Engineering, 2011, 3, 500-507
doi:10.4236/eng.2011.35058 Published Online May 2011 (http://www.SciRP.org/journal/eng)
Gas Turbine Performance Optimization Using Compressor
Online Water Washing Technique
Ezenwa Alfred Ogbonnaya
Department of Marine Engineering Rivers State University of Science and Technology,
Nkpolu, Port Harcourt, Nigeria
Received February 10, 2011; revised March 22 , 20 1 1; accepted April 6, 2011
Abstract
The ability to predict the behaviour of a gas turbine engine and optimize its performance is critical in eco-
nomic, thermal and condition monitoring studies. Having identified fouling as one of the major sources of
compressor and therefore gas turbine deterioration, a computer-based engine model was developed to
optimize the performance of gas turbines. The paper thus presents an analysis of compressor hand cleaning,
on and offline compressor washing to actualize the technique using a computer program in Visual Basic
programming language with data collected over a period of fifteen weeks for 2 gas turbine plants GT1 and
GT2. The results of the data collected, when collated, shows that after washing, the overall operational
efficiency changed from 39.2% to 46.25%. To optimize the performance of gas turbine engines, it is
therefore recommended that operators should perform a combination of compressor hand cleaning, offline
and online washing simultaneously.
Keywords: Gas Turbine, Turbomachinery Components, Fouling, Performance Optimization, Operational
Practices, Compressor Water Washing
1. Introduction
Gas Turbines (GTs) have wide range of industrial appli-
cations. Proper maintenance and operating practices can
significantly affect the level of performance degradation
and thus time between repairs or overhauls of a GT [1-5].
The correct construction and operation of the compo-
nents of GT plants are also necessary for proper under-
standing and monitoring. Furthermore, the function of a
GT is the result of the fine-tuned cooperation of many
different components of the plant. [6-9]. The emphasis of
this work is on the turbomachinery or aerodynamic
components of the GT. Hence the special contribution of
this work to GT operation and maintenance is that it
helps to prolong the life of the turbomachinery com-
ponents of the engine.
From the point of view of application, the GT’s com-
pressor is affected by the environmental conditions of the
site [10-12]. With increasing operatin g time, degradation
of the compressor manifest in the form of reduced per-
formance. The major cause of reduction in compressor
efficiency and inlet air mass flow is fouling. Others are
abrasion, corrosion and erosion of the blade surfaces.
The degradations of the GT compressor has direct influ-
ence on the GT power plant efficiency, pressure ratio and
power. With a view to prevent degradation, optimize
performance and increase availability, GTs are equipped
with sophisticated air filter systems. These air filter sys-
tems significantly reduce the amount of contaminants
that GTs are subjected to but cannot filter out the con-
taminant completely [11]. This present work applied
online water washing to optimize the performance of a
GT plant on industrial duty for electricity generation in
Sapele, Delta State of Nigeria. It further looked into the
plus and minus of other GTs maintenance techniques.
Operation of a GT at steady outputs can lead to depo-
sition from the combustion gas on the blades. Deposits
cause output and efficiency to drop by reducing the effi-
ciency of energy transfer and eventually restricting the
flow of the combustion gases [5,13]. There are quite a
number of wear out problems associated with GT. The
blades may break off or corrode, while bearings may
wear out due to friction and vibration. Common faults on
the rotor include rubbing, temporary unbalance,
eccentricity cracking or/and misalignment [10]. The
compressor may experience air leakage problems while

