Finite element model updating of a space vehicle first stage motor based on experimental test results

This paper discusses about a procedure to minimize the differences between analytical and experimental results of a space vehicle model by applying the finite element model updating procedure, in order to optimize the structures and processes before hardware is acquired. The material and geometric parameter set is formed for modal updating based on sensitivity analysis. Optimal values of experimental model parameters are determined using orthogonal array method. The updated finite element model produces more reliable results with the measured values. The method avoids irregularity and mismatch between the experimental and analytical model data sets, allowing flexible but automated model updating using neural network predicted parameters. The numerical results are compared with the experimental measurements and the divergences are measured by natural frequency difference and modal assurance condition. By training the neural network model based on the results and simultaneously adjusting the structural parameters, it is possible to reduce the difference between the measured and the predicted frequency values.