PREVIOUS ISSUES » Volume 9 Issue 4 October-December 2021

Volume 9 Issue 4 

October-December 2021

All articles in this issue under licence


Country – INDIA

DOI –10.55083/irjeas.2021.v09i04006

Paresh Korde, Dr. Shailendra Dwivedi

Abstract: An air diffuser while placing in an indoor office space has been the most important consideration for human thermal comfort.To obtain a maximum thermal comfort in an open indoor space the velocity of air and swirl produced by diffuser also has a huge impact.In this thesis we have worked to obtain a better temperature distributed in an indoor space as well as to get a better effect of swirl. We have observed the performance of placing a ceiling swirl diffuser by placing it on a diffuser angle of 9⁰,10⁰,11 in an indoor space. Swirl produce by diffuser depends on it angle and proper swirl mixing with air produce better temperature distribution inside the room to obtain thermal comfort so by this experimental work we just want to evaluate and analysis the proper flow of air by swirl diffuser to design more angle validated ceiling diffusers.

Cite this Article

Paresh Korde, Dr. Shailendra Dwivedi. STUDY OF PERFORMANCE OF CEILING SWIRL DIFFUSER PLACEMENT IN INDOOR ENVIRONMENT. International Research Journal of Engineering & Applied Sciences, (IRJEAS), 9(4), pp. 20-24, 2021.


Country – INDIA

DOI –10.55083/irjeas.2021.v09i04005

Kamal Bhan Singh, Yogesh Mishra

Abstract: In the current scenario, wind turbine energy accounts for 70% of total renewable energy used in India. The Indian wind energy sector has a capacity of 20 GW installed (as on 31.5.2017). In terms of installed wind power capacity, India ranks fifth in the world and is regarded as a major player in the global wind energy market. For the current work, a design method based on modelling in Uni-graphics and followed by simulation using the CFD (Computational Fluid Dynamics) star ccm + programme is chosen, so that the expense and time required to find the optimum aerodynamic design of a wind turbine blade by experiment can be minimised. The wind turbine aerodynamic theory is used in the analysis. This simulation analyses the characteristics of aerofoils by using star ccm + two-dimension models of wind turbine blades. The SST K- turbulent model is used to analyse two-dimensional aero foils NREL S807 and NREL-S809. The dimensionless lift, drag, and pitching moment coefficients of wind turbine blades at various angles of attack were calculated.Two different aero foils, NRELS807 and NREL S809, with ten different angle of attack are also analysed, for a total of 20 cases with a wind speed of 20m/sec. Uni-graphics is used for modelling. The firstaero foil NREL S807 has a maximum lift coefficient of 1.25 at a 12o angle of attack and a maximum drag coefficient of 0.134 at a 16o angle of attack. Maximum lift for aero foil S809 is 1.15 at an angle of attack (AOA) of 16o, and maximum drag is around 0.176.

Cite this Article

Kamal Bhan Singh, Yogesh Mishra. TWO DIMENSIONAL AEROFOIL WIND TURBINE BLADE DESIGN AND ANALYSIS USING CFD ANALYSIS. International Research Journal of Engineering & Applied Sciences, (IRJEAS), 9(4), pp. 25-27, 2021.


Country – INDIA

DOI –10.55083/irjeas.2021.v09i04007

Deepa Singh, Laxman Solankee

Abstract: Photovoltaic (PV) systems have received a lot of attention in recent decades due to their accessibility and advancements in PV technology. The protection of PV systems from faults such as String to String (SS), String to Ground (SG), Open circuit (OC), and partial shading are the key challenges to the realization of cost-effective and environmentally friendly PV systems. Such unusual circumstances reduce the maximum available PV power. Partial shading and breakdowns in a PV array must therefore be noticed quickly for enhanced system efficiency and reliability. The significant fault current in PV systems can be detected using the existing safety devices in PV systems, such as fuses and residual current detectors. The flowing fault current being of low order is not significant enough for current protection devices to detect if the solar and/or fault mismatch is modest and the fault resistance is high.As a result, under cloudy and low irradiance conditions, the traditional protection devices fail to identify problems, resulting in reliability concerns and photovoltaic fire threats. In this context, a fault diagnosis scheme for PV systems is presented in this paper, which includes feature extraction using the Discrete wavelet transform, and classification of various defects on the PV system using Decision tree.

Cite this Article

Deepa Singh, Laxman Solankee. MACHINE LEARNING BASED FAULT DIAGNOSIS SCHEME FOR GRID-CONNECTED PV SYSTEM. International Research Journal of Engineering & Applied Sciences, (IRJEAS), 9(4), pp. 28-33, 2021.

