A Deterministic Global Design Optimization Method for Nonconvex Generalized Polynomial Problems

1990 ◽  
Author(s):  
Chihsiung Lo ◽  
Panos Y. Papalambros
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Global Solutions ◽  
Optimization Method ◽  
Original Model ◽  
Two Phase ◽  
Generalized Polynomials ◽  
Generalized Polynomial ◽  
New Variables ◽  
Global Design Optimization

Abstract A new design optimization method is described for finding global solutions of models with a nonconvex objective function and nonlinear constraints. All functions are assumed to be generalized polynomials. By introducing new variables, the original model is transformed into one with a linear objective function, one convex and one reversed convex constraint. A two-phase algorithm that includes global feasible searches and local optimal searches is used for globally optimizing the transformed model. Several examples illustrate the method.

A Deterministic Global Design Optimization Method for Nonconvex Generalized Polynomial Problems

1996 ◽  
Vol 118 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Chihsiung Lo ◽  
P. Y. Papalambros
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Global Solutions ◽  
Optimization Method ◽  
Original Model ◽  
Two Phase ◽  
Generalized Polynomials ◽  
Generalized Polynomial ◽  
New Variables ◽  
Global Design Optimization

A new design optimization method is described for finding global solutions of models with a nonconvex objective function and nonlinear constraints. All functions are assumed to be generalized polynomials. By introducing new variables, the original model is transformed into one with a linear objective function, one convex and one reversed convex constraint. A two-phase algorithm that includes global feasible search and local optimal search is used for globally optimizing the transformed model. Several examples illustrate the method.

Cavitating Flow Suppression for a Two-Phase Liquefied Natural Gas Expander Through Collaborative Fine-Turning Design Optimization of Impeller and Exducer Geometric Shape

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Peng Song ◽  
Jinju Sun ◽  
Changjiang Huo
Keyword(s):  
Natural Gas ◽  
Objective Function ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Liquefied Natural Gas ◽  
Cavitating Flow ◽  
Two Phase ◽  
Phase Liquid ◽  
Adaptive Kriging

Abstract Cryogenic liquid turbine expanders have been increasingly used in liquefied natural gas (LNG) production plants to save energy. However, high-pressure LNG commonly needs to be throttled to or near a two-phase state, which makes the LNG turbine expander more vulnerable to cavitation. Although some work has been reported on cryogenic turbomachine cavitation, no work has been reported on designing a cavitation-resistant two-phase LNG liquid turbine expander. Motivated by the urgent requirement for two-phase liquid turbine expanders, an effective design optimization method is developed that is well-suited for designing the cavitation-resistant two-phase liquid turbine expanders. A novel optimization objective function is constituted by characterizing the cavitating flow, in which the overall efficiency and local cavitation flow behavior are incorporated. The adaptive-Kriging surrogate model and cooperative coevolutionary algorithm (CCEA) are incorporated to solve the highly nonlinear design optimization problem globally and efficiently. The former maintains high-level prediction accuracy of the objective function but uses much reduced computational fluid dynamics (CFD) simulations while the later solves the complex optimization problem at a high convergence rate through decomposing them into some readily solved parallel subproblems. By means of the developed optimization method, the impeller and exducer blade geometries and their axial gap and circumferential indexing are fine-tuned. Consequently, cavitating flow in both the impeller and exducer of the two-phase LNG expander is effectively mitigated.

On Global Feasible Search for Global Design Optimization With Application to Generalized Polynomial Models

1992 ◽  
Author(s):  
Chihsiung Lo ◽  
Panos Y. Papalambros
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Model Transformation ◽  
Special Problem ◽  
Search Algorithms ◽  
Deterministic Global Optimization ◽  
Generalized Polynomial ◽  
Polynomial Models ◽  
Feasible Solutions ◽  
Global Design Optimization

Abstract A powerful idea for deterministic global optimization is the use of global feasible search, namely, algorithms that guarantee finding feasible solutions of nonconvex problems or prove that none exists. In this article, a set of conditions for global feasible search algorithms is established. The utility of these conditions is demonstrated on two algorithms that solve special problem classes globally. Also, a new model transformation is shown to convert a generalized polynomial problem into one of the special classes above. A flywheel design example illustrates the approach. A sequel article provides further computational details and design examples.

