RIM driven propellers design using a simulation based design optimization approach

Author(s):  
S. Gaggero
2019 ◽  
Vol 145 (6) ◽  
pp. 3795-3804 ◽  
Author(s):  
Robin Tournemenne ◽  
Jean-François Petiot ◽  
Bastien Talgorn ◽  
Joël Gilbert ◽  
Michael Kokkolaras

2013 ◽  
Vol 30 (2) ◽  
pp. 161-174 ◽  
Author(s):  
Daniel Sieger ◽  
Stefan Menzel ◽  
Mario Botsch

Author(s):  
Fatma Ayancik ◽  
Erdem Acar ◽  
Kutay Celebioglu ◽  
Selin Aradag

In recent years, optimization started to become popular in several engineering disciplines such as aerospace, automotive and turbomachinery. Optimization is also a powerful tool in hydraulic turbine industry to find the best performance of turbines and their sub-elements. However, direct application of the optimization techniques in design of hydraulic turbines is impractical due to the requirement of performing computationally expensive analysis of turbines many times during optimization. Metamodels (or surrogate models) that can provide fast response predictions and mimic the behavior of nonlinear simulation models provide a remedy. In this study, simulation-based design of Francis type turbine runner is performed by following a metamodel-based optimization approach that uses multiple types of metamodels. A previously developed computational fluid dynamics-based methodology is integrated to the optimization process, and the results are compared to the results obtained from on-going computational fluid dynamics studies. The results show that, compared to the conventional methods such as computational fluid dynamics-based methods, metamodel-based optimization can shorten the design process time by a factor of 9.2. In addition, with the help of optimization, turbine performance is increased while cavitation on the turbine blades, which can be harmful for the turbine and reduce its lifespan, is reduced.


Author(s):  
Bastien Talgorn ◽  
Sébastien Le Digabel ◽  
Michael Kokkolaras

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. The remedy for all these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary design optimization problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.


2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Bastien Talgorn ◽  
Sébastien Le Digabel ◽  
Michael Kokkolaras

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima, and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.


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