scholarly journals An Efficient Surrogate-Based Optimization Method for BWBUG Based on Multifidelity Model and Geometric Constraint Gradients

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Daiyu Zhang ◽  
Bei Zhang ◽  
Zhidong Wang ◽  
Xinyao Zhu
Keyword(s):  
Shape Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Geometric Constraint ◽  
High Fidelity ◽  
Surrogate Based Optimization ◽  
Parameterized Model ◽  
Effectiveness And Efficiency ◽  
Optimization Efficiency

Performing shape optimization of blended-wing-body underwater glider (BWBUG) can significantly improve its gliding performance. However, high-fidelity CFD analysis and geometric constraint calculation in traditional surrogate-based optimization methods are expensive. An efficient surrogate-based optimization method based on the multifidelity model and geometric constraint gradient information is proposed. By establishing a shape parameterized model, deriving analytical expression of geometric constraint gradient, constructing multifidelity surrogate model, the calculation times of high-fidelity CFD model and geometric constraints are reduced during the shape optimization process of BWBUG, which greatly improve the optimization efficiency. Finally, the effectiveness and efficiency of the proposed method are verified by performing the shape optimization of a BWBUG and comparing with traditional surrogate-based optimization methods.

Freeform Skeletal Shape Optimization of Compliant Mechanisms

2003 ◽  
Vol 125 (2) ◽  
pp. 253-261 ◽  
Author(s):  
Dong Xu ◽  
G. K. Ananthasuresh
Keyword(s):  
Shape Optimization ◽  
Compliant Mechanisms ◽  
Optimization Methods ◽  
Optimization Method ◽  
Analytical Sensitivity ◽  
Adaptive Finite Element ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Element Discretization ◽  
Analytical Sensitivity Analysis

Compliant mechanisms are elastic continua used to transmit or transform force and motion mechanically. The topology optimization methods developed for compliant mechanisms also give the shape for a chosen parameterization of the design domain with a fixed mesh. However, in these methods, the shapes of the flexible segments in the resulting optimal solutions are restricted either by the type or the resolution of the design parameterization. This limitation is overcome in this paper by focusing on optimizing the skeletal shape of the compliant segments in a given topology. It is accomplished by identifying such segments in the topology and representing them using Bezier curves. The vertices of the Bezier control polygon are used to parameterize the shape-design space. Uniform parameter steps of the Bezier curves naturally enable adaptive finite element discretization of the segments as their shapes change. Practical constraints such as avoiding intersections with other segments, self-intersections, and restrictions on the available space and material, are incorporated into the formulation. A multi-criteria function from our prior work is used as the objective. Analytical sensitivity analysis for the objective and constraints is presented and is used in the numerical optimization. Examples are included to illustrate the shape optimization method.

Topology and Size–Shape Optimization of an Adaptive Compliant Gripper with High Mechanical Advantage for Grasping Irregular Objects

Robotica ◽  
2019 ◽  
Vol 37 (08) ◽  
pp. 1383-1400 ◽  
Author(s):  
Chih-Hsing Liu ◽  
Chen-Hua Chiu ◽  
Mao-Cheng Hsu ◽  
Yang Chen ◽  
Yen-Pin Chiang
Keyword(s):  
Topology Optimization ◽  
Optimal Design ◽  
Shape Optimization ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Experimental Studies ◽  
Design Procedure ◽  
Optimization Methods ◽  
Optimization Method ◽  
Mechanical Advantage

SummaryThis study presents an optimal design procedure including topology optimization and size–shape optimization methods to maximize mechanical advantage (which is defined as the ratio of output force to input force) of the synthesized compliant mechanism. The formulation of the topology optimization method to design compliant mechanisms with multiple output ports is presented. The topology-optimized result is used as the initial design domain for subsequent size–shape optimization process. The proposed optimal design procedure is used to synthesize an adaptive compliant gripper with high mechanical advantage. The proposed gripper is a monolithic two-finger design and is prototyped using silicon rubber. Experimental studies including mechanical advantage test, object grasping test, and payload test are carried out to evaluate the design. The results show that the proposed adaptive complaint gripper assembly can effectively grasp irregular objects up to 2.7 kg.

