Network Target Coordination for Optimal Design of Decomposed Systems With Consensus Optimization

2012 ◽  
Author(s):  
Wenshan Wang ◽  
Paolo Guarneri ◽  
Georges Fadel ◽  
Vincent Blouin
Keyword(s):  
Original System ◽  
Decomposition Methods ◽  
Master Problem ◽  
Optimization Approach ◽  
Distributed Coordination ◽  
Coordination Model ◽  
Design Variables ◽  
Convergence Results ◽  
Network Target Coordination ◽  
Consensus Optimization

The complexity of managing multidisciplinary engineering systems offers an unprecedented opportunity to investigate decomposition methods, which separate a system into a number of smaller subsystems that can be designed in multiple physical locations and coordinate the design of the subsystems to collaboratively achieve the original system design. This paper studies a network target coordination model for optimizing subsystems that are distributed as multiple agents in a network. To solve these coupled subsystems concurrently, we consider the “consensus optimization” approach by incorporating subgradient algorithms so that the master problem or auxiliary design variables required by most distributed coordination methods are not needed. The method allows each agent to conduct its optimization by locally solving for coupling variables with the information obtained from other agents in the network in an iteratively improving process. The convergence results of a geometric programming problem that satisfies the convexity assumption is provided. Moreover, two non-convex examples are tested to investigate the convergence characteristics of the proposed methods.

Frequency optimization of laminated functionally graded carbon nanotube reinforced composite quadrilateral plates using smoothed FEM and evolution algorithm

2017 ◽  
Vol 52 (14) ◽  
pp. 1971-1986 ◽  
Author(s):  
T Vo-Duy ◽  
T Truong-Thi ◽  
V Ho-Huu ◽  
T Nguyen-Thoi
Keyword(s):  
Carbon Nanotube ◽  
Optimization Problem ◽  
Volume Fraction ◽  
Differential Evolution Algorithm ◽  
Composite Plates ◽  
Functionally Graded ◽  
Optimization Approach ◽  
Reinforced Composite ◽  
Design Variables ◽  
Evolution Algorithm

The paper presents an efficient numerical optimization approach to deal with the optimization problem for maximizing the fundamental frequency of laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates. The proposed approach is a combination of the cell-based smoothed discrete shear gap method (CS-DSG3) for analyzing the first natural frequency of the functionally graded carbon nanotube reinforced composite plates and a global optimization algorithm, namely adaptive elitist differential evolution algorithm (aeDE), for solving the optimization problem. The design variables are the carbon nanotube orientation in the layers and constrained in the range of integer numbers belonging to [−900 900]. Several numerical examples are presented to investigate optimum design of quadrilateral laminated functionally graded carbon nanotube reinforced composite plates with various parameters such as carbon nanotube distribution, carbon nanotube volume fraction, boundary condition and number of layers.

Optimization of an Axial Turbine Stage for Aerodynamic Inlet Blockage

2011 ◽  
Author(s):  
Mohammad Arabnia ◽  
Vadivel K. Sivashanmugam ◽  
Wahid Ghaly
Keyword(s):  
Pressure Loss ◽  
Suction Side ◽  
Loss Coefficient ◽  
Von Mises Stress ◽  
Optimization Approach ◽  
Design Point ◽  
Pressure Loss Coefficient ◽  
Design Variables ◽  
Blade Shape ◽  
Von Mises

This paper presents a practical and effective optimization approach to minimize 3D-related flow losses associated with high aerodynamic inlet blockage by re-stacking the turbine rotor blades. This approach is applied to redesign the rotor of a low speed subsonic single-stage turbine that was designed and tested in DLR, Germany. The optimization is performed at the design point and the objective is to minimize the rotor pressure loss coefficient as well as the maximum von Mises stress while keeping the same design point mass flow rate, and keeping or increasing the rotor blade first natural frequency. A Multi-Objective Genetic Algorithm (MOGA) is coupled with a Response Surface Approximation (RSA) of the Artificial Neural Network (ANN) type. A relatively small set of high fidelity 3D flow simulations and structure analysis are obtained using ANSYS Workbench Mechanical. That set is used to train and to test the ANN models. The stacking line is parametrically represented using a quadratic rational Bezier curve (QRBC). The QRBC parameters are directly related to the design variables, namely the rotor lean and sweep angles and the bowing parameters. Moreover, it results in eliminating infeasible shapes and in reducing the number of design variables to a minimum while providing a wide design space for the blade shape. The aero-structural optimization of the E/TU-3 turbine proved successful, the rotor pressure loss coefficient was reduced by 9.8% and the maximum von Mises stress was reduced by 36.7%. This improvement was accomplished with as low as four design variables, and is attributed to the reduction of 3D-related aerodynamic losses and the redistribution of stresses from the hub trailing edge region to the suction side maximum thickness area. The proposed parametrization is a promising one for 3D blade shape optimization involving several disciplines with a relatively small number of design variables.

