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Abstract Modified Zehner-Schlunder models are proposed for calculations of the stagnant thermal conductivity of two types of porous media with spatially periodic structures. The area-contact model is developed to take into consideration finite area contacts between spheres in a packed-sphere bed. It is shown that results based on the area-contact model are in better agreement with experimental data than those based on the original Zehner-Schlunder model especially at high solid/fluid thermal conductivity ratios. The phase-symmetry model is developed for a sponge-like porous medium where each phase is continuously connected and phase symmetry must be maintained in the expression of the stagnant thermal conductivity. The characteristics of these two models are illustrated.

AbstractThe Environmental Kuznets Curve hypothesis suggests that, as a country's national income grows, environmental degradation subsides as the population demands a cleaner environment. On the other hand, critics of the Environmental Kuznets Curve claim that many polluting industries simply relocate offshore, where environmental compliance is less costly. They then export their products back to their previous home countries. This is known as the Pollution Haven hypothesis. This article demonstrates how pollution havens can falsely give the appearance of an Environmental Kuznets Curve by analysing lead emissions from the US automotive tyre manufacturing industry.

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.

In this paper the real analyticity of all conically self-similar free-vortex solutions to the Navier-Stokes equations is proven. Furthermore, it is mathematically established that such solutions are uniquely determined by the values of three derivatives on the symmetry axis, and hence a numerical method, invented and successfully used by Shtern & Hussain (1993,1996), is justified mathematically. In addition, it is proven that these results imply that for any conically self-similar free-vortex solution to the Navier--Stokes equations there exists a second order non-swirling correction term. For this term it is also shown that the second order contribution to the total axial flow force vanishes in the cases of the entire space and a half-space, but that it need not vanish for general conical domains. In doing so an old claim by Burggraf & Foster (1977) is established mathematically, however not for Long's problem but for Shtern & Hussain's (1996) extension of this problem to the full Navier-Stokes equations and the entire space.

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.

The dynamic behaviors of a proton exchange membrane (PEM) fuel cell have been studied both experimentally and numerically. The objective of this paper is to investigate the effects of cathode inlet humidification on PEM fuel cell load change operations and the fuel cell performance during a simulated start-up process. The PEM fuel cell was found to respond quickly and reproducibly to load changes. It was also found that an increase in the cathode inlet humidification significantly influences the start-up performance of a PEM fuel cell. The cathode inlet relative humidity (RH) under 30% significantly dropped the cell dynamic performance. Extensive numerical simulations, with the transient processes of load jump and gradual changes considered, were performed to characterize dynamic responses of a singe-channel PEM fuel cell under different inlet humidification levels. The results showed that the response time for a fuel cell to reach steady state depends on water accumulation in the membrane, which is consistent with the experimental results. Copyright © 2010 John Wiley & Sons, Ltd.

Portugal faces water scarcity challenges, yet studies on per-household water consumption are limited. This study aims to address this gap by employing cluster analyses to assess how population trajectories, a previously overlooked aspect, and the regional location influence per-household monthly water consumption across 122 municipalities. Findings highlight higher consumption in the South despite lower prices. Municipalities experiencing population growth and those with long-term population declines show higher per-household water consumption but lower prices. Interestingly, while higher prices correlate with lower consumption, southern municipalities show increased prices without reduced consumption. Clustering reveals slight changes in consumption patterns from 2011 to 2020.