scholarly journals Blade shape optimization of an aircraft propeller using space mapping surrogates

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401986507
Author(s):  
Usama T Toman ◽  
Abdel-Karim SO Hassan ◽  
Farouk M Owis ◽  
Ahmed SA Mohamed
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shape Optimization ◽  
Stokes Equations ◽  
Optimization Method ◽  
Space Mapping ◽  
High Fidelity ◽  
Blade Shape ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

Propeller performance greatly influences the overall efficiency of the turboprop engines. The aim of this study is to perform a propeller blade shape optimization for maximum aerodynamic efficiency with a minimal number of high-fidelity model evaluations. A physics-based surrogate approach exploiting space mapping is employed for the design process. A space mapping algorithm is utilized, for the first time in the field of propeller design, to link two of the most common propeller analysis models: the classical blade-element momentum theory to be the coarse model; and the high-fidelity computational fluid dynamics tool as the fine model. The numerical computational fluid dynamics simulations are performed using the finite-volume discretization of the Reynolds-averaged Navier–Stokes equations on an adaptive unstructured grid. The optimum design is obtained after few iterations with only 56 computationally expensive computational fluid dynamics simulations. Furthermore, an optimization method based on design of experiments and kriging response surface is used to validate the results and compare the computational efficiency of the two techniques. The results show that space mapping is more computationally efficient.

A novel surrogate-based aerodynamic optimization method using field approximate model

2017 ◽  
Vol 233 (3) ◽  
pp. 883-895 ◽  
Author(s):  
Wenjie Wang ◽  
Zeping Wu ◽  
Donghui Wang ◽  
Weihua Zhang
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Optimization Method ◽  
Approximate Model ◽  
Dynamics Simulation ◽  
Aerodynamic Optimization ◽  
Optimal Point ◽  
Design Variables ◽  
Sampling Set ◽  
Optimization Efficiency

An efficient surrogate-based aerodynamic shape optimization method is developed to improve the optimization efficiency. In this method, the field approximate model is presented firstly to predict the flow field parameters of interest for specific aerodynamic optimization problems with respect to the design variables and sequentially updated. The differential evolution is used to locate the optimum of field approximate model coupled with the analytical post-processing to calculate the objective and constraints for aerodynamic optimization. This optimal point is calculated by time-consuming computational fluid dynamics simulation and the result is added to the sampling set to update the sampling points and field approximate model. The proposed method is compared with conventional sequential approximate optimization and shows great advantages in accuracy and efficiency. Two shape optimization test cases are provided to verify the efficacy and efficiency of the proposed method.

Research on Topology Optimization Method for Continuum Structure under Geometric Constraints

2011 ◽  
Vol 291-294 ◽  
pp. 1589-1592
Author(s):  
Li Ren ◽  
Rui Yang ◽  
Wen Xiao Zhang
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Objective Function ◽  
Optimization Technique ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Optimization Process ◽  
Optimal Topology ◽  
Penalty Function Method ◽  
Continuum Structure

A new topology optimization model with holes’ geometric constraints for continuum structure is presented. It is solved by an evolutionary optimization method based on interval relaxation, in which the problem is divided into two subproblems of topology optimization process and size/shape optimization process. The optimal topology of structure can be found gradually by introducing interval relaxation factor to adjust holes’ size constraints, delete noneffective holes and by generating new holes based on the sensitivity analysis of objective function. Interior penalty-function method is employed as an optimization technique for the size/shape optimization of the structure corresponding to the topology. When the holes’ size bounds are the actual values, the optimal solution is the smallest objective function structure in the various topologies. Thus realizes the holes’ geometric size and location design, topology design and layout design together. The optimization results of example shows the method proposed is of good effectiveness and engineering applicability.

Adaptive response prediction for aerodynamic shape optimization

2017 ◽  
Vol 34 (5) ◽  
pp. 1485-1500
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Shape Optimization ◽  
Adaptive Response ◽  
Simulation Models ◽  
Response Prediction ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Aerodynamic Shape Optimization ◽  
Content Type ◽  
Aerodynamic Shape ◽  
Fidelity Model

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Sign in / Sign up

Export Citation Format

Share Document