Abstract:
Over the last two decades, technological transformation has resulted in a massively enriched supply of Unmanned Aerial Vehicles (UAVs); structural configuration is being hierarchically identified as the most decisive factor in determining both operational capability and mission efficiency. This review and meta-analysis studies the very basic theoretical design, development and structural analysis of the Delta shaped UAV, a special type of configuration that has been gaining increased attention for its unique aerodynamic and structural advantages [4]. The effective performance of delta wing configuration based on -specific changes to geometry, materials, and their portrayal across wind tunnel experiments is consolidated through systematic synthesis of 30 peer-reviewed sources across structural design methodologies, computational analysis methods, and experimental validation campaigns. The delta plan form, characterized by its characteristic swept-wing geometry and fuselage-integrated configuration, offers outstanding maneuverability and stability compared to more conventional UAV layouts. Higher aerodynamic efficiency is also achieved through specific surface treatments and stiffened structures, allowing for modifications. Literature survey documents improvements in lift to drag ratio of 15–30%, structural weight reductions of 20–25% and measurable improvements in maneuverability indices [5]. In this review, the most significant gaps in the literature is also identified and it includes limited research on fatigue behavior of composite structures under varying cyclic load and thermal stress distribution under extreme operating conditions and multi-objective optimization algorithms on FDM parts. So, what does the study mean for the future of delta UAVs Emerging technologies such as additive manufacturing, automated fiber placement, physics-informed neural networks, and structural health monitoring are identified as key enablers to help facilitate the next genera
Area: Department of Aeronautical Engineering
Author: Sakharam Chouhan 1, Dr. Vishwjeet Ambade2, Dr. Arepally Shushrutha3
DOI: MJAP/05/1010
Abstract:
This work provides the structural optimization, design and high-fidelity parametric assessment of a new low-volume, high-power-density DC-DC resonant power converter specifically designed for future electric vehicle (EV) battery charging infrastructure. Facing the pressing requirements for compactness and thermal efficiency in fast-charging topologies, this work explores a silicon carbide (SIC) based P–I coupled interleaved full-bridge LLC resonant topology with an elevated switching frequency of 500 kHz. Weconducteda multi-parametric empirical study along a range of loading conditions (10% to 110% of the nominal36 kW output rating) and an input DC-link voltage between 400 V and 800 V in order to characterization experimental boundaries of efficiency profiles. In order to achieve this, raw operational data comprised of switching transitions, magnetizing loop dynamics, core losses, and synchronous rectification parameters were carefully logged and collated over various stages. The experimental results analyzed exhibit a maximum conversion efficiency of 97.85% at full-load allowing the proposed system to give an impressive volumetric power density of 2.14 kW/L making it a considerable benchmark improvement over traditional silicon based systems. Mathematical formulations and statistical regression analyses give us confidence that high-frequency switching transitions are able to achieve ZVS across the full operating envelope without incurring thermal or electromagnetic interferences that would be prohibitive. This work provides a repeatable data-driven framework to church-tune the core geometries and bridge-switching paradigms, showing how wide-bandgap co-integration with efficient planar magnetics meets the promises of ultra-compact pervasive infrastructure for sustainable automotive-based transportation.
