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Abstract Thermal energy storage systems based on Phase change materials (PCM) are an attractive option to bridge the temporal and spatial gap between the energy demand and supply. But, these systems possess poor thermal conductivity causing reduced rate of heat transfer. The objective of the present study is to numerically analyze the melting process in an optimized finned latent heat storage system dispersed with varying volume fraction of Graphene nano plates (GNP). The individual effect of incorporating fins, GNP and a combination of both at different volume fraction has been studied. Effective thermal conductivity of nano-composite PCM has been theoretically evaluated including the effect of aspect ratio, interfacial thermal resistance, anisotropy, non-linear effects as well as concentration for the dispersed GNP. In this work, Dynamic differential scanning calorimetry tests are performed to evaluate the phase change temperature, latent heat and specific heat of the sugar alcohol (d- mannitol). Transient variation of liquid fraction, average temperature and radial/longitudinal temperature differentials are presented which would be useful for designing medium temperature (160–200 °C) storage systems for various applications. Fin height is varied to obtain an optimum fin size such that natural convection currents are not impeded. Various heat transfer models (including natural convection) are analysed using the actual plant data of a double effect solar absorption system at different arrangements of fins and GNP. Effect of Reynolds number and inlet temperature of HTF on the system performance have also been studied. A reduction of 68% in total melting time is observed in finned LHSS with 5% GNP as compared to a conventional system.

Abstract Hybrid nanofluids are a novel class of colloidal fluids which have drawn significant attention due to potential tailoring of their thermo-physical properties for heat transfer enhancement by a combination of more than one nano-additive to meet specific requirements of an application. In the present work, ceramic copper oxide/carbon (SiO2-CuO/C) nanoparticles in 80:20 (wt%) composition were prepared by ultrasonic-assisted wet mixing technique. The hybrid nanofluid was formulated by dispersing the nanoparticles into a base fluid mixture of 60:40 (% by mass) glycerol and ethylene glycol (G/EG) using the two-steps method. The influence of nanoparticles on the augmentation of specific heat, thermal conductivity and viscosity was examined in the volume concentration range of 0.5–2.0% in the temperature range of 303.15–353.15 K. The results demonstrate that the synthesized SiO2-CuO/C hybrid nanoparticles enhanced the thermo-physical properties of the base fluid mixture which is higher than using SiO2 alone. In the case of SiO2–G/EG nanofluid, the specific heat capacity decremented by a maximum value of 5.7% whereas the thermal conductivity and viscosity incremented by 6.9% and 1.33-times as compared with G/EG at maximum volume concentration of 2.0% at a temperature of 353.15 K. Comparatively, a reinforcement of 80% SiO2 with 20% CuO/C in G/EG mixture led to thermal conductivity and viscosity enhancement by 26.9% and 1.15-times, respectively with a significant reduction of specific heat by 21.1%. New empirical correlations were proposed based on the experimental data for evaluation of thermophysical properties.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Abstract It is well known that Espinosa-parades et al. (Espinosa-Paredes et al., 2011) proposed a fractional neutron point kinetic (FNPK) model with multi-group of delayed neutrons to describe dynamic behavior in a nuclear reactor. In (Aboanber and Nahla, 2016b), Aboanber and Nahla presented an extension of Espinosa-parades et al. FNPK model. This new model is called as the corrected fractional neutron point kinetic (CFNPK) model. The present study is concerned with the numerical solution of the CFNPK model (Aboanber and Nahla, 2016b) with six groups of delayed neutron precursors. The fractional derivative is described in the sense of Grunwald-Letnikov. An implicit finite difference method (FDM) is constructed for the solution of CFNPK model. The stability analysis of the method is carried out. We analyze the results of neutron density for different values of anomalous diffusion order, reactivity function, relaxation time and time step size. In addition, we compare the results corresponding to CFNPK model (Aboanber and Nahla, 2016b) with the results corresponding to the FNPK model proposed by Espinosa-parades et al. (Espinosa-Paredes et al., 2011). It is shown that as anomalous diffusion order decreases or simulation time increases the difference between the values of neutron density increases. We have investigated the effects of each term of the CFNPK.

The leafy seadragon certainly is among evolution's most "beautiful and wonderful" species aptly named for its extraordinary camouflage mimicking its coastal seaweed habitat. However, limited information is known about the genetic basis of its phenotypes and conspicuous camouflage. Here, we revealed genomic signatures of rapid evolution and positive selection in core genes related to its camouflage, which allowed us to predict population dynamics for this species. Comparative genomic analysis revealed that seadragons have the smallest olfactory repertoires among all ray-finned fishes, suggesting adaptations to the highly specialized habitat. Other positively selected and rapidly evolving genes that serve in bone development and coloration are highly expressed in the leaf-like appendages, supporting a recent adaptive shift in camouflage appendage formation. Knock-out of bmp6 results in dysplastic intermuscular bones with a significantly reduced number in zebrafish, implying its important function in bone formation. Global climate change-induced loss of seagrass beds now severely threatens the continued existence of this enigmatic species. The leafy seadragon has a historically small population size likely due to its specific habitat requirements that further exacerbate its vulnerability to climate change. Therefore, taking climate change-induced range shifts into account while developing future protection strategies.

Abstract This paper describes the development of a meta-heuristic based algorithm to solve the optimal transmission switching (OTS) problem. The approach solves the mixed-integer non-linear problem considering simultaneous optimization of transmission topology and generation dispatch. The algorithm is the first ever application of particle swarm optimization (PSO) to model OTS and operates using combined real and binary (CRB) variables to solve a weighted sum of interdependent multiple objective functions. The unique stochastic generation principle of combinatorial variables is based on a blend of both uniform and biased Gaussian probability distribution functions. The required binary values of swarms are generated from the continuous values of the random Gaussian distribution with the application of the Heaviside function. All the transmission lines are considered as potential switch variables the operations of which are limited by different aspects. The algorithm can tackle the complex optimization problem with many constraints of varying difficulty. The randomness in CRB variables and unpredictability in switching may generate infeasible particle(s) resulting in island formation. The algorithm also proposes regeneration of these infeasible particle(s) by modifying them to a feasible one to avoid this islanding as well as to have stable switching possibilities. The improvement in the computational efficiency of the algorithm is proposed with the adoption of Micro-PSO for small load deviations. A wide range of unique solutions are obtained based on the preferences of the system operator. This OTS algorithm is tested using the IEEE 57 bus and the IEEE 118 bus system and encouraging results are obtained.

The Geomagnetically induced current (GIC) produces unwanted adverse consequences in the power system. It causes problems in the power system like transformer core saturation, really miss operation, blackout of system, voltage instability and heating of transformers, disturbance in communication systems, corrosion of pipelines, railway tracks etc. This paper provides information about GIC and its different effects in various technological systems. Also present the possible solution to reduce GIC.

Abstract The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the unit commitment (UC) problem. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to minimize the total generation cost over a scheduled time period while adhering to several constraints such as generation limits, meeting load demand, spinning reserves and minimum up and down time. The minimum up/down time constraints have been coded in a direct manner without using the penalty function method. The proposed algorithm was tested and validated using 10 units and 24 h system. The most important merit of the proposed methodology is high accuracy and good convergence speed as it is a derivative free algorithm. The simulation results of the proposed algorithm have been compared with the results of other tested algorithms for UC such as shuffled frog leaping, particle swarm optimization, genetic algorithm and Lagrangian relaxation and bacterial foraging algorithm.