IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p2065-d1074653.html
   My bibliography  Save this article

Comparative Performance Evaluation of Gas Brayton Cycle for Micro–Nuclear Reactors

Author

Listed:
  • Sungwook Choi

    (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

  • In Woo Son

    (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

  • Jeong Ik Lee

    (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

Abstract

Gas Brayton cycles have been considered the next promising power cycles for microreactors. Especially the open-air and closed supercritical CO 2 (S-CO 2 ) Brayton cycles have received attention due to their high thermal efficiency and compact component sizes when compared to the steam Rankine cycle. In this research, the performances of the open-air and closed S-CO 2 Brayton cycle at microreactor power range are compared with polytropic turbomachinery efficiency. When optimizing the cycle, three different optimization parameters are considered in this paper: maximum efficiency, maximum cycle specific work, and maximum of the product of both indicators. For the air Brayton cycle, the maximum of the product of both indicators allows to consider both efficiency and specific work while optimizing the cycle. However, for the S-CO 2 Brayton cycle, the best performing conditions follow either maximum efficiency or the maximum cycle specific work conditions. In general, the S-CO 2 power cycle should be designed and optimized to maximize the cycle specific work for commercial-scale application. The results show that the air Brayton cycle can achieve near 45% efficiency when it can couple with a microreactor with a core outlet temperature higher than 700 °C. However, the S-CO 2 power cycle can still achieve above 30% efficiency when it is coupled with a microreactor with a core outlet temperature higher than 500 °C, whereas the air Brayton cycle cannot even reach breakeven condition.

Suggested Citation

  • Sungwook Choi & In Woo Son & Jeong Ik Lee, 2023. "Comparative Performance Evaluation of Gas Brayton Cycle for Micro–Nuclear Reactors," Energies, MDPI, vol. 16(4), pages 1-27, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2065-:d:1074653
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/2065/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/4/2065/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jacopo Buongiorno & Ben Carmichael & Bradley Dunkin & John Parsons & Dirk Smit, 2021. "Can Nuclear Batteries Be Economically Competitive in Large Markets?," Energies, MDPI, vol. 14(14), pages 1-20, July.
    2. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    3. Pandey, V. & Kumar, P. & Dutta, P., 2020. "Thermo-hydraulic analysis of compact heat exchanger for a simple recuperated sCO2 Brayton cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.
    2. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    3. Hiau LooiKee & Alessandro Nicita & Marcelo Olarreaga, 2009. "Estimating Trade Restrictiveness Indices," Economic Journal, Royal Economic Society, vol. 119(534), pages 172-199, January.
    4. Dabao Zhang, 2022. "Coefficients of Determination for Mixed-Effects Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 674-689, December.
    5. Wam, Hilde Karine & Pedersen, Hans Chr. & Hjeljord, Olav, 2012. "Balancing hunting regulations and hunter satisfaction: An integrated biosocioeconomic model to aid in sustainable management," Ecological Economics, Elsevier, vol. 79(C), pages 89-96.
    6. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    7. Jaimovich, Dany, 2015. "Missing Links, Missing Markets: Evidence of the Transformation Process in the Economic Networks of Gambian Villages," World Development, Elsevier, vol. 66(C), pages 645-664.
    8. Sanghamitra Bandyopadhyay & Frank A. Cowell & Emmanual Flachaire, 2009. "Goodness-of-Fit: An Economic Approach," Economics Series Working Papers 444, University of Oxford, Department of Economics.
    9. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    10. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    11. Scuotto, Veronica & Garcia-Perez, Alexeis & Nespoli, Chiara & Messeni Petruzzelli, Antonio, 2020. "A repositioning organizational knowledge dynamics by functional upgrading and downgrading strategy in global value chain," Journal of International Management, Elsevier, vol. 26(4).
    12. Mo'ath ALSHANNAQ & Rana IMAM, 2020. "Evaluating The Safety Performance Of Roundabouts," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 15(1), pages 141-152, March.
    13. Cynthia Wu & Jessica G Woo & Nanhua Zhang, 2017. "Association between urinary manganese and blood pressure: Results from National Health and Nutrition Examination Survey (NHANES), 2011-2014," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-6, November.
    14. Ozili, Peterson K, 2023. "The acceptable R-square in empirical modelling for social science research," MPRA Paper 115769, University Library of Munich, Germany.
    15. Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    16. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    17. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    18. Selen CAKMAKYAPAN & Atilla GOKTAS, 2013. "A Comparison Of Binary Logit And Probit Models With A Simulation Study," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(1), pages 1-17, JULY.
    19. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    20. Jui-Sheng Chou & Chang-Ping Yu & Dinh-Nhat Truong & Billy Susilo & Anyi Hu & Qian Sun, 2019. "Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning," Sustainability, MDPI, vol. 11(24), pages 1-22, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2065-:d:1074653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.