In order to study the fuel mass flow rate regulation characteristics of tube-in-hole for solid fuel ramjet, the flow field of turbulent combustion was numerically analyzed.
为了研究固体燃料冲压发动机内部套管结构调节燃料流量的特性，对某模型发动机的湍流燃烧流场进行了数值分析。
Solid mass rate at different gas flow is forecasted by using improved BP neural network in dense phase pneumatic conveying.
结果表明，BP网络能对不同实验条件下的固体质量流率进行较好的预测。
The result shows improved BP neural network can successfully forecast solid mass rate under different experimental conditions and solid mass rate contour diagram at different gas flow is plotted.
结果表明，BP网络能对不同实验条件下的固体质量流率进行较好的预测。
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