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.

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.

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.

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.

scholarly journals Application of hybrid Kalman filter for improving water level forecast

2016 ◽  
Vol 18 (5) ◽  
pp. 773-790 ◽  
Author(s):  
Xuan Wang ◽  
Vladan Babovic
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Model Updating ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Data Driven ◽  
Parameter Uncertainties ◽  
Hybrid Data Assimilation ◽  
Complex Nonlinear Systems ◽  
Set Up

Numerical modeling is one of the popular means to simulate and forecast the state of oceanographic systems. However, it still suffers from some limitations, e.g., parameter uncertainties, simplification of model assumptions, and absence of data for proper boundary and initial conditions. This paper proposes a hybrid data assimilation scheme, which combines the Kalman filter (KF) with a data-driven model (local linear model (LM)), to directly correct numerical model outputs at locations without measurements. Two different types of KF (unscented Kalman filter and two-sample Kalman filter) are tested and compared. A local LM is utilized to describe the evolution of the model state and then assimilated into the KF. This in turns simplifies the application of KF for highly complex nonlinear systems such as the dynamic motion of Singapore regional water. The proposed scheme is first examined using a simple hypothetical bay experiment followed by an operational model of the Singapore Regional Model (SRM), in which both are set up in the Delft3D modeling environment. This combination of KF and data-driven model provides insights into the influence of different error covariance estimations on the model updating accuracy. This research also provides guidance to offline utilization of KF in updating of numerical model output.

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 dynamic observer to capture and control perturbation energy in noise amplifiers

2014 ◽  
Vol 758 ◽  
pp. 728-753 ◽  
Author(s):  
Juan Guzmán Iñigo ◽  
Denis Sipp ◽  
Peter J. Schmid
Keyword(s):  
System Identification ◽  
Degrees Of Freedom ◽  
Feedforward Control ◽  
State Space Model ◽  
Plate Boundary ◽  
Identification Technique ◽  
Identification Approach ◽  
Set Up ◽  
And Control ◽  
Proper Orthogonal

AbstractIn this article, we introduce techniques to build a reduced-order model of a fluid system that accurately predicts the dynamics of a flow from local wall measurements. This is particularly difficult in the case of noise amplifiers where the upstream noise environment, triggering the flow via a receptivity process, is not known. A system identification approach, rather than a classical Galerkin technique, is used to extract the model from time-synchronous velocity snapshots and wall shear-stress measurements. The technique will be illustrated for the case of a transitional flat-plate boundary layer, where the snapshots of the flow are obtained from direct numerical simulations. Particular attention is directed to limiting the processed data to data that would be readily available in experiments, thus making the technique applicable to an experimental set-up. The proposed approach combines a reduction of the degrees of freedom of the system by a projection of the velocity snapshots onto a proper orthogonal decomposition basis combined with a system identification technique to obtain a state-space model. This model is then used in a feedforward control set-up to significantly reduce the kinetic energy of the perturbation field and thus successfully delay transition.

scholarly journals On Kaufmann's theory of the impact of the pianoforte hammer

1920 ◽  
Vol 97 (682) ◽  
pp. 99-110 ◽  
Keyword(s):  
Initial Conditions ◽  
Practical Importance ◽  
Prima Facie ◽  
Point Of View ◽  
Infinite Length ◽  
Modes Of Vibration ◽  
Rigorous Treatment ◽  
The Subject ◽  
Set Up ◽  
The Impact

The theory of the vibrations of the pianoforte string put forward by Kaufmann in a well-known paper has figured prominently in recent discussions on the acoustics of this instrument. It proceeds on lines radically different from those adopted by Helmholtz in his classical treatment of the subject. While recognising that the elasticity of the pianoforte hammer is not a negligible factor, Kaufmann set out to simplify the mathematical analysis by ignoring its effect altogether, and treating the hammer as a particle possessing only inertia without spring. The motion of the string following the impact of the hammer is found from the initial conditions and from the functional solutions of the equation of wave-propagation on the string. On this basis he gave a rigorous treatment of two cases: (1) a particle impinging on a stretched string of infinite length, and (2) a particle impinging on the centre of a finite string, neither of which cases is of much interest from an acoustical point of view. The case of practical importance treated by him is that in which a particle impinges on the string near one end. For this case, he gave only an approximate theory from which the duration of contact, the motion of the point struck, and the form of the vibration-curves for various points of the string could be found. There can be no doubt of the importance of Kaufmann’s work, and it naturally becomes necessary to extend and revise his theory in various directions. In several respects, the theory awaits fuller development, especially as regards the harmonic analysis of the modes of vibration set up by impact, and the detailed discussion of the influence of the elasticity of the hammer and of varying velocities of impact. Apart from these points, the question arises whether the approximate method used by Kaufmann is sufficiently accurate for practical purposes, and whether it may be regarded as applicable when, as in the pianoforte, the point struck is distant one-eighth or one-ninth of the length of the string from one end. Kaufmann’s treatment is practically based on the assumption that the part of the string between the end and the point struck remains straight as long as the hammer and string remain in contact. Primâ facie , it is clear that this assumption would introduce error when the part of the string under reference is an appreciable fraction of the whole. For the effect of the impact would obviously be to excite the vibrations of this portion of the string, which continue so long as the hammer is in contact, and would also influence the mode of vibration of the string as a whole when the hammer loses contact. A mathematical theory which is not subject to this error, and which is applicable for any position of the striking point, thus seems called for.

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.

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