Area: Department of Electrical Engineering
Author: Sulochana Gendre¹, Dr. Mithilesh Singh²
DOI: MJAP/05/1009
Abstract:
The power distribution systems are the key final link to provide end-users with access to the electrical grid, making their operational reliability one of the primary drivers influencing global power quality and customer satisfaction. This review paper tells a comprehensive higher-fidelity meta An analysis of the historical work associated with systematic normalization of distribution network reliability indices. This study evaluates and classifies the previous success of various grid improvement approaches by synthesizing both empirical datasets and architectural frameworks from thirty foundational studies printed over the previous two decades. In particular, the analysis focuses on key performance indicators such as System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI) and Momentary Interruption Frequency Index (MAIFI). Historical literature, you can be categorized in the field of three main types of technical intervention: optimal positioning of automatic transformers switches, integration of distribution price generator and network reconfiguration strategies. This paper makes a statistical synthesis of case study outcomes to identify historical performance correlations, demonstrating that the highest levels of incremental reduction in SAIDI and SAIFI, with an average mitigation between 35% and 52%, corresponded to hybrid topologies with automated sectionalizing links deployed along with decentralized DG resources. Moreover, this review methodologically dissects the predictive frameworks developed previously by inspecting their evolution from analytical models to state-of-the-art meta-heuristic optimization algorithms and deep learning architectures. This work reviews the methodological pitfalls highlighted in earlier research, including insufficient modeling of stochastic operational parameters and the omission of high-impact, low-probability (HILP) weather disturbances. In conclusion this systematic review provi
Area: Department of Electrical Engineering
Author: Punam Gendre¹, Dr. Mithilesh Singh²
DOI: MJAP/05/1008
Abstract:
The digital era has fundamentally transformed pedagogical practices globally, forcing a shift from traditional face-to-face instruction to flexible, technology-driven approaches. Among these, the hybrid learning model—combining physical classroom interaction with asynchronous and synchronous online learning—has emerged as a core standard in higher education. This research paper evaluates the effectiveness of hybrid learning models specifically concerning English language comprehension and communication skills among university-level students. Through a structured review of ten authentic academic studies and an analysis of current pedagogical practices, this study examines how blended environments affect reading comprehension, listening skills, spoken fluency, and written communication. The findings indicate that while hybrid models significantly boost self-paced vocabulary acquisition and multi-modal comprehension, they present challenges regarding authentic conversational spontaneity and digital equity. The paper concludes with actionable recommendations for curriculum designers to optimize hybrid English language delivery.
Area: Department of Education
Author: Dr. Kanchan Jain
DOI: MJAP/05/1007
Abstract:
It is the surface integrity the total of surface roughness, microhardness, residual stress, and wear resistance that largely controls the in-service performance of finished aluminum aerospace components rather than any single response as previously considered in isolation. This study is a comparison of surface integrity of three commercial aluminum alloys when roller burnished with five nanofluid blends. These include: four binary formulations (Al₂O₃–CuO [75:25(c); 25:75(wt.%)], Al₂O₃–graphene [60:40(c); 40:60(wt.%)], CuO–MWCNT [50:50(c); 50:50(wt.%)], TiO₂–SiO₂ [60:40(c); 40:60(wt.%)] ) and one ternary formulation (Al₂O₃–CuO–graphene [50:30:20(c); 50:30:20(wt.%)] ) Denses, 2021. Surface-integrity components including arithmetic surface roughness (Rₐ), Vickers microhardness (HV), residual compressive stress measured by X-ray diffraction (σᵣ) and pin-on-disc wear loss were measured on the Al6061-T6, Al7075-T6 and Al2024-T3 workpieces and aggregated into a composite Surface Integrity Index (SII) via equal weighting. The best SII of 0.91 on Al7075-T6 was obtained from the ternary Al₂O₃–CuO–graphene blend, which represented a 15.2 % improvement over the best binary blend (Al₂O₃–graphene, SII = 0.79) and was nearly six times larger than the dry-burnishing baseline (SII = 0.15). The ternary benefit stems from the in-situ activation of three synergistic tribological mechanisms provided by hard γ-Al₂O₃ particles in micro abrasion, CuO carriers for thermal regulation, and graphene nanoplatelets for friction-reducing tribofilm construction. Results showed strain-hardening depths of ~0.4 mm under all the hybrid blends, with the corresponding maximum microhardness (165 HV) and the most significant absolute residual compressive stress (−432 MPa) produced by the ternary blend and confirmed by subsurface microhardness profiles. These results define the role of the new ternary Al₂O₃–CuO–graphene formulation as a truly multi-mechanism surface-integrity optimizer for the finish-burni
Area: Deogiri Institute of Engineering and Management Studies
Author: Murarikar Ganesh Balaji1, Mr. Vishal Vijay Chahare2
DOI: MJAP/05/1006
Abstract:
