Aeronautical Development Establishment

Abstract Enhancing airfoil performance through passive, energy free flow control is a key goal in modern aerodynamics. This study investigates a modified NACA 0012 airfoil incorporating a rectangular convergent duct at the leading edge. The internal passage accelerates lower surface airflow through the Venturi effect, creating a localized low-pressure region that enhances lift and stabilizes the boundary layer without external power input or mechanical actuation. Experiments in a subsonic open circuit wind tunnel at 10 m/s (Re ≈ 1.5 × 10 5 ) and steady state CFD simulations using the k – ω SST model were conducted under identical conditions. Both results confirm that the ducted configuration improves aerodynamic efficiency in the pre stall regime, achieving a 10 %–13 % increase in lift, 10%–15 % reduction in drag, and 30 %–35 % improvement in lift to drag ratio compared with the baseline NACA 0012. CFD and experimental findings agree within 10 %–15 % across all angles of attack. Flow field visualization shows a high momentum jet emerging from the duct outlet and a persistent low-pressure core beneath the lower surface, demonstrating Venturi driven acceleration that delays separation and sustains lift. The proposed passive internal flow control concept provides a simple, lightweight, and energy free means to enhance aerodynamic efficiency for low Reynolds number applications such as unmanned aerial vehicles and small wind energy systems.

This paper presents a detailed simulation-based performance comparison of Orthogonal Frequency Division Multiplexing (OFDM) and filtered-OFDM (f-OFDM) systems in terms of Peak-to-Average Power Ratio (PAPR), channel capacity, and Power Spectral Density (PSD). Using a Quadrature Amplitude Modulation (16-QAM) scheme over 600 subcarriers, the systems are tested under Jakes fading channel conditions. We propose a hybrid channel estimation technique that combines Least Squares (LS) estimation at pilot positions with Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation across subcarriers to enhance accuracy in MIMO-f-OFDM systems under Jakes fading conditions. The f-OFDM system realizes a Hamming-windowed (Finite Impulse Response) FIR filter to confine the signal's bandwidth and mitigate out-of-band emissions. Simulation results show that the f-OFDM system demonstrates a PAPR improvement of approximately 0.8–1dB at a CCDF of 10-3, lowering the power amplifier back-off requirement. The f-OFDM has a lower PAPR compared to traditional OFDM, indicating better power efficiency. Spectral analysis reveals that f-OFDM achieves a 30–40 dB reduction in out-of-band emissions, highlighting superior spectral localization. In terms of channel capacity, f-OFDM and OFDM have nearly identical channel capacities, indicating comparable spectral efficiency across varying SNR values (0–17.5 dB). Compared to related works in the literature, this simulation exhibits a PAPR reduction of up to 1 dB and PSD suppression exceeding 30 dB, validating the effectiveness of the implemented filtering strategy. These findings reinforce the potential of f-OFDM as a strong waveform candidate for next-generation wireless 5G systems where spectral efficiency, robustness, and power efficiency are critical.

Water pumping systems play a crucial role in sustaining human life by addressing essential needs such as drinking water, agricultural irrigation, and various industrial processes. Implementing renewable energy sources, particularly standalone water pumping models supplied by Photovoltaic (PV) technology, can offer a more efficient solution for water supply across diverse regions. In numerous instances, the presence of several water pumping units is crucial to meet the demand. This research focuses on two water pumping systems that are managed by a shared PV system and a common converter. The switched reluctance motor (SRM) is especially distinguished for its outstanding performance for these applications, where the role of an electric motor is vital for effective water pumping. The adoption of PV-fed SRM in place of batteries allows the system to reduce costs and maintenance associated with battery operation. To enhance regulation, sensorless speed control and a sliding mode control (SMC) have been integrated. Partial shading is a common occurrence in PV systems, which can adversely affect power generation. The maximum power point under partial shading conditions (PSCs) may not be accurately estimated using the Perturbed and Observe (P&O) approach alone. The combination of the Mayfly algorithm and the P&O method has the potential to enhance efficiency in the management of PSCs. This research examines the effectiveness of maximum power point tracking (MPPT) across different PSC scenarios, and it contrasts the findings with those derived from the Modified Grey Wolf Optimizer (MGWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The integration of the recommended MPPT algorithm into SMC could transform the converter into an effective MPPT operation. Further proposed control methodology utilized TS-Fuzzy controller to achieve effective operation. Hardware-in-the-loop (HIL) testing has been used to verify the effectiveness of the suggested method on the OPAL-RT platform.

