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Abstract This research attempted to analyze a triple-tube heat exchanger (TTHE) fitted with innovative crimped-spiral ribs to enhance thermal efficiency. The crimped-spiral rib was placed on the top of the internal tube, and the water–alumina nanofluid was employed as the hot fluid within the ribbed side, whereas the two other fluids were pure water. The numerical solutions were performed through the two-phase mixture method, while the turbulent flow was modeled via the Reynolds Stress Model (RSM). The overall heat transfer coefficient (U), heat transfer rate, effectiveness, and performance index enhanced remarkably by using the nanofluid. The greatest increment in the U and effectiveness reached around 44.91 and 41 %, respectively, by the increment of the volume fraction by 0.02. The crimp intensity had an appreciable contribution to the thermal performance. The main mechanism was stronger swirl flow generated by the crimped-spiral rib that yielded boundary layer destruction. The heat transfer capability was increased via the rib pitch decrement and rib height increment. The performance index showed great values even at low volume fractions, which manifested the great merit of the implemented refinements. The thermal attributes were investigated at the first of the result section and then, the hydraulic characteristics were researched.

Energy management is considered the major concern in cloud computing, which supports the rapid growth of data centers and computing centers; therefore, energy and load balancing have become crucial issues in cloud data centers. To address this issue, the present paper proposed a two-phase energy-aware load balancing (EALB) scheduling algorithm using the virtual machine migration through the Particle Swarm Optimization (PSO) algorithm to be applicable to dynamic voltage frequency scaling-enabled cloud data centers, which is called EALBPSO. In the first phase, an objective function was employed to deactivate a large number of physical machines in order to reduce energy consumption. The main idea of the algorithm was to maximize load balancing in the second phase, in which the remaining virtual and physical machines were used as the PSO inputs, and an objective function was also defined to distribute the load appropriately among the physical machines. In addition, a dataset was developed to test different parameters and scenarios with the aim of assessing the effectiveness of the proposed EALBPSO algorithm in comparison with other algorithms already proposed in the literature for similar purposes. The experimental results demonstrated that the proposed algorithm was capable of saving up to 0.896%, 9.716%, and 10.8% energy compared with the MDPSO algorithm, Kumar et al.’s algorithm, and Dahsti and Rahmani algorithm, respectively, and also it showed 5.91%, 16%, and 16.267% improvements for the number of virtual machines migrations, and 3.867%, 8.623%, and 6.953% improvements for the deviation of processors, all compared with their competitors stated above, respectively.

Abstract Gas cyclone separators have been widely used in different industries. In this study, to find the best geometrical ratios of Stairmand cyclone separator, computational fluid dynamics (CFD), design of experiments (DOE), multi-gene genetic programming (MGGP), and ten meta-heuristic algorithms were combined. Six geometrical dimensions of the gas cyclone separator including inlet height and width, vortex finder length and its diameter, cylinder height and cone-tip diameter were optimized. The obtained models from MGGP were optimized by ten meta-heuristic algorithms and non-dominated Pareto fronts were analyzed using six unary and binary metrics and PROMETHEE II as a decision making method. According to the optimization results, multi-objective Particle Swarm Optimization (MOPSO) showed the best performance and generated more preferred designs than Stairmand design compared to other algorithms. These preferred designs increased the collection efficiency within 0.36 to 6% and decreased the pressure drop within 3.3 to 27.5% compared to the Stairmand.

The assessment of voltage stability margins is a promising direction for wide-area monitoring systems. Accurate monitoring architectures for long-term voltage instability are typically centralized and lack scalability, while completely decentralized approaches relying on local measurements tend towards inaccuracy. Here we present distributed linear algorithms for the online computation of voltage collapse sensitivity indices. The computations are collectively performed by processors embedded at each bus in the smart grid, using synchronized phasor measurements and communication of voltage phasors between neighboring buses. Our algorithms provably converge to the proper index values, as would be calculated using centralized information, but but do not require any central decision maker for coordination. Modifications of the algorithms to account for generator reactive power limits are discussed. We illustrate the effectiveness of our designs with a case study of the New England 39 bus system. 10 pages, submitted for publication

Abstract The implementation of occupancy-controlled and daylighting-dimmed lighting systems has an impact on the energy consumption of residential buildings. The BAC factor method of EN ISO 52120-1 estimates that 8% of the lighting energy can be saved compared to conventional manual control. However, it is assumed that their ability to potentially lower the lighting energy consumption is strongly related to external factors, such as the extent of daylight entrance and the behaviour of the inhabitants. By means of simulations in EnergyPlus, the performances of automated and manual lighting control are compared for an apartment and semi-detached building located in Brussels (Belgium) with variation in the occupant behaviour and orientation. It appears that an automated lighting control including 0-100% dimmer reduces the lighting energy demand for all investigated cases with savings up to 38.4%, whereas a similar control without dimmer does not necessarily reduce the lighting electricity demand. However, the results show a considerable variation, making prediction methods as the BAC factor method highly inaccurate. The actual relative energy performance depends on the automation system, type of building, orientation and occupant behaviour (i.e. number of inhabitants and occupancy rate). Hereby, the number of inhabitants has the most considerable impact on the relative energy performances with differences up to 50%, while the occupancy rate shows a significant correlation, especially for low numbers of inhabitants.

Nowadays, a large part of energy is provided by steam turbines; thus, increasing the efficiency and improving the steam turbines performance are of special importance. The presence of the liquid phase in the low-pressure stage of the steam turbine can cause energy loss, efficiency drop, and erosion/corrosion problems; therefore, one of the essential issues is to identify wet steam flow and try to reduce condensation loss. In order to decrease the liquid fraction, the drainage groove technique can be applied. The drainage groove sucks the water droplets from the turbine blade surface and drains them into the condenser. In this study, the effect of the drainage groove location on the surface of steam turbine blades has been investigated on the condensation, droplet radius, inlet mass flow, erosion rate, liquid drainage ratio, condensation losses, and total drainage ratio. For modeling the condensing flow, the Eulerian–Eulerian approach has been applied. The results show that the location of the drainage groove affects the groove performance and flow pattern in the turbine blade. In the selected drainage, the liquid drainage ratio, condensation losses, and erosion rate are reduced by 7.6%, 12%, and 88%, respectively, compared with the no-drainage groove case. Also, the total drainage ratio is 7.2% in the selected drainage. The outcomes of the present work have been a major step forward in the techniques having a great influence on the lifetime, repair and maintenance, and the output power of steam power generation facilities.

Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.