Global Nonlinear Modeling of Gas Turbine Dynamics Using NARMAX Structures

2002 ◽  
Vol 124 (4) ◽  
pp. 817-826 ◽  
Author(s):  
N. Chiras ◽  
C. Evans ◽  
D. Rees
Keyword(s):  
Gas Turbine ◽  
Nonlinear Model ◽  
Linear Models ◽  
Nonlinear Modeling ◽  
Search Space ◽  
Frequency Domain Analysis ◽  
Superior Performance ◽  
Linear Modeling ◽  
Large Signal ◽  
Identification Techniques

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modeling and nonlinear analysis is used to restrict the search space for nonlinear modeling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modeling can be effectively combined to enhance the modeling of an aircraft gas turbine.

Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

2001 ◽  
Author(s):  
Neophytos Chiras ◽  
Ceri Evans ◽  
David Rees
Keyword(s):  
Gas Turbine ◽  
Nonlinear Model ◽  
Linear Models ◽  
Search Space ◽  
Frequency Domain Analysis ◽  
Superior Performance ◽  
Large Signal ◽  
Nonlinear Modelling ◽  
Frequency Domains ◽  
Identification Techniques

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modelling can be effectively combined to enhance the modelling of an aircraft gas turbine.

scholarly journals Identifying a Dynamic Model for a GE Frame 7 Gas Turbine

1994 ◽  
Author(s):  
Howard Kaufman ◽  
R. Ravi
Keyword(s):  
Gas Turbine ◽  
Linear Models ◽  
Controller Design ◽  
Guide Vane ◽  
Selection Criterion ◽  
Inlet Guide Vane ◽  
Data Sets ◽  
Similar Work ◽  
Identification Techniques ◽  
Vane Angle

Several tests were conducted on a GE Frame 7 gas turbine to determine its dynamic characteristics. The objective is to obtain a model that can be used for controller design. The tests consisted of adding sequences of square waves to the two inputs — the fuel reference and the inlet guide vane angle reference — and recording the inputs and the outputs. This method of exciting the system afforded us with a way of separating the data sets into two categories, the first, in which the fuel reference was changed, and the second, in which the guide vane angle reference was changed. Least-squares system identification techniques were used to obtain linear models using a selection criterion that was a measure of how well a model fit both the sets of data. This brought in a measure of robustness to the models thus making them ideal for use in controller design. This paper summarizes the results from these tests, contains plots that show how well the linear models are able to fit the recorded data, and finally, provides some recommendations for others doing similar work.

Nonlinear Gas Turbine Modeling Using Feedforward Neural Networks

2002 ◽  
Author(s):  
Neophytos Chiras ◽  
Ceri Evans ◽  
David Rees
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Linear Models ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Shaft Speed ◽  
Large Signal ◽  
Turbine Engine ◽  
Fuel Flow ◽  
Relationship Of

In this paper a feedforward neural network is used to model the fuel flow to shaft speed relationship of a Spey gas turbine engine. The performance of the estimated model is validated against a range of small and large signal engine tests. It is shown that the performance of the estimated models is superior to that of the estimated linear models.

scholarly journals Nonlinear modeling growth body weight of Mangalarga Marchador horses

2017 ◽  
Vol 47 (4) ◽  
Author(s):  
Felipe Amorim Caetano Souza ◽  
Tales Jesus Fernandes ◽  
Raquel Silva de Moura ◽  
Sarah Laguna Conceição Meirelles ◽  
Rafaela Aparecida Ribeiro ◽  
...  
Keyword(s):  
Body Weight ◽  
Linear Models ◽  
Nonlinear Modeling ◽  
Growth Curves ◽  
Live Weight ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Cross Sectional ◽  
Adult Body Weight ◽  
The Cross

