An Adjoint-Based Design Methodology for CFD Optimization Problems

2003 ◽  
Author(s):  
Rainald Lohner ◽  
Orlando Soto ◽  
Chi Yang
Keyword(s):  
Design Methodology ◽  
Optimization Problems ◽  
Cfd Optimization

ON THE ASYMPTOTIC PROPERTIES OF RECURRENT NEURAL NETWORKS FOR OPTIMIZATION

1991 ◽  
Vol 05 (04) ◽  
pp. 581-601 ◽  
Author(s):  
JUN WANG
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Recurrent Neural Networks ◽  
Design Methodology ◽  
Optimization Problems ◽  
Asymptotic Properties ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Operating Characteristics

Asymptotic properties of recurrent neural networks for optimization are analyzed. Specifically, asymptotic stability of recurrent neural networks with monotonically time-varying penalty parameters for optimization is proven; sufficient conditions of feasibility and optimality of solutions generated by the recurrent neural networks are characterized. Design methodology of the recurrent neural networks for solving optimization problems is discussed. Operating characteristics of the recurrent neural networks are also presented using illustrative examples.

scholarly journals Parametric design methodology for manufacturing of metallic and composite structures

2021 ◽  
Author(s):  
Saptarshi Datta
Keyword(s):  
Product Development ◽  
Case Studies ◽  
Conceptual Design ◽  
New Product Development ◽  
Composite Structures ◽  
Design Methodology ◽  
Design Space ◽  
Optimization Problems ◽  
Functional Requirements ◽  
Development Approach

A parametric, concurrent design methodology for manufacturing of metallic and composite structures is established. Often, during a new product development, designs prepared using the “Sequential” or “Waterfall” approach are rejected or require significant rework during manufacturing, as designers are not always versed with manufacturing principles. Similarly, manufacturers are not always versed in design principles resulting in designs that do not cater to the functional requirements. The goal of this study is to establish a methodology right from the scope to the detailed design for developing manufacturable structures using the “Concurrent Engineering” approach. Existing literature on “Design Optimization for Manufacturing” predominantly focus on single variable optimization problems geared towards conceptual designs. The designs developed through such optimization cater towards functional performance within a “Fixed Design Space” while not accounting for manufacturing or operational challenges. The methodology developed in this study enables “Design for Manufacturing” for “Detailed Designs” through selection of a conceptual design and subsequently optimizing the selected conceptual design for a set of functional parameters. An “Integrated Product Development” approach is used, whereby, the functional requirements are linked to both design and manufacturing variables and optimization is conducted in an “Augmented Design Space” which is not available when only considering design or manufacturing variables. Three case studies involving both “Conceptual” and “Detailed” designs have been used to illustrate the methodology presented. Case I documents the design of a Flight Control System Bracket. Case II illustrates the use of “2D” composite structures to fabricate a roll frame. Case III involves the development of a “3D” composite door for a light unpressurized aircraft. For each of the three case studies a separate development approach has been employed. Case I uses an analytical approach, Case II uses FEM while CASE III employs a hybrid approach comprising of both FEM and analytical techniques.

Optimal design methodology for sizing a fuel cell/battery hybrid power source

2020 ◽  
pp. 095765092091034 ◽  
Author(s):  
Adriano Ceschia ◽  
Toufik Azib ◽  
Olivier Bethoux ◽  
Francisco Alves
Keyword(s):  
Fuel Cell ◽  
Optimal Design ◽  
Design Methodology ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Power Source ◽  
Operating Conditions ◽  
Hybrid Power ◽  
Component Sizing ◽  
Hybrid Power Source

This paper presents an optimal design methodology enabling to exhibit the best parameters of a complex energy system combing several components and their related control parts. It is based on a particle swarm optimization technique for component sizing, combined with optimal control to consider energy management constraints. This approximate resolution is valuable since it allows to achieve a robust and effective optimal design using low computational resources: it enables to tackle large search spaces in engineering time constraints. The selected use case is a fuel cell/battery hybrid power source based on a power-split parallel architecture. Its performance index is defined as the fuel consumption. Regarding this objective, the drivetrain components size and the control parameters values are both strongly coupled and physically constrained. In this context, the methodology makes a tradeoff between component sizing and energy saving. Simulation results show the relevance and robustness of this approach regarding different driving cycles and operating conditions. It validates the replicability of this method to other optimization problems in the field of energy optimization. A comprehensive review of the simulation tests highlights the present limits of this optimization and provides new perspectives for future works.

Improvements of bat algorithm using crossover technique and hybridization with Nelder-Mead simplex method

2019 ◽  
Vol 38 (3) ◽  
pp. 977-989
Author(s):  
Bourahla Kheireddine ◽  
Belli Zoubida ◽  
Hacib Tarik
Keyword(s):  
Finite Element ◽  
Simplex Method ◽  
Design Methodology ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Content Type ◽  
Genetic Algorithm Method ◽  
Element Computation

Purpose This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering. Design/methodology/approach For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm. Findings First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB. Originality/value The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.