Enhancing Cloud Computing Security Through Deep Learning: An Artificial Neural Network Approach [pp 34-38]

Country – INDIA

DOI -10.55083/irjeas.2021.v09i04009

Dr. Sajeeda Parveen Shaik

Abstract: Cloud computing, while offering unparalleled benefits in scalability and efficiency, faces escalating security challenges in the contemporary digital landscape. This paper proposes an innovative approach to fortify cloud computing security through the integration of deep learning, with a specific focus on artificial neural networks (ANNs). By harnessing the adaptive capabilities of ANNs, the study aims to detect and mitigate evolving security threats within diverse cloud environments. The research methodology involves the meticulous selection of neural network architectures, comprehensive training datasets, and rigorous evaluations, including considerations for real-world scenarios and dynamic threat landscapes. Results and analysis showcase the effectiveness of the artificial neural network approach, providing nuanced insights into detection accuracy, false positive rates, and response times under various conditions. Moreover, the paper discusses the potential for transfer learning and ongoing adaptation mechanisms to enhance the robustness of the proposed security framework. This contribution adds significant depth to the discourse on cloud security, offering a detailed roadmap for practitioners and decision-makers seeking advanced, adaptive solutions in the face of increasingly sophisticated and dynamic cyber threats. The integration of deep learning, particularly ANNs, emerges as a promising avenue for elevating the security posture of cloud environments in an ever-evolving digital ecosystem.

Synthesis and Characterization of Copper Nanoparticles: Investigating Antibacterial Potential via Size and Surface Charge Modulations [pp 39-44]

Country – INDIA

DOI -10.55083/irjeas.2021.v09i04010

Dr. Shrikrishna H Gurlhosur

Abstract: This research delves into a comprehensive investigation of copper nanoparticle synthesis and characterization, with a specific emphasis on exploring their antibacterial potential by manipulating size and surface charge. Employing a customized approach, precise control over nanoparticle dimensions and surface properties was achieved. Variations in size were attained by fine-tuning reaction parameters, while surface charge modifications were implemented through ligand functionalization. Thorough characterization using diverse analytical techniques, such as transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential measurements, and Fourier-transform infrared spectroscopy (FT-IR), was conducted to elucidate morphological aspects and surface features. This multifaceted characterization aimed to provide a comprehensive understanding of the synthesized copper nanoparticles. Antibacterial assessments against various bacterial strains, spanning Gram-positive and Gram-negative species, were carried out, and the results were meticulously correlated with variations in nanoparticle size and surface charge. The outcomes not only advance the knowledge of copper nanoparticle synthesis but also shed light on the intricate relationship between nanoparticle properties and their antibacterial efficacy. This research holds promise for the development of tailored antibacterial agents with specific physicochemical properties, offering potential applications in antimicrobial materials and biomedical interventions, contributing to the evolving landscape of nanotechnology-driven solutions for combating bacterial infections.

Preventing Card Fraud and Scam Using Artificial Intelligence [pp 45-53]

Country -USA

DOI -10.55083/irjeas.2021.v09i04011

Shubhodip Sasmal

Abstract: Card fraud and scams present escalating challenges to the security of financial transactions, necessitating innovative solutions to counter evolving threats. This research paper delves into the realm of Artificial Intelligence (AI) as a robust tool for preventing and mitigating card fraud and scams. The paper provides an in-depth analysis of the current landscape of card fraud, emphasizing the financial and societal impacts, as well as the shortcomings of traditional fraud detection methods. Central to this study is the exploration of AI’s pivotal role, leveraging its capabilities in data analysis, pattern recognition, and real-time decision-making.
Various AI-based approaches are scrutinized for their efficacy in combating card fraud. Machine learning models, encompassing both supervised and unsupervised techniques, are examined for their capacity to discern patterns associated with fraudulent transactions. Anomaly detection algorithms are explored to identify deviations from typical transaction behavior, serving as a critical line of defense against emerging fraud tactics. The paper also investigates the application of behavioral analytics, creating user behavior profiles to pinpoint abnormal patterns indicative of potential fraud.
Ethical considerations surrounding the use of AI in card fraud prevention are addressed, highlighting concerns related to privacy, data security, and potential biases in algorithmic decision-making. The study also acknowledges the limitations and challenges inherent in implementing AI-based solutions, including the necessity for extensive datasets, the dynamic nature of fraud tactics, and the potential for false positives.
Looking forward, the paper explores future directions for AI in card fraud prevention, considering advancements such as the integration of blockchain, federated learning, and adaptive strategies to stay ahead of emerging threats. In conclusion, this research underscores the critical importance of integrating AI into card fraud prevention strategies, offering a comprehensive and forward-looking perspective to fortify the security of electronic transactions for financial institutions, businesses, and consumers alike.