Global optimization of recirculation flow type casing treatment in centrifugal compressors of turbochargers

Sakaguchi, Daisaku; Tun, Min Thaw; Numakura, Ryusuke; Wang, BT

doi:10.1177/0954406216679435

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…Kramer (2017) illustrated a workflow to embed a supervised learning model into a GA where the real fitness evaluations are performed only to the solutions that fulfil criterion in the predictive model. This way of integration is easily recognizable in different engineering applications (Marcelin 2004;Sreekanth and Datta 2011;Ibaraki, Tomita and Sugimoto 2015;Sato and Fujita 2016;Sakaguchi et al 2018). Exploiting a strategy from these engineering problems is certainly valuable to our survey-design problem because a large computation effort to calculate the objective function inherently restricts the number of fitness evaluations in our case.…”

Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 96%

“…This way of integration is easily recognizable in different engineering applications (Marcelin ; Sreekanth and Datta ; Ibaraki, Tomita and Sugimoto ; Sato and Fujita ; Sakaguchi et al . ). Exploiting a strategy from these engineering problems is certainly valuable to our survey‐design problem because a large computation effort to calculate the objective function inherently restricts the number of fitness evaluations in our case.…”

Section: Survey‐parameter Updatementioning

confidence: 97%

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Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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Blended acquisition along with efficient spatial sampling is capable of providing high‐quality seismic data in a cost‐effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio‐inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.

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Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 96%

Section: Survey‐parameter Updatementioning

confidence: 97%

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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“…A similar study, which is based on the DE algorithm and a traditional surrogate model, requires about 130 CFD evaluated samples to achieve an optimal solution. 14 This means that the developed algorithm (EHGO) is much more efficient for the black box optimization with intensive computation.…”

Section: Optimization Of Rctmentioning

confidence: 99%

“…This method is therefore usually used for casing treatment design of turbomachinery. 14 For example, Tun et al. 15 integrated artificial neural network and design of experiment to implement the optimization of a centrifugal compressor with RCT.…”

Section: Introductionmentioning

confidence: 99%

“…This method is therefore usually used for casing treatment design of turbomachinery. 14 For example, Tun et al 15 integrated artificial neural network and design of experiment to implement the optimization of a centrifugal compressor with RCT. Kim et al 16 combined a Kriging surrogate model with a genetic algorithm to conduct the optimization of the casing treatment for an axial compressor.…”

Section: Introductionmentioning

confidence: 99%

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Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method

Liu

Zhao

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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As a typical black-box problem, recirculating casing treatment (RCT) optimization of compressor stages is computationally intensive and time consuming, even though surrogate models are usually employed. In order to improve efficiency and robustness of the optimization, an expected-improvement (EI) based hybrid global optimization (EHGO) algorithm is developed by coupling an EI-based surrogate model with a hybrid optimization algorithm. Highly nonlinear and multiple modality mathematical tests show that the EHGO algorithm is able to create a high-fidelity surrogate model near targeted regions with less evaluated samples, and to obtain the global optimal solution simultaneously. The RCT of a compressor stage is optimized based on this algorithm. The number of CFD simulations required for obtaining an optimum solution is greatly reduced, as compared to similar studies using conventional methods. The optimization results show that the aerodynamic performance is improved over the whole speed line and the flow range is also extended. The dominant factors for the performance improvements and the enhanced stall margin are addressed by analyzing the local flow characteristics before and after optimization. It is found that those factors include: removing a larger amount of low-momentum fluid, achieving a more uniform flow of impeller passage in circumferential direction, and reducing the radial distortion of impeller inlet flow. The proposed algorithm has the potential to considerably speed up the optimization process and make the optimization much more accessible. It can be generalized to deal with other computationally intensive black-box problems, for example, turbomachinery optimization.

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Stability improvement without efficiency penalty of a transonic centrifugal compressor by casing treatment and impeller/diffuser coupling optimization

Liu²,

Zhao³

et al. 2022

Aerospace Science and Technology

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Global optimization of recirculation flow type casing treatment in centrifugal compressors of turbochargers

Cited by 6 publications

References 13 publications

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method

Stability improvement without efficiency penalty of a transonic centrifugal compressor by casing treatment and impeller/diffuser coupling optimization

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