Aimed at the problem that the conventional absorption heat pumps and compression heat pumps cannot take into account the temperature rise and efficiency, this paper proposes the use of a thermally-coupled hybrid compression-absorption heat pump to achieve high-efficiency and high-temperature output. To meet the demands of different scenarios, a large-temperature-lift cycle and a high-temperature-output cycle are constructed. R245fa and lithium bromide aqueous solution are used as working substance. For output temperature above 100℃, Aspen Plus software is used to establish a mathematical model to predict the cycle performance for calculation. The results show that the optimized coefficient of performance(COP) can be 2.58 or higher when the large-temperature-lift cycle is used to recover the waste heat at 30-40℃. When the high-temperature-output cycle is used to recycle waste heat at 60-70℃, the optimized COP of the cycle can reach 2.83. The cycles proposed are more advantageous than the R245fa compression cycle on temperature lift, output temperature, and efficiency.
In response to the problems of boiler low water level and pump cavitation caused by the leakage of feedwater unloading pipeline in the process of boiler load raising of the conventional steam power system, the relational model of water-lift, mass flow, revs of booster pump, and each impeller of the feedwater pump are identified by using a differential evolution algorithm based on residual correction. The relational model of mass flow and resistance of feedwater pipelines and valves, the calculational model of working fluid parameters in feedwater system, and the performance degradation model of feedwater unloading control valve are established by using be mechanism modeling method. On this basis, the process of boiler load raising is simulated, and the main parameters of feedwater system are obtained at different feedwater unloading flow rates. It is found that the performance degradation of the control valve of the feedwater unloading pipeline is one of the main causes for boiler water low level and pump cavitation in the process of boiler load raising. In order to further clarify the degradation rule of the performance reliability of the feedwater system with the working time, a simulation method integrating the mathematical model and Monte Carlo random sampling is adopted to the performance reliability simulation of a certain type of marine water supply system in the process of boiler load raising. Thus the whole performance reliability changes of this boiler and the performance reliability life of this feedwater unloading control valve are obtained. The research results reveal the mechanism and degeneration rule of a unique fault of the steam power system, which has certain theoretical and engineering application value.
A gradient energy surface with micropillared structures is prepared by photolithography using polydimethylsiloxane (PDMS) as the substrate. The dynamic characteristics of vibrated droplets on the gradient energy surface with micropillared structures are studied by a high-speed camera. The influence of geometric parameters of the gradient energy surface with micropillared structures on the motion characteristics of vibrated droplets is analyzed. It is found that the droplets begin to wriggle when a certain vibration frequency is exerted on the gradient energy surface with micropillared structures and the vibration amplitude reaches a certain threshold. With the increase of the amplitude, the droplets move from the larger area fraction to the smaller area fraction. At the same vibration frequency, the acceleration of droplets gradually decreases as the amplitude increases. At the same time, compared to the region with a smaller area fraction, the acceleration of droplets motion is smaller in the region with a larger area fraction. In the region with a large area fraction, the range of wet contact diameter has a greater variation than the region with a small area fraction. A model is established using the mechanics and surface physical chemistry theory, and the influence of the surface microstructure on droplet motion characteristics is analyzed.
The stability of gas-fired flame is studied by combining the optical flow method and deep learning. The optical flow vector of the flame image is directly calculated by using the optical flow method. The pulsation of the flame in the two-dimensional image is observed, and an optical flow pulsation evaluation model is proposed to evaluate the stability of the flame. In addition, a deep convolutional neural network based on VGG-Nets is built and fine adjustments are made on ImageNet pre-training weights. Combining the static and dynamic characteristics of flames, the classification and recognition of five typical combustion states are achieved. The results show that this method has a good judgment ability for different combustion states of flames and a high recognition rate for unstable combustion flames.
The volatility and randomness of wind speed have caused potential safety hazards to wind power grid integration. Improving wind speed forecasting is crucial to the stability of wind power systems and the development of wind energy. A novel short-term wind speed forecasting model (MI-RNN) was proposed based on mutual information (MI) and recursive neural network (RNN). In this model, the MI theory was introduced to select the optimal length of historical wind speed sequence (τ), and the method of using each τ step to forecast wind speed at the next time point was adopted to input the historical wind speed data into RNN for model training. The final wind speed forecasting result was output by the trained RNN model. Besides, the MI-RNN model was applied to the wind speed dataset collected from a wind farm and the forecasting accuracy was compared with that of the traditional wind forecasting methods. The results show that the MI-RNN model has achieved a higher forecasting accuracy compared with the commonly used wind farm wind speed forecasting methods, and can accurately forecast the future wind direction, which is expected to be applied to wind speed forecasting of wind farms with spatial dimensions.
