Deep Autoencoder for Off-Line Design-Space Dimensionality Reduction in Shape Optimization

2018 ◽  
Author(s):  
Danny D'Agostino ◽  
Andrea Serani ◽  
Emilio F. Campana ◽  
Matteo Diez
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space ◽  
Line Design

scholarly journals A GAN-based dimensionality reduction technique for aerodynamic shape optimization

2021 ◽  
Author(s):  
Jun Zhang ◽  
Wenzheng Wang ◽  
Qiuyu Wu ◽  
Liwei Hu
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space ◽  
Reduction Technique ◽  
Aerodynamic Shape Optimization ◽  
Generative Adversarial Network ◽  
Aerodynamic Shape ◽  
Adversarial Network ◽  
Dimensionality Reduction Technique ◽  
Dynamics Simulations

Abstract Aerodynamic shape optimization (ASO) based on computational fluid dynamics simulations is extremely computationally demanding because a search needs to be performed in a high-dimensional design space. One solution to this problem is to reduce the dimensionality of the design space for aircraft optimization. Hence, in this study, a dimensionality reduction technique is designed based on a generative adversarial network (GAN) to facilitate ASO. The novel GAN model is developed by combining the GAN with airfoil curve parameterization and can directly produce realistic and highly accurate airfoil curves from input data of aerodynamic shapes. In addition, the respective interpretable characteristic airfoil variables can be obtained by extracting latent codes with physical meaning, while reducing the dimensionality of the airfoil design space. The results of simulation experiments show that the proposed technique can significantly improve the optimization convergence rate of the ASO process.

scholarly journals Stochastic Shape Optimization via Design-Space Augmented Dimensionality Reduction and RANS Computations

2019 ◽  
Author(s):  
Andrea Serani ◽  
Matteo Diez ◽  
Jeroen Wackers ◽  
Michel Visonneau ◽  
Frederick Stern
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space

scholarly journals Assessing the Interplay of Shape and Physical Parameters by Unsupervised Nonlinear Dimensionality Reduction Methods

2019 ◽  
Author(s):  
Andrea Serani ◽  
Danny D’Agostino ◽  
Emilio Fortunato Campana ◽  
Matteo Diez
Keyword(s):  
Dimensionality Reduction ◽  
Shape Optimization ◽  
Design Space ◽  
Mean Squared Error ◽  
Physical Parameters ◽  
Nonlinear Dimensionality Reduction ◽  
Data Set ◽  
Reduced Dimensionality ◽  
Nonlinear Design ◽  
Reduction Methods

The article presents an exploratory study on the application to ship hydrodynamics of unsupervised nonlinear design-space dimensionality reduction methods, assessing the interaction of shape and physical parameters. Nonlinear extensions of the principal component analysis (PCA) are applied, namely local PCA (LPCA) and kernel PCA (KPCA). An artificial neural network approach, specifically a deep autoencoder (DAE) method, is also applied and compared with PCA-based approaches. The data set under investigation is formed by the results of 9000 potential flow simulations coming from an extensive exploration of a 27-dimensional design space, associated with a shape optimization problem of the DTMB 5415 model in calm water at 18 kn (Froude number, <inline-formula><mml:math><mml:mrow><mml:mtext>Fr</mml:mtext><mml:mo>=</mml:mo><mml:mn>.25</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="JOSR09180056inf1.tif"/></inline-formula>). Data include three heterogeneous distributed and suitably discretized parameters (shape modification vector, pressure distribution on the hull, and wave elevation pattern) and one lumped parameter (wave resistance coefficient), for a total of <inline-formula><mml:math><mml:mrow><mml:mn>9000</mml:mn><mml:mtext> </mml:mtext><mml:mo>×</mml:mo><mml:mtext> </mml:mtext><mml:mn>5101</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="JOSR09180056inf2.tif"/></inline-formula> elements. The reduced-dimensionality representation of shape and physical parameters is set to provide a normalized mean squared error smaller than 5%. The standard PCA meets the requirement using 19 principal components/parameters. LPCA and KPCA provide the most promising compression capability with 14 parameters required by the reduced-dimensionality parametrizations, indicating significant nonlinear interactions in the data structure of shape and physical parameters. The DAE achieves the same error with 17 components. Although the focus of the current work is on design-space dimensionality reduction, the formulation goes beyond shape optimization and can be applied to large sets of heterogeneous physical data from simulations, experiments, and real operation measurements.

Torsional Robustness of the Combined Cycle Power Train Arrangement: Application of Statistical Methods to Accelerate Shaft-Line Design Cycles

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Mateusz Golebiowski ◽  
John Ling ◽  
Eric Knopf ◽  
Andreas Niedermeyer
Keyword(s):  
Statistical Methods ◽  
Design Space ◽  
Short Circuit ◽  
Probabilistic Approach ◽  
Combined Cycle ◽  
Power Train ◽  
Shaft Line ◽  
Main Challenge ◽  
Severe Constraint ◽  
Line Design

This article presents the application of statistical methods to guide the rotordynamic design of a turbogenerator shaft-line. One of the basic requirements is all shaft components must survive the event of a short circuit at the terminals of the generator. This is typically assessed via a transient response simulation of the complete machine train (including generator's electrical model) to check the calculated response torque against the allowable value. With an increasing demand of a shorter design cycle and competition in performance, cost, footprint, and safety, the probabilistic approach is starting to play an important role in the power train design process. The main challenge arises with the size of the design space and complexity of its mapping onto multiple objective functions and criteria which are defined for different machines. In this paper, the authors give an example demonstrating the use of statistical methods to explore (design of experiment (DoE)) and understand (surface response methods) the design space of the combined cycle power train with respect to the typically most severe constraint (fault torque torsional response), which leads to a quicker definition of a turbogenerator's arrangement. Further statistical analyses are carried out to understand the robustness of the chosen design against future modifications as well as parameters' uncertainties.

Shape Optimization of Microchannels Using Surrogate Modelling

2018 ◽  
Author(s):  
Muhammad Ansab Ali ◽  
Tariq S. Khan ◽  
Saqib Salam ◽  
Ebrahim Al Hajri
Keyword(s):  
Shape Optimization ◽  
Heat Sink ◽  
Pareto Front ◽  
Sampling Method ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Optimum Shape ◽  
Microchannel Heat Sink ◽  
Objective Functions ◽  
Computational Resources

To minimize the computational and optimization time, a numerical simulation of 3D microchannel heat sink was performed using surrogate model to achieve the optimum shape. Latin hypercube sampling method was used to explore the design space and to construct the model. The accuracy of the model was evaluated using statistical methods like coefficient of multiple determinations and root mean square error. Thermal resistance and pressure drop being conflicting objective functions were selected to optimize the geometric parameters of the microchannel. Multi objective shape optimization of design was conducted using genetic algorithm and the optimum design solutions are presented in the Pareto front. The application of the surrogate methods has predicted the performance of the heat sink with the sufficient accuracy employing significantly lower computational resources.

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