage error (RMSPE), Correlation Coefficient (ρ), CHI-Squared goodness of fit test and R-squared. The results that are plotted as a linear regression scatter plot clearly shows that with very small residuals the proposed prediction model is performing well for TEC prediction. The overall RE predicted by the model is within the scale of 1–12 meters. The error parameters calculated between true TEC and predicted TEC is found out to be in the scale of 0.88 to 5.06% for RMSPE, 0.9308 to 0.9981 for correlation coefficient, 4.97 to 107.94 (TECU) for chi squared and 0.78 to 0.98 (TECU) for R squared.

Satellite communication and navigation systems have become more essential to everyday life, but at the same time, understanding the effect of solar activity on these systems is vital. Total electron content (TEC) is a key factor affecting satellite signals. Solar flares affect the TEC variations, and this research examines the forecast of TEC during various X‐class solar flares that occurred in February, March, May, June, July, and August 2024, employing a bidirectional long short‐term memory (Bi‐LSTM) coupled with the Adam optimizer (Bi‐LSTM‐AO). The forecasted results were validated with the IRI‐2020. This study uses a robust dataset encompassing more than 1 year of TEC data from the IONOLAB database, along with key solar and geomagnetic parameters such as Kp, Ap, SSN, and F10.7 obtained from NASA OMNIWeb. These potent solar flares were scrutinized to evaluate the model’s performance in forecasting TEC variations under extreme solar activity. The Bi‐LSTM‐AO model exhibited exceptional accuracy in predicting TEC values across these dates, consistently outperforming the IRI‐2020 model. For example, on May 14, 2024, coinciding with the X8.79 solar flare, the Bi‐LSTM‐AO model achieved impressive performance metrics, including a root‐mean‐square error of 3.52, a mean absolute percentage error of 6.88%, a mean absolute gross error of 2.97, and a centered mean square deviation of 9.93. In contrast, the IRI‐2020 model showed significantly higher error metrics, with an RMSE of 13.18, a MAPE of 23.61%, and a MAGE of 10.93. This research provides the development of a more accurate space weather forecasting model to increase the positional accuracy in navigation systems. The improved predictions can enhance the reliability of satellite‐dependent systems, which are increasingly important for global communication and navigation systems.

The Indian Regional Navigation Satellite System (IRNSS) is an indigenously developed satellite navigation system to meet practical needs. The system, comprising a constellation of seven satellites in GEO orbit, has been fully operational since 2018. It is essential to evaluate the performance of the IRNSS continuously for various applications. As a part of ISRO field trial and data collection program an IRNSS Standard Positioning Service User Receiver (UR) placed at ACS College of Engineering (ACSCE), Bangalore, for independent field trial and data collection. The receiver is operational on a 24×7\\(24\\times 7\\) basis. A MATLAB Graphical User Interface has been developed to analyze and plot the data variation of signal strength, elevation angle, visibility of satellites, user position, position error, geometric dilution of precision (GDOP) and find the availability of the number of satellites plotted for every second based on the received data. From the results, it is observed that the signal strength (C/No) is good, i.e. above 40 dBHz, visibility of satellites at receiver location is good. The mean position at user location is found to be X=1349700m\\(X=1349700~\\mbox{m}\\), Y=6070902m\\(Y = 6070902~\\mbox{m}\\), Z=1413860m\\(Z = 1413860~\\mbox{m}\\) and latitude of 12.8914 degree, longitude of 77.465 degree and altitude of 739 m. The mean position from IRNSS is compared with Google map results showing a good match. The geometry distance of receiver location with respect to Earth center is estimated and observed. The RMS of position error for L1, L5 and dual frequency (L5+S) at ACSCE, Bangalore, is 9.7444 m, 6.6873 m and 5.6667 m, respectively. Hence, as expected the dual-frequency (L5+S) receiver gives an accurate position rather than the single-frequency signals. The IRNSS TEC is measured using Ionospheric group delay and the pseudo-range of L5 and S band which is collected from ACSCE receiver. A third order Savitzky-Golay-Filtered technique is used for TEC smoothing. RMSE between IRNSS TEC from pseudo-range and GPS TEC is 0.6482 TECU and the correlation coefficient is 0.9981. RMSE between IRNSS TEC from ionospheric delay of L5/S and GPS TEC is 1.971 TECU and the correlation coefficient is 0.9966. Finally, smoothed TEC values derived from pseudo-range measurements give a good result and can be used to generate daily TEC maps. In order to analyze the performance of IRNSS over the Indian region, we have chosen three other receiver stations located at Osmania University (Hyderabad), University of Burdwan (Bardhaman, West Bengal) and Shri Mata Vaishno Devi University (Katra, Jammu and Kashmir). Based on the results, we conclude that the IRNSS constellation is performing well and providing good signals for accurate user position determination and ionospheric data analysis.

The total electron content (TEC) is the number of electrons present in a ray path between satellite and receiver. TEC affects the propagation of radio signal from satellite to receiver, which causes a ranging error. In the case of single frequency user receiver, the prediction of TEC helps to correct the range errors. TEC is measured using TEC unit (TECU), where 1TECU=1×1016electrons/m2\\(1~\\text{TECU} = 1 \\times 10^{16}~\\text{electrons}/\\text{m}^{2}\\). The vertical total electron content (VTEC) of the L1 band is estimated by using data collected from GPS receiver which is installed at the ACSCE station Bangalore. In this work, an ordinary kriging (OK)-based surrogate model algorithm and Matlab code is developed and used to predict the hourly basis ionospheric VTEC. Six parameters such as time, sun spot number (SSN), the solar flux index at 10.7 cm (F10.7), Kp and Ap and observed TEC are used to build the surrogate model. These parameters are related to the ionospheric diurnal variations, solar cycle and geomagnetic activities. In order to cover all regions of the world during different solar activity periods six input parameters are collected from low-, mid- and high-latitude regions during low, medium and high solar activity periods. The root mean square error (RMSE) of the OK-based surrogate model ranges from 0.6–3.6 TECU, and the correlation coefficient varies between 0.79–0.99 and range error varies from 0.04654–0.3017244 m at three regions.The TEC prediction results from the OK-based surrogate model are compared with results obtained from (i) a genetic algorithm-based neural network (GA-NN), (ii) a back-propagation neural network (BP-NN) and (iii) the IRI-2012 model at the CHAN station. For a typical dataset the RMSE of the OK-based surrogate model is 4.523 TECU and the correlation coefficient is 0.9733. The RMSE values for the GA-NN, BP-NN and IRI-2012 models are 5.3529, 6.2913 and 6.7179 TECU and The correlation coefficients are 0.8343, 0.7869 and 0.7797, respectively. The OK model is also compared with a time series method for the CHAN station; it is observed that, for 3 days (7-1-2008 to 9-1-2008) prediction, the OK model gives a 75% result for ΔTEC<1TECU\\(\\Delta \\textit{TEC} <1~\\text{TECU}\\) condition, the time series method gives 39% for the same condition. The results indicate that the OK-based surrogate model is suitable for applications in ionospheric TEC predictions.