Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20170000261

Drag De-Orbit Device (D3): A Retractable Device for CubeSat Attitude and Orbit Control using Aerodynamic Forces

David Guglielmo·Sanny R Omar·Riccardo Bevilacqua·Laurence Fineberg·Justin Treptow·Bradley Poffenberger·Yusef Johnson
View original source ↗Export metadata ↓

Abstract

The increasing number of CubeSats being launched has raised concerns about orbital debris since most of these satellites have no means of active orbit control. Some technologies exist to increase the surface area of a CubeSat and expedite de-orbit due to aerodynamic drag in low Earth orbit, but most of these devices cannot be retracted and hence cannot be used for orbital maneuvering. This paper discusses the De-Orbit Drag Device (D3) module that is capable of de-orbiting a 12U, 15kg CubeSat from a 700 km circular orbit in under 25 years and can be deployed and retracted to modulate the aerodynamic drag force experienced by the satellite. This facilitates orbital maneuvering using aerodynamic drag and the active targeting of a de-orbit location. In addition, the geometry of this drag device provides 3-axis attitude stabilization of the host CubeSat using aerodynamic and gravity gradient torques which is useful for many missions and provides a predictable aerodynamic profile for use in orbital maneuvering algorithms.

Explore related subjects

algorithmreentrypropagation

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

David Guglielmo, Sanny R Omar, Riccardo Bevilacqua, Laurence Fineberg, Justin Treptow, Bradley Poffenberger, Yusef Johnson. 2018-09-14. Drag De-Orbit Device (D3): A Retractable Device for CubeSat Attitude and Orbit Control using Aerodynamic Forces. https://ntrs.nasa.gov/citations/20170000261

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2024

Demonstration of the rodeo algorithm on a quantum computer

The rodeo algorithm is an efficient algorithm for eigenstate preparation and eigenvalue estimation for any observable on a quantum computer. This makes it a promising tool for studying the spectrum and structure of atomic nuclei as well as other fields of quantum many-body physics. The only requirement is that the initial state has sufficient overlap probability with the desired eigenstate. While it is exponentially faster than well-known algorithms such as phase estimation and adiabatic evolution for eigenstate preparation, it has yet to be implemented on an actual quantum device. In this work, we apply the rodeo algorithm to determine the energy levels of a random one-qubit Hamiltonian, resulting in a relative error of 0.08% using mid-circuit measurements on the IBM Q device Casablanca. This surpasses the accuracy of directly-prepared eigenvector expectation values using the same quantum device. We take advantage of the high-accuracy energy determination and use the Hellmann-Feynman theorem to compute eigenvector expectation values for a different random one-qubit observable. For the Hellmann-Feynman calculations, we find a relative error of 0.7%. Here, we conclude by discussing possible future applications of the rodeo algorithm for multi-qubit Hamiltonians.

algorithm↗
DOE OSTI2023

AI and ML Applications for PV Reliability and System Performance

This poster discusses AI and ML topics in PV reliability and system performance. In particular, automated metadata extraction and QA for fielded solar installations is covered for the PV Fleets Project. Additionally, statistical learning topics for the PVInsight Project are addressed, as well as development of the PV Validation Hub.

algorithm↗
DOE OSTI2023

Linearized Distribution Optimal Power Flow for OEDI SI

This research is to meant to demonstrate the OEDI SI use case for distributed optimal power flow (DOPF). The goal was to formulate the optimal power flow problem in the distribution system for active and reactive power setpoints of PV systems using topology information and voltage measurements. The co-simulation runs every 15 minutes as outlined within the scenario file for the given feeder configuration. The linked GitHub repository includes five federates to achieve DOPF for the small, medium, large, and IEEE 123 feeder scenarios. We are using the OEDI SI framework, as well as the example feeder, sensor, recorder, and estimator federates provided in the example repository for OEDI SI. We also provide a runner script for switching between scenarios.

algorithm↗