Design optimization of axial flow hydraulic turbine runner: Part II?multi-objective constrained optimization method

2002 ◽  
Vol 39 (6) ◽  
pp. 533-548 ◽  
Author(s):  
Guoyi Peng ◽  
Shuliang Cao ◽  
Masaru Ishizuka ◽  
Shinji Hayama
Keyword(s):  
Constrained Optimization ◽  
Design Optimization ◽  
Axial Flow ◽  
Hydraulic Turbine ◽  
Optimization Method ◽  
Multi Objective ◽  
Turbine Runner

Development of a Multi-Objective Preliminary Design Optimization Approach for Axial Flow Compressors

2018 ◽  
Author(s):  
Youwei He ◽  
Jinju Sun ◽  
Peng Song ◽  
Xuesong Wang ◽  
Da Xu
Keyword(s):  
Design Optimization ◽  
Axial Flow ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Approach ◽  
Preliminary Design ◽  
Stringent Requirement ◽  
Stall Margin ◽  
Multi Objective ◽  
Axial Flow Compressors

A preliminary design optimization approach of axial flow compressors is developed. Loss correlations associated with airfoil geometry are introduced to relax the stringent requirement for the designer to prescribe the stage efficiency. In face of the preliminary design complexity resulted from the large number of design variables together with their stringent variation ranges and multiple design goals, the multi-objective optimization algorithm is incorporated. With such a developed preliminary design optimization method, the design space can be then explored extensively and the optimum designs of both high level overall efficiency and wide stall margin can be readily achieved. The preliminary design optimization method is validated in two steps. Firstly, an existing 5-stage compressor is redesigned without optimization. The obtained geometries and flow parameters are compared to the existing data and a good consistency is achieved. Then, the redesigned compressor is used as initial design and optimized by the developed multi-objective preliminary design optimization method, and significant performance gains are obtained, which demonstrates the effectiveness of the developed optimization methods.

Multi-Objective Design Optimization of Cooling Turbine Blade Based on Kriging Model

2012 ◽  
Vol 184-185 ◽  
pp. 316-319
Author(s):  
Liang Bo Ao ◽  
Lei Li ◽  
Yuan Sheng Li ◽  
Zhi Xun Wen ◽  
Zhu Feng Yue
Keyword(s):  
Design Optimization ◽  
Turbine Blade ◽  
Optimization Model ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Optimization Method ◽  
Approximate Model ◽  
Multi Objective ◽  
Pin Fin

The multi-objective design optimization of cooling turbine blade is studied using Kriging model. The optimization model is created, with the diameter of pin fin at the trailing edge of cooling turbine blade and the location, width, height of rib as design variable, the blade body temperature, flow resistance loss and aerodynamic efficiency as optimization object. The sample points are selected using Latin hypercube sampling technique, and the approximate model is created using Kriging method, the set of Pareto-optimal solutions of optimization objects is obtained by the multi-object optimization model using elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) based on the approximate model. The result shows that the conflict among all optimization objects is solved effectively and the feasibility of the optimization method is improved.

DBD Plasma Actuator Multi-Objective Design Optimization at Reynolds Number 63,000: Baseline Case

2013 ◽  
Author(s):  
Taufik Sulaiman ◽  
Satoshi Sekimoto ◽  
Tomoaki Tatsukawa ◽  
Taku Nonomura ◽  
Akira Oyama ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Design Optimization ◽  
Linear Trend ◽  
Lift Coefficient ◽  
Optimization Method ◽  
Input Power ◽  
Plasma Actuator ◽  
Dbd Plasma ◽  
Multi Objective ◽  
Design Variables

The working parameters of the dielectric barrier discharge (DBD) plasma actuator were optimized to gain an understanding of the flow control mechanism. Experiments were conducted at a Reynolds number of 63,000 using a NACA 0015 airfoil which was fixed to the stall angle of 12 degrees. The two objective functions are: 1) power consumption (P) and 2) lift coefficient (Cl). The goal of the optimization is to decrease P while maximizing Cl. The design variables consist of input power parameters. The algorithm was run for 10 generations with a total population of 260 solutions. Although the number of generations and population size was limited due to experimental constraints, the algorithm was able to converge and the approximate Pareto-front was obtained. From the objective function space, we observe a relatively linear trend where Cl increases with P and after a certain threshold, the value of Cl seems to saturate. We discuss the results obtained in the objective space in addition to scatter plot matrix and color maps. This article, with its experiment-based approach, demonstrates the robustness of a Multi-Objective Design Optimization method and its feasibility for wind tunnel experiments.

Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures

2016 ◽  
Author(s):  
Kai Liu ◽  
Duane Detwiler ◽  
Andres Tovar
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Conceptual Design ◽  
Optimization Method ◽  
Machine Learning Techniques ◽  
Efficient Global Optimization ◽  
Element Analysis ◽  
Multi Objective ◽  
Design Generation ◽  
Optimal Material

This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Efficient Global Optimization (EGO) followed by multi-objective design optimization to find the optimal material distribution. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method.

A Methodology for Trading-Off Performance and Robustness Under Uncertainty

2005 ◽  
Author(s):  
Zissimos P. Mourelatos ◽  
Jinghong Liang
Keyword(s):  
Design Optimization ◽  
Pareto Frontier ◽  
Mathematical Optimization ◽  
Optimization Method ◽  
Mean Value ◽  
Preference Aggregation ◽  
Efficient Design ◽  
Multi Objective ◽  
Reliability Based Design ◽  
Reliability And Robustness

Mathematical optimization plays an important role in engineering design, leading to greatly improved performance. Deterministic optimization however, may result in undesired choices because it neglects uncertainty. Reliability-based design optimization (RBDO) and robust design can improve optimization by considering uncertainty. This paper proposes an efficient design optimization method under uncertainty, which simultaneously considers reliability and robustness. A mean performance is traded-off against robustness for a given reliability level of all performance targets. This results in a probabilistic multi-objective optimization problem. Variation is expressed in terms of a percentile difference, which is efficiently computed using the Advanced Mean Value (AMV) method. A preference aggregation method converts the multi-objective problem to a single-objective problem, which is then solved using an RBDO approach. Indifference points are used to select the best solution without calculating the entire Pareto frontier. Examples illustrate the concepts and demonstrate their applicability.

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