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Strategies for Automation of Model Tuning in Multifidelity Trajectory Uncertainty Propagation

Multi-model Monte Carlo methods are efficient strategies to perform forward uncertainty quantification studies in entry, descent, and landing (EDL) applications. These multi-model methods are based on the classical Monte Carlo estimator, but fuse predictions from several low-fidelity models to obtain estimators with greater precision given a prescribed computational budget. The effectiveness of these approaches relies on the magnitudes of correlations between the low-fidelity models and the high-fidelity model, as well as the relative computational costs of all models. Identifying and exploiting the best trade-off between correlation and cost, which ultimately depends on the selection of hyperparameters in the low-fidelity models, is a task often performed by hand or simply inspired by the deterministic understanding available for a specific application. This work extends a preliminary effort,

Marten Thompson↗
DOE OSTI2020

De-tuning tolerant loop antenna

A loop antenna is described herein that is suitable for operation in free space or in proximity to a ground plane. The loop antenna comprises a looped conductive element with a gap formed therein and a tab extending toward the gap. A length of the tab of the loop antenna is selected such that a detuning loss of the loop antenna from operation in free space to operation in proximity to a ground plane is low. In an exemplary embodiment, the length of the tab is selected such that a reactance of the loop antenna in free space is within a threshold level of a reactance of the loop antenna when the antenna is in proximity to a ground plane.

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