Uncertainty quantification study of the aerodynamic performance of high-altitude propellers
[EN] Performance evaluations for propellers operating at high altitudes are subject to increased uncertainty due to scarce experimental or flight data and difficulties in modeling low Reynolds number flows. For this reason, the Polynomial Chaos Expansion (PCE) method is used in this paper to assess...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/17724 |
| Acceso en línea: | https://hdl.handle.net/10612/17724 |
| Access Level: | acceso abierto |
| Palabra clave: | Aeronáutica BEMT High-altitude flight condition Polynomial chaos expansion 3301 Ingeniería y Tecnología Aeronáuticas |
| Sumario: | [EN] Performance evaluations for propellers operating at high altitudes are subject to increased uncertainty due to scarce experimental or flight data and difficulties in modeling low Reynolds number flows. For this reason, the Polynomial Chaos Expansion (PCE) method is used in this paper to assess the performance uncertainty of propellers operating at high altitudes. Aleatoric (i.e. linked to the geometry or operating conditions) and epistemic (i.e. linked to the mathematical model describing the flow) uncertainty variables are included in this study to estimate the total uncertainty related to performance predictions made by two physical models, namely 3D RANS with the use of transition model and Blade Element Momentum Theory (BEMT). In order to validate the proposed method, multipoint uncertainty quantification (UQ) studies are performed for two benchmark propeller geometries under various operating conditions for which experimental data are available. The UQ method is further illustrated on a propeller operating at high altitude. The efficacy of UQ with Computational Fluid Dynamics (CFD) and BEMT is compared and the most influential uncertain variables are found using Sobol's total order indices. As a result of the CFD-based uncertainty quantification studies, two major uncertain variables are identified, providing a direction for more computationally affordable UQ studies. |
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