A tuning scheme of cycle reference point for gas turbine adaptive performance simulation with field data

2020 ◽  
Vol 34 (12) ◽  
pp. 5279-5294
Author(s):  
Binbin Yan ◽  
Minghui Hu ◽  
Kun Feng ◽  
Zhinong Jiang
Keyword(s):  
Gas Turbine ◽  
Reference Point ◽  
Field Data ◽  
Adaptive Performance ◽  
Performance Simulation ◽  
Tuning Scheme

Multi-Fluid Gas Turbine Components Scaling for a Generation IV Nuclear Power Plant Performance Simulation

2018 ◽  
Author(s):  
Emmanuel O. Osigwe ◽  
Arnold Gad-Briggs ◽  
Theoklis Nikolaidis ◽  
Pericles Pilidis ◽  
Suresh Sampath
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Gas Turbine ◽  
Nuclear Power ◽  
Working Fluid ◽  
Simulation Tool ◽  
Closed Cycle ◽  
Performance Simulation ◽  
Working Fluids ◽  
Component Map

One major challenge to the accurate development of performance simulation tool for component-based nuclear power plant engine models is the difficulty in accessing component performance maps; hence, researchers or engineers often rely on estimation approach using various scaling techniques. This paper describes a multi-fluid scaling approach used to determine the component characteristics of a closed-cycle gas turbine plant from an existing component map with their design data, which can be applied for different working fluids as may be required in closed-cycle gas turbine operations to adapt data from one component map into a new component map. Each component operation is defined by an appropriate change of state equations which describes its thermodynamic behavior, thus, a consideration of the working fluid properties is of high relevance to the scaling approach. The multi-fluid scaling technique described in this paper was used to develop a computer simulation tool called GT-ACYSS, which can be valuable for analyzing the performance of closed-cycle gas turbine operations with different working fluids. This approach makes it easy to theoretically scale existing map using similar or different working fluids without carrying out a full experimental test or repeating the whole design and development process. The results of selected case studies show a reasonable agreement with available data.

A Fully Integrated Approach to Component Zooming Using Computational Fluid Dynamics

2004 ◽  
Vol 128 (3) ◽  
pp. 579-584 ◽  
Author(s):  
Vassilios Pachidis ◽  
Pericles Pilidis ◽  
Fabien Talhouarn ◽  
Anestis Kalfas ◽  
Ioannis Templalexis
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Gas Turbine ◽  
Engine Performance ◽  
Integrated Approach ◽  
High Fidelity ◽  
Performance Simulation ◽  
Fully Integrated ◽  
Engine Component ◽  
Simulation Strategy

Background . This study focuses on a simulation strategy that will allow the performance characteristics of an isolated gas turbine engine component, resolved from a detailed, high-fidelity analysis, to be transferred to an engine system analysis carried out at a lower level of resolution. This work will enable component-level, complex physical processes to be captured and analyzed in the context of the whole engine performance, at an affordable computing resource and time. Approach. The technique described in this paper utilizes an object-oriented, zero-dimensional (0D) gas turbine modeling and performance simulation system and a high-fidelity, three-dimensional (3D) computational fluid dynamics (CFD) component model. The work investigates relative changes in the simulated engine performance after coupling the 3D CFD component to the 0D engine analysis system. For the purposes of this preliminary investigation, the high-fidelity component communicates with the lower fidelity cycle via an iterative, semi-manual process for the determination of the correct operating point. This technique has the potential to become fully automated, can be applied to all engine components, and does not involve the generation of a component characteristic map. Results. This paper demonstrates the potentials of the “fully integrated” approach to component zooming by using a 3D CFD intake model of a high bypass ratio turbofan as a case study. The CFD model is based on the geometry of the intake of the CFM56-5B2 engine. The high-fidelity model can fully define the characteristic of the intake at several operating condition and is subsequently used in the 0D cycle analysis to provide a more accurate, physics-based estimate of intake performance (i.e., pressure recovery) and hence, engine performance, replacing the default, empirical values. A detailed comparison between the baseline engine performance (empirical pressure recovery) and the engine performance obtained after using the coupled, high-fidelity component is presented in this paper. The analysis carried out by this study demonstrates relative changes in the simulated engine performance larger than 1%. Conclusions. This investigation proves the value of the simulation strategy followed in this paper and completely justifies (i) the extra computational effort required for a more automatic link between the high-fidelity component and the 0D cycle, and (ii) the extra time and effort that is usually required to create and run a 3D CFD engine component, especially in those cases where more accurate, high-fidelity engine performance simulation is required.

Training Future Gas Turbine Performance Engineers

2007 ◽  
Author(s):  
V. Pachidis ◽  
P. Pilidis ◽  
I. Li
Keyword(s):  
Gas Turbine ◽  
Thermal Power ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Performance Simulation ◽  
Turbine Engine ◽  
Turbine Performance ◽  
Auxiliary Power ◽  
Engine Testing ◽  
The Uk

The performance analysis of modern gas turbine engine systems has led industry to the development of sophisticated gas turbine performance simulation tools and the utilization of skilled operators who must possess the ability to balance environmental, performance and economic requirements. Academic institutions, in their training of potential gas turbine performance engineers have to be able to meet these new challenges, at least at a postgraduate level. This paper describes in detail the “Gas Turbine Performance Simulation” module of the “Thermal Power” MSc course at Cranfield University in the UK, and particularly its practical content. This covers a laboratory test of a small Auxiliary Power Unit (APU) gas turbine engine, the simulation of the ‘clean’ engine performance using a sophisticated gas turbine performance simulation tool, as well as the simulation of the degraded performance of the engine. Through this exercise students are expected to gain a basic understanding of compressor and turbine operation, gain experience in gas turbine engine testing and test data collection and assessment, develop a clear, analytical approach to gas turbine performance simulation issues, improve their technical communication skills and finally gain experience in writing a proper technical report.

