Organic phase change materials have the merits of high latent heat storage, small temperature changes, high chemical/thermal stability, low corrosion, and reusability, and thus have attracted extensive attention in the research of low and medium temperature energy storage media. However, pure phase change material has the deficiency of low thermal conductivity and easy to leakage, which seriously affect their large-scale applications in practical situations. Therefore, in this paper, expanded graphite (EG) with high thermal conductivity and good adsorption characteristics was used as skeleton material and thermal conductivity enhancement, and erythritol with high latent heat (327.24J/g) was selected as the phase change material to prepare a series of composite phase change material. Based on the harmonic wave method, the thermal conductivity of erythritol/EG at room temperature was obtained to be 6 W/m·K, which was 8.5 times higher than that of erythritol (0.6 W/m·K). The melting latent heat of 95wt% erythritol/EG is 290.52 J/g, and the solidification latent heat is 239.52 J/g. Compared with pure erythritol, the supercooling degree is reduced from 92.41℃ to 43.75℃, which helps to release the latent heat in time matches the storage-release temperature and improve the utilization efficiency of heat storage. The composite phase change material developed here exhibits extraordinary heat transfer and storage characteristics, which has great application prospects in the fields of solar photothermal conversion utilization.
Increasing shares of renewables in the energy matrix is linked to increased power price fluctuations, which, in turn, increases the financial risks for electricity market participants. In this context, understanding the key factors driving the power prices and thereby improving price forecasts is increasingly important. Here we analyze the main drivers of power prices with the help of machine learning. We show how the selection of the predictors set and length of historical data affect the forecast accuracy of the power prices. Using the developed model, we project how high energy and carbon prices may affect future electricity prices.
When a transformer fault occurs, the transformer oil will decompose and produce a large amount of dissolved gas in the oil, based on the dissolved gas in the oil to diagnose whether there is a fault in the transformer, known as dissolved gas analysis (DGA), in order to effectively predict whether a transformer fault will occur in the future, so as to prevent the development of the fault in time at the early stage of the fault, proposed A model for predicting the dissolved gas concentration in transformer oil based on the firefly algorithm (FA) optimized random forest (RF), which uses the random forest as the prediction model and adjusts the parameters in the RF by means of the firefly algorithm. The experimental results show that the FA algorithm can effectively optimize the parameters in the RF and improve the prediction accuracy of the model, overcoming the shortcomings of the traditional RF algorithm which uses random parameters with low accuracy, and the model can predict the dissolved gas concentration in oil more accurately than the existing methods.
Global warming concerns have motivated the study of new approaches that can decarbonize fossil fuels to produce clean fuels and commodities. A promising approach is solar-thermal methane pyrolysis to convert natural gas into clean hydrogen fuel and high-quality carbon product with virtually zero CO2 emissions by utilizing concentrated solar power. However, one of the challenges to continuous methane pyrolysis is deactivation of catalyst, when present, and establishing a facile means of extracting the valuable carbon product. In this work, a scalable route to continuous solar-thermal methane pyrolysis is presented that employs a roll-to-roll mode of operation. A high-flux solar simulator is used to mimic concentrated solar power and to allow operation at temperatures of approximately 1500 K, where methane rapidly decomposes onto the fibers of a porous carbon roll, collecting graphitic solid carbon and exhausting clean hydrogen fuel in addition to unconverted methane. The efficacy of the roll-to-roll approach for methane decomposition is investigated, and the technique is observed to be effective in achieving a continuous process. The roll-to-roll mechanism maintains stable and relatively high methane conversion compared to a stationary substrate, where enhancement in methane conversion as high as 42% is observed. The quality of the carbon product obtained is generally high, with Raman D/G peak ratios near 0.5. This work therefore establishes a proven baseline for continuous production of graphitic carbon from solar pyrolysis
Thermal fatigue in a T-junction is of crucial importance issue for the coolant system of nuclear energy plants. The dynamic mode decomposition (DMD) is employed to analyze the snapshot data from simulation results with applying large eddy simulation (LES). The thermal mixing flow in a square T-junction is simulated at the impinging jet (MR = 0.2). The temperature difference of hot and cold fluids is 15 K. The corresponding Reynolds number is about 20000. The results show that the frequency of the velocity modes is not equal to that of the temperature mode. The frequency of the temperature mode 1 is more than 40% higher than that of the velocity mode 1. The main spatial structures of the temperature field and the velocity field are alternately arranged along the trajectory of the branch fluid entering the main duct. The main coherent structure of the velocity field arrives at the bottom wall of x/Dm = 1, whereas for the temperature field, it basically appears in the region of x/Dm = 0.6 – 0.8. The negative structures of the velocity mode 1 induces the positive structures of the temperature mode 1. Also, the coherent structures of Modes 1 and 2 grow along the normal direction during downstream propagation.
The application of an internally cooled desiccant enhanced evaporative cooling system (ICDEVap) in Hong Kong is a promising scheme for energy saving and emission reduction. It consists of liquid desiccant dehumidification (LDD) and regenerative indirect evaporative cooling (RIEC) and can operate without a power-intensive compressor. The hot and humid air is first dehumidified by the internal cooling-LDD, and then sensibly cooled by the RIEC. To ensure efficient energy utilization and better indoor air quality simultaneously, the return air is indirectly utilized in the internal cooling of the LDD, which alleviates the efficiency deterioration of the desiccant. The influence of the return air ratio on the system performance is analyzed. The results show that the ICDEVap system operating at the optimal return air ratio saves 48% of the energy consumption compared to the mechanical vapor compressor refrigeration (MVCR) system in Hong Kong summer.