Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Aerodynamics (1)
- Aerothermodynamics (1)
- Collision avoidance (1)
- Deep Learning (1)
- Drag (1)
-
- Drag Coefficient (1)
- GSI (1)
- Gas Surface Interaction (1)
- Machine Learning (1)
- Machine learning (1)
- Modeling (1)
- Orbital Debris (1)
- RSM (1)
- Re-entry (1)
- Response Surface Modeling (1)
- Satellite (1)
- Satellite drag (1)
- Software (1)
- Space Debris (1)
- Space weather (1)
- Thermodynamics (1)
- Thermosphere (1)
- Uncertainty (1)
Articles 1 - 3 of 3
Full-Text Articles in Astrodynamics
Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii
Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii
Graduate Theses, Dissertations, and Problem Reports
In the modern space age, private companies are crowding the already-congested low Earth orbit (LEO) regime with small satellite mega constellations. With over 25,000 objects larger than 10 cm already in LEO, this rapid expansion is forcing us towards the enterprise on Space Traffic Management (STM). STM is an operational effort that focuses on conjunction assessment and collision avoidance between objects. While the equations of motion for objects in orbit are well-known, there are many uncertain parameters that result in the uncertainty of an object's future position. The force that the atmosphere exerts on satellite - known as drag - …
Updates And Improvements To The Satellite Drag Coefficient Response Surface Modeling Toolkit, Phillip Logan Sheridan
Updates And Improvements To The Satellite Drag Coefficient Response Surface Modeling Toolkit, Phillip Logan Sheridan
Graduate Theses, Dissertations, and Problem Reports
For satellites in Low Earth Orbit, the drag coefficient is a major area of uncertainty. Researchers at the Los Alamos National Laboratory have created a Response Surface Modeling (RSM) toolkit to provide the community with a resource for simulating and modeling satellite drag coefficients in Free Molecular Flow. The toolkit combines the high fidelity of numerical simulation techniques with the speed of regression modeling. Specifically, it uses a training sample of drag coefficients simulated with the Test Particle Monte Carlo method with the robust Gaussian Process Regression approach. The RSM toolkit is the prime process to become a toolkit of …
Next-Generation Re-Entry Aerothermodynamic Modeling Of Space Debris Using Machine Learning, Nicholas Sia
Next-Generation Re-Entry Aerothermodynamic Modeling Of Space Debris Using Machine Learning, Nicholas Sia
Graduate Theses, Dissertations, and Problem Reports
The number of resident space objects re-entering the atmosphere is expected to rise with increased space activity over recent years and future projections. Predicting the survival and impact location of the medium to large sized re-entering objects becomes important as they can cause on ground casualties and damage to property. Uncertainties associated with the re-entry process makes necessary a probabilistic approach, which can be computationally expensive when using high-fidelity numerical methods for estimating aerothermodynamic properties. To date, object-oriented analysis is the dominant tool used for atmospheric re-entry modeling and simulation, where aerothermodynamic coefficients are used to determine the risk a …