Gas Turbine Simulation Programming

Abstract:State-of-the-art thermodynamic simulation of energy conversion processes requires proprietary software. This article is an attempt to refute this statement. Based on object-oriented programming a simulation and exergy analysis of a combined cycle gas turbine is carried out in a free and open-source framework. Relevant basics of a thermodynamic analysis with exergy-based methods and necessary fluid property models are explained. Thermodynamic models describe the component groups of a combined heat and power system. The procedure to transform a physical model into a Python-based simulation program is shown. The article contains a solving algorithm for a precise gas turbine model with sophisticated equations of state. As an example, a system analysis of a combined cycle gas turbine with district heating is presented. Herein, the gas turbine model is validated based on literature data. The exergy analysis identifies the thermodynamic inefficiencies. The results are graphically presented in a Grassmann chart. With a sensitivity analysis a thermodynamic optimization of the district heating system is discussed. Using the exergy destruction rate in heating condensers or the overall efficiency as the objective function yields to different results.Keywords: simulation; object-oriented programming; energy conversion process; combined cycle gas turbine; combined heat and power; exergy analysis; simultaneous modular approach

The world is facing new challenges on sustainability and global warming and, as a result, propulsion and power technologies will play an even greater role in shaping the future. The solution of these problems very often demands engineers who are versed in the latest know-how in system modeling and simulation. Aviation has been and is at the forefront in this respect, and the power sector has always benefited from such innovation. In this unique course, you will advance your system modeling skills, which are at the core of the design process and essential for predicting and evaluating performance.

Given an engineering problem related to propulsion and power systems, you will use the 9-step method to create or select the appropriate model and run and interpret simulations in order to obtain a good solution of such a problem, and communicate the results.

You will be guided by instructors during dedicated online sessions and, if you can participate in person, during practical workshops. You will be encouraged to collaborate with your peers, as you would do in a professional environment. You will use software tools including OpenModelica and GSP to develop and configure the models required to run the simulations for your specific analysis. You can choose an engineering problem related to an aero engine, industrial gas turbine or another energy system.

In addition, the student will apply these new techniques to become competent in more specific problems to be chosen among those involving aero engines, gas turbines, power and thermal control systems. To this end, teams of students will work on an assignment which requires them to develop a system model, run simulations, interpret results and write a short report. Specific learning objectives related to the practical part of the course are therefore:

Based on the detailed analysis of collaborative running interface of Simulink/Fluent, a system simulation for the rated working condition as well as variable working condition of marine gas turbine has been achieved, which can improve the simulation efficiency of marine gas turbine by developing simulation model of combustor with Fluent and simulation models of other components with Simulink. The result shows that the Simulink/Fluent collaborative simulation zooming can make the inner working conditions of combustor be observed specifically, based on the overall performance matching analysis; thus an effective technical means for the structural optimization design of combustor has been provided.

In order to expand the application field and enhance the versatility, Fluent provides the following interface for users [1]: Fluent/UDF is written based on C language to deal with the nonfixed boundary conditions or dynamic grid computing and other personalized applications; Fluent/Journal is used to record the operation sequence on the Fluent/GUI and then Fluent can be started by the external program including command with Journal parameter, so that simulation task is automatically operated according to the operation sequence in Journal. The simulation results at calculation regional boundary can be stored in specified text file by Fluent to facilitate sharing data with external program.

Making use of external interface of Fluent and Simulink, the following three approaches can achieve Simulink/Fluent collaborative simulation: Fluent is embedded in Simulink [4, 5]; that is, Fluent simulation model is encapsulated to a simulation module which has the same attribute and operation style with the build-in module and participates in the running of the Simulink simulation model; Simulink is embedded in Fluent [1]; that is, Simulink simulation model is compiled into C function for Fluent/UDF to call; Simulink and Fluent run parallelly that is to achieve simulation collaboration through the global variate [1] or a kind of process blocking technology [2, 3]. As the first approach has more advantages in versatility and flexibility, it has become the mainstream of current application.

The general idea to develop the collaborative running interface of Simulink/Fluent, based on the first approach, is as follows [4, 5]: develop interface program based on Simulink/S-Function; start Fluent with command including Journal parameter; store the update values of the S-Function input ports into a text file; access the text file by Fluent/UDF and using these values to deal with nonfixed boundary conditions; at last, Fluent will store the simulation results of the calculation regional boundary into another text file and S-Function will access this text file to propagate its values to Simulink via output ports. Because many boundary conditions and physical parameters in Fluent simulation model cannot be modified by UDF, the interface developed by the idea mentioned above cannot be applied in some specific fields.

In Methodology, a new collaborative running interface of Simulink/Fluent is developed, which is also inside the range of the first approach. Compared with the existing one, the new collaborative running interface provides boundary conditions for Fluent environment by modifying parameters of Journal directly, instead of tedious UDF programming.

In Application, the simulation zooming research is conducted that takes triaxial gas turbine of a certain type as physical model, establishing 2D simulation model for the combustor with Fluent, establishing system simulation model which consists of 0D component simulation models with Simulink. The 2D simulation model of combustor is embedded in the Simulink environment by encapsulating the written collaborative running interface of Simulink/Fluent to S-Function module.

This paper takes triaxial gas turbine of a certain type as physical model (as it is shown in Figure 3). The main mathematical model established according to the traditional method of volume inertia is as follows:where is the volume between low-pressure compressor and high-pressure compressor; subscript , subscript , and subscript are air, gas, and fuel; and subscript in and subscript out are inlet and outlet of combustor; refer to Figure 3 for the meaning of other subscripts.

The triaxial gas turbine of a certain type is mainly used in mechanical or electric propulsion of marine, whose main performance parameters are shown in Table 1, where and are efficiency and output power of marine gas turbine; refer to Figure 3 for the meaning of other subscripts.

Discretization of Computational Domain. Taking the 3D solid model of loop-type flame tube of combustor of a certain type triaxial gas turbine as reference, a 2D axisymmetric model is built by the way of area average, and the discrete computational domain is also built on the basis of structured grid. As it is shown in Figure 4, the 2D model maintains the proper swirl holes, primary holes, mixing holes, head, and cooling holes, the corresponding domain of grid is properly encrypted, and the total number of grids is 5355.

Adding Mathematical Models and Boundary Conditions. The mathematical models described previously are sequentially added to the simulation model (e.g., RNG turbulence model). Considering the working condition of combustor and the algorithm condition used in calculation, the following boundary conditions are added to the simulation model [7]: the inlet of air, including mass flow, temperature, turbulence intensity, hydraulic diameter, and average mixing fraction; the inlet of fuel, including fuel type (using liquid fuel C7H16), injection mode and swirl angle of pressure swirl atomizer, mass flow, and temperature; the outlet of gas, including reflux temperature, turbulence intensity, and hydraulic diameter; making sure the wall is the heat insulating wall and has no slip velocity, whose parameters are zero, including turbulence parameter, concentration, and normal gradient of square value of concentration pulsation.

The Simulink environment is selected to develop the system simulation model which consists of 0D component simulation models [19, 20]. The 2D simulation model of combustor is embedded in the Simulink environment by encapsulating the written collaborative running interface of Simulink/Fluent to S-Function module which is named fluent_sub, to carry out the simulation zooming. As it is shown in Figure 5, the assistant module on one hand plays a role of volume module, to provide combustor inlet pressure for Fluent simulation model; on the other hand, it outputs the air-fuel ratio and gas constant of combustor outlet , which cannot be outputted by Fluent simulation model. 2b1af7f3a8