E. A. OGBONNAYA ET AL. 501
deposits on the blades can create blade washing effects
[14]. Performance analysis can be applied to both rotat-
ing and stationary parts of the GT. It is one condition
monitoring technique which allows the optimum time for
restorative maintenance to be calculated, where the dete-
rioration may result in increased fuel consumption or in
reduced outpu t or both [15-21]. The types of compressor
cleaning methods with their merits and demerits are
detailed as follows:
1.1. Compressor Abrasive Cleaning
The application of abrasive materials for cleaning of
compressors such as the injection of rive or walnut—
shell into the compressor for its clearing is becoming
outdated [11,22,23]. This is because the erosion impact is
more followed by the danger that these non-liquid mate-
rials are capable of clogging the passages of the second-
dary air system. Furthermore, these non-liquid particles
may cause damage to the compressor blade coating.
1.2. Compressor Hand Cleaning
This entails cleaning th e inlet guide vanes (IGV) and the
blades of the first compressor row with brushes and a
detergent [24]. Although this method is effective for re-
moving particles sticking to the blade surface, its short-
coming is that it is time consu ming and requires the GTs
to be shutdown. Hence this method should be supported
by offline washing.
1.3. Compressor Offline Washing
In this method, the GT has to be shutdown and cooled,
followed by flushing the compressor with demineralized
water [24]. This approach enables compressor fouling to
be removed virtually completely. With a view to avoid
GTs non-availability, this method should be used during
normal inspection interval.
1.4. Compressor Online Washing
This technique is normally done during GTs base-load
operation with the IGVs in the fully open con dition. It is
achieved by installing an online washing system at the
air inlet of the GTs [25,11]. Although this method is
known to diminish compressor fouling, it can not com-
pletely eliminate it.
2. M et h od o lo g y
The test engines are GTs 1 and 2 of the same capacity
(45 MW) on industrial duty for electricity generation in
Sapele, Delta State of Nigeria. Both GTs were commis-
sioned on the same day. After a period of three month
GT1 and GT2 were shutdown for maintenance. The
online washing method was done on weekly intervals for
a period of fifteen weeks. Deminerialized water was in-
jected through a configuration of small nozzles in the air
flow before the first compressor stage. At the end of the
exercise, the data gathered from both plants on daily ba-
sis were sampled and the mean used in this research.
These parameters enabled the calculation of the com-
pressor efficiency and other relevant operational pa-
rameters. In this work, it was assumed that the compres-
sor inlet condition s correspond to the ambient conditions.
The analyses were performed with the equations be-
low, while the experimental setup is shown in Figure 1.
Figure 2 also shows the enthalphy versus entropy map of
a compressor. It is between 2 and 2' that water
washing takes place. Therefore 2
H
 is the enthalpy after
water washing.
Figure 1. Experimental setup for GTs 1 and 2.
Figure 2. Enthalpy versus entrophy map of compression [11].

E. A. OGBONNAYA ET AL. 502
The compressor component is modeled as:
1
'
22
11
TP
TP





 (1)
Similarly, the turbine component is expressed as:
1
33
'4
4
TP
P T





 (2)
Furthermore, the isentropic efficiency of the com-
pressor is expressed as;
c
I
sentropic enthalphy drop
A
ctual enthalpy drop
 
'1
1
2
2
c
hh
hh


 (3)
Also, the isentropic efficiency of the turbine is
given by:
T
A
ctual enthalphy drop
I
sentropic enthalpydrop
 
34
34
T
TT
TT




(4)
The compressor work is modeled as:


21c
Whh (5)
Since, dh = Cpdt,


21cp
WCTT (6)
The turbine work is modeled as:


43T
Whh  (7)


43Tp
WCTT  (8)
The heat input is expressed as:
32in
Qhh (9)


32in p
QCTT  (10)
Similarly, the heat output is given by:


14out
Qhh (11)


14out p
QCTT 
(12)
The Network output and heat input is expressed
respectively as:
cT
WWW
 (13)
in out
QQ Q
 (14)
The overall efficiency of the test engine is modeled as:
th
Net work
NetHeatSupplied
  (15)
Tc
th
in
WW
Q


 (16)
By appropriate substitutions,
1
1
1
th
p









(17)
Noting that
p

is called the pressure ratio and is ex-
pressed as:
3 2
14
p
P P
PP
  (18)
3. R es u lt s
The results of the analyses carried out on GT 1, and GT2
are shown in Tables 1 and 2. Table 2 shows the data
taken before water washing. Table 3 shows the
monitored data for GT2 during the combination of the 3
water washing methods considered in this work while
Table 4 shows the values of percentage derivations in
outlet parameter. The values in these tables were used to
plot the various graphs shown from Figures 5 to 9.
These graphs are further used to explain the impact of
this work to engineering pratise. Figure 3 shows a pro-
gramme flowchart to determine the performance of the
GTs. The flowchart was constructed from Equations 6, 8
and 17.
4. Discussion of Results
The trajectories of the GTs when the compressor outlet
pressure is plotted against date in weeks are shown in
Figure 4. It shows that before the washing exercise, the
plants were operating at 7.00 bar, as depicted in trend for
GT1. The compressor outlet pressure is an indication of
fouling, as this menace causes a reduction of the pressure.
Following the application of water washing to GT1, it
was observed that the pressure increased to 7.88 bar.
These pressures were maintained by the regular weekly
online washing. Furthermore, the application of com-
pressor hand cleaning, online and offline washing during
shutdown resulted to a pressure of 8.8 bar against the
design value of 9.4 bar. It is reasonable to say that al-
though onlin e water washing yielded a meaningfu l result
but a combination of compressor hand cleaning during
shutdown, online and offlin e washing resulted to a better
performance.

