Multi-fidelity orbit uncertainty propagation

Jones, Brandon A.; Weisman, Ryan M.

doi:10.1016/j.actaastro.2018.10.023

Cited by 25 publications

(9 citation statements)

References 23 publications

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“…In this way, stochastic analysis is primarily performed via a low‐resolution model, which drastically decreases the computational complexity. Similar multifidelity approaches for acceleration of parametric studies have been recently reported in different areas, such as molecular dynamics simulations, orbit‐state uncertainty propagation, and combustion and turbulence modeling …”

Section: Introductionmentioning

confidence: 71%

“…Similar multifidelity approaches for acceleration of parametric studies have been recently reported in different areas, such as molecular dynamics simulations, orbit-state uncertainty propagation, and combustion and turbulence modeling. [23][24][25][26] We provide theoretical discussions that motivate the success of our approach, and we show via numerical examples that using a low-resolution model that is far coarser than necessary for any meaningful physical predictive power is able to give substantial insight into parametric variation. Our method is nonintrusive, ie, it is implemented with minimal modification to the existing codes for topology optimization.…”

Section: Introductionmentioning

confidence: 99%

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Parametric topology optimization with multiresolution finite element models

Keshavarzzadeh

Kirby

Narayan

2019

Numerical Meth Engineering

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Summary We present a methodical procedure for topology optimization under uncertainty with multiresolution finite element (FE) models. We use our framework in a bifidelity setting where a coarse and a fine mesh corresponding to low‐ and high‐resolution models are available. The inexpensive low‐resolution model is used to explore the parameter space and approximate the parameterized high‐resolution model and its sensitivity, where parameters are considered in both structural load and stiffness. We provide error bounds for bifidelity FE approximations and their sensitivities and conduct numerical studies to verify these theoretical estimates. We demonstrate our approach on benchmark compliance minimization problems, where we show significant reduction in computational cost for expensive problems such as topology optimization under manufacturing variability, reliability‐based topology optimization, and three‐dimensional topology optimization while generating almost identical designs to those obtained with a single‐resolution mesh. We also compute the parametric von Mises stress for the generated designs via our bifidelity FE approximation and compare them with standard Monte Carlo simulations. The implementation of our algorithm, which extends the well‐known 88‐line topology optimization code in MATLAB, is provided.

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Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Parametric topology optimization with multiresolution finite element models

Keshavarzzadeh

Kirby

Narayan

2019

Numerical Meth Engineering

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“…Possible candidates are pure analytic solutions (it was recently showed that near-optimality can be achieved even with analytic predictor-correctors [21]), or by a combination of analytic and numeric propagators through multi-fidelity approaches such as in Refs. [17,18].…”

Section: Discussionmentioning

confidence: 99%

“…Alternative methods that may be used to improve numerical tractability include the possibility of using analytic predictors instead of numeric ones, or a multi-fidelity approach [17,18], by which in this case analytic solutions may be combined with numeric solutions for high accuracy, high speed solutions.…”

Section: Computational Efficiencymentioning

confidence: 99%

Two Stage Optimization for Aerocapture Guidance

Zucchelli

Hanasusanto

Brandon-Jones

et al. 2021

AIAA Scitech 2021 Forum

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This paper proposes a two-stage optimization approach for aerocapture guidance. In classical entry guidance systems, deterministic optimization is used. Large-scale and shortscale density perturbations may strongly affect the performance of the guidance system, and variations in those are usually not accounted for when computing the command. In this work, perturbations that affect the trajectory at future time-steps are taken into consideration when computing the commanded bank angle. The chosen command is optimal based on a set of possible future perturbations, after the observation of which, a correction can be made. Both two-stage stochastic and two-stage robust optimization are proposed as a solution. In a Monte Carlo analysis consisting of 50 runs, the two-stage robust optimization guidance outperforms an optimal, deterministic, numeric predictor-corrector guidance. Excluding one outlier, also the two-stage stochastic optimization makes the guidance perform better than an optimal deterministic numeric predictor-corrector. With either approach, the computational demands are increased by about thirty times compared to an optimal numeric predictor-corrector. Much of the computation time increase may be reduced by parallelization. On the other hand, the extensive tuning required for the optimal numeric predictor-corrector is not needed for the twostage optimization guidance, making this approach conceptually more robust. Better modeling of the environment may help further improve the performance. Finally, an approximation to the two-stage robust optimization approach is developed. The guidance has computational requirements only four times larger than those of the optimal numeric predictor-corrector guidance, but can be parallelized into two threads, and, except for two outliers, it offers improved performance.

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“…Modeling and simulation of autonomous spacecraft have made momentous strides in recent years, and the space industry, and other aerospace professions are on the verge of being able to use computing power. The aim of this usage is to simulate reality for all kinds of applications in space engineering such as autonomy of nanosatellites [1], flight simulation [2], space object registration [3], and orbit propagation [4]. In space engineering, rapid simulation of space orbits and trajectories is essential in different aspects of space engineering including trajectory optimization [5], orbit transfers, orbit determination and attitude control, and gravitational modeling.…”

Section: Introductionmentioning

confidence: 99%

Simulation Framework for Orbit Propagation and Space Trajectory Visualization

Shirazi

Ceberio

Lozano

2021

IEEE Aerosp. Electron. Syst. Mag.

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In this paper, an interactive tool for simulation of satellites dynamics and autonomous spacecraft guidance is presented. Different geopotential models for orbit propagation of Earth-orbiting satellites are provided, which consider Earth's gravitational field with various accuracies. The presented software is a 3D visualization platform for space orbit simulation with analytical capabilities through various modules. Taking advantage of a graphical user interface, it can evaluate, analyze and illustrate the motion of satellite based on different orbit propagation schemes. Several cases of satellites and autonomous spacecraft in the space rendezvous mission are simulated regarding different propagation models to demonstrate the performance of the application in space mission analysis, ground track visualization and trajectory optimization. Results are validated by comparing with other state-of-the-art tools, such as AGI System Toolkit (STK).

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Multi-fidelity orbit uncertainty propagation

Cited by 25 publications

References 23 publications

Parametric topology optimization with multiresolution finite element models

Parametric topology optimization with multiresolution finite element models

Two Stage Optimization for Aerocapture Guidance

Simulation Framework for Orbit Propagation and Space Trajectory Visualization

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