Combining uncertainty and sensitivity using multi-fidelity probabilistic aerodynamic databases for aircraft maneuvers
Abstract/Contents
- Abstract
- Balancing cost and accuracy is a fundamental trade throughout the engineering design process. More accurate results typically take more time or resources to generate. To most efficiently use the resources available, it is critical to understand where the increased accuracy is needed. This thesis covers two major areas of research: developing a framework to quantify the uncertainty over the domain of interest and calculating the sensitivity of some defined performance metric to that uncertainty. Probabilistic aerodynamic databases are functions that store a distribution of possible aerodynamic coefficients over the entire flight envelope. Built with Gaussian processes, an additive correction hierarchical model is applied to combine multiple fidelity levels. Each of these fidelity levels has a subject matter expert (SME) provided error term assigned to each individual sample. Deterministic instances of the database respecting physics and SME uncertainties are created through Monte Carlo sampling to provide inputs to currently used industry analysis tools. By repeatedly running the trajectory analysis with different samples, distributions of the performance metrics are created. In the event these distributions of potential solutions exhibit too large an uncertainty on the quantity of interest, an adjoint-based sensitivity method was developed to guide further analyses. Extended from optimal control theory, sensitivities of the objective function with respect to each aerodynamic coefficient at each time step in the trajectory can be calculated for approximately the same cost as solving the forward trajectory problem. Multiple indicator functions combining the uncertainty and sensitivity were proposed. On a cannon ball example where the drag coefficient was uncertain, these different indicator functions were compared to an exhaustive search of adding a single analysis at each point in the domain. The best performing indicator was then applied to the National Aeronautics and Space Administration (NASA) Common Research Model (CRM). Both the cannon ball and NASA CRM were studied through an adaptive sampling methodology. The cannon ball adaptive sampling, guided by the uncertainty-sensitivity indicator functions, was three-to-four times better than uncertainty-only indicators and one-to-two orders of magnitude better than not adaptive sampling when maximizing accuracy for a fixed cost. When minimizing cost for a tolerable accuracy requirement, the uncertainty-sensitivity adaptive sampling reduced the cost by a factor of two compared to the uncertainty-only sampling. In the CRM case, only one indicator was used. Using 10 percent of the computation budget, a 50 percent increase in accuracy was seen compared to sampling over the entire maneuver domain.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wendorff, Andrew David | |
|---|---|---|
| Associated with | Stanford University, Department of Aeronautics and Astronautics. | |
| Primary advisor | Alonso, Juan José, 1968- | |
| Thesis advisor | Alonso, Juan José, 1968- | |
| Thesis advisor | Kochenderfer, Mykel J, 1980- | |
| Thesis advisor | Kroo, Ilan | |
| Advisor | Kochenderfer, Mykel J, 1980- | |
| Advisor | Kroo, Ilan |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Andrew David Wendorff. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Andrew David Wendorff
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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