Defence Institute of Advanced Technology (DIAT) is the premier engineering training institute under the Department of Defence Research & Development, Ministry of Defence, Government of India. DIAT (DU) is specialized in the training of officers of Defence Research Organizations, IOFS (Indian Ordnance Factories), Defence PSUs (Like Hindustan Aeronautics Limited, Bharat Electronics, Bharat Dynamics Limited), ship building agencies like Garden Reach Shipbuilders & Engineers, Cochin and Goa Shipyards, Mazagon Dock Shipbuilders and armed forces of friendly countries (like Sri Lanka and Afghanistan,) and other central and state government agencies.Ministry of Human Resource Development, Government of India has placed DIAT in Category 'A' Deemed University & accredited by National Assessment and Accreditation Council and National Board of Accreditation. During the last few years, researchers in DIAT have filed more than 50 patent applications with Indian Patent Office and published more than 2000 papers in various journals of International repute..
The research in the area of electromagnetic wave absorbing materials (EAMs) has been actively pursued for at least five decades. Electro-magnetic wave absorbing materials are specialized to narrow the reflection or increase the absorption properties of the materials of electro-magnetic signals to provide effective shielding capability, which is a core component of passive measurements in combat applications. The electro-magnetic characteristics of high entropy alloys (HEAs) could be optimized by adjusting their surface geometry, microstructure, and composition. This review addresses the role of HEAs core effects in electromagnetic wave shielding effect and its influence by core effects. To bridge the deficit in our understanding of EAMs, a thorough depiction of HEAs deployed as EAMs and their conceivable electromagnetic radiation (EMR) loss pathways are offered. The existing obstacles and envisaged paths for the evolution of future HEAs as EMR absorption materials are also addressed with reference to published literature.
The aviation sector faces challenges in enhancing safety, efficiency, and sustainability. The integration of emerging Industry 4.0 technologies presents a promising pathway to address these concerns. This paper presents a comprehensive review of current trends in Industry 4.0 applications in aviation, utilizing technologies such as artificial intelligence (AI), the Internet of Things (IoT), digital twins (DT), and additive manufacturing (AM). The bibliometric analysis of over 250 reviewed publications from 1997 to 2025 reveals the publication trend, sources, top countries and institutions, collaboration network, key technologies, and their development, which define the current trend and future research direction in this domain. Countries like China, Germany, and the USA emerged as key contributors supported by institutions such as Beihang University, Rzeszów University of Technology, and Hamburg University of Technology. This research is mainly funded by national and international funding agencies, including the European Union, National Science Foundations of various countries, such as China, and ministries focused on education. However, there are some limitations in using these technologies. AI can predict failures, but it cannot clearly explain the reasons behind them. Digital twins are expensive and need real-time data. IoT lacks common standards, and Virtual Reality (VR)/Augmented Reality (AR) tools are costly and unsupported everywhere. In the future, improving data sharing, reducing costs, and enhancing the system’s ease of use and security will facilitate the adoption of Industry 4.0 in aviation.
Abstract Access to freshwater remains a critical global challenge, particularly in arid and off-grid regions where conventional desalination technologies are energy-intensive. Solar stills offer a sustainable alternative; however, the effect of still geometry on thermal behavior and freshwater productivity remains insufficiently understood. Existing studies focus mainly on single geometries or hybrid configurations, leaving a clear knowledge gap in the direct comparative evaluation of different pyramid architectures under identical operating conditions. This study addresses this gap by conducting a combined mathematical and experimental comparison of two newly fabricated solar stills, a Pentagonal Pyramid Solar Still (PPS) and a Square Pyramid Solar Still (SPS), each constructed with an identical basin area of 0.25 m2. Experiments were performed under outdoor tropical climatic conditions (solar intensity 600–950 W/m2), and hourly data for temperature, humidity, and distillate yield were recorded. A Python-based transient heat and mass transfer model was developed and validated using statistical indicators (R2 = 0.96, MAPE ≈ 3–5%). The findings show that PPS consistently outperformed SPS, producing 18.3% higher daily distillate output, 15.1% greater thermal efficiency, and 17.6% higher exergy efficiency. The performance enhancement is primarily attributed to the additional inclined glass face in the pentagonal geometry, which increases optical exposure, internal radiation trapping, and the effective condensation surface. Economic analysis further demonstrates that PPS reduces the cost per liter of freshwater by nearly 12% compared with SPS. Overall, the study establishes that geometric optimization plays a decisive role in solar desalination performance, and the validated mathematical model provides a practical framework for future solar still design and scaling in decentralized water-scarce regions.
The growing adoption of solar energy underscores the need for efficient defect detection in photovoltaic (PV) modules, as manufacturing flaws and environmental stressors (e.g., cracks, corrosion, humidity) degrade performance and longevity. Hence, it is deemed necessary to identify these defects effectively. Electroluminescence (EL) imaging is an established technique for identifying critical defects like microcracks. Manual inspection remains laborious and error-prone. To address this, we propose a hybrid lightweight deep learning (DL) framework that integrates transformer architectures and attention mechanisms—including DETR, Biformer, and CBAM into the YOLOv8 and YOLOv11 frameworks. This integration enhances feature representation and contextual reasoning while preserving computational efficiency. By automating defect detection through AI-driven computer vision techniques, our approach reduces reliance on manual intervention and improves robustness under real-world conditions. The proposed YOLOv11+CBAM model achieves 93.0
Propellers are devices that help convert rotational energy to thrust. The need for improved aerodynamic performance, noise reduction, and flight stability drives the implementation of tubercles on drone propeller blades. Inspired by the leading-edge bumps on humpback whale flippers, tubercles help delay flow separation, reduce drag, and enhance lift, resulting in greater thrust efficiency. They also smooth out turbulent airflow, significantly reducing noise and vibrations. This research presents an optimized design methodology for propellers through the implementation of leading-edge tubercles. The study explores the aerodynamic benefits of biomimetic modifications, focusing on improving efficiency. Computational Fluid Dynamics (CFD) simulations were conducted to evaluate the performance of conventional propeller designs against those modified with sinusoidal tubercles of varying amplitudes and wavelengths. The optimized design demonstrated significant improvements in thrust generation and overall propulsion efficiency, alongside a notable reduction in flow separation. These findings suggest that tubercle-modified propellers offer a promising approach to enhancing propulsion systems.