E. A. OGBONNAYA ET AL. 503
Figure 3. Flow chart for performance trending draw n from
Equations (6), (8) and (17).
The path of the GTs when the turbine inlet tempera-
ture is plotted against date in weeks is shown in Figure 5.
The trend shows that the use of online water washing
improved the firing temperature of GT1 but a combina-
tion of online and offline water washing resulted in an
increase in the firing temperature of 1900k for GT2.
The graphs of the GTs when the Network output is
plotted against date in weeks are shown in Figure 6. It
shows that GT1 had initial high network output which
later decreased due to redeposition of foulants in the later
stages of the compressor. Furthermore, Figure 6 shows
that with a combination of compressor hand cleaning,
offline and online water washing, the network output of
Figure 4. Graph of compressor outlet pressure against date
in weeks.
Figure 5. Graph of turbine inlet temperature against date
in weeks.
Figure 6. Graph of network against date in weeks.

E. A. OGBONNAYA ET AL. 504
Table 1. Monitored Data for GT2 before water-washing (GTo).
Date (wks) T1 (k) T2 (k) P1 (bar) P2 (bar) T3 (k) T4 (k) Wnet (kJ/kg) 0

c

1 300 520.7246 1.013 6.98 1600 950 432.15 0.392 0.78
2 300 521.5418 1.013 7.00 1550 954 376.88 0.393 0.77
3 300 520.7211 1.013 6.99 1595 953 424.11 0.392 0.76
4 300 520.7246 1.013 6.98 1600 954 428.13 0.392 0.78
5 300 521.5480 1.013 7.00 1600 950 431.15 0.393 0.78
6 300 520.7246 1.013 6.98 1585 953 414.06 0.392 0.77
7 300 520.2246 1.013 6.98 1586 954 414.06 0.392 0.77
8 300 521.5480 1.013 7.00 1596 953 424.11 0.393 0.76
9 300 520.7211 1.013 6.99 1600 950 432.15 0.392 0.78
10 300 520.7211 1.013 6.99 1585 950 471.08 0.392 0.76
11 300 520.7246 1.013 6.98 1580 953 409.04 0.392 0.76
12 300 521.5480 1.013 7.00 1585 954 412.05 0.393 0.77
13 300 521.5480 1.013 7.00 1580 953 408.03 0.393 0.78
14 300 520.7246 1.013 6.98 1590 954 418.08 0.392 0.78
15 300 520.7211 1.013 6.99 1595 954 423.11 0.392 0.77
Table 2. Monitored data for GT1 with online water-washing.
Date (wks) T1 (k) T2 (k) P1 (bar) P2 (bar) T3 (k) T4 (k) Wnet (kJ/kg) 0

c

1 300 541.1492 1.013 7.88 1800 983.2679 578.460 82 0.457 0.82
2 300 550.1683 1.013 7.88 1795 977.8053 572.141 50 0.457 0.82
3 300 559.1875 1.013 7.88 1790 975.0740 565.822 24 0.457 0.81
4 300 568.2066 1.013 7.88 1785 972.3427 559.502 95 0.457 0.82
5 300 577.2258 1.013 7.88 1780 969.6114 553.183 66 0.457 0.82
6 300 586.2449 1.013 7.88 1775 966.8801 546.864 96 0.457 0.81
7 300 595.2641 1.013 7.88 1770 964.1488 540.545 07 0.457 0.81
8 300 604.2832 1.013 7.88 1765 961.4175 534.225 72 0.457 0.81
9 300 613.3024 1.013 7.88 1760 958.6862 527.906 49 0.457 0.81
10 300 622.3215 1.013 7.88 1755 955.9541 521.587 19 0.457 0.82
11 300 631.3407 1.013 7.88 1750 953.2236 515.2679 0.457 0.81
12 300 640.3598 1.013 7.88 1745 953.2236 508.94861 0.457 0.82
13 300 649.3790 1.013 7.88 1740 950.4923 502.629 32 0.457 0.82
14 300 658.3982 1.013 7.88 1735 949.7620 496.310 02 0.457 0.82
15 300 667.4173 1.013 7.88 1730 945.0297 489.990 73 0.457 0.82
Table 3. Monitored data for GT2 during a combination of compressor hand cleaning, online and offline water-washing.
Date (wks) T1 (k) T2 (k) P1 (bar) P2 (bar) T3(k) T4(k) Wnet (kJ/kg) 0

