Search results for: Upendra Bhandarkar

2 Effect of Particle Size in Aviation Turbine Fuel-Al2O3 Nanofluids for Heat Transfer Applications

Authors: Sandipkumar Sonawane, Upendra Bhandarkar, Bhalchandra Puranik, S. Sunil Kumar

Abstract:

The effect of Alumina nanoparticle size on thermophysical properties, heat transfer performance and pressure loss characteristics of Aviation Turbine Fuel (ATF)-Al2O3 nanofluids is studied experimentally for the proposed application of regenerative cooling of semi-cryogenic rocket engine thrust chambers. Al2O3 particles with mean diameters of 50 nm or 150 nm are dispersed in ATF. At 500C and 0.3% particle volume concentration, the bigger particles show increases of 17% in thermal conductivity and 55% in viscosity, whereas the smaller particles show corresponding increases of 21% and 22% for thermal conductivity and viscosity respectively. Contrary to these results, experiments to study the heat transfer performance and pressure loss characteristics show that at the same pumping power, the maximum enhancement in heat transfer coefficient at 500C and 0.3% concentration is approximately 47% using bigger particles, whereas it is only 36% using smaller particles.

Keywords: Heat transfer performance, Nanofluids, Thermalconductivity, Viscosity

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1 Production and Remanufacturing of Returned Products in Supply Chain using Modified Genetic Algorithm

Authors: Siva Prasad Darla, C. D. Naiju, K. Annamalai, Y. Upendra Sravan

Abstract:

In recent years, environment regulation forcing manufactures to consider recovery activity of end-of- life products and/or return products for refurbishing, recycling, remanufacturing/repair and disposal in supply chain management. In this paper, a mathematical model is formulated for single product production-inventory system considering remanufacturing/reuse of return products and rate of return products follows a demand like function, dependent on purchasing price and acceptance quality level. It is useful in decision making to determine whether to go for remanufacturing or disposal of returned products along with newly produced products to satisfy a stationary demand. In addition, a modified genetic algorithm approach is proposed, inspired by particle swarm optimization method. Numerical analysis of the case study is carried out to validate the model.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Production, Remanufacturing.

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