Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO<sub>2</sub>-BC). At the same time, the turbine design and optimization process for the sCO<sub>2</sub>-BC is complicated, and its relevant investigations are still absent in the literatu...

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Autores principales: Muhammad Saeed, Abdallah S. Berrouk, Burhani M. Burhani, Ahmed M. Alatyar, Yasser F. Al Wahedi
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
turbine design
supercritical CO<sub>2</sub>
artificial neural network
optimization
multi-objective genetic algorithm
machine learning
Technology
T
Acceso en línea:https://doaj.org/article/f13ca11f33a84b61aa1237239a36052f
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id oai:doaj.org-article:f13ca11f33a84b61aa1237239a36052f
record_format dspace
institution DOAJ
collection DOAJ
language EN
topic turbine design
supercritical CO<sub>2</sub>
artificial neural network
optimization
multi-objective genetic algorithm
machine learning
Technology
T
spellingShingle turbine design
supercritical CO<sub>2</sub>
artificial neural network
optimization
multi-objective genetic algorithm
machine learning
Technology
T
Muhammad Saeed
Abdallah S. Berrouk
Burhani M. Burhani
Ahmed M. Alatyar
Yasser F. Al Wahedi
Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
description Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO<sub>2</sub>-BC). At the same time, the turbine design and optimization process for the sCO<sub>2</sub>-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO<sub>2</sub> power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO<sub>2</sub> are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, hub ratio <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, speed ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>ν</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>) and inlet flow angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>α</mi><mn>3</mn></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula> and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO<sub>2</sub>-turbine performance parameters are most sensitive to the design parameter speed ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>ν</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>), followed by inlet flow angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>α</mi><mn>3</mn></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, and are least receptive to shroud ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac></mrow></semantics></math></inline-formula>). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO<sub>2</sub>-BC.
format article
author Muhammad Saeed
Abdallah S. Berrouk
Burhani M. Burhani
Ahmed M. Alatyar
Yasser F. Al Wahedi
author_facet Muhammad Saeed
Abdallah S. Berrouk
Burhani M. Burhani
Ahmed M. Alatyar
Yasser F. Al Wahedi
author_sort Muhammad Saeed
title Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
title_short Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
title_full Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
title_fullStr Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
title_full_unstemmed Turbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network
title_sort turbine design and optimization for a supercritical co<sub>2</sub> cycle using a multifaceted approach based on deep neural network
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/f13ca11f33a84b61aa1237239a36052f
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AT abdallahsberrouk turbinedesignandoptimizationforasupercriticalcosub2subcycleusingamultifacetedapproachbasedondeepneuralnetwork
AT burhanimburhani turbinedesignandoptimizationforasupercriticalcosub2subcycleusingamultifacetedapproachbasedondeepneuralnetwork
AT ahmedmalatyar turbinedesignandoptimizationforasupercriticalcosub2subcycleusingamultifacetedapproachbasedondeepneuralnetwork
AT yasserfalwahedi turbinedesignandoptimizationforasupercriticalcosub2subcycleusingamultifacetedapproachbasedondeepneuralnetwork
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spelling oai:doaj.org-article:f13ca11f33a84b61aa1237239a36052f2021-11-25T17:28:53ZTurbine Design and Optimization for a Supercritical CO<sub>2</sub> Cycle Using a Multifaceted Approach Based on Deep Neural Network10.3390/en142278071996-1073https://doaj.org/article/f13ca11f33a84b61aa1237239a36052f2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7807https://doaj.org/toc/1996-1073Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO<sub>2</sub>-BC). At the same time, the turbine design and optimization process for the sCO<sub>2</sub>-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO<sub>2</sub> power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO<sub>2</sub> are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, hub ratio <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, speed ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>ν</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>) and inlet flow angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>α</mi><mn>3</mn></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula> and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO<sub>2</sub>-turbine performance parameters are most sensitive to the design parameter speed ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>ν</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>), followed by inlet flow angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi>α</mi><mn>3</mn></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, and are least receptive to shroud ratio (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfrac><mrow><msub><mi>r</mi><mrow><mi>s</mi><mn>4</mn></mrow></msub></mrow><mrow><msub><mi>r</mi><mn>3</mn></msub></mrow></mfrac></mrow></semantics></math></inline-formula>). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO<sub>2</sub>-BC.Muhammad SaeedAbdallah S. BerroukBurhani M. BurhaniAhmed M. AlatyarYasser F. Al WahediMDPI AGarticleturbine designsupercritical CO<sub>2</sub>artificial neural networkoptimizationmulti-objective genetic algorithmmachine learningTechnologyTENEnergies, Vol 14, Iss 7807, p 7807 (2021)

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