An adaptive supercritical carbon dioxide turbine design method

A carbon dioxide, design method technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as poor accuracy and long design cycle

Active Publication Date: 2019-02-19
XI AN JIAOTONG UNIV
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Problems solved by technology

The Gauss Process Regression (GPR, Gauss Process Regression) method in machine learning provided by the present invention, combined with a fast adaptive design-optimization method established by the simulated annealing method, can solve the traditional supercritical carbon dioxide turbine design and optimization The problem of poor method accuracy and long design cycle has a stable and fast optimization effect, and has wide applicability to different engineering problems

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  • An adaptive supercritical carbon dioxide turbine design method
  • An adaptive supercritical carbon dioxide turbine design method
  • An adaptive supercritical carbon dioxide turbine design method

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Embodiment 1

[0203] see Figure 1 to Figure 5 , utilizing a kind of self-adaptive supercritical carbon dioxide turbine design method provided by the present invention, a 450kW supercritical carbon dioxide turbine has been designed-optimized, specifically as follows:

[0204] a) Design parameters

[0205] The key thermal design parameters of the supercritical carbon dioxide turbine are shown in Table 1. The design power is 450 kW, the speed is set at 40,000 rpm, the temperature and pressure at the nozzle inlet are 505 °C and 13.9 MPa, respectively, and the impeller outlet pressure is 7.85 MPa.

[0206] Table 1 Thermal design parameters

[0207]

[0208] b) Initial Design Scheme

[0209] According to general design experience and thermal design parameters, the initial design scheme of supercritical carbon dioxide turbine can be obtained, and its key parameters are shown in Table 2.

[0210] Table 2 Key parameters of the initial design scheme

[0211]

[0212] figure 2 The three-d...

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Abstract

The invention discloses an adaptive supercritical carbon dioxide turbine design method, which comprises the following steps: determining design variables, sampling and obtaining an initial design scheme; thermal design; aerodynamic analysis; setting initial annealing temperature T and design scheme; Gauss proces regression model learning; generating a new design scheme and carrying out thermal design according to Step 2; Gauss process regression model forecast; inspection criteria 1; the isentropic efficiency is obtained by aerodynamic analysis according to step 3; testing criterion 2, the optimal design scheme is obtained. The Gauss process regression method based on machine learning provided by the invention, combined with a rapid adaptive design and optimization method established by the simulated annealing method, which can solve the poor precision and long design cycle of the traditional supercritical carbon dioxide turbine design and optimization method, has a stable and fast optimization effect, and has wide applicability to different engineering problems.

Description

technical field [0001] The invention belongs to the technical field of special equipment design, in particular to an adaptive supercritical carbon dioxide turbine design method. Background technique [0002] The supercritical carbon dioxide Brayton cycle power plant has two advantages: 1) High energy conversion efficiency of the system: the cycle efficiency exceeds 45% at 500°C, which is higher than steam and helium cycles; 2) Low cost of system layout: ① Large working medium density , the system structure is compact; ②The system has no phase change process, no condenser is needed, and the cycle structure is simple; ③The operating temperature is low, conventional stainless steel materials can be used, and the manufacturing cost is low. It can be widely used in gas turbine waste heat utilization, space power system, ship power, etc., to improve the energy utilization efficiency of the system. Among them, the efficiency and reliability of the turbine is the key to ensure the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/17G06F2111/10
Inventor 谢永慧施东波刘天源张荻
Owner XI AN JIAOTONG UNIV
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