Real-time Direct Normal Irradiance (DNI) prediction is crucial for reliable and economic operation of Concentrated photothermal Solar Power (CSP) system in arid desert areas. However, the stochastic characteristics of short-term multidimensional meteorological time series make intra-hour DNI prediction a challenging task. In this study, we have proposed a deep learning model called TLD, which is combined with topological features captured by Topology Data Analysis (TDA) and temporal features captured by LSTM to address this challenge. Experimental results demonstrated that TLD outperformed the five latest models (Ridge, RF, C_GRU, BiLSTM, and GBRT) on seven solar radiation datasets in arid desert areas. Further analysis revealed that the proportion of cloudy days is a key factor affecting the model's performance. To enhance the forecast ability of TLD, we developed a physics-informed hybrid model named TLDP based on TLD and a smart persistence model, which fully combines the DNI prediction ability of TLD under cloudy conditions and that of the smart persistence model under sunny conditions. Experimental results of eight datasets collected from real-world solar photothermal power stations indicated that TLDP outperformed existing models, which may lay a foundation for more economical and stable operation of CSP plants in arid desert areas.

As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

In order to explore the mechanism of heat and mass transfer of metal foam absorber based on electrodeposition, the numerical simulation study of metal foam absorbers with different structures was carried out, and the heat transfer model of the coated metal foam tube bundle with a tube spacing of 3D × 1.5D was analyzed and established. Compared with the smooth tube bundle, with the Re number varying from 100 to 1500, the Rext of the tube bundle coated with stainless steel foam decreased from 0.061 K/W to 0.009 K/W, and the thermal resistance ratio outside the tube increased from 2.75 to 4.76. The pressure loss of the tube rose from 1.89 Pa to 80.10 Pa, and the pressure loss outside the tube dropped from 5.15 to 3.12. The overall performance index PEC of the tube increased from 1.59 to 3.26. Compared with rows of staggered tube bundles from 2.5D to 2.2D, the heat transfer coefficient can reach 4621 W/m2/K and the pressure loss is 1.67%.

采用室内实验研究管外包覆金属泡沫圆管在紧凑型错列管束中强化传热特性,分析不同金属泡沫材料和表面接触方式对换热的影响,发现管外包覆金属泡沫层换热管束中强制对流换热在总体换热中占据主导地位,新型粉末焊接可大幅度降低管束接触热阻.

The flow characteristics of the supercritical fluid in the micro-fin tube is the theoretical basis for the development of heat transfer enhancement and flow resistance reduction in the micro-fin tube. For micro-fin tubes with different fin shapes, this paper considered the physical properties of nitrogen in the supercritical state and adopted the enhanced wall function, reasonable turbulence equations, and control equations, etc., to numerically simulate the flow of nitrogen in a 2 mm micro-fin tube under supercritical pressure. The distribution of velocity field, turbulence and pressure field of supercritical nitrogen in the micro-fin tube was analyzed. The turbulent flow mechanism of the micro-fins was obtained, and it is found that the existence of the viscous bottom layer slows down the inter-costal fluid velocity and increases the frictional resistance during the flow process.

H2S is an important element to high-temperature corrosion for the water-cooled wall of coal-fired boilers, thus, it is an effective means to prevent high-temperature corrosion through reducing the concentration of H2S near the boiler wall. Since the concentration of H2S in the boiler is closely related to the concentration of O2 and CO, the research on the distribution of H2S atmosphere in the boiler furnace was conducted in this paper. With the air distribution regulation as the means, local O2 concentration is increased, to avoid the accumulation of H2S near the wall and reduce high-temperature corrosion.

