Development of a High-Fidelity Time-Dependent Aero-Structural Capability for Analysis and Design

Mavriplis, Dimitri J.; Anderson, Evan M.; Fertig, Ray S.; Garnich, Mark R.

doi:10.2514/6.2016-1175

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…The definition of the eigenvalue problem (14), symmetry of K and M (15), and mass normalization (16) are used to simplify further to…”

Section: Tangent Modelmentioning

confidence: 99%

“…The adjoint implementation (figure 8) involves a black-box AD differentation of the assembly (step 2). As was the case for the linear stress analysis, one would want to avoid differentiating through an iterative solver for the generalized eigenvalue problem of (14). A similar approach can also be used to compute the adjointsK andM .…”

Section: Adjoint Modelmentioning

confidence: 99%

“…To compute the required sensitivites efficiently, an adjoint structural solver that offers stress and vibration analysis has been developed. While recent works [14,9] have differentiated structural solvers for multidiscplinary optimizations (MDOs) using a more analytical approach, this work focuses on the efficient use of algorithmic differentiation (AD). The discrete adjoint implementations of the linear stress and vibration analysis are discussed in sections 3 and 4, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrete adjoint gradient evaluations for linear stress and vibration analysis

Schwalbach

Verstraete

Gauger

2019

Comput. Visual Sci.

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This paper presents methods used to perform discrete adjoint gradient evaluations for linear stress and vibration analysis. The methods are implemented within the framework of a discrete adjoint structural solver being developed for multidisciplinary adjoint optimizations of turbomachinery components. The code is differentiated using the algorithmic differentiation (AD) tool CoDiPack in tandem with manual treatment of the iterative solvers.Stress analysis leads to a linear system of equations that is typically solved by an iterative solver (e.g. GMRES). To ensure accuracy, the adjoint problem is formulated as a new linear system of equations to be solved.Vibration analysis results in a generalized eigenvalue problem that is also typically solved by an interative solver. The adjoint problem takes out the generalized eigenvalue solve and replaces it by one outer product per eigenfrequency, leading to significantly cheap eigenfrequency gradients for vibration analysis.

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“…The definition of the eigenvalue problem (14), symmetry of K and M (15), and mass normalization (16) are used to simplify further to…”

Section: Tangent Modelmentioning

confidence: 99%

Section: Adjoint Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discrete adjoint gradient evaluations for linear stress and vibration analysis

Schwalbach

Verstraete

Gauger

2019

Comput. Visual Sci.

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“…However, this approach is not appropriate for transonic flow. Several authors (Keye et al, 2014;Keye and Rudnik, 2015;Mavriplis et al, 2016) performed FSI simulations using the first approach, but imposing a full 3D structural model is cumbersome and computationally expensive. Alternatively, (Elsayed, et al, 2009) adopted a simplified finite element (FE) wing approach based on a beam stiffness properties extraction method.…”

Section: Introductionmentioning

confidence: 99%

Model structure effect on static aeroelastic deformation of the NASA CRM

Bdeiwi

Ciarella

Peace

et al. 2019

HFF

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Purpose This paper aims to present a computational aeroelastic capability based on a fluid–structure interaction (FSI) methodology and validate it using the NASA Common Research Model (CRM). Focus is placed on the effect of the wind tunnel model structural features on the static aeroelastic deformations. Design/methodology/approach The FSI methodology couples high-fidelity computational fluid dynamics to a simplified beam representation of the finite element model. Beam models of the detailed CRM wind tunnel model and a simplified CRM model are generated. The correlation between the numerical simulations and wind tunnel data for varying angles of attack is analysed and the influence of the model structure on the static aeroelastic deformation and aerodynamics is studied. Findings The FSI results follow closely the general trend of the experimental data, showing the importance of considering structural model deformations in the aerodynamic simulations. A thorough examination of the results reveals that it is not unequivocal that the fine details of the structural model are important in the aeroelastic predictions. Research limitations/implications The influence of some changes in structural deformation on transonic wing aerodynamics appears to be complex and non-linear in nature and should be subject to further investigations. Originality/value It is shown that the use of a beam model in the FSI approach provides a reliable alternative to the more costly coupling with the full FE model. It also highlights the non-necessity to develop precise, detailed structural models for accurate FSI simulations.

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“…Martins et al [25][26][27] have developed systematic aerostructural optimization design codes, which use coupled high-fidelity sensitivity analysis based on discrete adjoint method, and have been applied to design full aircraft configurations with coupled adjoint method. Mavriplis et al [28][29][30] have developed timedependent aeroelastic shape optimization based on discrete adjoint method.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Optimization Based on Continuous Adjoint Method for a Flexible Wing

Xia

2016

International Journal of Aerospace Engineering

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Aerodynamic optimization based on continuous adjoint method for a flexible wing is developed using FORTRAN 90 in the present work. Aerostructural analysis is performed on the basis of high-fidelity models with Euler equations on the aerodynamic side and a linear quadrilateral shell element model on the structure side. This shell element can deal with both thin and thick shell problems with intersections, so this shell element is suitable for the wing structural model which consists of two spars, 20 ribs, and skin. The continuous adjoint formulations based on Euler equations and unstructured mesh are derived and used in the work. Sequential quadratic programming method is adopted to search for the optimal solution using the gradients from continuous adjoint method. The flow charts of rigid and flexible optimization are presented and compared. The objective is to minimize drag coefficient meanwhile maintaining lift coefficient for a rigid and flexible wing. A comparison between the results from aerostructural analysis of rigid optimization and flexible optimization is shown here to demonstrate that it is necessary to include the effect of aeroelasticity in the optimization design of a wing.

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Development of a High-Fidelity Time-Dependent Aero-Structural Capability for Analysis and Design

Cited by 5 publications

References 36 publications

Discrete adjoint gradient evaluations for linear stress and vibration analysis

Discrete adjoint gradient evaluations for linear stress and vibration analysis

Model structure effect on static aeroelastic deformation of the NASA CRM

Aerodynamic Optimization Based on Continuous Adjoint Method for a Flexible Wing

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