Multi-Objective Design Optimization for a Steam Turbine Stator Blade Using LES and GA

Shimoyama, Koji; Yoshimizu, Shu; Jeong, Shinkyu; Obayashi, Shigeru; Yokono, Yasuyuki

doi:10.1299/jcst.5.134

Cited by 20 publications

(7 citation statements)

References 20 publications

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“…Genetic Algorithms (GAs) [40] have been employed in literature for a multitude of applications, solving problems ranging from optimal design of antennas and structural components, to control strategies for robotic applications, to aerodynamic optimization of turbomachinery [41][42][43][44]. Generally, GAs are best suited for problems featuring an expensive objective function depending on multiple variables.…”

Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

et al. 2019

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Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.Aerospace 2019, 6, 94 2 of 15 occurrence of a failure before the scheduled substitution of the interested system. Monitoring the behavior of an actuator could enable to detect the early signs of different progressive faults, in order to timely correct them. If a tool would be available with a high grade of robustness to isolate those incipient faults, most failure modes of the flight control system could be identified before they start affecting the performance of the system in terms of dynamical response, stability, stall force, or positioning accuracy. The discipline aimed to this purpose is called Prognostics and Health Management (PHM) (as reported in [1]). The application of PHM strategies involves the analysis of functional parameters of the system in form of electrical signals: for this reason, the use of electrical actuators someway represents an advantage, because no conversion of the signals (and therefore no additional sensors) is needed. Due to the complexity and to the multi-disciplinary nature of the monitored systems, the FDI task on EMA systems is particularly challenging, since several failure modes interact and an acceptable accuracy is hardly ac...

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Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

et al. 2019

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“…The hypervolume improvement HV I[f 1 (x), f 2 (x), · · · , f m (x)] is defined as the hypervolume bounded above by the non-dominated front of the current dataset of sample points, as illustrated in Fig. 2(b), and its expected value (11) where F 1 , F 2 , · · · , F m are the Gaussian random variables…”

Section: B Expected Hypervolume Improvement (Ehvi)mentioning

confidence: 99%

“…Moreover, Jeong et al [8] proposed an extension of EGO for multi-objective problems (EGOMOP), which evaluates EI of each objective function and maximizes multiple EIs in terms of all objective functions. EGOMOP has been demonstrated through several applications to real-world design problems, where expensive numerical simulations need to be conducted for function evaluation: supersonic transport [9], automobile tire [10], steam turbine [11], etc. Most of them succeeded in finding desirable design candidates, which are better than the baseline design in terms of all objective functions, within realistic computational time.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the criteria for updating Kriging response surface models in multi-objective optimization

Shimoyama

Sato

Jeong

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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This paper compares the criteria for updating the Kriging response surface models in multi-objective optimization: expected improvement (EI), expected hypervolume improvement (EHVI), estimation (EST), and those combination (EHVI+EST). EI has been conventionally used as the criterion considering the stochastic improvement of each objective function value individually, while EHVI has been recently proposed as the criterion considering the stochastic improvement of the front of non-dominated solutions in multi-objective optimization. EST is the value of each objective function, which is estimated non-stochastically by the Kriging model without considering its uncertainties. Numerical experiments were implemented in the welded beam design problem, and empirically showed that, in a non-constrained case, EHVI keeps a balance between accurate and wide search for non-dominated solutions on the Kriging models in multi-objective optimization. In addition, the present experiments suggested future investigation into the techniques for handling uncertain constraints to enhance the capability of EHVI in a constrained case.

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“…N real-world problems, optimization is typically formulated as a multiobjective problem with tradeoffs between objective functions [1,2]. Evolutionary algorithms (EA) have been developed to solve these multiobjective optimization problems successfully and obtain diverse and converging nondominated solutions (NDS) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Expected Improvement of Penalty-Based Boundary Intersection for Expensive Multiobjective Optimization

Namura

Shimoyama

Obayashi

2017

IEEE Trans. Evol. Computat.

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Computationally expensive multiobjective optimization problems are difficult to solve using solely evolutionary algorithms (EA) and require surrogate models, such as the Kriging model. To solve such problems efficiently, we propose infill criteria for appropriately selecting multiple additional sample points for updating the Kriging model. These criteria correspond to the expected improvement of the penalty-based boundary intersection (PBI) and the inverted PBI. These PBI-based measures are increasingly applied to EAs due to their ability to explore better nondominated solutions than those that are obtained by the Tchebycheff function. In order to add sample points uniformly in the multiobjective space, we assign territories and niche counts to uniformly distributed weight vectors for evaluating the proposed criteria. We investigate these criteria in various test problems and compare them with established infill criteria for multiobjective surrogate-based optimization. Both of the proposed criteria yield better diversity and convergence than those obtained with other criteria for most of the test problems. Index Terms-Efficient global optimization (EGO), expected improvement (EI), penalty-based boundary intersection (PBI), expensive optimization, multiobjective optimization.1089-778X (c)

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Multi-Objective Design Optimization for a Steam Turbine Stator Blade Using LES and GA

Cited by 20 publications

References 20 publications

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Comparison of the criteria for updating Kriging response surface models in multi-objective optimization

Expected Improvement of Penalty-Based Boundary Intersection for Expensive Multiobjective Optimization

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