c

1 300 556.36 1.013 8.80 1900 983 663.943 0.4608 0.85
2 300 556.36 1.013 8.80 1900 986 660.923 0.4608 0.85
3 300 558.16 1.013 8.90 1900 988 657.109 0.4625 0.85
4 300 558.16 1.013 8.90 1880 985 640.024 0.4625 0.85
5 300 556.36 1.013 8.80 1890 985 651.883 0.4608 0.83
6 300 558.16 1.013 8.90 1900 987 658.114 0.4625 0.84
7 300 556.36 1.013 8.80 1895 988 653.893 0.4608 0.84
8 300 556.36 1.013 8.80 1895 986 655.903 0.4608 0.85
9 300 558.16 1.013 8.90 1900 986 659.119 0.4625 0.85
10 300 558.16 1.013 8.90 1900 987 658.114 0.4625 0.85
11 300 558.16 1.013 8.90 1900 987 658.114 0.4625 0.85
12 300 556.36 1.013 8.80 1885 987 644.848 0.4608 0.85
13 300 558.16 1.013 8.90 1885 985 645.049 0.4625 0.85
14 300 558.16 1.013 8.90 1885 986 644.044 0.4625 0.85
15 300 558.16 1.013 8.90 1900 985 660.124 0.4625 0.85

E. A. OGBONNAYA ET AL. 505
Table 4. Values of percentage deri vation in c ompre ssor outle t pre ssur e under various operational practices.
Date P2 (bar) GT1 P2 (bar) GT2 P2 (bar) GT3
(wks) Design
Value Operational Value %D Design Value Operational Value %D Design Value Operational Value %D
1 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.80 –6.38
2 9.40 7.00
–25.53 9.40 7.88 –16.17 9.40 8.80 –6.38
3 9.40 6.99
–25.64 9.40 7.88 –16.17 9.40 8.90 –5.32
4 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.90 –5.32
5 9.40 7.00
–25.53 9.40 7.88 –16.17 9.40 8.80 –6.38
6 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.90 –5.32
7 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.80 –6.38
8 9.40 7.00
–25.53 9.40 7.88 –16.17 9.40 8.80 –6.38
9 9.40 6.99
–25.64 9.40 7.88 –16.17 9.40 8.90 –5.32
10 9.40 6.99
–25.64 9.40 7.88 –16.17 9.40 8.90 –5.32
11 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.90 –5.32
12 9.40 7.00
–25.53 9.40 7.88 –16.17 9.40 8.80 –6.38
13 9.40 7.00
–25.53 9.40 7.88 –16.17 9.40 8.90 –5.32
14 9.40 6.98
–25.74 9.40 7.88 –16.17 9.40 8.90 –5.32
15 9.40 6.99
–25.64 9.40 7.88 –16.17 9.40 8.90 –5.32
GT2 increased to 480.0kJ/kg.
The graph of compressor efficiency is shown in Fig-
ure 7. From the graph, GT2 has the highest compressor
efficiency of 85%. This is as a result of a combination of
compressor hand cleaning and offline/online water
washing. Also, GT1 yielded compressor efficiency of
82% as a result of applying online water washing only. It
also implies that the air pumping capacity of the GT2
compressor has increased.
The graph of overall GTs operational efficiency is
shown in Figure 8. It is observed that a small change of
the compressor efficiency have a significant effect on
theoverall GT performance and efficiency. GT1 has com-
Figure 7. Graph of isotropic compressor outlet efficiency
against date in weeks
Figure 8. Graph of overall operational efficiency against
date in weeks.
Figure 9. Percentage deviation in compressor outlet against
date in weeks.

