IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6183-d644996.html
   My bibliography  Save this article

Numerical Analysis of Engine Exhaust Flow Parameters for Resolving Pre-Turbine Pulsating Flow Enthalpy and Exergy

Author

Listed:
  • Beichuan Hong

    (Competence Center for Gas Exchange (CCGEx), Department of Machine Design, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden)

  • Varun Venkataraman

    (Competence Center for Gas Exchange (CCGEx), Department of Machine Design, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden)

  • Andreas Cronhjort

    (Competence Center for Gas Exchange (CCGEx), Department of Machine Design, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden)

Abstract

Energy carried by engine exhaust pulses is critical to the performance of a turbine or any other exhaust energy recovery system. Enthalpy and exergy are commonly used concepts to describe the energy transport by the flow based on the first and second laws of thermodynamics. However, in order to investigate the crank-angle-resolved exhaust flow enthalpy and exergy, the significance of the flow parameters (pressure, velocity, and temperature) and their demand for high resolution need to be ascertained. In this study, local and global sensitivity analyses were performed on a one-dimensional (1D) heavy-duty diesel engine model to quantify the significance of each flow parameter in the determination of exhaust enthalpy and exergy. The effects of parameter sweeps were analyzed by local sensitivity, and Sobol indices from the global sensitivity showed the correlations between each flow parameter and the computed enthalpy and exergy. The analysis indicated that when considering the specific enthalpy and exergy, flow temperature is the dominant parameter and requires high resolution of the temperature pulse. It was found that a 5% sweep over the temperature pulse leads to maximum deviations of 31% and 27% when resolving the crank angle-based specific enthalpy and specific exergy, respectively. However, when considering the total enthalpy and exergy rates, flow velocity is the most significant parameter, requiring high resolution with a maximum deviation of 23% for the enthalpy rate and 12% for the exergy rate over a 5% sweep of the flow velocity pulse. This study will help to quantify and prioritize fast measurements of pulsating flow parameters in the context of turbocharger turbine inlet flow enthalpy and exergy analysis.

Suggested Citation

  • Beichuan Hong & Varun Venkataraman & Andreas Cronhjort, 2021. "Numerical Analysis of Engine Exhaust Flow Parameters for Resolving Pre-Turbine Pulsating Flow Enthalpy and Exergy," Energies, MDPI, vol. 14(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6183-:d:644996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Buyu & Pamminger, Michael & Wallner, Thomas, 2019. "Impact of fuel and engine operating conditions on efficiency of a heavy duty truck engine running compression ignition mode using energy and exergy analysis," Applied Energy, Elsevier, vol. 254(C).
    2. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    3. Guillermo Valencia & Armando Fontalvo & Yulineth Cárdenas & Jorge Duarte & Cesar Isaza, 2019. "Energy and Exergy Analysis of Different Exhaust Waste Heat Recovery Systems for Natural Gas Engine Based on ORC," Energies, MDPI, vol. 12(12), pages 1-22, June.
    4. Hong Zhang & Hang Zhang & Zhuo Wang, 2017. "Effect on Vehicle Turbocharger Exhaust Gas Energy Utilization for the Performance of Centrifugal Compressors under Plateau Conditions," Energies, MDPI, vol. 10(12), pages 1-18, December.
    5. Mahabadipour, Hamidreza & Srinivasan, Kalyan Kumar & Krishnan, Sundar Rajan & Subramanian, Swami Nathan, 2018. "Crank angle-resolved exergy analysis of exhaust flows in a diesel engine from the perspective of exhaust waste energy recovery," Applied Energy, Elsevier, vol. 216(C), pages 31-44.
    6. Luján, José Manuel & Serrano, José Ramon & Piqueras, Pedro & Diesel, Bárbara, 2019. "Turbine and exhaust ports thermal insulation impact on the engine efficiency and aftertreatment inlet temperature," Applied Energy, Elsevier, vol. 240(C), pages 409-423.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dariusz Kozak & Paweł Mazuro, 2023. "Numerical Analysis of Two-Stage Turbine System for Multicylinder Engine under Pulse Flow Conditions with High Pressure-Ratio Turbine Rotor," Energies, MDPI, vol. 16(2), pages 1-46, January.

    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. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    2. Dettù, Federico & Pozzato, Gabriele & Rizzo, Denise M. & Onori, Simona, 2021. "Exergy-based modeling framework for hybrid and electric ground vehicles," Applied Energy, Elsevier, vol. 300(C).
    3. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    4. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    5. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    6. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    7. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    8. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    9. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Magni, Carlo Alberto, 2016. "Capital depreciation and the underdetermination of rate of return: A unifying perspective," Journal of Mathematical Economics, Elsevier, vol. 67(C), pages 54-79.
    11. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    12. Matteo Fontana & Massimo Tavoni & Simone Vantini, 2020. "Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models," Papers 2006.13850, arXiv.org, revised Apr 2024.
    13. Stefano Cucurachi & Carlos Felipe Blanco & Bernhard Steubing & Reinout Heijungs, 2022. "Implementation of uncertainty analysis and moment‐independent global sensitivity analysis for full‐scale life cycle assessment models," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 374-391, April.
    14. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    15. Guillermo Valencia Ochoa & Carlos Acevedo Peñaloza & Jorge Duarte Forero, 2019. "Thermoeconomic Optimization with PSO Algorithm of Waste Heat Recovery Systems Based on Organic Rankine Cycle System for a Natural Gas Engine," Energies, MDPI, vol. 12(21), pages 1-21, October.
    16. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Ziemele, Jelena & Gravelsins, Armands & Blumberga, Andra & Blumberga, Dagnija, 2017. "Sustainability of heat energy tariff in district heating system: Statistic and dynamic methodologies," Energy, Elsevier, vol. 137(C), pages 834-845.
    18. Thomas H. Jørgensen, 2023. "Sensitivity to Calibrated Parameters," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 474-481, March.
    19. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    20. Magni, Carlo Alberto & Marchioni, Andrea, 2020. "Average rates of return, working capital, and NPV-consistency in project appraisal: A sensitivity analysis approach," International Journal of Production Economics, Elsevier, vol. 229(C).

    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:14:y:2021:i:19:p:6183-:d:644996. 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.