基于感知器仿真的底排药剂燃烧状态预测模型

以往研究结果表明,底排药剂在瞬态卸压工况下的燃烧行为分为稳定复燃、低频振荡燃烧和永久熄灭三种状态;具体燃烧状态主要由卸压前最大压力(20～90MPa)和最大卸压速率(1.2×103～6×103MPa.s-1)决定,三种燃烧状态与两者之间的对应关系基本上是线性可分的模式。为了预测不同瞬态卸压工况下的底排药剂燃烧状态,本研究基于神经网络理论,建立了单层和双层感知器神经网络模型并利用试验数据对模型进行训练,得到了包含卸压前最大压力与卸压过程中最大卸压速率的底排药剂瞬态卸压工况下燃烧状态界定判据。采用蒙特卡罗随机抽样的方法验证了所建立的感知器神经网络模型的正确性与可靠性,可以用来对瞬态卸压工况下的底排药剂燃烧状态进行预测。

Prediction Model for Combustion State of Base Bleed Propellant Based on Perceptron Simulation

The previous investigation shows that the combustion behavior of base bleed propellant is classified to three states under the condition of transient depressurization, which are re-ignition,low frequency oscillation combustion and extinguishment, respectively. Variant combustion state was determined mainly by the maximum pressure before transient depressurization (20-90 MPa) and the maximum depressurization velocity (1.2×10³-6×10³ MPa?s-¹) during whole process. The relationship of combustion state between the maximum pressure before transient depressurization and the maximum depressurization velocity is linear separable pattern approximately. Two kinds of perceptron models, single layer and double layers, were constructed according to artificial neural networks. Both of the two peceptron models were trained by using test data. The decision boundary of combustion states of base bleed propellant was obtained, which included both the maximum pressure before transient depressurization and the maximum depressurization velocity factors. The correction and reliably of the peceptron were validated by Monte-Carlo random sampling. The peceptrons could be used to predict the combustion behavior of base bleed