Startseite Technik Modeling of Relative Exergy Destruction for Turboprop Engine Components Using Deep Learning Artificial Neural Networks
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Modeling of Relative Exergy Destruction for Turboprop Engine Components Using Deep Learning Artificial Neural Networks

  • Tolga Baklacioglu , Onder Turan EMAIL logo und Hakan Aydin
Veröffentlicht/Copyright: 19. Januar 2019
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International Journal of Turbo & Jet-Engines
Aus der Zeitschrift International Journal of Turbo & Jet-Engines Band 38 Heft 4

Abstract

This study illustrates an deep learning approach supported by a metaheuristic design targeting the foremost features and parameters of artificial neural network (ANN) framework used in predicting relative exergy destruction ( ( f r e l , d e s t * ) ) of a turboprop components. The development of deep ANN comprising of three-hidden layers using data obtained considering multiple engine input parameters was accomplished. Once the deep learning ANN frameworks were hybridized with a metaheuristic approach, such as genetic algorithms (GAs). The analysis of errors revealed a close fit involving the predicted values of the model and references made on data in f r e l , d e s t ∗ for the engine main components. The use of appropriately chosen values in preceding networks weights produced more accurate testing results (Linear correlation coefficient values (R) for the engine components are found to be between 0.996497 and 0.998986) in networks using three hidden layers compared to those using lower hidden layers. Furthermore, optimizing deep ANNs using GAs delivers not only further improved accuracy (R is calculated to be in the range of 0.998929–0.999966 for the engine components) but also an effective utilization of time in the resulting models.

Keywords: turboprop; energy; relative exergy destruction; artificial neural networks; deep learning; genetic algorithms
PACS: Entropy thermodynamics; 05.70.-a; Air transportation; 89.40.Dd; Irreversible thermodynamics; 05.70.Ln; Energy conservation in classical mechanics; 45.20.dh; Thermal analysis; 81.70.Pg; Thermodynamic considerations; 88.05.De

GLOSSARY

Abbreviations

 

ANN

Artificial neural network

BP

Back-propagation

Comb

Combustor

CV

Cross-validation

GA

Genetic algorithm

gen

Generated

HL

Hidden layer

MAE

Mean absolute error

MLP

Multilayer perceptron

MSE

Mean squared error

nMSE

Normalized mean squared error

per

Perfect

T

Training

Symbols

 

a

Air

b

Bias constant

C

Compressor

ch

Chemical

Cp

Specific heat

dest

Destruction

dk

kth component of the desired output vector

e

Specific energy

ek

Error function of neuron k

ex

Specific exergy rate

E

Energy rate; Mean squared error function of output layer

Ex

Exergy rate

f

Fuel

gg

Gas generator turbine

hn, yj

Output of the nth (jth) neuron in the hidden layer

hpr

Fuel heating value

i

initial

in

Inlet

k

kth component; Number of nodes in the output layer

K

Number of output processing elements

N

Number of heat reservoirs

ok

kth component of the actual output vector

out

Outlet

P

Pressure; Pattern size for relative exergy destruction

ph

Physical

pt

Power turbine

R

Specific gas constant; Linear correlation coefficient

rev

Reversible

s

Specific entropy

S

Entropy

t

Iteration number

tot

Total

u

Useful

v

Specific volume

V

Volume

vji

Weight value in the connection between the jth hidden and the ith output neurons

xi

Input layer

wn,i

Weight between the ith input neuron and nth hidden neuron

wm,n

Weight between the nth hidden neuron and mth neuron in the output layer

Δw

Weight increment

W

Work transfer interaction

Yactual

Actual value of the relative exergy destruction

Ym

Output of the mth neuron in the output layer

Ymean

Mean value of the relative exergy destruction

Ypredicted

Predicted relative exergy destruction

β

Momentum factor

η

Step-size (learning rate)

φ

Transfer function

φj’(αj)

Derivative of the transfer function of hidden layer

φk’(αk)

Derivative of the transfer function of output layer

λ

Combustion equation constant

0

Reference (environment) state

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Received: 2018-12-16
Accepted: 2019-01-09
Published Online: 2019-01-19
Published in Print: 2021-12-20

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Assessment of Exit Temperature Pattern Factors in an Annular Gas Turbine Combustor: An Overview
  4. Original Research Articles
  5. Optimization of Trenched Film Cooling Using RSM Coupled CFD
  6. Modeling of Relative Exergy Destruction for Turboprop Engine Components Using Deep Learning Artificial Neural Networks
  7. Direct Thrust Inverse Control of Aero-Engine Based on Deep Neural Network
  8. Entropy, Energy and Exergy for Measuring PW4000 Turbofan Sustainability
  9. A Centrifugal Compressor Performance Map Empirical Prediction Method for Automotive Turbochargers
  10. Unsteady Numerical Simulation in a Supersonic Compressor Cascade with a Strong Shock Wave
  11. CFD Study of Combined Impingement and Film Cooling Flow on the Internal Surface Temperature Distribution of a Vane
  12. CFD Analysis of Flow and Performance Characteristics of a 90°curved Rectangular Diffuser: Effects of Aspect Ratio and Reynolds Number
  13. Effects of the Recess Length of the Pilot Stage on the Lean Blowout Limits for the Multipoint Lean Direct Injection Combustors
  14. Stress and Vibration Analysis of a PDC (Pulse Detonation Chamber)
  15. Transverse Injection Experiments within an Axisymmetric Scramjet Combustor
https://doi.org/10.1515/tjj-2018-0047
Schlagwörter für diesen Artikel
turboprop; energy; relative exergy destruction; artificial neural networks; deep learning; genetic algorithms

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Assessment of Exit Temperature Pattern Factors in an Annular Gas Turbine Combustor: An Overview
  4. Original Research Articles
  5. Optimization of Trenched Film Cooling Using RSM Coupled CFD
  6. Modeling of Relative Exergy Destruction for Turboprop Engine Components Using Deep Learning Artificial Neural Networks
  7. Direct Thrust Inverse Control of Aero-Engine Based on Deep Neural Network
  8. Entropy, Energy and Exergy for Measuring PW4000 Turbofan Sustainability
  9. A Centrifugal Compressor Performance Map Empirical Prediction Method for Automotive Turbochargers
  10. Unsteady Numerical Simulation in a Supersonic Compressor Cascade with a Strong Shock Wave
  11. CFD Study of Combined Impingement and Film Cooling Flow on the Internal Surface Temperature Distribution of a Vane
  12. CFD Analysis of Flow and Performance Characteristics of a 90°curved Rectangular Diffuser: Effects of Aspect Ratio and Reynolds Number
  13. Effects of the Recess Length of the Pilot Stage on the Lean Blowout Limits for the Multipoint Lean Direct Injection Combustors
  14. Stress and Vibration Analysis of a PDC (Pulse Detonation Chamber)
  15. Transverse Injection Experiments within an Axisymmetric Scramjet Combustor
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