E. A. OGBONNAYA ET AL. 506
pressor efficiency of 82% which resulted to overall GT
efficiency of 45.8%. Also, GT2 has compressor effi-
ciency of 85% and it resulted to overall operational effi-
ciency of 46.25%.
Also, the graph of percentage derivation in compressor
outlet pressure against date in weeks is shown in Figure
9(a).
5. Conclusions
A comparative analysis has been carried out on three
GTs on industrial duty for electricity generation. These
GTs were commissioned at the same time before the re-
search was carried out on them. GTo served as a control
while compressor online washing was applied on GT1, a
combination of compressor hand cleaning and
online/offline water washing was applied to GT2. The
exercise lasted for fifteen weeks. The result of the analy-
sis shows that with the use of compressor online water
washing on GT1 yielded a compressor efficiency of 82%
and overall operational efficiency of 45.8%. Also, the
use of compressor hand cleaning, online and offline wa-
ter washing on GT2 yielded a compressor efficiency of
85% and overall operational efficiency of 46.25% the
results are handy to conclude that an acceptable balance
between these maintenance and operational practices
improves the GTs performance and optimize their avail-
ability.
6. References
[1] I. S. Diakunchak, “Performance Degradation in Industrial
Gas T urbines,” ASME Paper 91-GT-228, 1991.
[2] P. C. Frith, “The Effect of Compressor Rotor Tip Crops
on Turbo- Engine Performance,” ASME Paper 92-GT-83,
1992.
[3] S. A. Khalid, A. S. Khal sa, I. I. A. Waitz, S. C. Tan, E. M.
Greitzer, N. A. Cumpsty, J. J. Adamczyk and F. E. Marble,
“Endwal Blockage in Axial Compressor,” ASME Paper
98-GT-203,1998.
[4] R. J. Kind, P. J. Serjak and M. W. P. Abbott,
“Measurements and Prediction of the Effects of Surface
Roughness on Profile Losses and Deviation in a Turbine
Cascade,” ASME Paper 96-GT-203, 1996.
[5] R. Kurz and K. Brun, “Degradation in Gas Turbine Sys-
tems,” ASME Journal of Engineering for Gas Turbines
and Power, Vol. 123, No. 1, 2001 pp. 70-77.
doi:10.1115/1.1340629
[6] R. K. Rajput, “A Textbook of Power Plant Engineering,”
Laximi Publications, New Delhi, 1995.
[7] D. G. Wilson, “The Design of High Efficiency Turbo-
machinery and Gas Turbines,” MIT Press, Mass, 1994.
[8] T. D. Eastop and A. McConkey “Applied Thermodynam-
ics for Engineering Technologists,” 5th Edition, Addi-
son-Wesley Longman, Ltd., UK, 1993.
[9] P. K. Nag, “Power Plant Engineering,” 2nd Edition, Tata
McGraw Hill Publishing Company Limited, New Delhi,
2001, p.764.
[10] E. A. Ogbonnaya, “Modeling Vibration Based Faults in
Rotor Shaft of Gas Turbines,” Ph.D Thesis, Rivers State
University of Science and Technology, Port Harcourt,
2004.
[11] E. Schneider, Dermiricioglu, S. Fraanco and D. Therkom,
“Analysis of Compressor Online Washing to Optimize
Gas Turbine Power Plant Performance,” Proceedings of
ASME Turbo Expo 2009, Orlando, 8-12 June 2009, pp.
1-9.
[12] E. A. Ogbonnaya and K. Theophilus Johnson, “Use of
Multiple Variable Mathematical Method for Effective
Condition Monitoring of Gas Turbine,” Proceedings of
ASME Turbo-Expo, Glasgow, 14-18 June 2010, pp. 1-4.
[13] R. J. Boyle, “Prediction of Surface Roughness and Inci-
dence Effects on Turbine Performance,” Journal of Tur-
bomachinery, Vol. 116, No. 4, 1994, pp. 512-519.
doi:10.1115/1.2929468
[14] E. A. Ogbonnaya, H. U. Ugwu and C. N. Johnson,
“Computer-Aided Solution to the Vibrational Effect of
Instabilities in Gas Turbine Compressors,” Engineering,
Vol. 2, No. 8, 2010, pp. 658-664.
doi:10.4236/eng.2010.28084
[15] R. Beebe, “Condition Monitoring by Performance Analy-
sis to Optimize Time for Overhaul of Centrifugal
Pumps,” 52nd Annual Meeting for Machinery Failure
Prevention Society, Virginia Beach, 3 August 2009, pp.
102-112.
[16] C. B. Meher-Homji, M. Chaka and A. E. Brouley, “The
Fouling of Axial Flow Compressor-Causes,Effects, Sus-
ceptibility and Sensitivity,” Proceedings of ASME Turbo
Expo Orlando, Florida, 8-12 June 2009, pp. 1-20.
www.teurbexpo.org
[17] M. Venturini, “Influence of Blade Determination on
Compressor and Turbine Performance” ASME Paper GT
2008-50043, 2008.
[18] R. E. Duudas, “The Use of Performance Monitoring to
Prevent Compressor and Turbine Failures,” ASME Inten-
tional Gas Turine and Aeroengine Congress, Paper No.
82-GT-66, 1982.
[19] E. D. Bently, C. T. Hatch and B. Grissom “Fundamental
of Rotating Machine Diagnositic,” Bently Pressurized
Bearing Press, Minden, 2002.
[20] E. A. Ogbonnaya, “Diagnosing and Prognosing Gas Tur-
bine Rotor Shaft Faults Using the MICE,” Proceedings of
ASME Turbo Expo GT 2009-59450, Orlando, 8-12 June
2009, pp. 1-10.
[21] H. C. Pussey, “Turbo-Machinery Condition Monitoring
and Failure Prognosis, Shock and Vibration Information
Analysis,” centre/H1-Test Laboratories, Proceedings of
Institute of Vibration, Virginia, 8 November 2007, pp
2-10.
[22] M. P. Boyce and F. Gonzales, “A Study of Online and
Offline Turbine Washing to Optimize the Operation of a

