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Abstract Temperature has an important influence on plant growth and development. In protected agriculture production, accurate prediction of temperature environment is of great significance. However, due to the time series, nonlinear and multi coupling characteristics of temperature, it is difficult to achieve accurate prediction. We proposed a method for building a solar greenhouse temperature prediction model based on a timeseries analysis, that considers the time series characteristics and dynamic temperature changes in the greenhouse system. The method would predict the temperature of greenhouse, and provide reference for the temperature change law in protected agriculture. A parameter analysis was performed on the nonlinear autoregressive exogenous (NARX) neural network, and a solar greenhouse temperature time series prediction model was established using the NARX regression neural network. The results showed that the proposed model depicted a maximum absolute error of 0.67 °C, and model correlation coefficient of 0.9996. Compared with the wavelet and BP neural networks, the NARX regression neural network accurately predicted and significantly outperformed in the solar greenhouse temperature prediction model. Moreover, the prediction model can accurately predict temperature trends within the solar greenhouse and is pivotal to obtaining precise control of solar greenhouse temperature.

Abstract This paper reports on the development of a magnetically driven high-velocity implosion experiment conducted on the CQ-3 facility, a compact pulsed power generator with a load current of 2.1 MA. The current generates a high Lorentz force between inner and outer liners made from 2024 aluminum. Equally positioned photonic Doppler velocimetry probes record the liner velocities. In experiment CQ3-Shot137, the inner liner imploded with a radial converging velocity of 6.57 km/s while the outer liner expanded at a much lower velocity. One-dimensional magneto-hydrodynamics simulation with proper material models provided curves of velocity versus time that agree well with the experimental measurements. Simulation then shows that the inner liner underwent a shock-less compression to approximately 19 GPa and reached an off-Hugoniot high-pressure state. According to the scaling law that the maximum loading pressure is proportional to the square of the load current amplitude, the results demonstrate that such a compact capacitor bank as CQ-3 has the potential to generate pressure as high as 100 GPa within the inner liner in such an implosion experiment. It is emphasized that the technique described in this paper can be easily replicated at low cost.