Variable Fidelity Methods and Surrogate Modeling of Critical Loads on X-31 Aircraft

Zhang, Mengmeng; Tomac, Maximillian; Wang, Cong; Rizzi, Arthur

doi:10.2514/6.2013-1081

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Tornado). For example, in the study of surrogate model generation for aero-data and aero-loads of X-31 aircraft [24], the cheap samples are provided by the Euler solver (L2) and the expensive samples are provided by the RANS solver (L3) on the very fine grids, with computational time substantially increased. Similar case was also found in [28].…”

Section: Domain Validity Issuesmentioning

confidence: 99%

Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

Zhang¹,

Bartoli

Jungo

et al. 2020

Progress in Aerospace Sciences

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In the modern aircraft design process numerical simulation is one of the key enablers. However, computational time increases exponentially with the level of fidelity of the simulation. In the EU Horizon 2020 project AGILE different aircraft design analysis tools relative to different levels of fidelity are used. One of the challenges is to reduce the computational time -e.g. to facilitate an efficient optimization process -by processing the analysis data of various fidelity levels in a global surrogate model. This paper focuses on fusion of data sets via an automatic iterative process embedded in the collaborative multidisciplinary analysis (MDA) chains as applied in AGILE. Surrogate modeling techniques are applied, taking into account the optimal sampling and the corresponding fidelities of the samples. This paper will detail the different steps of the proposed collaborative approach. As a test case handling qualities analysis of the AGILE reference conventional aircraft is performed, by fusing the computed aerodynamic coefficients and derivatives. A full set of aerodynamic data computed either with different levels of fidelity or with only a low-fidelity tool has been derived and evaluated. The data set with multiple levels of fidelity significantly improved the accuracy of the flight performance analysis, especially for the transonic region in which the low fidelity aerodynamic method is not reliable. Moreover, the test case shows that by combining a collaborative surrogate modelling approach with fusion of the data sets, the fidelity of the analysis data can be significantly improved giving maximum relative prediction error less than 5% with minimum computation effort.

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Section: Domain Validity Issuesmentioning

confidence: 99%

Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

Zhang¹,

Bartoli

Jungo

et al. 2020

Progress in Aerospace Sciences

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“…1 The Multidisciplinary Design Optimization Process Chain as used in the Digital-X project [36] liminary [37,35,66] design level. These architectures exploit often surrogate models [62,50,18,27,73,42] (e.g. response surfaces, Kriging models, artificial neural networks, support-vector machines, radial basis functions and metamodels) in order to reduce the computational effort of the design.…”

Section: Introductionmentioning

confidence: 99%

Reduced-order models for aerodynamic applications, loads and MDO

et al. 2018

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This article gives an overview of reduced order modeling work performed in the DLR project Digital-X. Parametric aerodynamic reduced order models (ROMs) are used to predict surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady aerodynamic applications are built using proper orthogonal decomposition (POD) and Isomap, a manifold learning method. Approximate solutions in the so obtained low-dimensional representations of the data are found with interpolation techniques, or by minimizing the corresponding steady flow-solver residual. The latter approach produces physics-based ROMs driven by the governing equations. The steady ROMs are used to predict the static aeroelastic loads in a multidisciplinary design and optimization (MDO) context, where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. An approach to correct a linear loads analysis model using steady CFD solutions at various Mach numbers and angles of attack and a ROM of the corrected Aerodynamic Influence Coefficients (AICs) is also shown. This results in a complete loads analysis model preserving aerodynamic nonlinearities while allowing fast evaluation across all model parameters. The different ROM methods are applied to a 3D test case of a transonic wing-body transport aircraft configuration. Keywords reduced order model • proper orthogonal decomposition • isomap • manifold learning • multidisciplinary design and optimization • aerodynamic influence coefficients • loads analysis • CFD

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Virtual Flight Simulation using Computational Fluid Dynamics

Akram¹,

Cristofaro

Ronch

2017

Advanced UAV Aerodynamics, Flight Stability and Control

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The aim of this Chapter is to present recent advances in the development of high-fidelity simulation software for virtual flight testing of unconventional and next-generation aircraft. The accurate and efficient simulation of the full set of physics associated with aircraft flight operations including flight control, aerodynamics, and structural dynamics is discussed. Pivotal to this framework is a novel adaptive design of experiment algorithm for the rapid generation of the aerodynamic database that is developed and demostrated on a complete aircraft with highly nonlinear aerodynamics. Throughout the Chapter, several worked-out examples and problems are introduced to aid the reader to gain competence and proficiency in the methods and computer codes accompanying this Chapter. The Chapter continues with Section 1.1 overviewing the state-of-the-art in virtual flight simulation. It continues in Section 1.2 with a discussion of the aerodynamic model traditionally used for flight simulation. Then, surrogate modelling and adaptive design of experiment are developed in Section 1.3. Finally, advanced models used for virtual flight simulation are presented in Section 1.4.

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Variable Fidelity Methods and Surrogate Modeling of Critical Loads on X-31 Aircraft

Cited by 6 publications

References 4 publications

Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

Reduced-order models for aerodynamic applications, loads and MDO

Virtual Flight Simulation using Computational Fluid Dynamics

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