Dynamic performance investigations of a turbojet engine using a cross-application visual oriented platform

2008 ◽  
Vol 112 (1129) ◽  
pp. 161-169 ◽  
Author(s):  
K. G. Kyprianidis ◽  
A. I. Kalfas
Keyword(s):  
Gas Turbine ◽  
Initial Conditions ◽  
Dynamic Performance ◽  
Simulation Models ◽  
Simulation Software ◽  
Performance Models ◽  
Programming Environments ◽  
Performance Simulation ◽  
Spreadsheet Program ◽  
Turbojet Engine

Abstract This paper presents the development of visual oriented tools for the dynamic performance simulation of a turbojet engine using a cross-application approach. In particular, the study focuses on the feasibility of developing simulation models using different programming environments and linking them together using a popular spreadsheet program. As a result of this effort, a low fidelity cycle program has been created, capable of being integrated with other performance models. The amount of laboratory sessions required for student training during an educational procedure, for example for a course in gas turbine performance simulation, is greatly reduced due to the familiarity of most students with the spreadsheet software. The model results have been validated using commercially available gas turbine simulation software and experimental data from open literature. The most important finding of this study is the capability of the program to link to aircraft performance models and predict the transient working line of the engine for various initial conditions in order to dynamically simulate flight phases including take-off and landing.

Statistical Methodologies for Reliability Assessment of Gas Turbine Measurements

2003 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini ◽  
M. Burgio
Keyword(s):  
Data Processing ◽  
Mathematical Models ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Field Data ◽  
Reliability Assessment ◽  
Field Measurements ◽  
Measurement Reliability ◽  
Student Distribution ◽  
Analysis Models

All measurements, although taken as accurately as possible, are subjected to uncertainty. So the analysis of errors and uncertainty is crucial in all applications since such errors need to be estimated and, when possible, reduced. In particular, when gas turbine mathematical models based on the processing of field measurements (such as the Gas Path Analysis models) are used, the evaluation of measurement reliability is a key point. In fact, it has been demonstrated that these kinds of techniques are sensitive to measurement errors: thus, tools for field data processing to evaluate the presence of the so-called outliers are advisable. In this paper, some statistical methodologies for the assessment of the reliability of the measurements taken on a gas turbine are presented. The methodologies, taken from literature and used for historical measurements, are discussed. Moreover, a new methodology, based on a modified t-Student distribution, is proposed.

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

2018 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Lucrezia Manservigi ◽  
Giovanni Bechini ◽  
Mauro Venturini
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Anomaly Classification ◽  
Monitoring And Diagnostics ◽  
Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

2019 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi
Keyword(s):  
Gas Turbine ◽  
Field Data ◽  
Health Management ◽  
Theoretical Background ◽  
Health State ◽  
Prediction Errors ◽  
Probabilistic Prediction ◽  
Future Health ◽  
Bayesian Hierarchical ◽  
Prediction Reliability

Abstract The prediction of the time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future health state thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM predicted outputs. Then, BHM is applied to both simulated and field data representative of gas turbine degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors were found to be lower than 1.0 % or 1.7 % for single- or multi-step prediction, respectively.

Design/off-design performance simulation and discussion for the gas turbine combined cycle with inlet air heating

Energy ◽  
2019 ◽  
Vol 178 ◽  
pp. 386-399 ◽  
Author(s):  
Yongping Yang ◽  
Ziwei Bai ◽  
Guoqiang Zhang ◽  
Yongyi Li ◽  
Ziyu Wang ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Combined Cycle ◽  
Performance Simulation ◽  
Design Performance ◽  
Air Heating

Advanced Modeling of Fluid Thermodynamic Properties for Gas Turbine Performance Simulation

2008 ◽  
Author(s):  
Vishal Sethi ◽  
Fulvio Diara ◽  
Sina Atabak ◽  
Anthony Jackson ◽  
Arjun Bala ◽  
...  
Keyword(s):  
Thermodynamic Properties ◽  
Gas Turbine ◽  
Fluid Model ◽  
Simulation Software ◽  
Working Fluid ◽  
Performance Simulation ◽  
Turbine Performance ◽  
Fluid Properties ◽  
Convergence Problems ◽  
The Impact

This paper describes the structure of an advanced fluid thermodynamic model which has been developed for a novel advanced gas turbine simulation environment called PROOSIS. PROOSIS (PRopulsion Object Oriented SImulation Software) is part of the VIVACE-ECP (Value Improvement through a Virtual Aeronautical Collaborative Enterprise - European Cycle Programme) project. The main objective of the paper is to determine a way to achieve an accurate, robust and reliable fluid model. The results obtained demonstrate that accurate modeling of the working fluid is essential to avoid convergence problems of the thermodynamic functions thereby increasing the accuracy of calculated fluid properties. Additionally, the impact of accurately modeling fuel thermodynamic properties, at the point of the injection, is discussed.

Multiple-Point Adaptive Performance Simulation Tuned to Aeroengine Test-Bed Data

2009 ◽  
Vol 25 (3) ◽  
pp. 635-641 ◽  
Author(s):  
Y. G. Li ◽  
L. Marinai ◽  
E. Lo Gatto ◽  
V. Pachidis ◽  
P. Philidis
Keyword(s):  
Multiple Point ◽  
Adaptive Performance ◽  
Test Bed ◽  
Performance Simulation
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