Shape optimization of annular S-ducts by CFD and high-order polynomial response surfaces

Immonen, Eero

doi:10.1108/ec-08-2017-0327

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…3a, and it could be understood that excluding small discrepancies near to both walls, predicted simulation and experimental results matched quite well. However, similar discrepancies were also reported by the Milanvoic et al and Immonen [40,42] because of the improper handling of the wall curvature by the turbulence model. Duenas [15] had also commented that it might be difficult the accurate prediction of flow under the highly curved wall surface by Boussinesq eddy-viscosity turbulence models.…”

Section: Numerical Methods Validationsupporting

confidence: 77%

“…With the assumption of uniform flow without swirl at all circumferential direction of the S-shaped duct inlet, a 3-D S-shaped duct can be represented well by the approach of 2-D axisymmetric. This will reduce complexity as well as computational time for convergence of the fluid domain [2,15,19,38,40,41]. Modeling and meshing of the 2-D axisymmetric S-shaped duct are accomplished by using 'GAMBIT' software.…”

Section: Geometry Creation Meshing and Case Formationmentioning

confidence: 99%

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Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Sharma

Baloni

2020

SN Appl. Sci.

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A high-bypass turbofan engine transfers air from low-to the high-pressure compressor through an S-shaped transition duct. Minimization of the total pressure loss and maximization of uniform flow are key factors to ensure the maximum performance of the S-shaped transition duct. The conventional design approach is time-consuming and does not guarantee an optimal solution. Hence, the present article is based on the application of optimization for the S-shaped compressor transition duct. The optimization is carried out on the basis of shape parameters to minimize objectives namely pressure loss coefficient and non-uniformity with the reduction of length of the S-shaped transition duct. The 2-D axisymmetric approach of computational fluid dynamics is used as a design tool for performance evaluation with optimization techniques. The simulation model is validated with the available experimental results of the literature. The correlation between the response and independent variables is established with the help of the second-order polynomial response surface methodology. Further, individual optimization is carried out for single-objective consideration using particle swarm optimization and gray wolf optimization algorithms. As the results of single-objective optimization depict the conflicts between both objectives, later optimization using multi-objective particle swarm optimization and multi-objective gray wolf optimization algorithms is carried out. The performance metrics are obtained and compared. The 'TOPSIS' method is applied to get best solution out of multiple solutions. The optimized duct reduced pressure loss coefficient and non-uniformity index by 28.14% and 43.33% with a reduction of 6.37% of length compared to baseline S-shaped duct.

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Section: Numerical Methods Validationsupporting

confidence: 77%

Section: Geometry Creation Meshing and Case Formationmentioning

confidence: 99%

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Sharma

Baloni

2020

SN Appl. Sci.

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“…Latin Hypercube Sampling scheme has been extensively used in a string of different ducts and channels optimization works. 9,14,19,42–45…”

Section: Optimization Methodologymentioning

confidence: 99%

“…Recent reviews on this subject consider that the most popular are response surface models (RSMs), artificial neural networks (ANNs), radial basis functions (RBFs) and Kriging. 2,3 An optimization of annular S-duct shape in respect to energy losses and exit flow uniformity was performed by Immonen (2018) with use of polynomial regression surrogate. 9 After initial study, author decided to use high-order polynomial (order of 4 or 5) as the most appropriate for such class of problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective surrogate model-based optimization of a small aircraft engine air-intake duct

Drężek

Kubacki

Żółtak

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aviation industry is constantly striving for more efficient design processes in respect to optimal time, human and computational resources utilization. This implies a need for application of an approximation techniques enabling for fast responses generation with maintained level of results quality. This study focuses on an advancement of aerodynamic shape optimization process of a small aircraft engine intake system by introduction of a surrogate modelling step into the design loop. The multi-objective metamodel assisted optimization is carried out in order to reduce pressure losses along the engine intake duct and increase flow homogeneity at the engine compressor intake plane. Latin Hypercube Design method is utilized in order to sample the design space. A set of initial objective function evaluations is generated with application of Reynolds-averaged Navier–Stokes solver. The ensemble of samples is further used to train a Kriging-based surrogate model. The Efficient Global Optimization algorithm basing on the Expected Improvement function is employed to gradually increase the metamodel prediction quality by usage of sequential sampling technique. Finally, the optimal point predicted by the Kriging surrogate is validated against the high-fidelity model with usage of the Computational Fluid Dynamics code. The paper presents an application of the abovementioned methodology to the design process of the I-31T aircraft turboprop engine intake system. Proposed Kriging-based optimization workflow is utilized in order to reduce pressure losses and improve flow homogeneity in the engine air-intake duct.

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“…Flow within the S-shaped duct is turbulent, and flow will significantly be affected by the boundary layers along duct walls as well. Hence, to resolve the viscous sub-layer, SST k-ω turbulence model with default model constants is selected for all 117 sets of simulations [26][27][28]. In the present paper, if the sum total of the normalized residual for each conservation equation is less than or equal to 10 −4 , the solution is considered as converge.…”

Section: Numerical Simulationmentioning

confidence: 99%

Design optimization of a compressor transition S-shaped duct using a teaching–learning-based optimization algorithm

Sharma

Baloni

2019

J Braz. Soc. Mech. Sci. Eng.

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A high bypass turbofan aero-engine delivers compressed air from the low-pressure to the high-pressure compressor through a compressor transition duct. Weight and design space limitations impel to its S-shaped design. Despite that, the compressor transition duct has to guide the flow very carefully to the high-pressure compressor without disturbances and flow separations. Hence, the present paper is devoted to elaborate on the application of the proposed heuristic optimization technique known as multi-objective teaching-learning-based optimization (MO-TLBO) algorithm in the area of S-shaped transition compressor duct. The present algorithm is applied for obtaining the optimum design of the S-shaped duct while minimizing the total pressure loss coefficient and non-uniformity index at the S-shaped duct outlet. These objectives are also optimized independently and as a multi-objective consideration as well. Because of the conflicting nature of the objective functions, a Pareto-optimal curve is also presented for a successful trade-off between the objectives. Pareto-optimal curve gives flexibility to the designer for selecting the best set of the parameters based on the objectives. Successful execution of the proposed MO-TLBO algorithm is examined by comparing the optimum results acquired by the multi-objective genetic algorithm and comparisons divulge that the TLBO algorithm can be successfully practiced to obtain the optimized design of compressor transition S-shaped duct. Optimized duct shows the reduction in total pressure loss and non-uniformity by 28.80% and 36.67%, respectively, despite the reduction of 14.74% in overall length.

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Shape optimization of annular S-ducts by CFD and high-order polynomial response surfaces

Cited by 14 publications

References 29 publications

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Multi-objective surrogate model-based optimization of a small aircraft engine air-intake duct

Design optimization of a compressor transition S-shaped duct using a teaching–learning-based optimization algorithm

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