Development of a Multistage Reliability-Based Design Optimization Method

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Eric J. Paulson ◽  
Ryan P. Starkey
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Technology Development ◽  
Computational Cost ◽  
Optimization Method ◽  
Comparison Method ◽  
Reliability Based Design Optimization ◽  
Numerical Example ◽  
Reliability Based Design ◽  
System Acquisition

Complex system acquisition and its associated technology development have a troubled recent history. The modern acquisition timeline consists of conceptual, preliminary, and detailed design followed by system test and production. The evolving nature of the estimates of system performance, cost, and schedule during this extended process may be a significant contribution to recent issues. The recently proposed multistage reliability-based design optimization (MSRBDO) method promises improvements over reliability-based design optimization (RBDO) in achieved objective function value. In addition, its problem formulation more closely resembles the evolutionary nature of epistemic design uncertainties inherent in system design during early system acquisition. Our goal is to establish the modeling basis necessary for applying this new method to the engineering of early conceptual/preliminary design. We present corrections in the derivation and solutions to the single numerical example problem published by the original authors, Nam and Mavris, and examine the error introduced under the reduced-order reliability sampling used in the original publication. MSRBDO improvements over the RBDO solution of 10–36% for the objective function after first-stage optimization are shown for the original second-stage example problem. A larger 26–40% improvement over the RBDO solution is shown when an alternative comparison method is used than in the original. The specific implications of extending the method to arbitrary m-stage problems are presented, together with a solution for a three-stage numerical example. Several approaches are demonstrated to mitigate the computational cost increase of MSRBDO over RBDO, resulting in a net decrease in calculation time of 94% from an initial MSRBDO baseline algorithm.

On Global Feasible Search for Global Design Optimization with Application to Generalized Polynomial Models

1995 ◽  
Vol 117 (3) ◽  
pp. 402-408 ◽  
Author(s):  
Chihsiung Lo ◽  
P. Y. Papalambros
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Model Transformation ◽  
Special Problem ◽  
Search Algorithms ◽  
Deterministic Global Optimization ◽  
Generalized Polynomial ◽  
Polynomial Models ◽  
Feasible Solutions ◽  
Global Design Optimization

A key idea for deterministic global optimization is the use of global feasible search, namely, algorithms that guarantee finding feasible solutions of nonconvex problems or prove that none exists. In this article, a set of conditions for global feasible search algorithms is established. The utility of these conditions is demonstrated on two algorithms that solve special problem classes globally. Also, a new model transformation is shown to convert a generalized polynomial problem into one of the special classes above. A flywheel design example illustrates the approach. A sequel article provides further computational details and design examples.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Topology Optimization of Hard-Coating Thin Plate for Maximizing Modal Loss Factors

Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 774
Author(s):  
Haitao Luo ◽  
Rong Chen ◽  
Siwei Guo ◽  
Jia Fu
Keyword(s):  
Topology Optimization ◽  
Objective Function ◽  
Composite Plates ◽  
Optimization Method ◽  
Hard Coating ◽  
Design Variables ◽  
Coating Composite ◽  
Damping Materials ◽  
Topology Optimization Method ◽  
Loss Factors

At present, hard coating structures are widely studied as a new passive damping method. Generally, the hard coating material is completely covered on the surface of the thin-walled structure, but the local coverage cannot only achieve better vibration reduction effect, but also save the material and processing costs. In this paper, a topology optimization method for hard coated composite plates is proposed to maximize the modal loss factors. The finite element dynamic model of hard coating composite plate is established. The topology optimization model is established with the energy ratio of hard coating layer to base layer as the objective function and the amount of damping material as the constraint condition. The sensitivity expression of the objective function to the design variables is derived, and the iteration of the design variables is realized by the Method of Moving Asymptote (MMA). Several numerical examples are provided to demonstrate that this method can obtain the optimal layout of damping materials for hard coating composite plates. The results show that the damping materials are mainly distributed in the area where the stored modal strain energy is large, which is consistent with the traditional design method. Finally, based on the numerical results, the experimental study of local hard coating composites plate is carried out. The results show that the topology optimization method can significantly reduce the frequency response amplitude while reducing the amount of damping materials, which shows the feasibility and effectiveness of the method.

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