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[1] Lampart, P. & Yershov, S., 3D Shape optimiastion of turbomachinery blading. TASK, 6(1), pp. 113–125, 2002.
[2] Lampart, P. & Hirt Ł., Complex multidisciplinary optimization of turbine blading systems. Archives of Mechanics, 64(2), pp. 153–175, 2012.
[3] Al Jubori, A., Al-Dadah, R.K., Mahmoud, S., Bahr Ennil, A.S. & Rahbar, K., Three dimensional optimization of small-scale axial turbine for low temperature heat source driven organic rankine cycle. Energy Conversion and Management, 133, pp. 411–426, 2016. [Crossref]
[4] Elmegaard, B. & Brix, W., Efficiency of Compressed Air Energy Storage, 24th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, pp. 1–12, 2012.
[5] Klonowicz, P., Heberle, F., Preißinger, M. & Brüggemann, D., Significance of loss correlations in performance prediction of small scale, highly loaded turbine stages working in organic rankine cycles. Energy, 72, pp. 322–330, 2012. [Crossref]
[6] Beyene, A. & Miller, A., Krytyczna ocena metod określania strat w turbinowych palisadach profile. Biuletyn Informacyjny Instytutu Techniki Cieplnej Politechniki Warszawskiej, pp. 19–34, 1986. (in Polish).
[7] Kosowski, K., Tucki, K. & Kosowski, A., Turbine stage design aided by artificial intelligence methods. Expert Systems with Applications, 36(9), pp. 11536–11542, 2009. [Crossref]
[8] Pierret, S. & Van den Braembussche, R.A., Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. Journal Turbomachinery, 121, pp. 326–332, 1999. [Crossref]
[9] Forrester, A.I.J., Sóbester, A. & Keane, A.J., Engineering Design via Surrogate Models, Wiley, pp. 23–24, 2008.
[10] Hedar, A. & Fukushima, M., Minimizing multimodal functions by simplex coding. Optimization Methods and Software, 18(3), pp. 265–282, 2003. [Crossref]
[11] Rodriguez, F.J., García-Martinez, C. & Lozano, M., Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: Taxonomy, comparison, and synergy test. IEEE Transactions on Evolutionary Computation, 16, pp. 787–800, 2012. [Crossref]
[12] Mahmuddin, M. & Yousof, Y., A hybrid simplex search and bio-inspired algorithm for faster convergence. International Conference Machine Learning and Computing, 3,pp. 203–207, 2011.
[13] Mirsadeghi, E. & Shariat Panahi, M., Hybridizing artificial bee colony with simulated annealing. International Journal Hybrid Information Technology, 5, pp. 11–18, 2012.
[14] Jovanovic, R., Kais, S. & Alharbi, F.H., Cuckoo search inspired hybridization of the Nelder-Mead simplex algorithm applied to optimization of photovoltaic cells. Applied Mathematics Information Science, 10, pp. 961–973, 2016. [Crossref]
[15] Yang, X.Y., A New Metaheuristic Bat-Inspired Algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Springer Berlin Heidelberg, pp. 65–74, 2010.
[16] Nelder, J.A. & Mead, R., A simplex method for function minimization. Computer Journal, 7(4), pp. 308–313, 1965. [Crossref]
[17] Yilmaz, S. & Küçüksille, E.U., A new modification approach on bat algorithm for solving optimization problems. Applied Soft Computing Journal, 28, 259–275, 2015. [Crossref]
[18] Rezaee Jordehi, A., Chaotic bat swarm optimisation (CBSO). Applied Soft Computing Journal, 26, pp. 523–530, 2015. [Crossref]
[19] Yang, X. & Gandomi, A.H., Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), pp. 464–483, 2012. [Crossref]
[20] Spendley, W., Hext, G. & Himsworth, F., Sequential application of simplex designs in optimisation and evolutionary operation. Technometrics, 4(4), pp. 441–461, 1962. [Crossref]
[21] Yang, X.S., Recent Advances in Swarm Intelligence and Evolutionary Computation, Springer, 2015.
[22] Lampart, P., Numerical Optimization of Stator Blade Sweep and Lean in an LP Turbine Stage. International Joint Power Generation Conference, ASME, pp. 579–592, 2002.
[23] Lampart, P., Control of LP turbine rotor blade underloading using stator blade compound lean at root. Journal of Thermal Science, 9(2), pp. 115–121, 2000. [Crossref]
[24] Rosic, B. & Xu, L., Blade lean and shroud leakage flows in low aspect ratio turbines. Journal of Turbomachinery, 134(3), pp. 31003-1–31003-12, 2012. [Crossref]
[25] Klonowicz, P., Surwiło, J., Witanowski, Ł., Suchocki, T., Kozanecki, Z. & Lampart, P., Design and numerical study of turbines operating with MDM as working fluid. Open Engineering, 5, pp. 485–499, 2015. [Crossref]
[26] Witanowski, Ł., Klonowicz, P. & Lampart, P., UWCAES Optimization of a radial-axial turbine working in a UWCAES system. Mechanik, 7, pp. 3–9, 2016.
