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Purpose The lean blowout (LBO) limit of the combustor is one of the important performance parameters for any gas turbine combustor design. This study aims to predict the LBO limits of an in-house designed swirl stabilized 3kW can-type micro gas turbine combustor. Design/methodology/approach The experimental prediction of LBO limits was performed on 3kW swirl stabilized combustor fueled with methane for the combustor inlet velocity ranging from 1.70 m/s to 6.80 m/s. The numerical prediction of LBO limits of combustor was performed on two-dimensional axisymmetric model. The blowout limits of combustor were predicted through calculated average exit gas temperature (AEGT) method and compared with experimental predictions. Findings The results show that the predicted LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity. Practical implications This LBO limits predictions will use to fix the operating boundary conditions of 3kW can-type micro gas turbine combustor. This methodology will be used in design stage as well as in the testing stage of the combustor. Originality/value This is a first effort to predict the LBO limits on micro gas turbine combustor through AEGT method. The maximum uncertainty in LBO limit prediction with AEGT is 6 % in comparison with experimental results.

The solar photovoltaic energy is becoming popular in the modern-day distribution networks due to the clean energy factor. The photovoltaic modules exhibit a nonlinearity in the output power concerning the environmental conditions. This work suggests an adaptive neuro-fuzzy inference system- (ANFIS-) based maximal power point tracker (MPPT) for the optimization of the solar photovoltaic system (SPVS). The controller modelled is utilized to optimize the output power of a DC-DC converter connected to a 400 W PV array. The entire model is analysed employing MATLAB/SIMULINK using primary features provided by the technical data. The behavior of the controller modelled is tested for various weather conditions and partial shading conditions. The findings show the controller’s tracking speed effectiveness and dynamic response in PSCs.

According to the International Energy Agency’s estimation, Indian primary energy demand is expected to increase by about 1250 Mtoe and 1500 Mtoe by 2030. The 3 M management concept now changed into a 4 M management concept that includes energy management. In the case of energy management, the electrical energy sector assumes critical importance. An energy cost occupies nearly 30-40% of the production cost in small and medium-size enterprises (SMEs). Bureau of Energy Efficiency (BEE) has initiated a separate energy efficiency intervention to the SME’s, where the energy-efficient technologies, operational procedures, and proper awareness programs to SMEs are stated. This paper discusses the complete electrical energy audit on a small-scale Siddha and Ayurveda medicine industry. The traditional medicine manufacturing technologies make the process complex (process loads are intermittent/interlinked) during analysis. Hence, the detailed energy auditing/conservation studies on the power factor/harmonics were carried out without affecting Indian traditional medicine manufacturing technology. The postauditing design and installation of an intelligent controller by considering the intermittent/interlinked loads are carried out, and the effectiveness of the intelligent controller is ascertained with the energy conservation and the carbon emission reduction of SME. In addition, the climate change mitigation is ensured through the design and deployment of grid-interactive rooftop solar photovoltaic power plant using Solar Pro v4.5 (Photovoltaic System simulation software) with the power analysis, economic analysis and life cycle carbon emission of the proposed plant. The proposed solar photovoltaic power plant ensures energy self-sufficiency and sustainable energy utilization by carbon emission reduction on electricity utilization of SMEs.

Purpose This paper aims to predict the effect of combustor inlet area ratio (CIAR) on the lean blowout limit (LBO) of a swirl stabilized can-type micro gas turbine combustor having a thermal capacity of 3 kW. Design/methodology/approach The blowout limits of the combustor were predicted predominantly from numerical simulations by using the average exit gas temperature (AEGT) method. In this method, the blowout limit is determined from characteristics of the average exit gas temperature of the combustion products for varying equivalence. The CIAR value considered in this study ranges from 0.2 to 0.4 and combustor inlet velocities range from 1.70 to 6.80 m/s. Findings The LBO equivalence ratio decreases gradually with an increase in inlet velocity. On the other hand, the LBO equivalence ratio decreases significantly especially at low inlet velocities with a decrease in CIAR. These results were backed by experimental results for a case of CIAR equal to 0.2. Practical implications Gas turbine combustors are vulnerable to operate on lean equivalence ratios at cruise flight to avoid high thermal stresses. A flame blowout is the main issue faced in lean operations. Based on literature and studies, the combustor lean blowout performance significantly depends on the primary zone mass flow rate. By incorporating variable area snout in the combustor will alter the primary zone mass flow rates by which the combustor will experience extended lean blowout limit characteristics. Originality/value This is a first effort to predict the lean blowout performance on the variation of combustor inlet area ratio on gas turbine combustor. This would help to extend the flame stability region for the gas turbine combustor.

