An efficient and accurate numerical method for simulating close-range blast loads of cylindrical charges based on neural network

Abstract

To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian (CEL) method in predicting close-range air blast loads of cylindrical charges, a neural network-based simulation (NNS) method with higher accuracy and better efficiency was proposed. The NNS method consisted of three main steps. First, the parameters of blast loads, including the peak pressures and impulses of cylindrical charges with different aspect ratios (L/D) at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations. Subsequently, incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network. Finally, reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model, including modifications of impulse and overpressure. The reliability of the proposed NNS method was verified by related experimental results. Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model. Moreover, huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method. The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^1/3. It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law, and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges. The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads, and it has significant application prospects in designing protective structures.