[27] Lagarias, J., Reeds, J., Wright, M. & Wrightm P., Convergence properties of Nelder-Mead simplex method in low dimensions. SIAM Journal on Optimization, 9, pp. 112–147, 1998. [Crossref]
[28] Kelly, C.T., Implicit Filtering, North Carolina State University, Raleigh, North Carolina, 2011.
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Open Access
Research article

3D Shape Optimisation of a Low-Pressure Turbine Stage

l. witanowski,
p. klonowicz,
p. lampart
The Szewalski Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Poland
International Journal of Energy Production and Management
|
Volume 3, Issue 1, 2018
|
Pages 10-21
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
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Abstract:

The possibility of reducing the flow losses in low-pressure turbine stage has been investigated in an iterative process using a novel hybrid optimisation algorithm. Values of the maximised objective func- tion that is isentropic efficiency are found from 3D RANS computation of the flowpath geometry, which was being changed during the optimisation process. To secure the global flow conditions, the constraints have been imposed on the mass flow rate and reaction. Among the optimised parameters are stator and rotor twist angles, stator sweep and lean, both straight and compound. Blade profiles remained unchanged during the optimisation. A new hybrid stochastic-deterministic algorithm was used for the optimisation of the flowpath. In the proposed algorithm, the bat algorithm was combined with the direct search method of Nelder-Mead in order to refine the best obtained solution from the standard bat algorithm. The method was tested on a wide variety of well-known test functions. Also, the results of the optimisation of the other stochastic and deterministic methods were compared and discussed. The optimisation gives new 3D-stage designs with increased efficiency comparing to the original design.

Keywords: CFD, Efficiency, Hybrid method, Low pressure steam turbine, Optimisation, Swarm intelligence
Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Lampart, P. & Yershov, S., 3D Shape optimiastion of turbomachinery blading. TASK, 6(1), pp. 113–125, 2002.
[2] Lampart, P. & Hirt Ł., Complex multidisciplinary optimization of turbine blading systems. Archives of Mechanics, 64(2), pp. 153–175, 2012.
[3] Al Jubori, A., Al-Dadah, R.K., Mahmoud, S., Bahr Ennil, A.S. & Rahbar, K., Three dimensional optimization of small-scale axial turbine for low temperature heat source driven organic rankine cycle. Energy Conversion and Management, 133, pp. 411–426, 2016. [Crossref]
[4] Elmegaard, B. & Brix, W., Efficiency of Compressed Air Energy Storage, 24th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, pp. 1–12, 2012.
[5] Klonowicz, P., Heberle, F., Preißinger, M. & Brüggemann, D., Significance of loss correlations in performance prediction of small scale, highly loaded turbine stages working in organic rankine cycles. Energy, 72, pp. 322–330, 2012. [Crossref]
[6] Beyene, A. & Miller, A., Krytyczna ocena metod określania strat w turbinowych palisadach profile. Biuletyn Informacyjny Instytutu Techniki Cieplnej Politechniki Warszawskiej, pp. 19–34, 1986. (in Polish).
[7] Kosowski, K., Tucki, K. & Kosowski, A., Turbine stage design aided by artificial intelligence methods. Expert Systems with Applications, 36(9), pp. 11536–11542, 2009. [Crossref]
[8] Pierret, S. & Van den Braembussche, R.A., Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. Journal Turbomachinery, 121, pp. 326–332, 1999. [Crossref]
[9] Forrester, A.I.J., Sóbester, A. & Keane, A.J., Engineering Design via Surrogate Models, Wiley, pp. 23–24, 2008.
[10] Hedar, A. & Fukushima, M., Minimizing multimodal functions by simplex coding. Optimization Methods and Software, 18(3), pp. 265–282, 2003. [Crossref]
[11] Rodriguez, F.J., García-Martinez, C. & Lozano, M., Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: Taxonomy, comparison, and synergy test. IEEE Transactions on Evolutionary Computation, 16, pp. 787–800, 2012. [Crossref]
[12] Mahmuddin, M. & Yousof, Y., A hybrid simplex search and bio-inspired algorithm for faster convergence. International Conference Machine Learning and Computing, 3,pp. 203–207, 2011.
