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Abstract Thermal management system requires robust design as well as suitable paraffin/expanded graphite composites for confining the temperature of heat source within safe limits. However, paraffin/expanded graphite composites provide thermal management only for a certain period of time i.e. until the attainment of saturation energy storage limit. Herein, design, fabrication and simulation of a compact paraffin based thermal management system equipped with thermal bridge are presented. By keeping the highest safe temperature limit of batteries i.e. 65 °C and phase transition of paraffin as the test standards, performance of heat source was evaluated in terms of its total temperature retardation time along with quantitative effect of paraffin/expanded graphite composite. In the light of fundamental theories on mass and energy balance, substantial design steps and certain empirical equations have been introduced with further validation through experimental analysis. With heat dissipation rate of 6 W, it has been found that 75 g paraffin/expanded graphite composite with melting temperature of 54 °C kept the heat source temperature under the highest safe limit for around 13,000 s, providing the longest temperature retardation time in comparison to other types of paraffin/expanded graphite composites. Further, differential scanning calorimeter depicted that latent heat of 5 wt% paraffin/expanded graphite composite was slightly reduced from 182 J/g to 174 J/g even after 400 accelerated thermal cycles, demonstrating the promising thermal reliability and long life, which fits well with the set criterion of overall life expectancy of batteries. Besides, finite element analysis conducted via computational fluid dynamics software Fluent established qualitatively reliable fitness with experimental results.

Abstract A new model of the optimum tilt angle of a soiled photovoltaic (PV) panel is proposed in this paper. The tilt angle is a key factor that influences the output power of PV panel, while dust deposition is an inevitable external element to be considered. In this paper, the solar radiation model is studied by analysing the Hay, Davies, Klucher, Reindl (HDKR) model. The cell temperature of a PV panel is also investigated to evaluate the power output. A fitting formula is derived to express the relationship between the dust deposition density and the tilt angle, and it is integrated in the output model of a fixed-type PV panel. Besides, the effect of dust deposition on the transmittance is analysed. An inverse correlation between the dust deposition density and tilt angle can be obtained, and the optimum tilt angle is calculated to maximize the power output of a soiled PV panel. Simulation is conducted by Matlab to demonstrate the validity of the proposed model of the optimum tilt angle with the shielding effect by dust. Furthermore, the relationship between dust deposition and the working temperature of PV panel is investigated by indoor experiments.

Abstract The realization of precise and reliable short-term wind speed prediction is extremely essential to wind power development, especially for its integration into traditional grid system. For this purpose, this study develops a novel forecasting method based on time varying filter-based empirical mode decomposition, auto-regressive integrated moving average model and group method of data handling-based hybrid model. This method mainly contains four individual steps for grasping the major behavioral characteristics of wind speed data. The first step adopts time varying filter-based empirical mode decomposition to handle the nonlinearity and nonstationarity of the raw wind speed data by decomposing them into a number of subseries with more stability and regularity. Then, auto-regressive integrated moving average model is applied to depict the linear characteristic hidden in the data. For the above modeling errors (i.e., the nonlinear residuals), the third step employs three nonlinear models with different action mechanisms (i.e., least square support vector machine, genetic programming algorithm and spatio-temporal radial basis function neural network) to systematically capture their complex nonlinear features. Finally, group method of data handling neural network is utilized to combine these nonlinear models and perform the selective prediction, where the involved models and their weights could be determined automatically. Four groups of the measured wind speed datasets with two different time intervals are used to assess the performance of the proposed method. The experimental results indicate it outperforms the other compared models and may have great potential for the practical application in power system.

Abstract Importance for the accurate forecast of wind region with multiple wind farms is gradually emerging. As influenced by the geographical features of the wind region, the power output from each wind farm is closely correlated to the local-patterns of its covered weather. However, modeling the highly time-varying nature of the local-patterns’ spatial distribution remains the key challenge to regional wind power forecast. For this purpose, a sub-region is proposed to represent the spatial scale of wind farms covered by the same local-pattern. All wind farms in the wind region are divided into multiple sub-regions. This classification is defined as the partition which represents a typical state of the wind region. To deal with the time-varying nature, partitions are considered on the adaptive process. In this paper, a regional wind power forecasting method based on adaptive partition and long-short-term matching is proposed. First, a refined partition set of wind region is determined by the Regional Hierarchical Clustering algorithm. Second, to identify the current states of the wind region, the partition with minimum forecasting error is chosen as Optimal Partition. Third, the long-short-term matching strategy is proposed to find the adaptive partition among the refined partition set with the indication of recent and historical Optimal Partitions. Eventually, for each time horizon, the forecasted power of each sub-regions in the adaptive partition is aggregated to achieve the final regional wind power forecasting results. The superior performance and robustness of the proposed methods are validated with actual wind generation data from a wind region which contains nine wind farms in China. The ability to capture wind farm local-pattern of the proposed method is also approved.

