Search NASA⌕ Search

NASA NTRS · 20160012488

Re-Entry Point Targeting for LEO Spacecraft using Aerodynamic Drag

Abstract

Most Low Earth Orbit (LEO) spacecraft do not have thrusters and re-enter atmosphere in random locations at uncertain times. Objects pose a risk to persons, property, or other satellites. Has become a larger concern with the recent increase in small satellites. Working on a NASA funded project to design a retractable drag device to expedite de-orbit and target a re-entry location through modulation of the drag area. Will be discussing the re-entry point targeting algorithm here.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Omar, Sanny, Bevilacqua, Riccardo, Fineberg, Laurence, Treptow, Justin, Johnson, Yusef, Clark, Scott. 2016-10-19. Re-Entry Point Targeting for LEO Spacecraft using Aerodynamic Drag. https://ntrs.nasa.gov/citations/20160012488

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

KEEP EXPLORING

Related reports

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↗

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↗

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↗