ABSTRACT: The analysis of the growth and development of various species has been done using the growth curves of the specific animal based on non-linear models. The objective of the current study was to evaluate the fit of the Brody, Gompertz, Logistic and von Bertalanffy models to the cross-sectional data of the live weight of the MangalargaMarchador horses to identify the best model and make accurate predictions regarding the growth and maturity in the males and females of this breed. The study involved recording the weight of 214 horses, of which 94 were males and 120 were non-pregnant females, between 6 and 153 months of age. The parameters of the model were estimated by employing the method of least squares, using the iteratively regularized Gauss-Newton method and the R software package. Comparison of the models was done based on the following criteria: coefficient of determination (R²); Residual Standard Deviation (RSD); corrected Akaike Information Criterion (AICc). The estimated weight of the adult horses by the models ranged between 431kg and 439kg for males and between 416kg and 420kg for females. The growth curves were studied using the cross-sectional data collection method. For males the von Bertalanffymodel was found to be the most effective in expressing growth, while in females the Brody model was more suitable. The MangalargaMarchador females achieve adult body weight earlier than the males.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

Behavioral Referents for Molar Ratings of Heterosocial Skill

1982 ◽  
Vol 50 (1) ◽  
pp. 139-146 ◽  
Author(s):  
W. Stephen Royce
Keyword(s):  
Multiple Regression ◽  
Linear Models ◽  
Eye Contact ◽  
Regression Analyses ◽  
Linear Modeling ◽  
Hand Gestures ◽  
Men And Women ◽  
Behavioral Cues ◽  
Wide Range ◽  
Highly Skilled

A linear modeling technique was used to identify valid behavioral referents of molar heterosocial skill ratings in both men and women. Videotapes of the heterosocial interactions of 30 men and 30 women representing a wide range of skill were shown to untrained peers who made molar heterosocial skill ratings and supplied lists of the behavioral cues they believed to be useful in discriminating skillful and unskillful subjects. The most widely endorsed cues were then scored for their rates of occurrence in the target subjects' interactions, and multiple regression analyses were used to construct linear models of behavioral referents for the molar heterosocial skill ratings. Highly skilled men were those who kept their gaze up, asked questions, and used appropriate hand gestures. Highly skilled women were those who kept their gaze up, made eye contact, and avoided speaking too softly.

Multi-Stage System Identification of a Gas Turbine

2017 ◽  
Author(s):  
Amit Pandey ◽  
Maurício de Oliveira ◽  
Chad M. Holcomb
Keyword(s):  
Gas Turbine ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Closed Loop Control ◽  
Input Output ◽  
Open Loop System ◽  
Loop Control ◽  
Multi Stage ◽  
Identification Techniques

Several techniques have recently been proposed to identify open-loop system models from input-output data obtained while the plant is operating under closed-loop control. So called multi-stage identification techniques are particularly useful in industrial applications where obtaining input-output information in the absence of closed-loop control is often difficult. These open-loop system models can then be employed in the design of more sophisticated closed-loop controllers. This paper introduces a methodology to identify linear open-loop models of gas turbine engines using a multi-stage identification procedure. The procedure utilizes closed-loop data to identify a closed-loop sensitivity function in the first stage and extracts the open-loop plant model in the second stage. The closed-loop data can be obtained by any sufficiently informative experiment from a plant in operation or simulation. We present simulation results here. This is the logical process to follow since using experimentation is often prohibitively expensive and unpractical. Both identification stages use standard open-loop identification techniques. We then propose a series of techniques to validate the accuracy of the identified models against first principles simulations in both the time and frequency domains. Finally, the potential to use these models for control design is discussed.

Large signal nonlinear model of anisotropic transformers for nonsinusoidal operation. I. lambda -i characteristics

1991 ◽  
Vol 6 (1) ◽  
pp. 1874-1886 ◽  
Author(s):  
E.F. Fuchs ◽  
M.A.S. Masoum ◽  
D.J. Roesler
Keyword(s):  
Nonlinear Model ◽  
Large Signal

scholarly journals Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 123
Author(s):  
María Jaenada ◽  
Leandro Pardo
Keyword(s):  
Generalized Linear Models ◽  
Influence Function ◽  
Regression Models ◽  
Linear Models ◽  
Asymptotic Properties ◽  
Significant Loss ◽  
Superior Performance ◽  
Test Statistics ◽  
Linear Regression Models ◽  
Type Test

Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.

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