High-Fidelity Gradient-Based Wing Structural Optimization Including a Geometrically Nonlinear Flutter Constraint

2022 ◽  
Author(s):  
Eirikur Jonsson ◽  
Cristina Riso ◽  
Bernardo Bahia Monteiro ◽  
Alasdair C. Gray ◽  
Joaquim R. Martins ◽  
...  
Keyword(s):  
Structural Optimization ◽  
High Fidelity ◽  
Geometrically Nonlinear ◽  
Nonlinear Flutter ◽  
Gradient Based

Design of Lightweight Composite Structures: A Parameter Free Structural Optimization Approach

2012 ◽  
Vol 504-506 ◽  
pp. 1391-1396
Author(s):  
Michael Fischer ◽  
Helmut Masching ◽  
Matthias Firl ◽  
Kai Uwe Bletzinger
Keyword(s):  
Finite Element ◽  
Structural Optimization ◽  
Industrial Application ◽  
Composite Structures ◽  
Large Scale ◽  
Finite Element Models ◽  
Optimization Approach ◽  
Geometrically Nonlinear ◽  
Gradient Based ◽  
Algorithmic Techniques

This contribution presents computational concepts and algorithmic techniques for simulation and gradient-based optimization of geometrically nonlinear and large-scale finite element models of composite structures. Several industrial application examples illustrate the methods, show the applicability to large problems, and prove the high parallel efficiency.

Efficient Gradient-Based Algorithms for the Construction of Pareto Fronts

2011 ◽  
Author(s):  
Sriram Shankaran ◽  
Brian Barr
Keyword(s):  
Pareto Front ◽  
Computational Cost ◽  
High Fidelity ◽  
Simulation Tools ◽  
Pareto Points ◽  
Uniform Spacing ◽  
Design Variables ◽  
Gradient Based ◽  
Pareto Fronts ◽  
Sampling Approach

The objective of this study is to develop and assess a gradient-based algorithm that efficiently traverses the Pareto front for multi-objective problems. We use high-fidelity, computationally intensive simulation tools (for eg: Computational Fluid Dynamics (CFD) and Finite Element (FE) structural analysis) for function and gradient evaluations. The use of evolutionary algorithms with these high-fidelity simulation tools results in prohibitive computational costs. Hence, in this study we use an alternate gradient-based approach. We first outline an algorithm that can be proven to recover Pareto fronts. The performance of this algorithm is then tested on three academic problems: a convex front with uniform spacing of Pareto points, a convex front with non-uniform spacing and a concave front. The algorithm is shown to be able to retrieve the Pareto front in all three cases hence overcoming a common deficiency in gradient-based methods that use the idea of scalarization. Then the algorithm is applied to a practical problem in concurrent design for aerodynamic and structural performance of an axial turbine blade. For this problem, with 5 design variables, and for 10 points to approximate the front, the computational cost of the gradient-based method was roughly the same as that of a method that builds the front from a sampling approach. However, as the sampling approach involves building a surrogate model to identify the Pareto front, there is the possibility that validation of this predicted front with CFD and FE analysis results in a different location of the “Pareto” points. This can be avoided with the gradient-based method. Additionally, as the number of design variables increases and/or the number of required points on the Pareto front is reduced, the computational cost favors the gradient-based approach.

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.

AN ADAPTIVE RBF METHOD FOR DESIGN OPTIMIZATION OF BUILDING STRUCTURES

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Optimization ◽  
Optimization Algorithm ◽  
Design Method ◽  
Building Structures ◽  
Design Constraint ◽  
Gradient Based ◽  
Structural Responses ◽  
Sample Points ◽  
Objective Function Values ◽  
Adaptive Metamodeling