Abstract Wind speed is the key factor of wind power generation. With the increase of the proportion of wind power generation in total power generation, the accurate prediction of wind speeds plays an important role in the stable operations of power grids. However, the strong randomness of wind speeds makes it difficult to accurately predict wind speeds. Thus, a wind speed prediction model combining empirical mode decomposition (EMD) with some novel recurrent neural networks (RNN) and the autoregressive integrated moving average (ARIMA) is proposed to solve the problem. The selected RNNs are long short-term memory network (LSTM) and the gated recurrent unit (GRU) network. In this model, EMD is used to decompose the wind speed sequence to reduce the complexity and non-stationary of the series. The entropy of the samples of the sub-sequences after decomposition is calculated. Consequently, LSTM is applied to predict the high frequency sub-sequences with large entropy while the ARIMA is employed to predict the remaining low frequency sub-sequences and one residual. Finally, the prediction results of each sub series are combined to obtain the final prediction results. To verify the accuracy and stability of the model, four wind speed data sets form Inner Mongolia, China, are used to test the proposed methods. Five models are established in four practical cases and their performances are compared with the performances of the proposed model. The results in this paper show the following: (1) the EMD method can improve the wind speed prediction performance when it is combined with LSTM; (2) after decomposition, LSTM is suitable for predicting high complexity subsequences and the ARIMA is suitable for effectively predicting low complexity subsequences based on the different sample entropies; and (3) the root mean squared errors (RMSEs) of the hybrid model on the four wind speed data sets are 0.4163, 0.2085, 0.1613, and 0.2790, respectively, which are basically lower than those of the five models compared. Therefore, it is feasible to apply the hybrid model to wind speed prediction.

Abstract Zeotropic mixtures exhibit great potential for further investigation of organic Rankine cycle (ORC) in the field of low temperature heat utilization because of the characteristics of varying temperature evaporation in two-phase region. However, few studies are contributed to the design of zeotropic mixture turbine and turbine seal. In this paper, the system optimization of ORC by means of genetic algorithm is conducted for different mixtures to obtain the optimal mixture working fluid and initial design parameters of turbine. The thermal design of the mixture ORC radial inflow turbine is carried out, and the CFD simulation is performed to investigate the three-dimensional flow characteristic of the designed turbine. Meanwhile, the helical groove seal of the turbine shaft is designed and analyzed through CFD method. Results show that the mixture ORC radial inflow turbine is well designed with an isentropic efficiency of 83.71%, and CFD results basically match with the thermal design results. What’s more, the CFD simulation result for the differential pressure of the helical groove seal has an only −5.83% deviation compared with the designed one, and the helical groove seal is well designed with desirable sealing performance.

To explore the mechanism of the influence of Ni-Fe bimetallic catalyst for the producing high-value carbon nanotubes (CNTs) with clean hydrogen from waste plastic pyrolysis, the pyrolysis-catalysis of plastics were performed using a two stage fixed bed reaction system with Ni and Fe loading at variant molar ratios. The catalysts and produced carbon were analysed with various characterization method, including temperature-programed reduction/oxidation, X-ray diffraction, scanning electron microscopy or/and Raman spectroscopy. Both the H2 concentration and H2 yield reached maximum values of 73.93 vol.% and 84.72 mg g−1 plastic, respectively, as the ratio of Ni:Fe at 1:3. The amount and quality of CNTs were greatly influenced by the catalyst composition, and Ni and Fe display different roles to the overall reactivity of Ni-Fe catalyst for the pyrolysis-catalysis of waste plastics. Catalyst with more Fe loading produced more hydrogen and deposited carbon, due to higher cracking ability and the relatively lower interaction between active sites and support. The presence of Ni in Ni-Fe bimetallic catalyst enhanced the thermal stability and graphitization degree of produced carbons. The thermal quality of filamentous carbons might be associated with carbon defects.