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- W3048878812 abstract "Blunt body shape is used for re-entry modules to withstand the aerodynamic heating loads in the hypersonic regime. During the terminal phase of descent, it encounters the low subsonic regime. In this phase, stable descent and favorable attitude for deceleration system deployment are of paramount importance. For ensuring the requisite state, the dynamic stability of the body during the period needs to be characterized. Captive experiments do not provide accurate results due to sting effects. Hence, free-flight tests are performed using a dynamically scaled model. But only the accelerations and angular rates during the flight can be measured. To estimate the dynamic stability derivatives from the data, the system must be identified. It is considered as a nonlinear least squares problem, which can be solved using an unconstrained nonlinear method. The total coefficients are extracted using an analytical approach which solves the 6-degree of freedom (6-DOF) equations of motion. A set of neural networks are designed to map the coefficients to the influencing parameters. The networks are trained and optimized using Levenberg–Marquardt backpropagation algorithm, which interpolates between Gauss–Newton and gradient descent methods. The parameters are perturbed to obtain the stability derivatives. The data from real-time free-flight tests of a re-entry module is used to estimate the parameters, using the proposed method." @default.
- W3048878812 created "2020-08-18" @default.
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- W3048878812 date "2020-01-01" @default.
- W3048878812 modified "2023-10-14" @default.
- W3048878812 title "Identification of Aerodynamic Derivatives of a Re-entry Module" @default.
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- W3048878812 doi "https://doi.org/10.1007/978-981-15-5432-2_25" @default.
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