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Journal ArticleDOI

Sensitivity Analysis for Navier-Stokes Equations on Unstructured Meshes Using Complex Variables

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TLDR
Although a step size parameter is required, the numerical derivatives are not subject to subtractive cancellation errors and, therefore, exhibit true second-order accuracy as the step size is reduced, in contrast to the use of finite differences.
Abstract
The use of complex variables for determining sensitivity derivatives for turbulent flows is examined. Although a step size parameter is required, the numerical derivatives are not subject to subtractive cancellation errors and, therefore, exhibit true second-order accuracy as the step size is reduced. As a result, this technique guarantees two additional digits of accuracy each time the step size is reduced one order of magnitude. This behavior is in contrast to the use of finite differences, which suffer from inaccuracies due to subtractive cancellation errors. In addition, the complex-variable procedure is easily implemented into existing codes

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Journal ArticleDOI

The complex-step derivative approximation

TL;DR: Improvements to the basic method are suggested that further increase its accuracy and robustness and unveil the connection to algorithmic differentiation theory.
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Survey of Shape Parameterization Techniques for High-Fidelity Multidisciplinary Shape Optimization

TL;DR: A survey of shape parameterization techniques for multidisciplinary applications of complex cone gurations using high-e delity analysis tools such as computational e uid dynamics and computational structural mechanics is provided in this article.
Journal ArticleDOI

Algorithm Developments for Discrete Adjoint Methods

TL;DR: In this paper, the authors present a number of algorithm developments for adjoint methods using the "discrete" approach in which the discretisation of the non-linear equations is linearised and the resulting matrix is then transposed.
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Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

TL;DR: This paper presents a review of all existing discrete methods for computing the derivatives of computational models within a unified mathematical framework that hinges on a new equation, the unifying chain rule, from which all the methods can be derived.
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Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches

TL;DR: The historical development of these approaches are examined, the theoretical background of each major method and the associated numerical techniques required to make them practical in an engineering setting are described, and what is considered to be the state-of-the-art in these methods are described.
References
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Journal ArticleDOI

An implicit upwind algorithm for computing turbulent flows on unstructured grids

TL;DR: An implicit, Navier-Stokes solution algorithm is presented for the computation of turbulent flow on unstructured grids using an upwind algorithm and a backward-Euler time-stepping scheme.
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Using Complex Variables to Estimate Derivatives of Real Functions

TL;DR: A method to approximate derivatives of real functions using complex variables which avoids the subtractive cancellation errors inherent in the classical derivative approximations is described.
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Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation

TL;DR: In this paper, a continuous adjoint approach for obtaining sensitivity derivatives on unstructured grids is developed and analyzed, and a second-order accurate discretization method is described.
Journal ArticleDOI

ADIFOR-Generating Derivative Codes from Fortran Programs

TL;DR: Experimental results show that ADifOR can handle real-life codes and that ADIFOR-generated codes are competitive with divided-difference approximations of derivatives, and studies suggest that the source transformation approach to automatic differentiation may improve the time to compute derivatives by orders of magnitude.
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