Widespread deployment of Internet of Things (IoT) sensor networks in sensitive industrial, medical and urban environments have elevated the fundamental questions regarding energy conservation, latency mitigation and structural security. It combines the unprecedented bandwidth and ultra-reliable low-latency communication (URLLC) of 5G communication architectures. Nonetheless, the nature of IoT nodes, which are highly distributed and vulnerable, makes it unable to withstand advanced cyber-attacks as sinkhole, blackhole and selective-forwarding attacks in traditional routing paradigms. This empirical exploration accounts application of a hybrid Particle Swarm Optimization (PSO) framework for potential applications to multi-objective secure routing in IoT sensor networks enabled by future 5G. The proposed Secure-PSO is using a dynamic evaluation matrix combined with standard physical constraints - including the residual energy of nodes in the transmission range, link quality and multi-hop distance - to model optimal cluster-head election and path selection trajectories through mathematics. Experiments were performed in multiple densities of 100-500 sensor nodes on a local 5G macro-cell grid. Localized adversarial injection Empirical data collection that was mainly based on quantified metrics (Network Lifetime, Average Energy Consumption, Packet Delivery Ratio (PDR), End-to-End Latency, and Throughput). Quantitative results showed that the Secure-PSO framework extended network lifetime by 34.2% over traditional Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols and kept an average PDR greater than 96.5% in case of 20% node attrition from malicious nodes as well. The mathematical parameters are significant, as statistically validated by rigorous Analysis of Variance (ANOVA) testing. Weed out of the box at the end, this paper makes two structural contributions to architectural engineering by demonstrating that metaheuristic algorithmic models can integrate defense
Area: Department ECE
Author: Vishal Gehlod1, Janmejay singh Solanki2, Amit Thakur3
DOI: MJAP/05/1005
Abstract:
Fire resistance is one of the most critical performance requirements for structural concrete in modern construction, particularly in high-rise buildings, tunnels, industrial structures, and underground facilities. Ordinary Portland Cement (OPC) concrete undergoes progressive deterioration including strength loss, spalling, and microcracking when exposed to temperatures above 300°C. This study investigates the development of fire-resistant concrete (FRC) mixes using advanced supplementary cementitious materials (SCMs) including silica fume (SF) and fly ash (FA), polypropylene (PP) fibers, and refractory aggregates. Four concrete mix designs were systematically formulated and experimentally evaluated: a control mix (M1), an SF-blended mix (M2), and FA-blended mix (M3), and a hybrid SF–PP fiber mix (M4). Specimens were subjected to ISO 834 standard fire exposure at temperatures of 25°C, 200°C, 400°C, 600°C, and 800°C. Evaluation parameters included residual compressive strength, flexural strength, splitting tensile strength, thermal conductivity, mass loss, spalling resistance, water absorption, and chloride ion penetration. Results demonstrate that Mix M4 retained 27.2% of its ambient compressive strength at 800°C nearly twice the retention of the control achieved a fire resistance rating exceeding four hours, and exhibited markedly superior spalling resistance attributed to the vapor pressure relief mechanism of PP fibers. The findings provide actionable material design strategies for fire-resistant structural concrete meeting international standards.
Area: Dept. of Civil Engineering
Author: Dr. U J Jadhav, Dr. P R Modak, Mrs. M S Chiwande, Mr. C S Misal
DOI: MJAP/05/1002
Abstract:
For civil infrastructure managers worldwide, the structural integrity of steel and concrete bridges is a critical concern. Fatigue life and deterioration rates have become crucial in determining maintenance schedules, especially with monitors for aging bridge networks and hundreds of thousands of bridges across the United States being rapidly overloaded. Although traditional empirical methods and physics-based models are cornerstones in the degradation mechanism modeling, they struggle with effectively capturing the complex nonlinear nature of deterioration for real-world bridge structures. For the last twenty years, machine learning (ML) algorithms have provided revolutionary data-driven high-fidelity predictive capabilities that are complementary to and often surpass traditional approaches. This review paper is a meta-analysis of the studies available in existing literature on predicting fatigue life and deterioration of bridge infrastructure using ML approaches such as artificial neural networks (ANN), support vector machine(s) (SVM), random forests (RF), gradient boosting methods(GBM, XGBoost, Light GBM)), convolutional neural networks (CNN) and physics-informed neural networks(PINN). This review (n = 30 studies from 2005–2024) systematically evaluates methodology, data-set characteristics, predictive accuracy and practical deployment barriers. They demonstrate that, when used alone on their own, single-algorithm approaches are outperformed consistently by both ensemble learning and deep learning models achieving MAE reductions as high as 35% compared to regression baselines. Important shortcomings include limited bridge typology generalization, minimal real-world sensor deployment and no standard benchmark datasets. The paper ends with directions for future research towards hybrid physics-ML frameworks, federated learning for distributed monitoring networks, and explainable AI (XAI) to garner engineer trust in decision support.
Area: Department of Engineering
Author: TVS Ramanjaneyulu1, Dr. Ananda Babu Kurakula2
DOI: MJAP/05/0024