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Applying Bayesian Inference to Estimate Uncertainties in the Aerodynamic Database of CALLISTO

Krummen, Sven and Tummala, Pavan and Wilken, Jascha and Dumont, Etienne and Ertl, Moritz and Ecker, Tobias and Riehmer, Johannes and Klevanski, Josef (2023) Applying Bayesian Inference to Estimate Uncertainties in the Aerodynamic Database of CALLISTO. In: 2022 IEEE Aerospace Conference, AERO 2022. IEEE Aerospace Conference, 04.-11. März 2023, Big Sky, Montana, USA. ISBN 978-166543760-8. ISSN 1095-323X. (Submitted)

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Abstract

The three national space centers DLR, CNES & JAXA have joined their efforts in the project CALLISTO to develop and mature key technologies for future operational Reusable Launch Vehicles (RLVs). The goal of this project is to develop, manufacture and test a reusable Vertical-Takeoff Vertical-Landing (VTVL) first stage demonstrator, which will be operated at the European Spaceport in French Guiana from late 2024. One important aspect in the development of RLVs, but also of aerospace vehicles in general, is the generation of an Aerodynamic Database (AEDB) which characterizes the aerodynamic flying qualities of the vehicle. These databases are commonly aggregated from Computational Fluid Dynamics (CFD) simulations and Wind Tunnel Tests (WTTs) via simple heuristic models. Whereas this classical approach is suitable for the estimation of nominal aerodynamic coefficients, the quantification of uncertainties in this pre-flight data with respect to the final flight behavior is still a difficult task that involves a lot of human expert knowledge and "gut feeling". Particularly for launch vehicles, these uncertainties are however essential to ensure robust guidance and control algorithms, as well as sufficient vehicle performance for a selected mission profile. For CALLISTO, in parallel to a classical approach, a new methodology has now been tested to estimate these uncertainties within the AEDB: To apply Bayesian Inference to predict a probability distribution over the aerodynamic coefficients, conditional on the available test and simulation results and on prior knowledge. This methodology has already been well-established in other data science domains, but for aerospace engineering only very few use-cases are known so far. With this new approach an objectively traceable modelling of the aerodynamic uncertainties should be possible. This paper presents the current development state of the Bayesian aerodynamic uncertainties model of CALLISTO. After problem definition and a short introduction to the underlying dataset, the paper mainly focuses on the used modelling techniques and the applicability of Bayesian methods to the aerodynamic characterization problem. Selected results are shown for Bayesian models and compared against the classical modelling approach, while advantages and disadvantages of the Bayesian methodology are discussed. It is shown that the implemented Bayesian Gaussian process model can infer the typical characteristics of the AEDB from the available datasets, while having comparable prediction qualities as the reference model. Observed differences in the variance and bias characteristics are discussed for both models.

Item URL in elib:https://elib.dlr.de/192816/
Document Type:Conference or Workshop Item (Speech)
Title:Applying Bayesian Inference to Estimate Uncertainties in the Aerodynamic Database of CALLISTO
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Krummen, SvenUNSPECIFIEDhttps://orcid.org/0000-0002-4126-688XUNSPECIFIED
Tummala, PavanUNSPECIFIEDhttps://orcid.org/0000-0002-3460-8044UNSPECIFIED
Wilken, JaschaUNSPECIFIEDhttps://orcid.org/0000-0001-5748-1261UNSPECIFIED
Dumont, EtienneUNSPECIFIEDhttps://orcid.org/0000-0003-4618-0572UNSPECIFIED
Ertl, MoritzUNSPECIFIEDhttps://orcid.org/0000-0002-1900-5122UNSPECIFIED
Ecker, TobiasUNSPECIFIEDhttps://orcid.org/0000-0001-7134-1185UNSPECIFIED
Riehmer, JohannesUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Klevanski, JosefUNSPECIFIEDhttps://orcid.org/0009-0002-4336-1116UNSPECIFIED
Date:March 2023
Journal or Publication Title:2022 IEEE Aerospace Conference, AERO 2022
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
ISSN:1095-323X
ISBN:978-166543760-8
Status:Submitted
Keywords:CALLISTO, Reusable Launch Vehicle (RLV), Vertical-Takeoff Vertical-Landing (VTVL), Aerodynamic Database (AEDB), Uncertainty Estimation, Bayesian Inference, Gaussian Process
Event Title:IEEE Aerospace Conference
Event Location:Big Sky, Montana, USA
Event Type:international Conference
Event Dates:04.-11. März 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transportation
DLR - Research area:Raumfahrt
DLR - Program:R RP - Space Transportation
DLR - Research theme (Project):R - Project CALLISTO [RP]
Location: Bremen , Göttingen , Köln-Porz
Institutes and Institutions:Institute of Space Systems > Space Launcher Systems Analysis
Institute of Space Systems > Systems Engineering and Project Office
Institute for Aerodynamics and Flow Technology
Deposited By: Krummen, Sven
Deposited On:21 Dec 2022 11:56
Last Modified:27 Oct 2023 15:29

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