Fouling is the most important performance degradation factor, so it is necessary to accurately predict the effect of fouling on engine performance. In the previous research, it is very difficult to accurately model the fouled axial flow compressor. This paper develops a new performance calculation method of fouled multistage axial flow compressor based on experiment result and operating data. For multistage compressor, the whole compressor is decomposed into two sections. The first section includes the first 50% stages which reflect the fouling level, and the second section includes the last 50% stages which are viewed as the clean stage because of less deposits. In this model, the performance of the first section is obtained by combining scaling law method and linear progression model with traditional stage stacking method; simultaneously ambient conditions and engine configurations are considered. On the other hand, the performance of the second section is calculated by averaged infinitesimal stage method which is based on Reynolds’ law of similarity. Finally, the model is successfully applied to predict the 8stage axial flow compressor and 16stage LM250030 compressor. The change of thermodynamic parameters such as pressure ratio, efficiency with the operating time, and stage number is analyzed in detail.
The performance of gas turbine is strongly influenced by environment conditions of the power plant. Gas turbine performance degradation over time is mainly due to the change of the blade profiles of compressor and turbine caused by fouling, corrosion, erosion, and foreign object damage (FOD). Among these degradation factors, compressor fouling is the most important reason for gas turbine performance deterioration. It is estimated that compressor fouling accounts for 70% to 85% of the gas turbine performance loss [
Particles, such as soil dust, pollen, seeds, and combustion products, mixed with oil vapors from internal and external leaks can readily adhere to the blade surface and annulus areas; thus, the shape of the airfoil is changed and the blade surface roughness is increased. The principle effect of compressor fouling is the reduction of mass flow rate, isentropic efficiency, pressure ratio, and power output.
Although fouling mechanism in axial flow compressor is well known, predicting to what extent the engine output and efficiency are affected is still a major challenge for performance engineers [
Some authors offer criteria and mathematical models for fouling to characterize the compressor sensitivity to fouling. Tarabrin [
Melino et al. [
Mohammadi and MontazeriGh [
The research revealed that fouling can progress into 40 to 50 percent of the compressor stages [
The diagram of sectionalized axial flow compressor.
The overall performance of clean multistage compressor can be evaluated which requires performance for every stage to be available. In order to simulate compressor stage performance, the following nondimensional parameters are used.
Flow coefficient is as follows:
Pressure rise coefficient is as follows:
Here,
Temperature rise coefficient is as follows:
Efficiency is as follows:
Here,
In order to model each compressor stage, generalized flow coefficient, generalized pressure rise coefficient, and generalized efficiency coefficient are used to evaluate the outlet performance parameters based on those parameters at inlet. Consider
The selection of reference coefficient is very important to construct generalized stage characteristic curve. The reference coefficient (
Generalized pressure rise coefficient relationship was set up based on stage pressure rise data from a number of sources, which is shown in Figure
Generalized stage pressure coefficient curve.
Generalized efficiency relationship is obtained from the curve proposed by Howell and Bonham [
Generalized stage efficiency curve.
Once the generalized stage performance curve is available, the stagestacking procedure is initiated by specifying compressor inlet parameters such as inlet flow coefficient, inlet total pressure, inlet total temperature, and compressor corrected speed. Thus, the outlet condition can be evaluated starting from the inlet ones.
Sandercock et al. [
The effect of fouling on stage performance parameters can be described using (
Here,
Fouling coefficients of mass flow rate and efficiency.
At the same operating time, the performance parameters such as flow rate, pressure ratio, efficiency, and temperature rise are different for different stages. Because the deposited mass of particles is not uniform, the performance degradation of each stage is different. In order to accurately predict the effect of fouling on engine performance, fouling coefficient of each stage should be modified.
Fouling level is affected by many factors such as particle concentration, particle size, particle material, temperature, and humidity. In this model, these influencing factors are considered by introducing fouling severity coefficient
Based on experiment data, the deposited particles are reduced from the first stage to the outlet, so the fouling severity coefficient for every stage should be different. In this model, linear progression model is applied based on the following assumptions, as shown in Table
The fouling severity can be described by coefficient
From the last research it can be seen that front stages of the compressors are more severely affected by fouling and the impact decreases linearly.
Only 40–50% of the compressor stages are affected.
Linear progression model [
Step 
Stage 1  Stage 2  Stage 



