Abstract: The ejection velocity of an ejection seat is a primary input parameter of the dual mode sequencer ope-ration which, along with the ejection altitude, determines the parachute-opening time. It is found that in certain circumstances large errors of the sensed velocity may exist which significantly influence the performance of the escape system. In this study, a method that predicts the ejection velocity by using load factor ( n_x ) in the x direction of the body-axis system is presented. A mathematical model of the egress phase is established, and its module simulation model is set up on the MSC.EASY5 fundamental platform. Furthermore, a solver based on the batch program is designed. According to numerical simulation, the relationship curves between the n_x and ejection velocity at various altitudes and gravities are obtained. Subsequently, back propagation (BP) neural networks are established in order to implement the sequential nonlinear mapping from input vectors (altitude and n_x ) to the ejection velocity. The impact of adverse attitude parameters on the relationship is analyzed, which is found to be negligible under engineering requirements. Finally, the predicted ejection velocity is compared with the experimental data and the error is acceptable for engineering applications.

Key words: load factor, ejection velocity, inertia measurement, simulation, neural network, adverse attitude