A fast multiobjective optimization approach to S-duct scoop inlets design with both inflow and outflow

2018 ◽  
Vol 233 (9) ◽  
pp. 3381-3394
Author(s):  
Lifang Zeng ◽  
Dingyi Pan ◽  
Shangjun Ye ◽  
Xueming Shao
Keyword(s):  
Multiobjective Optimization ◽  
Total Pressure ◽  
Optimization Method ◽  
Navier Stokes ◽  
Integral Model ◽  
Optimization Approach ◽  
Navier Stokes Equation ◽  
Cross Sectional ◽  
Design Variables ◽  
Computational Fluid Dynamics Analysis

A fast multiobjective optimization method for S-duct scoop inlets considering both inflow and outflow is developed and validated. To reduce computation consumption of optimization, a simplified efficient model is proposed, in which only inflow region is simulated. Inlet pressure boundary condition of the efficient model is specified by solving an integral model with both inflow and outflow. An automated optimization system integrating the computational fluid dynamics analysis, nonuniform rational B-spline geometric representation technique, and nondominated sorting genetic algorithm II is developed to minimize the total pressure loss and distortion at the exit of diffuser. Flow field is numerically simulated by solving the Reynolds-averaged Navier–Stokes equation coupled with k–ω shear stress transport turbulence model, and results are validated to agree well with previous experiment. S-duct centreline shape and cross-sectional area distribution are parameterized as the design variables. By analyzing the results of a suggested optimal inlet chosen from the obtained Pareto front, total pressure recovery has increased from 97% to 97.4%, and total pressure distortion DC60 has decreased by 0.0477 (21.7% of the origin) at designed Mach number 0.7. The simplified efficient model has been validated to be reliable, and by which the time cost for the optimization project has been reduced by 70%.

scholarly journals Robust Optimization Approach Using Scenario Concepts for Artillery Firing Scheduling Under Uncertainty

2019 ◽  
Vol 9 (14) ◽  
pp. 2811
Author(s):  
Choi ◽  
Yun ◽  
Kim ◽  
Jin ◽  
Kim
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Numerical Experiments ◽  
Master Problem ◽  
Model Parameters ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Robust Solution ◽  
Reasonable Time ◽  
Scheduling Under Uncertainty

Real wars involve a considerable number of uncertainties when determining firing scheduling. This study proposes a robust optimization model that considers uncertainties in wars. In this model, parameters that are affected by enemy’s behavior and will, i.e., threats from enemy targets and threat time from enemy targets, are assumed as uncertain parameters. The robust optimization model considering these parameters is an intractable model with semi-infinite constraints. Thus, this study proposes an approach to obtain a solution by reformulating this model into a tractable problem; the approach involves developing a robust optimization model using the scenario concept and finding a solution in that model. Here, the combinations that express uncertain parameters are assumed by scenarios. This approach divides problems into master and subproblems to find a robust solution. A genetic algorithm is utilized in the master problem to overcome the complexity of global searches, thereby obtaining a solution within a reasonable time. In the subproblem, the worst scenarios for any solution are searched to find the robust solution even in cases where all scenarios have been expressed. Numerical experiments are conducted to compare robust and nominal solutions for various uncertainty levels to verify the superiority of the robust solution.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

On the Use of Multi-Disciplinary Optimisation Methods for Road Vehicle Passive Suspension Design

2019 ◽  
Author(s):  
Liunan Yang ◽  
Federico Ballo ◽  
Giorgio Previati ◽  
Massimiliano Gobbi ◽  
Gianpiero Mastinu
Keyword(s):  
Total Mass ◽  
Decomposition Methods ◽  
Analytical Target Cascading ◽  
Spring Stiffness ◽  
Objective Functions ◽  
Road Vehicle ◽  
Passive Suspension ◽  
Design Variables ◽  
Target Cascading ◽  
Optimisation Methods