 
1 

—  — 
2 


— 



— 




So, the relative fouling severity coefficient for every stage
Here,
Thus, stage performance parameters are modified as (
Fouling severity assignment table [
FS 




0  1.00  1.00  1.00 
1  0.96  0.99  0.95 
2  0.92  0.98  0.90 
3  0.88  0.97  0.85 
4  0.84  0.96  0.80 
5  0.80  0.95  0.75 
6  0.76  0.94  0.70 
7  0.72  0.93  0.65 
8  0.68  0.92  0.60 
9  0.64  0.91  0.55 
10  0.60  0.90  0.50 
Cherkez [
Configuration coefficient.
Engine configuration 


Singleshaft  1.1 
Twoshaft  1.0 
Threeshaft  0.9 
Based on above analysis, the stage performance parameters are modified as follows:
Stages in downstream are recognized as clean stages, so the performance prediction can be performed based on averaged infinitesimal stage characteristics method. The second method decomposed compression process into limitless infinitesimal compression section based on the following assumptions.
The fluid state inside the compressor is characterized by a turbulent flow, which satisfies the selfmodeling criterion. Subsequently, the Reynolds’ dynamical similarity is satisfied automatically.
The change of density from the inlet to outlet of the stage is negligible, while the amount of infinitesimal stage tends to be infinite. Subsequently, similar work conditions satisfy both the geometrical similarity and kinematical similarity criteria.
The performance characteristics of each infinitesimal stage are similar; thus, different infinitesimal stages satisfy the same relative characteristic map.
Every stage denotes equal relative pressure and relative temperature increase ratio at the design point. Therefore, the efficiencies at the design point of each stage are equal.
In order to overcome the limitation of Reynolds’ law of similarity, the compressor is imagined to be built up of a number of infinitesimal stages. Finally, the compressor’s characteristic is calculated section by section. Based on Reynolds’ law of similarity, the characteristics of each infinitesimal stage are being extrapolated, the performance curve of each infinitesimal stage is obtained, and furthermore the performance curve of the whole compressor is constructed. In this model, the second section is decomposed into 10 infinitesimal stages; the entrance parameter of the following stage is equal to the exit parameter of upstream stage [
The computation procedure is as follows.
The reference point of each stage is defined by design point.
The performance characteristics parameter at offdesign condition is obtained based on Reynolds’ law of similarity.
The stage performance is calculated based on above calculation result.
Finally, the total performance of axial flow compressor can be obtained.
Assuming that the compressor is decomposed into
Here,
The flow coefficient of infinitesimal stage is
The stage characteristics at offdesign condition can be obtained based on Reynolds’ law of similarity by corrected mass flow rate and rotational speed. Consider
Here,
The second section performance is obtained by the following equation:
The investigation of compressor blade contamination carried out at the Pervomayskaya gas piping compressor station [
Based on above discussion, after the performance was predicted, the whole compressor performance can be computed using
In order to demonstrate the validity of stagestacking method, an 8stage axial compressor is applied. The detailed design parameters are as follows.
Mass flow rate is 10.84 kg/s, air pressure at ambient conditions is 101.4 KPa and air temperature is 288 K. The total pressure ratio is 11.53 and polytropic efficiency is 88.8%. The clean compressor map is shown in Figures
Pressure ratio characteristic curve for clean compressor.
Efficiency characteristic curve for clean compressor.
Based on fouling coefficient reference and fouling coefficient of each stage, fouled compressor performance can be calculated by modified stagestacking method. The relation between stage pressure ratio and operating time is shown in Figure
The relation between stage pressure ratio and operating time for fouled stages.
Assuming that the fouling level of the first four stages is uniform, the pressure ratio curve and the efficiency curve are shown from Figure
The pressure ratio curve in design and fouled condition when operating time is 400 hours.
The pressure ratio curve in design and fouled condition when operating time is 2000 hours.
The efficiency curve in design and fouled condition when operating time is 400 hours.
The efficiency curve in design and fouled condition when operating time is 2000 hours.
The performance of fouled multistage axial compressor is computed based on the prediction model developed in this paper; the pressure ratio curve and efficiency curve are shown from Figures
The pressure ratio curve with 400 hours due to fouling with different relative fouling severity coefficient.
The pressure ratio curve with 2000 hours due to fouling with different relative fouling severity coefficient.
The efficiency curve with 400 hours due to fouling with different relative fouling severity coefficient.
The efficiency curve with 400 hours due to fouling with different relative fouling severity coefficient.
The reference engine in this work is General Electric 2500 [
LM250030 compressor design parameter.
Parameter  Unit  Literature 

