Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-22T11:29:49.558Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling of Energy Efficiency of a Turboprop Engine using Ant Colony Optimisation

Published online by Cambridge University Press:  30 October 2019

A. Piskin ,
T. Baklacioglu Open the ORCID record for T. Baklacioglu [Opens in a new window] ,
O. Turan  and
H. Aydin
A. Piskin
Affiliation:
Tusas Engine Industries, INC.Esentepe Mahallesi EskisehirTurkey
T. Baklacioglu*
Affiliation:
[email protected]
O. Turan
Affiliation:
Faculty of Aeronautics and AstronauticsEskisehir Technical [email protected]
H. Aydin
Affiliation:
Tusas Engine Industries, INC.Esentepe Mahallesi EskisehirTurkey
*
(*Corresponding author)
Get access Rights & Permissions [Opens in a new window]

Abstract

Exergy efficiency can be used as an objective function in order to improve systems efficiency. Thus, the most efficient regions for the operation parameters can be searched easily. Exergy efficiency data of a turboprop engine’s components that have been calculated using basic engine parameters in the previous studies are modeled using cubic spline curve fitting methodology. Spline curves are on the two dimensional plane, where x axis is the input parameter and y axis is the exergy efficiency of the component. A spline curve is defined by the points subject to arbitrary selection of number and position. Initially positions of the points are located with two different methods and then in order to obtain better accuracy point positions are improved by ‘Ant colony’ and ‘Goldsection’ optimisation methods. Sum of Squares of the errors between the fitted value and data value was used as the fitness function. Least square error of 5 × 10−9 is assumed as acceptable accuracy which yields to a minimum R = 0.9998 linear correlation coefficient. In the optimisation step, independent engine variable versus calculated engine performance parameters were checked against spline fitted values. Improvement of the fitness function is observed as the number of fitting points is increased. Ant colony optimisation in engine exergy efficiency parametric modeling is a new approach in authors’ point of view.

Keywords

AircraftTurbopropEnergyExergyAnt Colony OptimisationEngine Performance
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1272 , February 2020 , pp. 237 - 256
Copyright
© Royal Aeronautical Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aydin, H., Turan, O., Karakoc, T.H. and Midilli, A. Component-based exergetic measures of the an experimental turboprop/turboshaft engine for propeller aircrafts and helicopters, International Journal of Exergy, 2012, 11, (3), pp 322–348.CrossRefGoogle Scholar
Aydin, H., Turan, O., Midilli, A. and Karakoc, T.H. Exergetic and exergo-economic analysis of a turboprop engine: a case study for CT7-9C, International Journal of Exergy, 2012, 11, (1), pp 69–82.CrossRefGoogle Scholar
Baklacioglu, T., Turan, O. and Aydin, H. Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks, Elsevier, Energy, 2015, 86, pp 709–721.CrossRefGoogle Scholar
Kutz, J.N. Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, Oxford University Press, 2013, Oxford, UK, ISBN 978-0-19-966033-9.Google Scholar
Wold, S. Spline functions in analysis, Technometrics ©, 1974, 16, (1), pp 1–11.CrossRefGoogle Scholar
Lindstrom, M.J. Penalised estimation of free-knot splines, J Computational and Graphical Statistics, 1999, 8, (2), pp 333–352.Google Scholar
Cham, T.J. and Cipolla, R. Automated B-spline curve representation incorporating MDL and error-minimizing control point insertion strategies, IEEE Transactions Pattern Analysis and Machine Intelligence, 1999, 21, (1), pp 49–53.CrossRefGoogle Scholar
Dierckx, P. Curve and Surface Fitting With Splines, Oxford University Press Inc., 1996, New York, NY, USA, ISBN 0-19-853440-X.Google Scholar
Lu, F. and Milios, E.E. Optimal spline fitting to planar shape, Signal Processing, 1994, 37, (1), pp 129–140, ISSN 0165-1684.CrossRefGoogle Scholar
de Boor, C. and Rice, J.R. Least Squares Cubic Spline Approximation I- Fixed knots, CDS TR 20, 1968, Department of Computer Sciences Purdue University, Houston, TX, USA.Google Scholar
de Boor, C. and Rice, J.R. Least Squares Cubic Spline Approximation II- Variable knots, CDS TR 21, 1968, Department of Computer Sciences Purdue University, Houston, TX, USA.Google Scholar
Jupp, D.L.B. Approximation to data by splines with free knots, SIAM Journal on Numerical Analysis, 1978, 15, (2), pp 328–343.CrossRefGoogle Scholar
Kokkonis, P.A. and Leute, V. Least squares splines approximation applied to multicomponent diffusion data, Computational Materials Science, 1996, 6, pp 103–111, Elsevier.CrossRefGoogle Scholar
Iglesias, A., Galvez, A. and Collantes, M. Bat algorithm for curve parameterization in data fitting with polynomial Bezier curves, International Conference on Cyberworlds, IEEE, 2015.CrossRefGoogle Scholar
Galvez, A. and Iglesias, A. Efficient particle swarm optimisation approach for data fitting with free knot B-splines, Computer-Aided Design, 2011, 43, pp 1683–1692, Journal homepage: www.elsevier.com/locate/cad.CrossRefGoogle Scholar
Galvez, A. and Iglesias, A. Firefly algorithm for explicit B-spline curve fitting to data points, Mathematical Problems in Engineering, 2013, 2013, Article ID 528215, Hindawi Publishing Corporation.CrossRefGoogle Scholar
Baklacioglu, T., Aydin, H. and Turan, O. Energetic and exergetic efficiency modeling of a cargo aircraft by a topology improving neuro-evolution algorithm, Energy, 2016, 103, pp 630–345.CrossRefGoogle Scholar
Baklacioglu, T., Turan, O. and Aydin, H. Metaheuristic approach for an artificial neural network: exergetic sustainability and environmental effect of a business aircraft, Transportation Research Part D: Transport and Environment, 2018, 63, pp 445–465.CrossRefGoogle Scholar
Uyar, K. and Ulker, E. B-spline curve fitting with invasive weed optimisation, Applied Mathematical Modeling, 2017, 52, pp 320–340, www.elsevier.com.CrossRefGoogle Scholar
Zhang, J. Computer Applications in Engineering and Construction, CVEN302-501, Texas A&M University Department of Civil Engineering College Station, 2008, Nagle Hall, TX, USA.Google Scholar
Buchanan, J.R. Cubic Spline Interpolation, MATH 375, Numerical Analysis, Department of Mathematics of Millersville University Pennsylvania, 2013, Millersville, PA, USA.Google Scholar
Deb, K. Optimisation for Engineering Design: Algorithms and Examples, Prentice Hall of India Private Limited, 2012, New Delhi, ISBN 81-203-0943-X.Google Scholar
Dorigo, M. Optimisation, learning and natural algorithms, PhD thesis, Politechnico di Milano, 1992.Google Scholar
Stützle, T. and Hoos, H.H. Max-Min Ant System, Elsevier Science, 1999, Dresden, Germany.Google Scholar
Rao, S.S. Engineering Optimisation, Theory and Practice, 4th ed, 2009, John Wiley & Sons, Inc., Hoboken, New Jersey, USA. ISBN10 0470183527, ISBN13 9780470183526.CrossRefGoogle Scholar