Abstract Two widely used decomposition-based multi-disciplinary optimisation (MDO) methods, namely analytical target cascading (ATC) and collaborative optimisation (CO), are applied to the design of the suspension system of a road vehicle. Instead of directly optimising the spring stiffness and the damping coefficient, three parameters of the spring and three parameters of the damper are selected as design variables. Discomfort, road holding, and the total mass of the spring-damper system, are considered as objective functions. An investigation is completed to analyse the performance of the two decomposition methods compared with the conventional all-in-one (AiO) formulation in terms of efficiency and applicability.

Study on Vibration Reduction Design of Steel-Composite Material Hybrid Mounting for Ship Based on Material Selection Optimization

2013 ◽  
Vol 694-697 ◽  
pp. 415-424
Author(s):  
Wei Wang ◽  
Lu Yun Chen ◽  
Yu Fang Zhang
Keyword(s):  
Power Flow ◽  
Optimization Problems ◽  
Material Selection ◽  
Vibration Reduction ◽  
Optimization Method ◽  
Optimization Approach ◽  
Structural Dynamic ◽  
Level Difference ◽  
Design Variables ◽  
Steel Composite

The material selection optimization for vibration reduction design is studied present article. By introducing the stacking sequence hypothesis of metal material, taking into account the power flow level difference and vibration level difference parameter, the mechanical parameters of the material and plies number are defined as design variables, and the mathematical model of structural dynamic optimization based on material selection optimization approach is established. Finally, a naval hybrid steel-composite mounting structure for example, by introducing genetic algorithm, the optimization problems is solved. The numerical results show that the optimization method is effective and feasible.

Runner Optimization for In-Mold Assembly of Multi-Material Compliant Mechanisms

2011 ◽  
Author(s):  
Wojciech Bejgerowski ◽  
Satyandra K. Gupta
Keyword(s):  
Optimization Problem ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Polymer Melt ◽  
Injection Molding Process ◽  
Optimization Approach ◽  
Assembly Process ◽  
Molding Process ◽  
Design Variables

The runner system in injection molding process is used to supply the polymer melt from injection nozzle to the gates of final part cavities. Realizing complex multi-material mechanisms by in-mold assembly process requires special runner layout design considerations due to the existence of the first stage components. This paper presents the development of an optimization approach for runner systems in the in-mold assembly of multi-material compliant mechanisms. First, the issues specific to the in-mold assembly process are identified and analyzed. Second, the general optimization problem is formulated by identification of all parameters, design variables, objective functions and constraints. Third, the implementation of the optimization problem in Matlab® environment is described based on a case study of a runner system for an in-mold assembly of a MAV drive mechanism. This multi-material compliant mechanism consists of seven rigid links interconnected by six compliant hinges. Finally, several optimization approaches are analyzed to study their performance in solving the formulated problem. The most appropriate optimization approach is selected. The case study showed the applicability of the developed optimization approach to runner systems for complex in-mold assembled multi-material mechanism designs.

scholarly journals Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Duc Nam Nguyen ◽  
Thanh-Phong Dao ◽  
Ngoc Le Chau ◽  
Van Anh Dang
Keyword(s):  
Elbow Joint ◽  
Strain Energy ◽  
Hybrid Approach ◽  
Hybrid Optimization ◽  
Assistive Device ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Design Variables

Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish the number of numerical experiments. The FEM is developed to retrieve the strain energy and the reaction torque of joint. And then, the Kigring metamodel is used as a black-box to find the pseudoobjective functions. Based on pseudoobjective functions, the MOGA is applied to find the optimal solutions. Traditionally, an evolutionary optimization algorithm can only find one Pareto front. However, the proposed approach can generate 6 Pareto-optimal solutions, as near optimal candidates, which provides a good decision-maker. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results found that the optimal strain energy is about 0.0033 mJ and the optimal torque is approximately 588.94 Nm. Analysis of variance is performed to identify the significant contribution of design variables. The sensitivity analysis is then carried out to determine the effect degree of each parameter on the responses. The predictions are in a good agreement with validations. It confirms that the proposed hybrid optimization approach has an effectiveness to solve for complex optimization problems.

Sign in / Sign up

Export Citation Format

Share Document