Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
Source abstract
Abstract
In the process of high-speed penetration, penetrating ammunition is prone to problems such as penetration overload signal vibration and mixings and projectile attitude deflection. It is easy to misjudge if a fuze relies only on the overload data from the ground or the utilized program, and the actual penetration overload measured under actual launch conditions cannot be taken as the dynamic judgement basis. Therefore, a real-time penetration overload prediction method based on a deep neural network is proposed, which can predict overload values according to the projectile parameter settings, the real-time collection of overload information, and the calculation speed and assist the fuze in judging the target layer and projectile attitude. In this paper, we adopt a deep learning model with multiple time series inputs and modify the input coding mode so that the model can output a 48 μs overload curve within 20 μs, meeting the real-time signal processing requirements of the high-speed missile penetration process. The mean squared error between the predicted curve and the actual curve is 0.221 for the prediction of multilayer penetrating targets and 0.452 for the prediction of thick penetrating targets. A penetration overload prediction function can be realized.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: High-Velocity Impact and Material Behavior · Advanced Measurement and Detection Methods · Nuclear Engineering Thermal-Hydraulics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Department of Mechanical Engineering, Nanjing University of Technology and Science, Nanjing 210094, China
Funding
Pre-research fund
References (32)
Show source-provided references
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