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The purpose of this paper is to present the influence of plastic pyrolysis oil blended with gasoline at 10 per cent with and without ethanol additive at 5 per cent in a three-cylinder petrol engine.

The engine is running at standard working processes. The result of 10PPO is compared with pure petrol and additive-added blend. The outcomes clears that, the engine performance is reduced by using plastic oil blended with petrol and NO_x emission rates are increasing substantially.

To control the emission rate, ethanol is added, and corresponding performance reveals that brake thermal efficiency is 4.52 per cent increase compared to pure petrol and 7.03 per cent increase compared to without additive blend.

Emissions such as CO and NOx are considerably controlled with additive blend.

The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V).

The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses.

The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS.

A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.

This paper aims to compare the performance, emission and combustion characteristics of E20 biodiesel with diesel-water emulsion and eucalyptus water emulsion.

This research expounds the trans-esterification process apparently. Various biodiesel blends were made to go through the trans-esterification process to make it suitable for feeding them into the low heat rejection (LHR) engine. E20 biodiesel – 20% of eucalyptus oil by volume with diesel was chosen to carry out the research as it was found to be the best blend with diesel. The volume of water content in diesel water emulsions was varied by 5, 10 and 15% in DWM1 (Diesel Water Mixture1), DWM2 (Diesel Water Mixture2) and DWM3 (Diesel Water Mixture3), respectively. Similarly, the volume of water content in eucalyptus water emulsions was varied with emulsification ratio of E20 biodiesel. Partially stabilized zirconia was coated over top surface of the piston and valve facing of the LHR engine.

From the researches carried out, DWM3 (Diesel Water Mixture3) was found to be superior when compared with other diesel-water emulsions in LHR engine. The overall efficiency was found to be higher for EWM3 than other biofuels tested the in LHR engine.

This investigational experiment can be further extended to multi-cylinder engine and to improve the cetane number, Di ethyl ester (DEE) fuel additives can be added.

The main theme of this paper is the effect of viscous dissipation Darcy–Forchheimer flow and heat transfer augmentation of a viscoelastic fluid over an incessant moving needle.

The governing partial differential equations of the current problem are diminished into a set of ordinary differential equations using requisite similarity transformations. Energy equation is extended by using Cattaneo–Christov heat flux model with variable thermal conductivity. By applying boundary layer approximation system of equations is framed.

Convective condition is also introduced in this analysis. Obtained set of similarity equations are then solved with the help of efficient numerical method four–fifth-order RKF-45.

The outcomes of various pertinent parameters on the velocity, temperature distributions are analysed by using portraits.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue.

This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review.

To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE.

The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process.

The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment.

The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.

This study aims to evaluate the link between climate/weather change and farmer migration in Bihar, India. The influence of cognitive conditions and climate-related stress on farmer migration decisions and the socioeconomic characteristics of migrating and non-migrating farm households are analysed. The focus is the role of migration in access to climate and agricultural extension services and the contribution of migration to enhanced farmer coping capacity.

A primary survey was conducted of farm households in seven districts of Bihar, India. Farmer perceptions of climate change were analysed using the mental map technique. The role of socioeconomic characteristics in farm household migration was evaluated using binary logistic regression, and the influence of migration on access to climate and agricultural extension services and the adaptive capacity of migrating households was investigated using descriptive statistics.

Climate-induced livelihood risk factors are one of the major drivers of farmer’s migration. The farmers’ perception on climate change influences migration along with the socioeconomic characteristics. There is a significant difference between migrating and non-migrating farm households in the utilization of instructions, knowledge and technology based climate and agriculture extension services. Benefits from receipt of remittance, knowledge and social networks from the host region enhances migrating households’ adaptive capacity.

This study provides micro-evidence of the contribution of migration to farmer adaptive capacity and access to climate and agricultural extension services, which will benefit analyses of climate-induced migration in other developing countries with higher agricultural dependence. In addition, valuable insights are delivered on policy requirements to reduce farmer vulnerability to climate change.

The purpose of this paper is to review the existing literature on Lean Six Sigma (LSS) for services, construct a morphological analysis (MA) framework and identify research gaps to point to future research possibilities and priorities.

The MA framework is based on literature review of 175 papers published from 2003 to 2015, across 67 journals recognised by Scopus or ABS Academic Journal Quality Guide 2015. A three-phased methodology is used by the authors, with Phase1 featuring a five-stage systematic review protocol to identify relevant journal papers for review; Phase2 presenting a framework for classifying the reviewed papers in terms of their fundamental, methodological, chronological and sector-wise orientations; and Phase3 constructing an MA framework on the classified papers and identifying the research gaps.

The MA framework constructed based on six dimensions, namely, organizational context of applications, desired outcomes, implementation systems, LSS tools and techniques, integration with other management philosophies and evaluation methods, involving 40 focused themes, has revealed 355 distinct research gaps as opportunities for future research.

This paper confirms the existence of substantial scope and points to specific topics for further research in the area of LSS for services. The findings demonstrate the gaps in academic research on the subject. In addition, the study also helps organisational leaders and practitioners to look at LSS from a holistic perspective in the services context.

The MA framework of the existing literature on LSS for services presents a unique, systematic effort to identify research opportunities. In addition, a five-stage systematic review protocol is proposed in this paper. This could be valuable to researchers and practitioners in enabling them to systematically review the literature on research subjects of interest to them.

This paper seeks to present a feedback error learning neuro‐controller for an unstable research helicopter.

Three neural‐aided flight controllers are designed to satisfy the ADS‐33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non‐linearity and parameter uncertainties. The basic building block of the neuro‐controller is a nonlinear auto regressive exogenous (NARX) input neural network. For each neural controller, the parameter update rule is derived using Lyapunov‐like synthesis. An offline finite time training is used to provide asymptotic stability and on‐line learning strategy is employed to handle parameter uncertainty and nonlinearity.

The theoretical results are validated using simulation studies based on a nonlinear six degree‐of‐freedom helicopter undergoing an agile maneuver. The neural controller performs well in disturbance rejection is the presence of gust and sensor noise.

The neuro‐control approach presented in this paper is well suited to unmanned and small‐scale helicopters.

The study shows that the neuro‐controller meets the requirements of ADS‐33 handling qualities specifications of a helicopter.

This paper aims to present the design of a stability augmentation system (SAS) in the longitudinal and lateral axes for an unstable helicopter.

The feedback controller is designed using linear quadratic regulator (LQR) control with full state feedback and LQR with output feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard (ADS‐33E‐PRF). A helicopter having a soft inplane four‐bladed hingeless main rotor and a four‐bladed tail rotor with conventional mechanical controls is used for the simulation studies. In the simulation studies, the helicopter is trimmed at hover, low speeds and forward speeds flight conditions. The performance of the helicopter SAS schemes are assessed with respect to the requirements of ADS‐33E‐PRF.

The SAS in the longitudinal axis meets the requirement of the Level 1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level 2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions. The requirements of the inter axis coupling is also met and shown for the coupled dynamics case. The SAS in lateral axis may require an additional control augmentation system or adaptive control to meet the Level 1 requirements.

The study shows that the design of a SAS using LQR control algorithm with full state and output feedbacks can be used to meet ADS‐33 handling quality specifications.