Uncertainty Quantification for Mars Atmospheric Entry using Polynomial Chaos and Spectral Decomposition

Jiang, Xiuqiang

doi:10.2514/6.2018-1317

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…17,18 In the case of considering the perturbation of aerodynamic parameters and initial state disturbance, the non-intrusive polynomial chaos expansion (NIPCE) and intrusive polynomial chaos expansion (IPCE) are both used for the deterministic quantification of uncertain dynamics models in order to analyze the dynamic uncertainty propagation of hypersonic reentry vehicle or Mars reentry spacecraft. 19,20 The simulation results verify that both methods can obtain the complete statistical characteristics of the random state. Furthermore, the IPCE requires fewer sampling points, which makes the computational efficiency higher, and does not need to know the prior information of the random response of the system state, which is more suitable for the characterization of uncertainty dynamic system.…”

Section: Introductionmentioning

confidence: 56%

Uncertain trajectory planning integrating polynomial chaos and convex programming

Tan

Jing

Gao

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The present work explores the robust trajectory optimization scheme considering both the initial state disturbance and multiple constraints. An uncertain multi-constraint optimization model has been first established. Due to the existence of the stochastic disturbance, standard numerical trajectory planning algorithms cannot be directly applied to address the considered issue. Hence, based on the intrusive polynomial chaos expansion, we present a deterministic quantification for the stochastic state and constraints, so that the transformed optimization model becomes solvable for standard numerical optimization methods. To obtain the enhanced computational performance, an hp pseudo-spectral sequential convex programming procedure combined with a penalty function and backtracking search is proposed. This is achieved by discretizing and convexifying the nonlinear dynamics/constraints using hp quadrature collocation and successive linearization, respectively, and by adjusting the confidence region manually in the iteration. The simulation of a three-dimensional interception with the specific impact angle is conducted to verify the effectiveness. The simulation results show that the initial solutions are insensitive to the convex optimization, and the control commands generated by the proposed algorithm are effective against the initial state disturbance.

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Section: Introductionmentioning

confidence: 56%

Uncertain trajectory planning integrating polynomial chaos and convex programming

Tan

Jing

Gao

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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Dimensionality Reduction for Onboard Modeling of Uncertain Atmospheres

Albert,

Doostan,

Schaub

2024

Journal of Spacecraft and Rockets

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Onboard density models are a key aspect of closed-loop guidance systems for hypersonic flight. Traditional approaches model density as a deterministic function of altitude, but a recent drive toward stochastic guidance approaches motivates onboard uncertainty propagation. Existing solutions for efficient uncertainty propagation generally treat density as an exponential function of altitude, but this approach is limited in its ability to capture relevant dispersions. This work models density as a Gaussian random field that is approximated by a Karhunen–Loève expansion, enabling a relatively high-fidelity, finite-dimensional parametric representation. Alternative models are also developed using a variational autoencoder architecture, resulting in greater dimensionality reduction at the expense of analytical description. Normalization schemes are presented and compared by their efficiency in capturing density variability in a limited number of terms, and normalization by reference dynamic pressure is shown to be the most compact approach. The model alternatives are compared both by their approximations of density itself and by their predictions of peak heat flux for dispersed direct-entry and aerocapture trajectories. An extension of this approach for modeling density as a function of multiple independent variables is also presented and demonstrated. Finally, it is shown that the Karhunen–Loève density model can be sequentially updated according to noisy density observations by formulating the problem as a Kalman measurement function.

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Finite-Dimensional Density Representation for Aerocapture Uncertainty Quantification

Albert

Doostan

Schaub

2021

AIAA Scitech 2021 Forum

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Uncertainty Quantification for Mars Atmospheric Entry using Polynomial Chaos and Spectral Decomposition

Cited by 5 publications

References 21 publications

Uncertain trajectory planning integrating polynomial chaos and convex programming

Uncertain trajectory planning integrating polynomial chaos and convex programming

Dimensionality Reduction for Onboard Modeling of Uncertain Atmospheres

Finite-Dimensional Density Representation for Aerocapture Uncertainty Quantification

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