[13] Mirsadeghi, E. & Shariat Panahi, M., Hybridizing artificial bee colony with simulated annealing. International Journal Hybrid Information Technology, 5, pp. 11–18, 2012.
[14] Jovanovic, R., Kais, S. & Alharbi, F.H., Cuckoo search inspired hybridization of the Nelder-Mead simplex algorithm applied to optimization of photovoltaic cells. Applied Mathematics Information Science, 10, pp. 961–973, 2016. [Crossref]
[15] Yang, X.Y., A New Metaheuristic Bat-Inspired Algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Springer Berlin Heidelberg, pp. 65–74, 2010.
[16] Nelder, J.A. & Mead, R., A simplex method for function minimization. Computer Journal, 7(4), pp. 308–313, 1965. [Crossref]
[17] Yilmaz, S. & Küçüksille, E.U., A new modification approach on bat algorithm for solving optimization problems. Applied Soft Computing Journal, 28, 259–275, 2015. [Crossref]
[18] Rezaee Jordehi, A., Chaotic bat swarm optimisation (CBSO). Applied Soft Computing Journal, 26, pp. 523–530, 2015. [Crossref]
[19] Yang, X. & Gandomi, A.H., Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), pp. 464–483, 2012. [Crossref]
[20] Spendley, W., Hext, G. & Himsworth, F., Sequential application of simplex designs in optimisation and evolutionary operation. Technometrics, 4(4), pp. 441–461, 1962. [Crossref]
[21] Yang, X.S., Recent Advances in Swarm Intelligence and Evolutionary Computation, Springer, 2015.
[22] Lampart, P., Numerical Optimization of Stator Blade Sweep and Lean in an LP Turbine Stage. International Joint Power Generation Conference, ASME, pp. 579–592, 2002.
[23] Lampart, P., Control of LP turbine rotor blade underloading using stator blade compound lean at root. Journal of Thermal Science, 9(2), pp. 115–121, 2000. [Crossref]
[24] Rosic, B. & Xu, L., Blade lean and shroud leakage flows in low aspect ratio turbines. Journal of Turbomachinery, 134(3), pp. 31003-1–31003-12, 2012. [Crossref]
[25] Klonowicz, P., Surwiło, J., Witanowski, Ł., Suchocki, T., Kozanecki, Z. & Lampart, P., Design and numerical study of turbines operating with MDM as working fluid. Open Engineering, 5, pp. 485–499, 2015. [Crossref]
[26] Witanowski, Ł., Klonowicz, P. & Lampart, P., UWCAES Optimization of a radial-axial turbine working in a UWCAES system. Mechanik, 7, pp. 3–9, 2016.
[27] Lagarias, J., Reeds, J., Wright, M. & Wrightm P., Convergence properties of Nelder-Mead simplex method in low dimensions. SIAM Journal on Optimization, 9, pp. 112–147, 1998. [Crossref]
[28] Kelly, C.T., Implicit Filtering, North Carolina State University, Raleigh, North Carolina, 2011.
Cite this:
APA Style
IEEE Style
BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Witanowski, L., Klonowicz, P., & Lampart, P. (2018). 3D Shape Optimisation of a Low-Pressure Turbine Stage. Int. J. Energy Prod. Manag., 3(1), 10-21. https://doi.org/10.2495/EQ-V3-N1-10-21
L. Witanowski, P. Klonowicz, and P. Lampart, "3D Shape Optimisation of a Low-Pressure Turbine Stage," Int. J. Energy Prod. Manag., vol. 3, no. 1, pp. 10-21, 2018. https://doi.org/10.2495/EQ-V3-N1-10-21
@research-article{Witanowski20183DSO,
title={3D Shape Optimisation of a Low-Pressure Turbine Stage},
author={L. Witanowski and P. Klonowicz and P. Lampart},
journal={International Journal of Energy Production and Management},
year={2018},
page={10-21},
doi={https://doi.org/10.2495/EQ-V3-N1-10-21}
}
L. Witanowski, et al. "3D Shape Optimisation of a Low-Pressure Turbine Stage." International Journal of Energy Production and Management, v 3, pp 10-21. doi: https://doi.org/10.2495/EQ-V3-N1-10-21
L. Witanowski, P. Klonowicz and P. Lampart. "3D Shape Optimisation of a Low-Pressure Turbine Stage." International Journal of Energy Production and Management, 3, (2018): 10-21. doi: https://doi.org/10.2495/EQ-V3-N1-10-21
WITANOWSK L， KLONOWICZ P， LAMPART P. 3D Shape Optimisation of a Low-Pressure Turbine Stage[J]. International Journal of Energy Production and Management， 2018， 3(1)： 10-21. https://doi.org/10.2495/EQ-V3-N1-10-21