Approximation based curvilinear local search for optimization problems

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Fatih Yaman ◽  
Asim Egemen Yilmaz ◽  
Kemal Leblebicioğlu
Keyword(s):  
Local Search ◽  
Design Methodology ◽  
Reference Data ◽  
Optimization Problems ◽  
Interpolation Method ◽  
Computational Cost ◽  
Search Space ◽  
Approximation Technique ◽  
Search Mechanism ◽  
Alpha Beta

Purpose At this work, we propose a local approximation based search method to optimize any function. For this purpose, an approximation method is combined with an estimation filter, and a new local search mechanism is constituted. Design/methodology/approach RBF network is very efficient interpolation method especially if we have sufficient reference data. Here, reference data refers to the exact value of the objective function at some points. Using this capability of RBFs, we can approximately inspect the vicinity each point in search space. Meanwhile, in order to obtain a smooth, rapid and better trajectory toward the global optimum, the alpha-beta filter can be integrated to this mechanism. For better description and visualization, the operations are defined in 2-dimensional search space; but the outlined procedure can be extended to higher dimensions without loss of generality. Findings When compared with our previous studies using conventional heuristic methods, approximation based curvilinear local search mechanism provide better minimization performance for almost all benchmark functions. Moreover computational cost of this method too less than heuristics. The number of iteration down to at least 1/10 compared to conventional heuristic algorithm. Additionally, the solution accuracy is very improved for majority of the test cases. Originality/value This paper proposes a new search approach to solve optimization problems with less cost. For this purpose, a new local curvilinear search mechanism is built using RBF based approximation technique and alpha-beta estimation filter.

Comparative study of swarm intelligence-based saliency computation

2017 ◽  
Vol 10 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Ning Xian
Keyword(s):  
Particle Swarm Optimization ◽  
Design Methodology ◽  
Optimization Problems ◽  
Aerial Images ◽  
Swarm Optimization ◽  
Content Type ◽  
Air Surveillance ◽  
Searching Ability ◽  
Practical Implications ◽  
Weight Coefficients

Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection. Design/methodology/approach To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation. Findings According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms. Practical implications The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images. Originality/value CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

scholarly journals An Optimal Design Methodology for the Trajectory of Hydraulic Excavators Based on Genetic Algorithm

2021 ◽  
Vol 33 (6) ◽  
pp. 1248-1254
Author(s):  
Takamichi Yuasa ◽  
◽  
Masato Ishikawa ◽  
Satoshi Ogawa
Keyword(s):  
Genetic Algorithm ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Reaction Force ◽  
Ground Level ◽  
Optimization Method ◽  
Work Efficiency ◽  
Cost Index ◽  
Hydraulic Excavators

Hydraulic excavators are one type of construction equipment used in various construction sites worldwide, and their usage and scale are diverse. Generally, the work efficiency of a hydraulic excavator largely depends on human operation skills. If we can comprehend the experienced operation skills and utilize them for manual control assist, semi-automatic or automatic remote control, it would improve its work efficiency and suppress personnel costs, reduce the operator’s workload, and improve his/her safety. In this study, we propose a methodology to design efficient machine trajectories based on mathematical models and numerical optimization, focusing on ground-level excavation as a dominant task. First, we express its excavation trajectory using four parameters and assume the models for the amount of excavated soil and the reaction force based on our previous experiments. Next, we combine these models with a geometrical model for the hydraulic excavating machine. We then assign the amount of soil to a performance index preferably to be maximized and the amount of work to a cost index preferably to be minimized, both in the form of functions of the trajectory parameters, resulting in an optimization problem that trades them off. In particular, we formulate (1) a multi-objective optimization problem maximizing a weighted linear combination of the amount of soil and the amount of work as an objective function, and (2) a single-objective optimization problem maximizing the amount of soil under a given upper bound on the amount of work, so that we can solve these optimization problems using the genetic algorithm (GA). Finally, we conclude this paper by suggesting our notice on design methodology and discussing how we should provide the optimization method as mentioned above to the users who operate hydraulic excavators.

Advanced Optimal Control-Based Design of a Gough-Stewart Platform

2021 ◽  
Author(s):  
Minglei Zhu ◽  
Shijie Song ◽  
Dawei Gong
Keyword(s):  
Optimal Control ◽  
Optimal Design ◽  
Design Methodology ◽  
Visual Servoing ◽  
Optimization Problems ◽  
Stewart Platform ◽  
Geometric Parameters ◽  
Design Parameters ◽  
Observation Error ◽  
Positioning Accuracy

Abstract Designing a robot with the best accuracy is always the attractive research direction in robot community. In order to create a Gough-Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough-Stewart platform: leg-direction-based visual servoing, line-based visual servoing and image moment visual servoing. Depend on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough-Stewart platform for each type of controller. Then, we perform the co-simulations on the three optimized Gough-Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps getting both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

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