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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.

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.

The results show that the predicted LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity.

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.

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.

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.

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.

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.

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.

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.

While pursuing energy management, firms simultaneously strive to boost sales as a path towards economic performance. Also, the literature suggests that family firms exhibit greater environmental commitment than their non-family counterparts. To examine these contentions, this review espouses contingency theory to interrogate the correlations of (1) energy consumption targets, (2) energy efficiency enhancing measures, (3) energy consumption monitoring and (4) the domestic sales performance of small family firms in Turkey's food sector.

Data were sourced from the World Bank Enterprise Survey. A sample of 137 family firms in food production, processing and retail was analysed using non-linear structural equation modelling. Path coefficients were determined to estimate the extent to which energy management practices predict domestic sales.

The path analysis revealed that although energy consumption targets do not directly increase sales performance, they stimulate firms' energy efficiency enhancement measures and energy consumption monitoring to produce this effect by 21%.

The contingency lens espoused leaves room to capture further antecedents in small family food firms' technical, managerial, ownership, operational and architectural configuration that may also interact with or predict the propensity for energy management.

For practitioners, the inherent findings demonstrate that there are firm-specific material benefits arising from adopting energy management practices. And, although small firms such as family food businesses may have low energy intensities, they can improve their sales performance by setting energy targets, installing energy efficiency enhancing measures and embarking on energy consumption monitoring.

Public stakeholders in Turkey such as the Ministry of Energy and Natural Resources, the General Directorate of Energy Affairs and affiliate institutions can reflect on these findings to develop a coherent national energy management policy for small firms. Such initiatives are especially relevant to Turkey and its ambitions to join the EU which requires member states to set up a national energy efficiency action plan.

This inquiry is one of the first to examine energy management in the food sector at the family firm level through the contingency lens. Theoretically, the results draw attention and shed new light on disparate energy management practices and their discrete yet substantial contribution to sales performance.

The purpose of this paper is to focus on life cycle cost analysis (LCCA) of 1 MW roof-top Solar Photovoltaic (PV) panels installed in warm and humid climatic region in Southern India. The effect of actual power generated from solar PV panels on financial indicators is evaluated.

LCCA is done using the actual power generated from solar PV panels for one year. The net present value (NPV), internal rate of return (IRR), simple payback period (SPP) and discounted payback period (DPP) are determined for a base case scenario. The effect of service life and the differences between the ideal power expected and the actual power generated is evaluated.

A base case scenario is evaluated using the actual power generation data, 25-year service life and 6 percent discount rate. The NPV, IRR, SPP and DPP are found to be INR 13m, 8 percent, 10.9 years and 18.8 years respectively. It is found that the actual power generated is about one-third less than the ideal power estimated by consultants prior to project bidding. The payback period increases by 70–120 percent when the actual power generated from solar PV panels is considered.

The return on investment calculated based on ideal power generation data without considering the operation and maintenance related aspects may lead to incorrect financial assessment. Hence, strategies toward solar power generation should also focus on the actual system performance during operation.

The mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.

A wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.

Consequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.

The simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

This study aims to illuminate the limited understanding of viable social business among corporate actors in developing countries. It addresses pressing environmental and societal problems, emphasizing the need for corporate participation in sustainable solutions. Additionally, the study explores the transformational business notion linking company achievement with social progress, an increasingly studied concept in management.

Based on a transdisciplinary case study (i.e. a university-industry collaboration [UIC]), this study proposed a structural framework and guiding principles to integrate the academic and practitioners’ different but complementary resources and expertise.

The outcomes could provide insights for social entrepreneurs to perform highly optimal decisions on their organisational strategies, in which the financial-then-social pathway could be an effective social business success mechanism.

Besides, the case study also generates each five learning lessons and challenges coping strategies that provide practical guidance on operationalising an effective UIC. The empirical findings contribute to social entrepreneurship and sustainability science literature.

The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances.

COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO₂). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test.

IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO₂), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials.

The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).

The purpose of this paper is to present, a novel boost‐active clamp bridge single stage high‐frequency zero voltage soft‐switching‐pulse width modulation (ZVS‐PWM) inverter, which converts the utility frequency AC power into high‐frequency AC power with an embedded controller. This single stage high‐frequency inverter is composed of a single‐phase diode bridge rectifier, a non‐smoothing filter, a boost‐active clamp bridge type ZVS‐PWM high‐frequency inverter, and an induction‐heated load with planar type litz wire working coil assembly. Also, the paper discusses how to extend the soft‐switching operation ranges and improve power conversion efficiency.

The proposed converter is simulated and it is implemented using embedded controller.

It was found that the single stage high‐frequency induction heating (IH) inverter using boosted voltage function can eliminate the DC and low‐frequency components of the working coil current and reduce the power dissipation of the circuit components and switching devices.

The paper shows that the PWM HF inverter is preferred for IH, since it has reduced switching losses and switching stresses. The paper can be extended to PC‐based wireless control, which can be part of a distributed control system in major industrial heating systems.

In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies.

A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed.

For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation.

From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications.

This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section.

From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

The purpose of this paper is to propose two energy management strategies (EMS) for hybrid electric vehicle, the power system is an hybrid architecture (fuel cell (FC)/battery) with two-converters parallel configuration.

First, the authors present the EMS uses a power frequency splitting to allow a natural frequency decomposition of the power loads and second the EMS uses the optimal control theory, based on the Pontryagin’s minimum principle.

Thanks to the optimal approach, the control objectives will be easily achieved: hydrogen consumption is minimized and FC health is protected.

The simulation results show the effectiveness of the control strategy using optimal control theory in term of improvement of the fuel consumption based on a comparison analysis between the two strategies.