Mass flow  Kg/s  65.8 
CDP  KPa  1722 
Power  KW  20134 
EGT  °C  504 
LM250030 compressor operating line data (General Electric, 1981).





9450  18.06  147.5  1.530 
9160  17.21  144.0  1.439 
8971  16.25  137.8  1.389 
8813  15.30  131.6  1.343 
8660  14.37  125.5  1.307 
8508  13.44  119.2  1.262 
8364  12.45  112.2  1.220 
8105  10.35  96.7  1.112 
7772  7.88  76.6  0.983 
The pressure ratio curve of LM250030 with 400 hours due to fouling with different relative fouling severity coefficient.
Fouling is the important influence factor of axial flow compressor performance degradation, so the prediction of the effect of fouling on compressor is crucial. In the previous research, CFD method and experiment study method are extensively used. Due to complex geometry and operating condition, it is very difficult to accurately predict the fouling phenomena.
This paper developed and validated a model which is able to evaluate the performance of fouled axial flow compressor. In multiple stage axial flow compressor, fouling is more serious in the first 50% stages than rear stages where particles deposited are less. So, the whole compressor is divided into two parts in this model. The performance of the first part is calculated by using stagestacking method for the simulation of compressor behavior. The model is able to reproduce the change of axial compressor performance maps due to fouling through a scaling technique and linear progression model. Moreover, the introduction of the fouling sensitivity factor allows taking into account the different sensitivity of different compressors to the fouling.
The performance of second part is calculated based on averaged infinitesimal stage method. In view of the shortcomings of the conventional way of extrapolating performance curve of axial compressors, the concept of average stage cascade performance, together with some rational assumptions, is being presented. The compressor is imagined to be built up of a number of infinitesimal stages and the compressor’s characteristic is calculated section by section.
The method was applied and tested using available data. The results of the application highlight the capability of the method to accurately predict the performance of multistage axial flow compressor due to fouling.
Pressure ratio
Mass flow rate
Temperature ratio of inlet total temperature and standard sealevel temperature
Pressure ratio of inlet total pressure and standard sealevel pressure
Rotational speed
Standard sealevel temperature
Standard sealevel pressure
Pressure coefficient
Specific heat at constant pressure
Stage pressure ratio
Ratio of specific heat
Tangential blade speed
Tangential blade speed at the midspan radius
Angular speed
Midspan radius
Axial flow velocity
Total temperature at the inlet
Effective section area
Temperature coefficient
Efficiency
Gas constant
Mach number
Compressor shaft speed
Inlet flow angle
Pressure
Ratio of fouling flow coefficient
Ratio of fouling pressure coefficient
Ratio of fouling efficiency coefficient
Operating time
Number of infinitesimal stage
Fouling severity coefficient
Configuration coefficient.
Stagnation index
Inlet
Outlet
Reference value
Fouling
The first section
The second section
Reference.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The work was carried out with the support of the National Natural Science Foundation of China (51105142) and the Fundamental Research Funds for the Central Universities (2014MS119).