Optimal design of semi-arch auxiliary teeth of stationary discontinuous armature PMLSM with concentrated winding using design of experiment

2014 ◽  
Author(s):  
Eui-Jong Park ◽  
Yong-Jae Kim ◽  
Sang-Yong Jung
Keyword(s):  
Optimal Design ◽  
Design Of Experiment

Optimal Design of Experiment for Parameter Estimation of a Single Particle Model for Lithiumion Batteries

2018 ◽  
Author(s):  
Andrea Pozzi ◽  
Gabriele Ciaramella ◽  
Krishnakumar Gopalakrishnan ◽  
Stefan Volkwein ◽  
Davide M. Raimondo
Keyword(s):  
Parameter Estimation ◽  
Optimal Design ◽  
Design Of Experiment ◽  
Single Particle ◽  
Particle Model ◽  
Single Particle Model

Optimal Design for Impeller of a Sludge Pump Using Design of Experiment

2019 ◽  
Author(s):  
Jeong-Min Jin ◽  
Hyo-Geun Ji ◽  
Youn-Jea Kim
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Design Of Experiment ◽  
Design Theory ◽  
Flow Analysis ◽  
Optimization Techniques ◽  
Navier Stokes ◽  
Pump Impeller ◽  
Computational Fluid Dynamics Cfd ◽  
Design Factors

Abstract Recently, many studies carried out to improve the performance of the pump with shape changes. In this paper, impeller optimization is performed to improve the pump performance. Design optimization techniques for the sludge pump impellers have been developed by using computational fluid dynamics (CFD) and optimal design theory. This paper describes the design optimization of a sludge pump impeller based on Response Surface Method (RSM) coupled with Navier-Stokes flow analysis. In particular, RSM which was based on the results of the design of experiment (DOE) helps to achieve the optimum point. In order to optimize the shape of the impeller, the thickness and the height of the blade were set as design factors. As a result, it was confirmed that the efficiency and the head were improved by 11.2% and 6.67%, respectively, compared to the referenced model.

Hydrodynamic Integrated Optimization of Two-D Hydrofoil Based on Kriging Model

2011 ◽  
Vol 421 ◽  
pp. 651-656 ◽  
Author(s):  
Zhuo Yi Yang ◽  
Ting Gao ◽  
Yong Jie Pang ◽  
Yan Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Design Of Experiment ◽  
Cfd Simulation ◽  
Pressure Coefficient ◽  
Kriging Model ◽  
Weighted Sum ◽  
Hydrodynamic Calculations ◽  
The Cost ◽  
Single Objective

Abstract. It is accurate to use CFD hydrodynamic model in optimal design, but its efficiency is low. According to variables design of experiment was arranged, then kriging model was established to by CFD hydrodynamic calculations of two-D hydrofoil. The two objectives which were the maximal ration of lift to resistance and minimal min-pressure coefficient were linearly weighted sum to a single objective. Genetic algorithm was carried out to solve the hydrodynamic performances optimization of the hydrofoil. After optimization, the ration of lift to resistance was improved and min-pressure coefficient was increased. The result shows that the Kriging model can reflect the identity of the objective, effectively reduce the cost of CFD simulation.

scholarly journals Cylinder Head Design of Experiment by using the Wartsila Digital Design Platform

2017 ◽  
Vol 50 (3) ◽  
pp. 330-332 ◽  
Author(s):  
Massimo Cattarinussi ◽  
Anton Leppänen ◽  
Juho Könnö ◽  
Tero Frondelius
Keyword(s):  
Optimal Design ◽  
Design Of Experiment ◽  
Cylinder Head ◽  
Digital Design ◽  
Geometrical Parameters ◽  
Response Sensitivity ◽  
Simulation Process ◽  
Structural Calculation ◽  
Fully Automatic ◽  
Parameter Interactions

Cylinder Head Design of Experiment (DOE) was carried out by using the Wartsila digital design platform. The overall goal has been to create a simulation process to perform a complete cylinder head thermal-structural calculation and create a DOE over that process to vary the inputs, the geometrical parameters under optimization, in a fully automatic workflow. The outputs, uploaded to the DOE activity, were used to rank the input effects on the output response, to capture response sensitivity to input changes, to identify parameter interactions and, at the end, to estimate the optimal design.

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