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Development of a Novel Modified Mean‐Line Approach for Performance Prediction of Axial Compressors.

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Title: Development of a Novel Modified Mean‐Line Approach for Performance Prediction of Axial Compressors.
Authors: Hassanlue, Sina1 (AUTHOR), Alhuyi Nazari, Mohammad1 (AUTHOR) nazari.mohammad@tuga.mapnagroup.com, Pakatchian, Mohammadreza1 (AUTHOR)
Source: Energy Science & Engineering. Feb2026, Vol. 14 Issue 2, p843-853. 11p.
Subject Terms: *Axial flow compressors, *Compressor performance, *Mathematical models, *Friction losses, *Multi-objective optimization
Abstract: There is significant importance in accurate prediction of axial flow compressor characteristics at different conditions. Different techniques and models have been proposed for performance prediction of axial compressors. Because of its lower computational cost and high speed the meanline algorithm has been widely applied in preliminary design and analysis of axial compressors; however, there are some inaccuracies at design and off‐design conditions since the method relies on empirical correlations, which may be weak when applied to unconventional airfoil types. Applying some modifications to the meanline algorithm could improve its performance for a wider operating range with higher accuracy. This study aims to propose a modification for accuracy enhancement of meanline techniques to obtain characteristics of axial flow compressors at design and different off‐design conditions. For this purpose, three scenarios are considered to modify the models. In the 1st scenario, coefficients are used for the deviation models while the pressure loss was not changed, in the 2nd scenario coefficients are applied for the pressure loss models and the deviation model is used as the base model and in the 3rd scenario, coefficients are used for both models. The coefficients are optimized by use of multi‐objective genetic algorithm. It was found that use of the 3rd scenario leads to the best accuracy. In this scenario, the average absolute error in the estimated isentropic efficiency is reduced from 2.41% to 0.75% at 80% design rotational speed while the improvement in estimated mass flow rate at this speed is relatively minor. However, at design rotational speed after optimization, the average absolute error in the estimated isentropic efficiency drops from 6.35% to 0.96% while the estimated mass flow rate decreases from 4.97% to 0.094%. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 191576652
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  Label: Title
  Group: Ti
  Data: Development of a Novel Modified Mean‐Line Approach for Performance Prediction of Axial Compressors.
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  Data: <searchLink fieldCode="AR" term="%22Hassanlue%2C+Sina%22">Hassanlue, Sina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alhuyi+Nazari%2C+Mohammad%22">Alhuyi Nazari, Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nazari.mohammad@tuga.mapnagroup.com</i><br /><searchLink fieldCode="AR" term="%22Pakatchian%2C+Mohammadreza%22">Pakatchian, Mohammadreza</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy+Science+%26+Engineering%22">Energy Science & Engineering</searchLink>. Feb2026, Vol. 14 Issue 2, p843-853. 11p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Axial+flow+compressors%22">Axial flow compressors</searchLink><br />*<searchLink fieldCode="DE" term="%22Compressor+performance%22">Compressor performance</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Friction+losses%22">Friction losses</searchLink><br />*<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: There is significant importance in accurate prediction of axial flow compressor characteristics at different conditions. Different techniques and models have been proposed for performance prediction of axial compressors. Because of its lower computational cost and high speed the meanline algorithm has been widely applied in preliminary design and analysis of axial compressors; however, there are some inaccuracies at design and off‐design conditions since the method relies on empirical correlations, which may be weak when applied to unconventional airfoil types. Applying some modifications to the meanline algorithm could improve its performance for a wider operating range with higher accuracy. This study aims to propose a modification for accuracy enhancement of meanline techniques to obtain characteristics of axial flow compressors at design and different off‐design conditions. For this purpose, three scenarios are considered to modify the models. In the 1st scenario, coefficients are used for the deviation models while the pressure loss was not changed, in the 2nd scenario coefficients are applied for the pressure loss models and the deviation model is used as the base model and in the 3rd scenario, coefficients are used for both models. The coefficients are optimized by use of multi‐objective genetic algorithm. It was found that use of the 3rd scenario leads to the best accuracy. In this scenario, the average absolute error in the estimated isentropic efficiency is reduced from 2.41% to 0.75% at 80% design rotational speed while the improvement in estimated mass flow rate at this speed is relatively minor. However, at design rotational speed after optimization, the average absolute error in the estimated isentropic efficiency drops from 6.35% to 0.96% while the estimated mass flow rate decreases from 4.97% to 0.094%. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/ese3.70386
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 843
    Subjects:
      – SubjectFull: Axial flow compressors
        Type: general
      – SubjectFull: Compressor performance
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Friction losses
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
    Titles:
      – TitleFull: Development of a Novel Modified Mean‐Line Approach for Performance Prediction of Axial Compressors.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Hassanlue, Sina
      – PersonEntity:
          Name:
            NameFull: Alhuyi Nazari, Mohammad
      – PersonEntity:
          Name:
            NameFull: Pakatchian, Mohammadreza
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20500505
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            – Type: volume
              Value: 14
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Energy Science & Engineering
              Type: main
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