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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.

Abstract Secondary concentrators are used in solar concentrating systems to redirect solar beams reflected by the primary concentrators to the focal point or line. These components allow to increase the concentrated solar flux density and hence to lower thermal radiation losses. Solar reflectors for secondary concentrators are permanently exposed to environmental conditions, high radiation fluxes and elevated temperatures that potentially cause stress and degradation throughout the time. Therefore, analyzing solar reflectors of secondary concentrators by simulating these conditions is crucial. No previous research works about the durability of solar reflector materials for secondary concentrators have been reported. The present work is focused on studying the degradation of the reflector materials by simulating accelerated aging, caused by several ambient parameters and the effect of concentrated radiation. Both cooled and uncooled systems for secondary concentrators are included in this study. According to results obtained, aluminum reflectors and thin silvered-glass reflectors glued to an aluminum structure showed minimum reflectance losses and structural degradation under the operation conditions of cooled 3D secondary concentrators (tower systems). Following critical aspects to avoid reflector degradation were identified: to select a suitable adhesive material to glue the thin silvered-glass reflector to the support aluminum structure, to properly protect reflectors edges, to design a suitable cooling system and to avoid the combination of high radiation fluxes with mechanical stress. In addition, laminated silvered-glass reflectors have shown to be suitable for uncooled 2D secondary concentrators (Fresnel collectors). Furthermore, a comparison with naturally aged secondary concentrators using silvered-glass reflectors glued to an aluminum structure revealed that the simulated degradation under accelerated conditions performed in this work did reproduce the most frequent degradation patterns suffered in real operating conditions.

This paper introduces a model predictive control (MPC) strategy for the purpose of fuel-optimal operation of a range-extender hybrid vehicle. The modern navigation system nowadays can provide abundant road information. Using this information, the proposed controller solves a global optimization problem offline in order to determine a preset trajectory of the state of charge (SoC). The online MPC uses the resulting SoC trajectory as set-points for the terminal state in every moving horizon. Repeating this process, the optimal energy management along the trip to be traveled can thus be calculated. This proposed control strategy is implemented in the commercial vehicle simulation environment IPG CarMaker. From the first simulation results, the proposed strategy shows a promising fuel saving potential with real-time capability.

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.

AbstractIn this paper a complete vehicle system simulation tool chain which applies a Multi Objective Optimization (MOO) methodology for designing the Electric Powertrain (ePT) of Battery Electric Vehicles (BEV) is presented. Optimization scope includes all relevant electric powertrain components from battery, inverter, electric machine to gear box. In addition to cost, vehicle dynamics, energy consumption and range are further optimization targets. For an overall minimal system cost design a multiplicity of interactions between all powertrain components has to be taken into account. High system complexity prevents an expert to consider all relevant correlations without the support of numeric simulation tools. The presented simulation tool chain enables fast identification of the best cost/benefit trade off regarding system cost while considering all defined system performance requirements. The approach enables experts to find unconventional solutions which would have been overlooked applying a classical straight forward approach and, thus, helps to sharpen the expert's knowledge in cause-effect relationships on the system level. Typical use cases are given and illustrated by several practical examples.

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].