Pimpri Chinchwad College of Engineering

Solar Photovoltaic (PV) systems typically convert solar irradiance into electricity, thereby helping to reduce the need for fossil fuels and the amount of greenhouse gases released. They provide a reliable and continuous renewable source of energy. However, PV systems are continuously exposed to diverse and changing environmental conditions, such as temperature, humidity, dust, and rain. Exposure to such conditions creates electrical and visible faults in the PV systems. These faults may reduce the PV system’s performance, reliability, and lifetime. In this regard, this paper aims to propose a framework/methodology for reliability modeling and assessment of large-scale grid-connected PV systems using a Fault Tree Analysis (FTA) approach. Specifically, an exhaustive literature survey is carried out to acquire the failure rates of different components/faults existing on the DC side of the PV system. Then, the Fussel-Vesely (F-V) importance measure is employed to identify critical faults and their criticality ranking. Results showed that solder bond failure, broken cell, broken interconnect (finger interruption), rack structure, grounding/lightning protection system, delamination, discoloration, and partial shading are the most critical faults which severely degrade the performance of the PV systems. The recommendations and scope for further study are provided to optimize operations and maintenance costs.

Understanding the combustion of ketones is crucial for their integration into modern energy systems as sustainable, efficient, and environmentally friendly alternatives to conventional fuels, as such studies help optimize engine performance, reduce emissions, and advance the development of next-generation renewable fuel technologies. To achieve these goals, the present study experimentally investigates the laminar burning velocity (LBV) of methyl isopropyl ketone (MIPK)/air mixtures at elevated temperatures up to 631 K and equivalence ratios (phi) between 0.8 and 1.3, using the externally heated diverging channel method. The models of Li et al. (Combust. Flame 38 (2021) 2135-2142) and Lin et al. (Combust. Flame 258 (2023) 113041) show good agreement with the experimental LBV data at low temperatures but underpredict the LBV at higher mixture temperatures. A sensitivity analysis identified reaction R84 (CH2 + O2 -> CO2 + 2H), adopted from NUIG-Mech1.1 (Combust. Flame 226 (2021) 229-242) in the Lin (Combust. Flame 258 (2023) 113041) model, as a critical target for modification in addressing this discrepancy. In the present work, the rate coefficients of reaction R84, adopted from the latest NUIGMech1.3 (Combust. Flame 248 (2023) 112562) mechanism, have been incorporated into the Lin model to improve its predictive accuracy. These rate coefficients exhibited a substantial temperature-dependent increase in the rate constant (approximately 174 % at 500 K and 293 % at 2500 K). Rate of production analysis shows that the modified Lin model enhances CH2 consumption and increases H radical formation relative to the base Lin model, while reaction flux analysis confirms that the revised R84 kinetics alter C2 species pathways, thereby contributing to improved model prediction. The modified Lin model shows significantly improved predictions of LBVs, demonstrating good agreement with both the present measurements and previously reported literature data over a wide range of temperatures.

Copper cobaltite (CuCo2O4) is a promising material for energy storage applications due to its excellent electrochemical properties, high conductivity, and superior energy storage capacity. Most previous reports have concentrated on compositional tuning or morphological modifications, while the effect of reaction time on its functional properties has remained largely unexplored. In this study, CuCo2O4 nanofibers were successfully synthesized via a hydrothermal method, and the influence of reaction time on their structural, optical, and electrochemical properties was systematically investigated. X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy confirmed the formation of a pure cubic spinel structure. Detailed electrochemical analysis revealed a specific capacitance of 647.43 F/g at 5 mA/cm2, demonstrating excellent cycling stability (>90 % retention) and efficient electron transport. An asymmetric supercapacitor fabricated using CuCo2O4 as the cathode exhibited high performance, with a power density of 4988.27 W/kg and an energy density of 28.81 Wh/kg, along with 94.22 % capacity retention over 4000 cycles. The present results not only support the suitability of CuCo2O4 as a high-performance electrode material but also demonstrate that optimizing the reaction time provides a novel and effective route for enhancing performance compared to existing reports.

It gives an innovative strategy for detecting and preventing fraud in banking applications, with a specific focus on incorporating face recognition technology. In this proposed methodology, a Seagull-based convolutional neural network (SbCNN) is utilized to tackle crucial challenges related to facial recognition, especially in scenarios where individuals are wearing masks. The primary objective is to elevate security measures by proficiently identifying and verifying individuals regardless of whether they are wearing masks or not. Leveraging the sophisticated capabilities of the CNN ensures precise and robust processing of facial features. A key concern addressed is the prevalent issue of individuals using masks, a challenge that has become a focal point in security applications. The proposed method actively acknowledges and aims to provide a comprehensive solution to mitigate potential risks associated with fraudulent activities within the banking sector. It underscores the significance of adapting security protocols to contemporary challenges, positioning the Seagull-based CNN as a proactive and effective approach to fortify the security of banking applications in response to evolving threats.

Indoor airborne microplastics (MPs) (1-10 mu m) are increasingly reported in occupied spaces, yet low-energy mitigation options remain limited. We evaluate an outlet-mounted Azolla biofilter using a multiscale framework that couples computational fluid dynamics (CFD; porous Darcy-Forchheimer with discrete particle modelling) to a size-resolved ordinary differential equation (ODE) model. CFD provides panel face velocity and pressure drop (< u(n)>, triangle p); these are mapped to a biofiltration sink k(bio)(,i) = (A/V)< u(n)>sigma(i) in the ODE, which predicts concentration decay C/C-0(t) and removal metrics eta(5) and eta(30). This bidirectional linkage enables direct, size-resolved validation and rapid generation of design charts. In a reference room (3 x 4 x 3 m(3), ACH approximate to 2 h(-1)), a panel with area A = 0.25 m(2) reduces room-average MP concentrations by similar to 45% in 5 min and similar to 80-85% in 30 min, while maintaining triangle p approximate to 0.14 Pa (<< 10 Pa). ODE predictions agree with CFD-DPM within <= 10% over 0-30 min across tested areas (0.05-0.25 m(2)). Residence-time distribution analysis shows truncation of long-tail exposure events, consistent with enhanced encounter at the outlet-mounted panel. Performance gains are strongest for respirable particles (1-2.5 mu m), which are of greatest health concern. Sensitivity analyses (including +/- 50% variation in sigma(i) and realistic ranges of biomass permeability/porosity) indicate robust sizing guidance. We provide validated design charts for eta(5)(A) and eta(30)(A) with uncertainty bands to support selection of panel area for target removal at negligible pressure drop. The coupled ODE-CFD approach offers an actionable, low- energy retrofit pathway for improving indoor air quality where conventional ventilation upgrades are constrained, and it establishes a foundation for pilot deployments and standardized chamber testing of plant-based biofilters for airborne MPs.