scholarly journals Multistage Turbomachinery Design Using the Discrete Adjoint Method Within the Open-Source Software SU2

2020 ◽  
Vol 36 (3) ◽  
pp. 465-478
Author(s):  
S. Vitale ◽  
M. Pini ◽  
P. Colonna
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Adjoint Method ◽  
Discrete Adjoint ◽  
Discrete Adjoint Method ◽  
Turbomachinery Design

Discontinuous Galerkin unsteady discrete adjoint method for real-time efficient tsunami simulations

2013 ◽  
Vol 232 (1) ◽  
pp. 416-430 ◽  
Author(s):  
Sébastien Blaise ◽  
Amik St-Cyr ◽  
Dimitri Mavriplis ◽  
Brian Lockwood
Keyword(s):  
Real Time ◽  
Discontinuous Galerkin ◽  
Adjoint Method ◽  
Discrete Adjoint ◽  
Discrete Adjoint Method

Sensitivity Analysis of Flexible Multibody Systems Based on the Motion Formalism and the Discrete Adjoint Method

2018 ◽  
Author(s):  
Alfonso Callejo ◽  
Valentin Sonneville ◽  
Olivier A. Bauchau
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Cross Sections ◽  
Adjoint Method ◽  
Optimization Methods ◽  
Sensitivity Analyses ◽  
Discrete Adjoint ◽  
Discrete Adjoint Method ◽  
Flexible Multibody ◽  
Forward Equations

The combination of analysis and optimization methods in mechanical engineering, also known as design optimization, has great potential in product development. Robust sensitivity analyses that provide reliable and efficient objective function gradients play a key role in design optimization. This paper presents a discrete adjoint method for the sensitivity analysis of flexible mechanical systems. The ultimate goal is to be able to relate the physical properties of beam cross-sections to the dynamic behavior of the system, which is key to design realistic flexible elements. The underlying flexible multibody formulation is one that supports large-amplitude motion, beams with sophisticated composite cross-sections, and kinematic joints. A summary of the kinematic and dynamic foundations of the forward equations is presented first. Then, a discrete adjoint method, along with meaningful examples and validation, is presented. The method has proven to provide extremely accurate and reliable sensitivities.

Development of Adjoint Solvers for Engineering Gradient-Based Turbomachinery Design Applications

2009 ◽  
Author(s):  
Andre C. Marta ◽  
Sriram Shankaran ◽  
D. Graham Holmes ◽  
Alexander Stein
Keyword(s):  
Adjoint Method ◽  
Automatic Differentiation ◽  
High Fidelity ◽  
Optimization Approach ◽  
Discrete Adjoint ◽  
Gradient Based ◽  
Computational Fluid Dynamics Cfd ◽  
And Function ◽  
Turbomachinery Design ◽  
Fine Tune

High-fidelity computational fluid dynamics (CFD) are common practice in turbomachinery design. Typically, several cases are run with manually modified parameters based on designer expertise to fine-tune a machine. Although successful, a more efficient process is desired. Choosing a gradient-based optimization approach, the gradients of the functions of interest need to be estimated. When the number of variables greatly exceeds the number of functions, the adjoint method is the best-suited approach to efficiently estimate gradients. Until recently, the development of CFD adjoint solvers was regarded as complex and difficult, which limited their use mostly to academia. This paper focuses on the problem of developing adjoint solvers for legacy industrial CFD solvers. A discrete adjoint solver is derived with the aid of an automatic differentiation tool that is selectively applied to the CFD code that handles the residual and function evaluations. The adjoint-based gradients are validated against finite-difference and complex-step derivative approximations.

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