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This work investigates the cut quality characteristics of SS321 using plasma arc cutting. The SS321 has a wide range of applications such as in chemical storage, exhaust manifolds of automotives and aircraft. The intricate shapes for this material are very difficult to cut using conventional machining process. Hence, plasma arc cutting is used. The input cutting parameters are cutting speed, current, stand off distance and gas pressure. For each input parameter, three levels are considered and, therefore, total numbers of experimental runs are 3 9 3 9 3 9 3 = 81. To minimize the number of runs, the Taguchi L9 orthogonal array is proposed which is having advantages of both minimum and maximum trial runs. The output parameters are surface roughness, kerf width and heat-affected zone. The experiments are carried out in Micro Step spol S.R.O. Plasma arc cutting machine. To find the best cutting parameters, the regression models are given as input of Matlab-Genetic Algorithm. The test results show that ANOVA models are significant. It is inferred that lower values of current and Standoff Distance give better surface roughness and minimum heat-affected zone.

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Nowadays, wind turbine blades are manufactured using several materials and methodologies to save cost and to increase their performance. This article gives a brief overview of blade materials and prevailing manufacturing traits to make them more reliable and cost‐efficient. The surface roughness, manufacturing defects, and fluctuating loads in flow fields significantly affect wind turbine power generation. However, these problems can be reduced by using appropriate materials. Therefore, selection of wind turbine materials is extremely important to maintain its performance even in harsh environmental conditions. This article describes an overview of current material solutions for overcoming material defects like delamination, structural distortion, and icing problems in the Polar Regions. It involves composites, smart alloys, protective coatings, etc. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13046, 2019 Highlights Types of loading, failure mechanisms and current manufacturing methods of wind turbine blades. Innovative blade material approach: Morphing concepts, Glass or Carbon fiber/Nano‐/Bio‐Composites. Active and passive strategies for anti‐icing or de‐icing of wind turbine blades.

This investigation studied the packed bed thermal energy storage system with concrete and air used as the energy storage material and working fluid respectively. Three different configurations of packed bed arrangements such as regular ring, staggered ring and staggered ring with clearance are studied. The temperature distribution outcome of the experimental values compared with three-dimensional transient based computational simulation. The flow influencing parameters such as pressure drop, turbulence intensity, design of packed beds, the surface area of packed beds, the void fraction of the system discussed. Experimental results are in excellent agreement with simulation results. It is observed that staggered ring arrangement with the clearance for packed bed have better charging and discharging profile compared to the other two arrangements.

In tandem with the burgeoning popularity of social media research in the field of sport communication and marketing, we are witnessing a concomitant rise in its epistemological sophistication. Despite this growth, the field has given less attention to methodological issues and implications. In light of the development of machine learning, the overarching goal of the current research was to answer the call for innovative methodological approaches to advance knowledge in the area of social media research. Specifically, we (a) assess the current state of sport social media research from a methodological perspective, with a particular focus on machine learning; (b) present an empirical illustration to demonstrate how sport scholars can benefit from the advancement in natural language processing and the derivative topic modeling techniques; (c) discuss how machine learning could enhance the rigor of social media research and improve theory development; and (d) offer potential opportunities and directions for the future sport social media research that utilizes machine learning.