Traditional Graph Neural Network (GNN) approaches for fake news detection (FND) often depend on auxiliary, non-textual data, such as user interaction histories or content dissemination patterns. However, these data sources are not always accessible, limiting the effectiveness and applicability of such methods. Additionally, existing models frequently struggle to capture the detailed and intricate relationships within textual information, reducing their overall accuracy. In order to address these challenges Advanced Text Analysis Graph Neural Network (ATA-GNN) is proposed in this paper. The proposed model is designed to operate solely on textual data. ATA-GNN employs innovative topic modelling (clustering) techniques to identify typical words for each topic, leveraging multiple clustering dimensions to achieve a comprehensive semantic understanding of the text. This multilayered design enables the model to uncover intricate textual patterns while contextualizing them within a broader semantic framework, significantly enhancing its interpretative capabilities. Extensive evaluations on widely-used benchmark datasets demonstrate that ATA-GNN surpasses the performance of current GNN-based FND methods. These findings validate the potential of integrating advanced text clustering within GNN architectures to achieve more reliable and text-focused detection solutions.

Abstract— Air quality index (AQI) calculations must be quick and precise since air pollution poses a hazard to both the environment and public health. This work introduces a neural network model-based deep learning approach for AQI estimation. The study makes use of daily measurements of a range of pollutants from different cities, including PM2.5, PM10, NO, NO2, CO, and others. The data is put through pre-processing procedures including modelling and missing value management in order to improve the model's efficacy. A neural network consisting of two hidden layers is trained using the data processing. Using mean squared error (MSE), the model was assessed after more than 150 training runs. Based on pollution data, the research findings demonstrate that the model can forecast AQI. Keywords—Air Quality Index (AQI), Neural Network, Machine Learning, Predictive Modeling, Mean Squared Error (MSE).

A FIR filter is used in digital signal processing to eliminate undesirable components or noise from a signal. In order to efficiently eliminate noise from the received channel data bits, this work presents the VLSI architecture of a Multi-Standard Digital Up Converter (DUC) based FIR filter. In the accumulation unit's existing architecture, carry skip addition was utilized. Shift add architecture is used in this proposed architecture to achieve an efficient adaptation delay, area, and power implementation. The architecture is designed using Carry look ahead adder, which is used in the accumulation unit. The results demonstrate that the proposed technique produces less area and power consumption than the existing DUC based RRC filter architecture. In this project, the suggested filter is implemented using the VERILOG programming language in Xilinx ise 14.7. In this project, the FIR filter's VERILOG coding is used, and simulation is used to view waveforms. The increasing need for high performance and low power DSP is a result of the multimedia applications' rapid expansion. The RRC digital filter is the primary component of a DSP system that is most frequently employed. Keywords— CarrySkipadder, DigitalUpconverter, Carry look ahead adder

This research explores the design and implementa- tion of an image and video processing platform using recon- figurable FPGA technology. With the increasing demand for real- time, high-performance multimedia processing in modern applications, a flexible and efficient solution is essential. The project utilizes an FPGA-based platform to handle image and video data processing through edge detection, image scaling, and real-time motion detection techniques. By integrating custom- built hardware components, this study aims to provide a low- power, high-throughput solution that meets the requirements of both image and video processing tasks. Materials and methods involved using Xilinx Virtex-II Pro FPGA for hardware imple- mentation, alongside embedded memory processors and multi- functional design elements such as zoom-in/out utilities. The system was evaluated through several experimental procedures, using various image and video datasets, to measure performance in terms of speed, resource utilization, and power consumption. Results indicated that the system achieved notable improvements in terms of efficiency, utilizing 25% of logic resources, 55% of on- chip memory, and 180mW of power. The discussion focuses on the adaptability of FPGA-based systems in meeting the diverse needs of multimedia processing, emphasizing scalability and low power consumption. In conclusion, the reconfigurable FPGA platform provides a versatile and efficient environment for image and video processing. The system’s resource efficiency, scalability, and potential for further enhancements make it a promising candidate for future multimedia applications. Keywords-Reconfigurable FPGA Architecture, Video and Image Processing, Edge Detection, Image Rescaling.

Biomass-based carbonaceous materials derived from Musa Sapientum have gained much attention in recent years for their application in energy storage devices, especially supercapacitors. In the present work, we synthesized carbonaceous material from banana peel as the biomass precursor by using a pyrolysis method carried out at various temperatures (600, 800, and 1000 °C). The characterization of the prepared carbonaceous materials BP600, BP800 and BP1000 was done by using different characterization techniques such as FTIR, XRD, FE-SEM, and TEM, studies. The electrochemical study of the synthesized material was carried out by cyclic voltammetry (CV), galvanostatic charge–discharge (GCD) electrochemical impedance spectroscopy (EIS). The supercapacitive performance of the material was studied using a 3-electrode system with 3M KOH as an electrolyte. As a result, the BP600 exhibited a better specific capacitance with higher energy and power densities along with a maximum cyclic stability of 16,000 cycles. To show the practical applicability of the material BP 600, two electrode system studies were carried out as well, which showed preferentially good values for specific capacitance with appreciable power and energy density values. The study provides us with a green approach for the fabrication of non-toxic, low-cost, and environmentally friendly potential porous carbonaceous electrode materials by converting bio-waste into a clean and renewable source of energy.