The entropy generation analysis of fully turbulent convective heat transfer to nanofluids in a circular tube is investigated numerically using the Reynolds Averaged Navier–Stokes (RANS) model. The nanofluids with particle concentration of 0%, 1%, 2%, 4% and 6% are treated as single phases of effective properties. The uniform heat flux is enforced at the tube wall. To confirm the validity of the numerical approach, the results have been compared with empirical correlations and analytical formula. The self-similarity profiles of local entropy generation are also studied, in which the peak values of entropy generation by direct dissipation, turbulent dissipation, mean temperature gradients and fluctuating temperature gradients for different Reynolds number as well as different particle concentration are observed. In addition, the effects of Reynolds number, volume fraction of nanoparticles and heat flux on total entropy generation and Bejan number are discussed. In the results, the intersection points of total entropy generation for water and four nanofluids are observed, when the entropy generation decrease before the intersection and increase after the intersection as the particle concentration increases. Finally, by definition of E_p, which combines the first law and second law of thermodynamics and attributed to evaluate the real performance of heat transfer processes, the optimal Reynolds number Re_op corresponding to the best performance and the advisable Reynolds number Re_ad providing the appropriate Reynolds number range for nanofluids in convective heat transfer can be determined.

Recent studies have shown that the construction of nanophononic metamaterials can reduce thermal conductivity without affecting electrical properties, making them promising in many fields of application, such as energy conversion and thermal management. However, although extensive studies have been carried out on thermal conductivity reduction in nanophononic metamaterials, the local heat flux characteristic is still unclear. In this work, we construct a heat flux regulator which includes a silicon nanofilm with silicon pillars. The regulator has remarkable heat flux regulation ability, and various impacts on the regulation ability are explored. Surprisingly, even in the region without nanopillars, the local heat current is still lower than that in pristine silicon nanofilms, reduced by the neighboring nanopillars through the thermal proximity effect. We combine the analysis of the phonon participation ratio with the intensity of localized phonon modes to provide a clear explanation. Our findings not only provide insights into the mechanisms of heat flux regulation by nanophononic metamaterials, but also will open up new research directions to control local heat flux for a broad range of applications, including heat management, thermoelectric energy conversion, thermal cloak, and thermal concentrator.

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.

Micro/nano-porous polymeric material is considered a unique industrial material due to its extremely low thermal conductivity, low density, and high surface area. Therefore, it is necessary to establish an accurate thermal conductivity prediction model suiting their applicable conditions and provide a theoretical basis for expanding their applications. In this work, the development of the calculation model of equivalent thermal conductivity of micro/nano-porous polymeric materials in recent years is summarized. Firstly, it reviews the process of establishing the overall equivalent thermal conductivity calculation model for micro/nanoporous polymers. Then, the predicted calculation models of thermal conductivity are introduced separately according to the conductive and radiative thermal conductivity models. In addition, the thermal conduction part is divided into the gaseous thermal conductivity model, solid thermal conductivity model and gas-solid coupling model. Finally, it is concluded that, compared with other porous materials, there are few studies on heat transfer of micro/nanoporous polymers, especially on the particular heat transfer mechanisms such as scale effects at the micro/nanoscale. In particular, the following aspects of porous polymers still need to be further studied: micro scaled thermal radiation, heat transfer characteristics of particular morphologies at the nanoscales, heat transfer mechanism and impact factors of micro/nanoporous polymers. Such studies would provide a more accurate prediction of thermal conductivity and a broader application in energy conversion and storage systems.

The detection of road infrared targets is essential for autonomous driving. Different from RGB images, the acquisition of infrared images is unaffected by visible light. However, the signal‐to‐noise ratio still presents significant challenges. This study demonstrates an improved infrared target detection model for road infrared target detection. An advanced EfficientNet is incorporated to replace the conventional structure and enhance feature extraction. A Dilated‐Residual U‐Net is also introduced to reduce the noise of infrared images. Meanwhile, the k ‐means algorithm and data enhancement are implemented to improve the detection performance. The experimental results show that the mean average precision of the proposed model is observed to be 0.89 for the infrared road dataset with an average detection speed of 10.65 s ⁻¹ .

Primary frequency regulation capability of the wind turbine generators is an appealing topic in order to consider safe increasing of the wind power integration into power grids. This study introduces improvements in the primary frequency contribution of the grid‐connected variable speed wind turbine generators, which operate under de‐loaded conditions. A new dynamic droop control approach based on the fuzzy logic system is proposed. In this control approach, the droop setting is continuously adjusted in response to the de‐loaded rotor speed, the frequency deviation, and the rate of change of frequency. In addition, a new dynamic de‐loading technique based on the Sugeno–Fuzzy inference system is proposed where the de‐loading percentage of the wind turbine generator is decided continuously according to the frequency deviation. The effectiveness of the proposed dynamic de‐loading and dynamic droop control approaches is verified through extensive comparative studies. The simulation results confirmed that the proposed control approaches have the ability to provide transient and steady‐state power‐sharing and improve effectively the frequency stabilisation of the power system within the stable and secure limits of the wind turbine.