E. A. OGBONNAYA ET AL. 507
Gas Turbine,” Journal of Engineering for Gas Turbine
and Power, Vol. 129, No. 1, 2007, pp. 114-129.
doi:10.1115/1.2181180
[23] J. P. Stalder, “Gas Turbine Compressor Washing State of
the Art: Field Experiences, Journal of Engineering for
Gas Turbine and Power, Vol. 123, No. 2, 2001, pp
363-370. doi:10.1115/1.1361108
[24] S. Demircioglu, “Detail Analysis of Compressor Online
Wash Impact in the Gas Turbine Performance,” Diploma
Thesis, F. H. Offenbury, 2008.
[25] K. R. Brun and H. Simmons, “Aerodynamic Instability
and Life Limiting Effects of Inlet and Interstage Water
Injection into Gas Turbines,” ASME Paper GT
2005-68007, 2005.
Nomenclature
Cp= Specific heat capacity at constant pressure
(kJ/kg)
D= Percentage derivation
ΣQ= Network output (kJ/kg)
ΣW= Network output (kJ/kg)
GT= Gas turbine
GTo= Monitored data before water washing of
GTs.
h1= Specific enthalpy at compressor inlet (kJ/kg)
h2= Specific enthalpy at compressor outlet
(kJ/kg)
h3= Specific enthalpy at turbine inlet (kJ/kg)
h4= Specific enthalpy at turbine outlet (kJ/kg)
nc= Isentropic efficiency of compressor
ητ= Isentropic efficiency of turbine
ηth= Overall thermal efficiency
P1= Compressor inlet pressure (bar)
P2= Compressor exit pressure (bar)
P3= Turbine inlet pressure (bar)
P4=Turbine outlet pressure (bar)
Qin=Heat input (kJ/kg)
Qout=Heat output (kJ/kg)
p

=Pressure ratio
T1=Compressor inlet temperature (k)
T2=Compressor outlet temperature (k)
T2
1= Compressor isentropic outlet temperature
(k)
T3=Turbine inlet temperature (k)
T4=Turbine outlet temperature (k)
T4
1= Turbine isentropic outlet temperature (k)
Wc=Compressor work (kJ/kg)
WT=Turbine work (kJ/kg)