An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Experimental Setup ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique

An annular pulsed detonation combustor (PDC) basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a setup without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental setup is considered in order to develop and test these methods.

An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique ◽  
Set Up

An annular pulsed detonation combustor basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a set-up without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental set-up is considered in order to develop and test these methods.

Application of Artificial Neural Networks for Misfiring Detection in an Annular Pulsed Detonation Combustor Mockup

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Sascha Wolff ◽  
Jan-Simon Schäpel ◽  
Rudibert King
Keyword(s):  
Neural Network ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Data Driven ◽  
Learning Approach ◽  
Experimental Setup ◽  
Physical Knowledge ◽  
Pulsed Detonation ◽  
Machine Learning Approach ◽  
The Given

An annular pulsed detonation combustor (PDC) basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a setup without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a supervised data driven machine learning approach is used for the misfiring detection. The features used as inputs for the classifier are extracted from measurements incorporating physical knowledge about the given setup. To this end, a neural network is trained based on labeled data which is then used for classification purposes, i.e., misfiring detection. A surrogate, nonreacting experimental setup is considered in order to develop and test these methods.

A physics-based approach to flow control using system identification

2012 ◽  
Vol 702 ◽  
pp. 26-58 ◽  
Author(s):  
Aurelien Hervé ◽  
Denis Sipp ◽  
Peter J. Schmid ◽  
Manuel Samuelides
Keyword(s):  
System Identification ◽  
Control Strategies ◽  
A Priori ◽  
Operating Conditions ◽  
Noise Sources ◽  
Subsequent Performance ◽  
Model Based ◽  
Noise Characteristics ◽  
Identification Technique ◽  
Auto Regressive

AbstractControl of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A model-based approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based system-identification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only time-sequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment.

Application of Artificial Neural Networks for Misfiring Detection in an Annular Pulsed Detonation Combustor Mockup

2016 ◽  
Author(s):  
Sascha Wolff ◽  
Jan-Simon Schäpel ◽  
Rudibert King
Keyword(s):  
Neural Network ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Data Driven ◽  
Learning Approach ◽  
Physical Knowledge ◽  
Pulsed Detonation ◽  
Machine Learning Approach ◽  
Set Up ◽  
The Given

An annular pulsed detonation combustor basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a set-up without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a supervised data driven machine learning approach is used for the misfiring detection. The features used as inputs for the classifier are extracted from measurements incorporating physical knowledge about the given set-up. To this end, a neural network is trained based on labeled data which is then used for classification purposes, i.e., misfiring detection. A surrogate, non-reacting experimental set-up is considered in order to develop and test these methods.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

A System Identification Method for Excitation-Frequency Dependent Rotordynamic Coefficients

2012 ◽  
Author(s):  
Timothy W. Dimond ◽  
Amir A. Younan ◽  
Paul Allaire
Keyword(s):  
System Identification ◽  
Measurement Uncertainty ◽  
Excitation Frequency ◽  
Frequency Dependent ◽  
Rotordynamic Coefficients ◽  
Adjoint Systems ◽  
Dynamic Coefficients ◽  
Backward Whirl ◽  
Identification Technique ◽  
Damped Systems

Experimental identification of rotordynamic systems presents unique challenges. Gyroscopics, generally damped systems, and non-self-adjoint systems due to fluid structure interaction forces mean that symmetry cannot be used to reduce the number of parameters to be identified. Rotordynamic system experimental measurements are often noisy, which complicates comparisons with theory. When linearized, the resulting dynamic coefficients are also often a function of excitation frequency, as distinct from operating speed. In this paper, a frequency domain system identification technique is presented that addresses these issues for rigid-rotor test rigs. The method employs power spectral density functions and forward and backward whirl orbits to obtain the excitation frequency dependent effective stiffness and damping. The method is highly suited for use with experiments that employ active magnetic exciters that can perturb the rotor in the forward and backward whirl directions. Simulation examples are provided for excitation-frequency reduced tilting pad bearing dynamic coefficients. In the simulations, 20 and 50 percent Gaussian output noise was considered. Based on ensemble averages of the coefficient estimates, the 95 percent confidence intervals due to noise effects were within 1.2% of the identified value. The method is suitable for identification of linear dynamic coefficients for rotordynamic system components referenced to shaft motion. The method can be used to reduce the effect of noise on measurement uncertainty. The statistical framework can also be used to make decisions about experimental run times and acceptable levels of measurement uncertainty. The data obtained from such an experimental design can be used to verify component models and give rotordynamicists greater confidence in the design of turbomachinery.

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