The Lean Blowout Limit of the combustor is one of the important performance parameters for a gas turbine combustor design. This study aims to predict the total pressure loss and Lean Blowout (LBO) limits of an in-house designed swirl stabilized 3kW can-type micro gas turbine combustor. The experimental prediction of total pressure loss and LBO limits was performed on a designed combustor fuelled with Liquefied Petroleum Gas (LPG) for the combustor inlet velocity ranging from 1.70 m/s to 11 m/s. The results show that the predicted total pressure drop increases with increasing combustor inlet velocity, whereas the LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity. The combustor total pressure drop was found to be negligible; being in the range of 0.002 % to 0.065 % for the measured inlet velocity conditions. These LBO limits predictions will be used to fix the operating boundary conditions of the gas turbine combustor.

Among the renewable forms of energy, solar energy is a convincing, clean energy and acceptable worldwide. Solar PV plants, both ground mounting and the rooftop, are mushrooming thought the world. One of the significant challenges is the fault identification of the solar PV module, since a vast power plant condition monitoring of individual panels is cumbersome. This paper attempts to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique. An ordinary and thermal image has been processed in the image processing tool and proved that thermal images record the hot spots. Similarly, the new and aged solar photovoltaic panels were compared in the image processing technique since any fault in the panel has been recorded as hot spots. The image recorded in the aged panels records hot spots, and performance has been analyzed using conventional metrics. The experimental results have also been verified.

Peste des petits ruminants (PPR), an acute febrile viral disease impacting goats and sheep flocks, manifests with pyrexia, mucopurulent nasal and ocular discharges, necrotizing and erosive stomatitis, pneumonia, and enteritis. The disease-instigating agent, PPR virus, pertains to the Morbillivirus caprinae genus in the Paramyxoviridae family. The endemic presence of PPR in India results in notable economic losses due to heightened mortality and morbidity in infected animals. Understanding viral pathogen evolution is pivotal for delineating their emergence in diverse environments. This study explores the molecular evolutionary patterns of PPRV, concentrating on the N and F structural genes isolated from Indian sheep and goats. Analyzing evolutionary rate, phylogenetics, selection pressure, and codon usage bias, we determined the time to the most recent common ancestor (tMRCA) as 1984, 1973, 2000, and 2004 for goat and sheep’s N and F genes, respectively, with evolutionary rates ranging from 2.859 x 10 3 to 4.995 x 10 4 . The F-gene is found to exhibit a faster evolution than the N-gene, indicating apparent virus transmission across the regions of India, as supported by phylogenetic analysis. Codon usage bias examination, incorporating nucleotide composition and various plots (effective number of codon plot, parity plot, neutrality plot), suggests the evolution in India influenced by both natural selection and mutational pressure, resulting in alterations in the virus’s codon bias. The integrated analysis underscores the significant role of selection pressures, implying PPRV’s co-evolution and adaptations influenced by various genes. Insights from this study can guide effective disease control and vaccine development, aiding in managing PPR outbreaks in India and beyond.

In Tamil Nadu, India, the old wind turbines installed in 1988 have to be repowered and replaced by modern/advanced technological wind turbines. A solar power plant can be set up using the vast area between the wind turbines on the farm. The economic evaluation must be carried out to determine whether this hybrid project is economically beneficial. This article aims to examine the financial viability of repowering old wind turbines with solar power plants’ inclusion. According to the initial investment costs specified within fixed areas and the distance between turbines, the issue is articulated as optimizing the net present value (NPV) of the project. In this study, the economic investigation on the wind farm located in Kayathar was explored by replacing 30 numbers of 200 kW old wind turbines with modern wind turbines of 2000–3000 kW and installing the solar power plant between wind turbines, excluding the shadow region of wind turbines. The HOMER Pro software and Excel spreadsheet were used to analyze the optimal allocation, economic, electricity production, and sensitivity analyses of the effective hybrid systems. The value of electricity prices was measured using the levelized cost of energy and NPV methods. Additionally, a sensitivity analysis of the chosen parameters for the internal rate of return and debt service coverage ratio was estimated for wind and solar hybridization. The economic analysis suggests satisfactory profitability for the project, even without depending on subsidies. Furthermore, sensitivity analysis helps one see the consequences of the risk on the life of the wind farm and its impact on the project’s productivity and performance.

Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions. SVM algorithm with Quadratic Kernel function trained using power signature features predicts weld conditions considered in this study with an accuracy of 99%.

Summary Traditional mineral oil is used as a liquid dielectric in distribution transformers. Because of its limited fire‐resistant characteristics and biodegradability and also because of the lack of fossil fuels, the search for alternate liquid dielectrics for distribution transformer is essential. This research work has been proposed on nonedible Karanja oil as a liquid dielectric for distribution transformers. Hence, the electrical properties, physical properties, and chemical properties of Karanja oil with catalyst are measured at the temperatures of 40°C, 60°C, 80°C, 100°C, and 120°C as per the international standards; results are compared with mineral oil. From the experimental analysis, it is found that an electrical characteristic of Karanja oil with catalyst is better than mineral oil with catalyst. Furthermore, Karanja oil with catalyst has much higher acidity than others, but it is beneficial. Also, Karanja oil with catalyst has low‐relative water content than mineral oil with catalyst. However, the viscosity of the oil with catalyst is much higher than mineral oil; so care must be taken for designing the cooling tubes of a transformer.