Uncertainty Analysis of Gas Turbine Overall Performance Based on Monte Carlo Method

doi:10.12096/j.2096-4528.pgt.24032

Abstract

Abstract:

Objectives In the design and manufacturing process of gas turbines, there are many uncertainties in the geometric structure and operating conditions of the components, leading to significant fluctuations in the thermal performance of the gas turbine. In order to accurately identify the operating state of the gas turbine, it is necessary to quantitatively evaluate the impact of these uncertainties on the overall performance of related components and the entire system. Methods Based on the Monte Carlo method and the thermal process mechanism equations of the gas turbine, an uncertainty analysis model for the overall turbine system is developed to study the quantitative propagation characteristics of component efficiency and flow uncertainty. For the structure of aero-derivative gas turbine, the uncertainty distribution of four independent parameters — compressor efficiency, compressor inlet flow, turbine efficiency, and cooling air flow are introduced. The probability distribution of the overall performance and component operation parameters of the gas turbine are obtained under the conditions of both coupled and independent uncertainties of each parameter. Results Compared with the other three parameters, the uncertainty of turbine efficiency has a more significant impact on the output power distribution, efficiency, and exhaust temperature of gas turbine. While compressor efficiency has a greater impact on the operating state of other components, its impact is reduced when combined with other uncertainties. The temperatures and output power at each point of the entire system are more susceptible to the uncertainty of the components. Conclusions The turbine should be a key focus in both design and operation. During gas turbine operation, monitoring temperature and output power is more effective in capturing fluctuations in component performance.

Key words: gas turbine, generator set, Monte Carlo method, uncertainty, overall performance, thermal performance, operating status

CLC Number:

TK 14

Wenqiang LU, Yan CHEN, Jinglun FU, Haisong LIU, Xiangling KONG. Uncertainty Analysis of Gas Turbine Overall Performance Based on Monte Carlo Method[J]. Power Generation Technology, 2025, 46(1): 126-134.

Figures/Tables 18

References 17

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参数	厂家公开数据	Gatecycle软件		GTPC软件
参数	厂家公开数据	计算结果	误差	计算结果	误差
进气损失/MPa	0	0	0	0	0
出气损失/MPa	0	0	0	0	0
P₀/MPa	0.101 3	0.101 3	0	0.101 3	0
T₀/℃	15	15	0	15	0
G₀/(kg/s)	83.150	83.15	0	83.159	0.011
G₁/(kg/s)	83.150	83.15	0	83.159	0.011
P₁/MPa	1.013	1.013	0	1.013	0.000
T₁/℃	15	15	0	15	0.000
G₂/(kg/s)	—	67.220	—	81.902	—
P₂/MPa	2.200 2	2.340 6	0.638 1	2.200 3	0.004
T₂/℃	476.480	486.720	2.149	476.490	0.002
η_comp/%	—	85	—	84.559	—
G_f /(kg/s)	1.730	1.670	-3.468	1.730	0
T_f /℃	—	15	—	15	—
G₃/(kg/s)	—	68.890	—	68.258	—
P₃/MPa	—	2.211 9	—	2.199 5	—
T₃ /℃	—	1 365.000	—	1 360.53	—
η_turb /%	—	90.000	—	85.664	—
G₄/(kg/s)	84.330	83.570	-0.901	84.889	0.663
P₄/MPa	0.047 31	0.049 65	0.049 46	0.047 31	0
T₄/℃	835.560	837.110	0.185	835.630	0.008
G_bleed3/(kg/s)	—	1.250	—	1.247	—
P_bleed3/MPa	0.584 7	0.6420	0.980 0	0.584 7	0
T_bleed3 /℃	280.630	241.800	13.837	280.630	0
n /(r/min)	9 687	9 586	1.043	9 687	0
效率/%	31.372	31.880	1.619	31.370	-0.006
功率/MW	41.120	40.170	-2.310	41.067	-0.129

参数	厂家公开数据	Gatecycle软件		GTPC软件
参数	厂家公开数据	计算结果	误差	计算结果	误差
进气损失/MPa	0	0	0	0	0
出气损失/MPa	0	0	0	0	0
P₀/MPa	0.101 3	0.101 3	0	0.101 3	0
T₀/℃	15	15	0	15	0
G₀/(kg/s)	83.150	83.15	0	83.159	0.011
G₁/(kg/s)	83.150	83.15	0	83.159	0.011
P₁/MPa	1.013	1.013	0	1.013	0.000
T₁/℃	15	15	0	15	0.000
G₂/(kg/s)	—	67.220	—	81.902	—
P₂/MPa	2.200 2	2.340 6	0.638 1	2.200 3	0.004
T₂/℃	476.480	486.720	2.149	476.490	0.002
η_comp/%	—	85	—	84.559	—
G_f /(kg/s)	1.730	1.670	-3.468	1.730	0
T_f /℃	—	15	—	15	—
G₃/(kg/s)	—	68.890	—	68.258	—
P₃/MPa	—	2.211 9	—	2.199 5	—
T₃ /℃	—	1 365.000	—	1 360.53	—
η_turb /%	—	90.000	—	85.664	—
G₄/(kg/s)	84.330	83.570	-0.901	84.889	0.663
P₄/MPa	0.047 31	0.049 65	0.049 46	0.047 31	0
T₄/℃	835.560	837.110	0.185	835.630	0.008
G_bleed3/(kg/s)	—	1.250	—	1.247	—
P_bleed3/MPa	0.584 7	0.6420	0.980 0	0.584 7	0
T_bleed3 /℃	280.630	241.800	13.837	280.630	0
n /(r/min)	9 687	9 586	1.043	9 687	0
效率/%	31.372	31.880	1.619	31.370	-0.006
功率/MW	41.120	40.170	-2.310	41.067	-0.129

变量参数	概率分布函数		样本点分布
变量参数	M	S	M	S
压气机入口空气质量/(kg/s)	105.00	5.25	104.84	5.26
压气机效率/%	86.41	0.50	86.41	0.50
高压透平效率/%	90.00	0.52	90.00	0.52
冷气相对流量/%	19.15	0.25	19.15	0.25

变量参数	概率分布函数		样本点分布
变量参数	M	S	M	S
压气机入口空气质量/(kg/s)	105.00	5.25	104.84	5.26
压气机效率/%	86.41	0.50	86.41	0.50
高压透平效率/%	90.00	0.52	90.00	0.52
冷气相对流量/%	19.15	0.25	19.15	0.25

变量参数	Case 1		Case 2		Case 3		Case 4
变量参数	M	S	M	S	M	S	M	S
压气机入口空气质量/(kg/s)	104.84	0	104.84	0	104.84	5.26	104.84	5.26
压气机效率/%	86.41	0.1、0.5、1.0、2.1	86.41	0	86.41	0.1、0.5、1.0、2.1	86.41	0.50
高压透平效率/%	90.00	0	90.00	0.1、0.5、1.0、2.1	90.00	0.52	90.00	0.1、0.5、1.0、2.1
冷气相对流量/%	19.15	0	19.15	0	19.15	0.25	19.15	0.25