With the application of supercritical fluid heat transfer equipment in industrial fields such as solar thermal power generation, chemical industry, aerospace, etc., studying the heat transfer characteristics of supercritical fluid in micro-fin tubes has become a key theoretical basis for the development of micro-fin low-resistance heat transfer enhancement technology. In view of micro-fin tubes with different fin shapes, this paper took into account thermophysical properties of nitrogen under supercritical conditions and completed a numerical simulation study on the heat transfer process of nitrogen in 2 mm micro-fin tubes under supercritical pressure. The temperature field distribution of supercritical nitrogen in the micro-fin tube was analyzed, and the turbulent flow mechanism of the micro-fin was studied. It was found that micro-fin could increase the heat exchange area, destroy the boundary layer, and improve the heat transfer coefficient. This paper took comprehensive heat transfer performance evaluation factor PEC to compare the influence of different fin shapes on heat transfer enhancement performance of the heat exchange unit. It was found that the comprehensive heat transfer factor of the square straight micro-fin tube was about 1.22 times that of the smooth round tube, and PEC of the triangular straight micro tube was about 1.08 times that of the smooth tube. The results suggest that square straight micro-fin tube has significantly superior heat transfer performance than smooth round tube and triangular straight micro-fin tube.

The promoter is a region located near the transcription start site (TSS) and responsible for the initiation and regulation of DNA transcription. Hence, accurate identification of promoters is essential for further building and understanding the mechanism of genetic regulatory networks. Numerous approaches for eukaryotic promoter identification were proposed. Nevertheless, the performances of these approaches are still unsatisfactory due to the variety nature of promoters. To extract more discriminative features and accurately identify eukaryotic promoters, here, we develop an effective hybrid deep learning model HDLMepi, which is able to characterize the original promoter sequences and the structural profiles of promoters simultaneously. We integrate the method we name PromoterClCce which characterizes the original promoter sequences and extracts sequence features, with an approach DSPN, which we design to model the structural profile of promoters and extract structure features, in HDLMepi for precisely eukaryotic promoter identification. We apply HDLMepi on both human and plants datasets and the experimental results demonstrate it is effective in promoter features extraction and can improve the performance of promoter identification significantly. HDLMepi is also open to add new features or new models and can be applied to other biology functional sequences.

Nowadays, the demand for medium temperature utilization of solar energy is becoming wide, not only in the field of building energy, but also in some industrial fields. While, the application of parabolic trough collector in the field of medium temperature requires to meet appropriate concentration ratio, compact construction and low initial cost. In this study, we proposed an optical design for small and medium sized parabolic trough collector. During the experiment, the Monte Carlo ray tracing method (MCRT) was used to calculate the peak optical efficiency of the collector, and the annual average optical efficiency of the DaXing District of Beijing was calculated by System Advisor Model (SAM). Finally, by comparing with mature products, the calculation results show that our design is feasible.

The design and optimization of Concentrated Solar Power-Photovoltaic (CSP-PV) hybrid system is a hot topic in the field of solar energy. By merging the advantages of these two forms of power generation, this system can provide the cheap and controllable solar energy. To match the demand of the grid, the proportion of different energy sources in this hybrid system need to be optimized carefully. However, most related works just consider the balance of energy output and consumption in the level of the power plant, while the key parameters of the CSP plant generally in varied forms with different situations. Thus, a method can optimize all the main parameters of the CSP and the hybrid system in the same time is in need. In this way, we process a method based on the artificial fish-swarm algorithm to complete the global optimization of CSP-PV Hybrid System, the experimental result proven the effectiveness of the method.

Nowadays, with the rapid development of related technologies, the solar power become an important component of the whole energy system. At present, there are two main forms of solar power generation: photovoltaics (PV) and concentrated solar power (CSP). Among them, PV is much cheaper, but more susceptible to resource conditions, while CSP is more controllable based on the thermal storage section, but still expensive. To integrate the advantages of these two kinds of technologies, the hybrid solar power system is proposed by many researchers. Some single objective optimization algorithms are used in the process of design such systems. However, it is still a dilemma to deal with the cost and stability in the hybrid system. In this paper, we try to propose an Multi-Objective Particle Swarm Optimization (MO_PSO) algorithm to solve this problem, which can consider the performance and cost of the project at the same time. The experimental result based on the real data shown that this algorithm can provide a feasible solution of the hybrid power system with stable output and acceptable cost. Furthermore, this method based on artificial intelligence can be used in other hybrid systems optimization in the smart grid.