Abstract
Aerodynamic vehicles come across the influence of impulsive forces and these are the major concerns associated with high-speed atmospheric vehicles. These shock wave induced impulsive forces impart hazardous effects on the surface of the vehicle. So, the magnitude of these forces is required for the design and modification of aerospace vehicles. Due to practical constraints, the real-time experiment is very difficult. Therefore, the ground-based test facilities are carried out using an aerodynamic model in shock tubes and shock tunnels. These models are required to be calibrated properly before carrying out the actual experiments. In the present study, a bi-cone model with a stress-wave force balance is used to perform the calibration task. The balance is mounted inside the model with strain gauge which records strain signal related to the applied force acting on the nose of bi-cone model. The strain signals of impulsive forces are captured for different magnitude and these signals are used for training and recovery of forces. Two different methods have been adopted for the recovery of the forces; one through classical de-convolution technique and another using the hybrid soft-computing approach, Adaptive neuro-fuzzy inference system (ANFIS). The forces recovered through both the techniques are compared with the known forces and also with each other. This provided an insight about the feasibility and applicability of the soft computing approach towards the inverse recovery of unknown forces for short duration experiments.
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Nayak, S., Sahoo, N. (2022). Dynamic Calibration of a Stress-Wave Force Balance Using Hybrid Soft Computing Approach. In: Banerjee, S., Saha, A. (eds) Nonlinear Dynamics and Applications. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-99792-2_54
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DOI: https://doi.org/10.1007/978-3-030-99792-2_54
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