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Most of the Catalonian population and urban areas are located along the Mediterranean coastal zone and for this analysis three largest Catalonia’s cities are chosen: Barcelona, Terrassa and Tarragona. Each city is a geographical representative of a different climate type according to the re-analysed Köppen-Geiger climate classification. The objective of this research is to demonstrate the effects of regional climate variations on the potential of natural ventilation (NV) application in building design. The first part of the article presents the availability of NV corresponding to yearly climate conditions applying the methodology “Climate Potential for Natural Ventilation” (CPNV). In continuation, the second analysis is calculated with building energy simulation software and displays possible refrigeration energy savings in three geographical locations. Mixed-mode (or hybrid-mode) and night-ventilation techniques are applied for the cooling process of a hypothetical low-rise office building model. Peer Reviewed

This paper proposes an improved L index which is adapted to analyze the voltage stability of power system with the ZIP loads. First it introduces derivation of the traditional L index. Then, it analyzes the differences between ZIP loads and PQ loads and then proposes an improved version of L index based on the derivation. Furthermore, the proposed improved index is used to analyze the voltage stability under different operation conditions of the power system with ZIP loads. The simulations show the effectiveness of the proposed improved L index.

Environmental and intrinsic factors interact to determine energy requirements in vertebrates. Glucocorticoid hormones (GCs) are key mediators of this interaction, as they fluctuate with energetic demands and regulate physiological and behavioral responses to environmental challenges. While a great body of research has focused on GC variation among individuals, the mechanisms driving GC variation across species and at broad spatial scales remain largely unexplored. Here, we adopted a macrophysiological approach to investigate the environmental factors and life-history traits driving variation in baseline GCs across lizard species. We tested three hypotheses: (1) If GCs increase with body temperature to meet higher metabolic demand, we expect an association between average baseline GCs and the mean species’ body temperature in the field (GC-temperature dependence hypothesis); (2) If GCs mediate behavioral responses to avoid thermal extremes, we expect that individuals frequently exposed to extreme conditions exhibit higher baseline GC levels (Behavioral thermoregulation hypothesis); (3) If GCs increase to support higher energy demands in active foragers during their period of activity, we expect that active foraging species have higher baseline GCs than sit-and-wait foragers, and that GC levels increase in relation to the duration of daily activity windows (Activity hypothesis). We used biophysical models to calculate operative temperatures and the activity patterns of lizards in sun-exposed and shaded microenvironments. Then, we tested the association between baseline GCs, body temperature, operative temperatures, foraging mode, and activity windows across 37 lizard species, using data from HormoneBase. Our comparative analyses showed that variation in baseline GCs was primarily related to the mean field body temperature and foraging mode, with higher baseline GCs in active foragers with higher body temperatures. Our results suggest that body temperature and foraging mode drive GC variation through their effects on energy requirements across lizard species.

Abstract In the last decade, there has been a growing trend towards flexible body wave energy converters (WECs) enabled by rubber-like elastomeric composite membrane structures that can simplify all aspects of WEC design. Currently, there are few literature studies detailing the implementations of membranes into WEC design. This paper aims to overcome this by reviewing the developments, material selection and modelling procedures for novel membrane based wave energy converters (mWECs), providing the reader with a comprehensive overview of the current state of the technology. In the first half of this paper, all of the possible WEC implementation areas are reviewed which include the primary mover, power take-off (PTO) and other sub-assembly systems. For the primary mover, the review has identified three main working surface approaches using membranes, these are: air-filled cells, water filled tubes and tethered carpets; which aim to reduce peak loads for enhanced reliability and survivability. In other areas, the PTO of WECs can benefit from using soft dielectric elastomer generators (DEGs) which offer a simpler designs compared with conventional mechanical turbomachinery. These have been implemented into the membrane working surface as well as replacing the PTO in existing WEC architectures. In the second half of the paper, a discussion is made on the material selection requirements with a few possible compositions presented. Following this, the potential modelling procedures for these devices is detailed. The device numerical models have altered existing procedures to take into account the non-linearities caused by the membrane interface and membrane PTO damping.

AbstractAlloy anodes composed of microsized particles receive increasing attention recently, which outperform the nanostructured counterparts in both the manufacturing cost and volumetric energy density. However, the pulverization of particles and fracture of solid electrolyte interphase (SEI) during cycling brings about fast capacity degradation. Herein, it is shown how normally considered fragile SEI can become highly elastic through electrolyte chemistry regulation. Compared to the SEI constructed in classic carbonate electrolyte, the atomic force microscopy tests reveal that the one built in ether‐based electrolyte doubles the maximum elastic strain to accommodate the repeated swelling‐contracting. Such an SEI effectively encapsulates the microsized Sb anodes to prevent the capacity loss from particle isolation. Coupled with an intercalation‐assisted alloying reaction mechanism, a sustained capacity of ≈573 mAh g−1 after 180 cycles at 0.1 A g−1 with outstanding initial Coulombic efficiency is obtained, which is among the highest values achieved in K‐ion batteries. This study emphasizes the significance of building robust SEI, which offers the opportunity to enable stable microsized alloy anodes.

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

This paper proposes a sustainability-oriented multiobjective optimization model for siting and sizing DG plants in distribution systems. Life cycle exergy (LCE) is used as a unified indicator of the entire system’s environmental sustainability, and it is optimized as an objective function in the model. Other two objective functions include economic cost and expected power loss. Chance constraints are used to control the operation risks caused by the uncertain power loads and renewable energies. A semilinearized simulation method is proposed and combined with the Latin hypercube sampling (LHS) method to improve the efficiency of probabilistic load flow (PLF) analysis which is repeatedly performed to verify the chance constraints. A numerical study based on the modified IEEE 33-node system is performed to verify the proposed method. Numerical results show that the proposed semilinearized simulation method reduces about 93.3% of the calculation time of PLF analysis and guarantees satisfying accuracy. The results also indicate that benefits for environmental sustainability of using DG plants can be effectively reflected by the proposed model which helps the planner to make rational decision towards sustainable development of the distribution system.