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Editorial Complex Optimization and Simulation in Power Systems

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 Concerns about power quality (PQ) in distribution networks have necessitated the use of PQ measuring/monitoring equipment to detect and analyse PQ problems. As the installation of such equipment at all locations is not economically feasible, minimum number and optimum locations of PQ monitors have been sought in recent researches to meet both economical efficiency and monitoring capability. Focusing on voltage sags, this paper attempts to further reduce the number of PQ monitors obtained by optimum allocation approaches while keeping the desired monitoring capability. Growing number of electric equipment with high sensitivity to voltage sags have raised more concerns about financial losses associated with voltage sags in comparison to other PQ problems. Here, a neural estimator placed at a monitored location is proposed to estimate instantaneous voltage-sag waveforms of a non-monitored sensitive load using local measurements. Echo state network (ESN) is used as the voltage-sag waveform estimator (VSWE). Because of increasing penetration of distributed generations (DGs) and their impacts on voltage-sag performance, they are considered to challenge the estimation task. The proposed ESN-based VSWE is examined on the IEEE 37-bus network. Tests for extensive unseen cases with different fault resistances, fault inception-angles, fault locations and fault types under different load demands and network conditions show acceptable high accuracy of estimations during fault and fault clearing sequences. Furthermore, the performance of the VSWE is investigated during transients due to switching capacitors, DGs, loads and lines.

Abstract The integrity of PWR pressure vessels is assured by keeping the crack tip stress intensity factor below the toughness of the material under monotonic isothermal loading. To study the effects of sudden cooling associated with a thermal gradient, a specially modified compact specimen has been developed. This has been used to carry out tests in the transition zone with different loading-temperature sequences liable to call the conventional criteria into question. The test is described in detail in Part I of this article [Chapuliot S, et al. Thermomechanical analysis of thermal shock fracture in the brittle/ductile transition zone. Part I: Description of the tests. Engng Fract Mech, 72, 2005, 661–73]. The second part describes numerical investigations to estimate the local mechanical fields at the crack tip and the overall parameters of the fracture mechanics. Finite element thermomechanical calculations are used to interpret the results of these new thermal shock tests using the master curve concept [ASTM E 1921–1997. Standard test method for determination of reference temperature To for ferritic steels in the transition range, 1997] and the Beremin statistical model [Beremin FM. A local criterion for cleavage fracture of a nuclear pressure vessel steel. Metall Trans A, 14A, November 1983, 2287–777].

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.

AbstractEfficiency of annular elliptical fin has been studied numerically. It has been shown that constant temperature lines do not preserve their circular shape in fins with high aspect ratio and therefore, the common methods such as equivalent fin area or sector method may not be applicable. For this reason, a new simple correlation has been proposed to approximate annular elliptical fin efficiency and then the fin geometry optimized, to maximize the rate of heat dissipation for a specified fin volume, when there is a space restriction on the fin minor axis. Results have been presented graphically and a correlation has been deduced also.

Abstract This work aims to predict the general performances of a pilot parabolic trough collector during transient periods. To do so, a one-dimensional thermal model has been developed. It has been validated with experimental results from two different experimental setups, in steady-state conditions, with a transmitted power maximum error of 3.4%. Since the model only predicts the collector's thermal behavior, the parabolic trough collector has been first optically qualified. Then, optical efficiencies were used as input for the model. Experimental results were obtained in steady-state conditions and compared to the model. Then, experimental and numerical results were compared during two period of time with varying inlet conditions (i.e. dynamic condition tests): the first one with stable conditions, and the other one with harsh conditions. The developed model showed a good capability of predicting the thermal behavior of the parabolic trough collector with unstable environment (DNI, mass flow, inlet temperature), with a 9.6% relative standard error in the worst case. As a conclusion, while previous studies only focused on steady-state conditions, it has been showed that this kind of model can be used to precisely predict the dynamic behavior of large power plants.