Design of building structures has long been based on a trial-and-error iterative approach. Structural optimization provides practicing engineers an effective and efficient approach to replace the traditional design method. A numerical optimization algorithm, such as a gradient-based method or genetic algorithm (GA), can be applied, in conjunction with a finite element (FE) analysis program. The FE program is used to compute the structural responses, such as forces and displacements, which represent the design constraint functions. In this method, reading and writing the input/output files of the FE program and interface programming are required. Another method to perform structural optimization is to create an approximate constraint function, which involves implicit structural responses. This is referred to as a surrogate or metamodeling method. The structural responses can be expressed as approximate functions, based on a number of preselected sample points. In this study, an adaptive metamodeling method was studied and applied to a building structure. The FE analyses were first performed at the sample points, and metamodels were constructed. A gradient-based optimization algorithm was applied. Additional samples were generated and additional FE analyses were conducted so that the model accuracy could be improved, close to the optimal design points. This adaptive scheme was continued, until the objective function values converged. The method worked well and optimal designs were found within a few iterations.

scholarly journals Adjoint-Based High-Fidelity Structural Optimization of Wind-Turbine Blade for Load Stress Minimization

AIAA Journal ◽  
2019 ◽  
Vol 57 (9) ◽  
pp. 4057-4070 ◽  
Author(s):  
Evan M. Anderson ◽  
Faisal Hasan Bhuiyan ◽  
Dimitri J. Mavriplis ◽  
Ray S. Fertig
Keyword(s):  
Structural Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
High Fidelity ◽  
Stress Minimization

scholarly journals Tiltrotor CFD Part II - aerodynamic optimisation of tiltrotor blades

2017 ◽  
Vol 121 (1239) ◽  
pp. 611-636
Author(s):  
A. Jimenez-Garcia ◽  
M. Biava ◽  
G.N. Barakos ◽  
K.D. Baverstock ◽  
S. Gates ◽  
...  
Keyword(s):  
Single Point ◽  
Optimal Solution ◽  
High Fidelity ◽  
Blade Design ◽  
Discrete Adjoint ◽  
Blade Shape ◽  
Gradient Based ◽  
Flow Gradients ◽  
Flight Regimes ◽  
Quasi Newton

ABSTRACTThis paper presents aerodynamic optimisation of tiltrotor blades with high-fidelity computational fluid dynamics. The employed optimisation framework is based on a quasi-Newton method, and the required high-fidelity flow gradients were computed using a discrete adjoint solver. Single-point optimisations were first performed to highlight the contrasting requirements of the helicopter and aeroplane flight regimes. It is then shown how a trade-off blade design can be obtained using a multi-point optimisation strategy. The parametrisation of the blade shape allowed the twist and chord distributions to be modified and a swept tip to be introduced. The work shows how these main blade shape parameters influence the optimal performance of the tiltrotor in helicopter and aeroplane modes, and how an optimised blade shape can increase the overall tiltrotor performance. Moreover, in all the presented cases, the accuracy of the adjoint gradients resulted in a small number of flow evaluations for finding the optimal solution, thus indicating gradient-based optimisation as a viable tool for modern tiltrotor design.

scholarly journals Gradient-based multifidelity optimisation for aircraft design using Bayesian model calibration

2011 ◽  
Vol 115 (1174) ◽  
pp. 729-738 ◽  
Author(s):  
A. March ◽  
K. Willcox ◽  
Q. Wang
Keyword(s):  
Bayesian Model ◽  
Model Calibration ◽  
Trust Region Method ◽  
Trust Region ◽  
Aircraft Design ◽  
High Fidelity ◽  
First Order ◽  
Gradient Based ◽  
Quadratic Programming Method ◽  
Optimisation Methods

Abstract Optimisation of complex systems frequently requires evaluating a computationally expensive high-fidelity function to estimate a system metric of interest. Although design sensitivities may be available through either direct or adjoint methods, the use of formal optimisation methods may remain too costly. Incorporating low-fidelity performance estimates can substantially reduce the cost of the high-fidelity optimisation. In this paper we present a provably convergent multifidelity optimisation method that uses Cokriging Bayesian model calibration and first-order consistent trust regions. The technique is compared with a single-fidelity sequential quadratic programming method and a conventional first-order trust-region method on both a two-dimensional structural optimisation and an aerofoil design problem. In both problems adjoint formulations are used to provide inexpensive sensitivity information.

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