A study of design velocity field computation for shape optimal design

Choi, Kyung K.; Chang, Kuang-Hua

doi:10.1016/0168-874x(94)90025-6

Cited by 132 publications

(75 citation statements)

References 15 publications

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“…these parameters are required, since they are not provided by today's commercial CAD packages. One approach to obtain these geometric sensitivities (also found in the literature as design velocities [10]) is to compute finite differences between the CAD model before and after a parameter perturbation. This approach has been identified by some researchers [11,12,13] to lack robustness against topology and labelling changes, which can occur even under small parameter perturbations.…”

Section: Introductionmentioning

confidence: 99%

Linking Parametric Cad With Adjoint Surface Sensitivities

Vasilopoulos

Agarwal²,

Meyer

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. The goal of this work is to present an efficient CAD-based adjoint process chain for calculating parametric sensitivities (derivatives of the objective function with respect to the CAD parameters) in timescales acceptable for industrial design processes. The idea is based on linking parametric design velocities (geometric sensitivities computed from the CAD model) with adjoint surface sensitivities. A CAD-based design velocity computation method has been implemented based on distances between discrete representations of perturbed geometries. This approach differs from other methods due to the fact that it works with existing commercial CAD packages (unlike most analytical approaches) and it can cope with the changes in CAD model topology and face labeling. Use of the proposed method allows computation of parametric sensitivities using adjoint data at a computational cost which scales with the number of objective functions being considered, while it is essentially independent of the number of design variables. The gradient computation is demonstrated on test cases for a Nozzle Guide Vane (NGV) model and a Turbine Rotor Blade model. The results are validated against finite difference values and good agreement is shown. This gradient information can be passed to an optimization algorithm, which will use it to update the CAD model parameters.

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Section: Introductionmentioning

confidence: 99%

Linking Parametric Cad With Adjoint Surface Sensitivities

Vasilopoulos

Agarwal²,

Meyer

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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show abstract

“…The advantage of this method is that it is easy to implement and interface with a commercial CAD package. However, the linear dependency of the DVF with respect to the design variable is not easy to maintain since there is not a general mathematical formula to relate the design variable with mesh changes, thus the derived struc tural response sensitivities may not be reliable to predict the approximated results of the mod ified finite element model [86]. Furthermore, applying Laplace smoothing techniques for large finite element models is inefficient [87].…”

Section: Finite Difference Methods With Automatic Mesh Generationmentioning

confidence: 99%

“…In both specifications, the DVF is determined by solving the discretized finite element equilibrium equation of an auxiliary linear elasticity model. The regularity and linear dependency of the DVF are auto matically satisfied [86], however, this method is inefficient since an additional finite element solution is required, and the flexibility of the boundary shape changes is limited.…”

Section: Boundary Displacement and Fictitious Load Methodsmentioning

confidence: 99%

“…The computation methods for DVF can be categorized as (1) finite difference methods with automatic mesh generation technique, (2) isoparametric map ping methods, (3) boundary displacement and fictitious load methods, and (4) hybrid methods of isoparametric mapping and boundary displacement techniques [86]. Detailed information on the theoretical and practical requirements of DVF can be found in reference [86].…”

Section: Introductionmentioning

confidence: 99%

“…DVF computation is important in determining shape design sensitivity coefficients and updating the finite element mesh for shape design optimization, since the accuracy of the structural response sensitivities and the optimal solution of the shape design is highly depen dent on the results of DVF [85]. The computation methods for DVF can be categorized as (1) finite difference methods with automatic mesh generation technique, (2) isoparametric map ping methods, (3) boundary displacement and fictitious load methods, and (4) hybrid methods of isoparametric mapping and boundary displacement techniques [86]. Detailed information on the theoretical and practical requirements of DVF can be found in reference [86].…”

Section: Introductionmentioning

confidence: 99%

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Applying virtual reality techniques to engineering design optimization

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the ori^nal or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reprodaction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the imlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletionOversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book.

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Shape Design Sensitivity Analysis and Optimization for Structural Durability

Chang

Choi

1997

Int. J. Numer. Meth. Engng.

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SUMMARYIn this paper, a design sensitivity analysis (DSA) method for fatigue life of 3-D solid structural components of mecanical systems with respect to shape design parameters is presented. The DSA method uses dynamic stress DSA obtained using an analytical approach to predict dynamic stress increment due to design changes; computes fatigue life of the component, including crack initiation and crack propagation, using the predicted dynamic stress; and uses the difference of the new life and the original life at the same critical point to approximate the sensitivity of fatigue life. A tracked vehicle roadarm is presented in this paper to demonstrate accuracy and efficiency of the DSA method. Also, this method is applied to support design optimization of the tracked vehicle roadarm considering crack initiation lives as design constraints. 1997 by John Wiley & Sons, Ltd.

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A study of design velocity field computation for shape optimal design

Cited by 132 publications

References 15 publications

Linking Parametric Cad With Adjoint Surface Sensitivities

Linking Parametric Cad With Adjoint Surface Sensitivities

Applying virtual reality techniques to engineering design optimization

Shape Design Sensitivity Analysis and Optimization for Structural Durability

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