In order to optimize the bifunctional electrocatalytic performance of La0.95FeO3-δ, the effects and influencing mechanism of carbon morphology, electrode ink preparation, and catalyst loading on the bifunctional electrocatalytic performance of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) were investigated. The results show that compared with La0.95FeO3-δ, the catalytic activity of La0.95FeO3-δ/C is significantly improved. The bifunctional electrocatalytic performance of La0.95FeO3-δ/C is mainly dependent on carbon morphology and electrode ink preparation. An optimum bifunctional performance is achieved in a composite electrode with 0.6 mg/cm2 La0.95FeO3-δ and 0.12 mg/cm2 EC600JD prepared by ultrasonic dispersion, ball milling, and ultrasonic dispersion. The optimized La0.95FeO3-δ/C has an excellent bifunctional electrocatalytic performance, simple preparation, and low cost, which is expected to be applied in Li-O2 batteries.
This paper presents a comprehensive analysis of the frictional pressure drop correlations in two-phase flow in mini-channel, describes the relationship of inheritance and development between the correlations, and points out the innovation between different correlations. In order to evaluate the universality and precision of various correlations, it establishes a large frictional pressure drop database, which is specialized for mini-channels. This database includes 1302 and 1576 data points under evaporation and condensation/adiabatic conditions, respectively. Finally, it evaluates and analyzes 26 correlations under different conditions. The results show that the Sempértegui-Tapia and the Kim correlation have the best prediction ability under evaporation conditions and condensation/adiabatic conditions, respectively. This paper provides some advice on the improvement of correlation.
In order to ensure the accuracy and efficiency of measurement, and reduce the dependence of the soft sensing on dataset, a soft-sensing method of compressor power based on interpretable neural network is proposed. When training on a dataset with good generalization in the experiment, the root mean squared error(RMSE) of the interpretable neural network model on the test set is 0.0094, which is 1.1% lower than that of the back propagation(BP) neural network model. When training on a dataset with poor generalization, the RMSE of the interpretable neural network model on the test set is 0.0128, which is 79.8% lower than that of the BP neural network model. The experimental results show that the soft-sensing method based on interpretable neural network not only has a high accuracy rate, but also can maintain a good measurement performance when training on a dataset with poor generalization.
A full-scale novel conical swirl-vane steam separator with a conical barrel is studied by test and numerical methods using steam and water as working fluids. The test results indicate that the separation efficiency of the separator decreases first and then increases with the increase of steam superficial velocity. But this phenomenon is not obvious at low steam superficial velocities. The pressure drop of the separator increases significantly with the increase of steam superficial velocity. Based on the commercial software STAR-CCM+ and Euler-Euler two-fluid model, a numerical simulation is conducted to study the effect of droplet size on the separation efficiency and pressure drop of the swirl-vane separator. The numerical results imply that large droplets promote the liquid separation. However, the pressure drop is insensitive to the droplet size. According to the test results, a numerical model that is in good agreement with the test is established, and the distribution of presssure, velocity, and water volume fraction are analyzed in detail.
Aimed at the lack of effective stability evaluation methods for the current steam power system, an operation stability assessment method suitable for single parameter is proposed. This method is a composite method, which first applied the midpoint and regression based empirical mode decomposition (MREMD) and singular value decomposition (SVD) to decompose the time series of operation parameters and extract their hidden trend terms. Then, the components are selected for reconstruction according to the optimal algorithm parameter permutation entropy (OAPPE) of each component. Finally, the auto-regressive integrated moving average (ARIMA ) model commonly used in the non-stationary time series analysis is utilized to predict the trend and the disturbance of parameters, and their distribution characteristics are also extracted in this process, based on which, the probability of instability (PI) of operation parameters at each point on the predicted trend are calculated, and their stabilities are quantitatively evaluated. The actual case proves that this method can effectively assess the operation stability of a single parameter of the steam power system, which has a certain theoretical innovation and engineering application value.
