Approximation and Reconstruction of the Electrostatic Field in Wire–Plate Precipitators by a Low-Order Model

2001 ◽  
Vol 170 (2) ◽  
pp. 893-916 ◽  
Author(s):  
F. Beux ◽  
A. Iollo ◽  
M.V. Salvetti ◽  
A. Soldati
Keyword(s):  
Electrostatic Field ◽  
Order Model ◽  
Low Order

Hybrid Reduced Model of Rotor

2013 ◽  
Vol 60 (3) ◽  
pp. 319-333
Author(s):  
Rafał Hein ◽  
Cezary Orlikowski
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Hybrid Model ◽  
Rotor System ◽  
Reduced Model ◽  
Order Model ◽  
Gyroscopic Effect ◽  
Reduced Models ◽  
Rigid Finite Element Method ◽  
Low Order

Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one model of the system. Thus a hybrid model is obtained. The advantage of the presented method is that the number of gyroscopic and damping interactions does not affect the model range

On-line thermal regulation of a capillary pumped loop via state feedback control using a low order model

2016 ◽  
Vol 108 ◽  
pp. 614-627 ◽  
Author(s):  
Etienne Videcoq ◽  
Manuel Girault ◽  
Vincent Ayel ◽  
Cyril Romestant ◽  
Yves Bertin
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Thermal Regulation ◽  
Order Model ◽  
Capillary Pumped Loop ◽  
On Line ◽  
Low Order

Gradient-Free Optimization in Thermoacoustics: Application to A Low-Order Model

2021 ◽  
Author(s):  
Johann Moritz Reumschüssel ◽  
Jakob G. R. von Saldern ◽  
Yiqing Li ◽  
Christian Oliver Paschereit ◽  
Alessandro Orchini
Keyword(s):  
Optimization Algorithms ◽  
Optimization Methods ◽  
Theoretical Studies ◽  
Order Model ◽  
Output Data ◽  
Fuel Injectors ◽  
Solution Strategies ◽  
The Past ◽  
Downhill Simplex ◽  
Low Order

Abstract Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimization can avoid cumbersome theoretical studies as input parameters are purely adapted based on output data. As no knowledge about the objective function is provided to the algorithms, this approach might reveal unconventional solutions to complex problems that were out of scope of classical solution strategies. In this study, the potential of these optimization methods on thermoacoustic problems is examined. The optimization algorithms are applied to a generic low-order thermoacoustic can-combustor model with several fuel injectors at different locations. We use three optimization algorithms — the well established Downhill Simplex Method, the recently proposed Explorative Gradient Method, and an evolutionary algorithm — to find optimal fuel distributions across the fuel lines while maintaining the amount of consumed fuel constant. The objective is to have minimal pulsation amplitudes. We compare the results and efficiency of the gradient-free algorithms. Additionally, we employ model-based linear stability analysis to calculate the growth rates of the dominant thermoacoustic modes. This allows us to highlight general and thermoacoustic-specific features of the optimization methods and results. The findings of this study show the potential of gradient-free optimization methods on combustor design for tackling thermoacoustic problems, and motivate further research in this direction.

scholarly journals Enhanced regime predictability in atmospheric low-order models due to stochastic forcing

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130286 ◽  
Author(s):  
Frank Kwasniok
Keyword(s):  
Noise Level ◽  
Coarse Grained ◽  
Climate Modelling ◽  
Order Model ◽  
Stochastic Forcing ◽  
Barotropic Flow ◽  
Optimal Value ◽  
Weather And Climate ◽  
Flow Over Topography ◽  
Low Order

Regime predictability in atmospheric low-order models augmented with stochastic forcing is studied. Atmospheric regimes are identified as persistent or metastable states using a hidden Markov model analysis. A somewhat counterintuitive, coherence resonance-like effect is observed: regime predictability increases with increasing noise level up to an intermediate optimal value, before decreasing when further increasing the noise level. The enhanced regime predictability is due to increased persistence of the regimes. The effect is found in the Lorenz '63 model and a low-order model of barotropic flow over topography. The increased predictability is only present in the regime dynamics, that is, in a coarse-grained view of the system; predictability of individual trajectories decreases monotonically with increasing noise level. A possible explanation for the phenomenon is given and implications of the finding for weather and climate modelling and prediction are discussed.

A correlation-based model order reduction approach for a diesel engine NOx and brake mean effective pressure dynamic model using machine learning

2020 ◽  
pp. 146808742093694
Author(s):  
Armin Norouzi ◽  
Masoud Aliramezani ◽  
Charles Robert Koch
Keyword(s):  
Support Vector Machine ◽  
Model Order Reduction ◽  
Order Reduction ◽  
High Order ◽  
Support Vector ◽  
Effective Pressure ◽  
Reduction Algorithm ◽  
Order Model ◽  
Model Order ◽  
Low Order

A correlation-based model order reduction algorithm is developed using support vector machine to model [Formula: see text] emission and break mean effective pressure of a medium-duty diesel engine. The support vector machine–based model order reduction algorithm is used to reduce the number of features of a 34-feature full-order model by evaluating the regression performance of the support vector machine–based model. Then, the support vector machine–based model order reduction algorithm is used to reduce the number of features of the full-order model. Two models for [Formula: see text] emission and break mean effective pressure are developed via model order reduction, one complex model with high accuracy, called high-order model, and the other with an acceptable accuracy and a simple structure, called low-order model. The high-order model has 29 features for [Formula: see text] and 20 features for break mean effective pressure, while the low-order model has nine features for [Formula: see text] and six features for break mean effective pressure. Then, the steady-state low-order model and high-order model are implemented in a nonlinear control-oriented model. To verify the accuracy of nonlinear control-oriented model, a fast response electrochemical [Formula: see text] sensor is used to experimentally study the engine transient [Formula: see text] emissions. The high-order model and low-order model support vector machine models of [Formula: see text] and break mean effective pressure are compared to a conventional artificial neural network with one hidden layer. The results illustrate that the developed support vector machine model has shorter training times (5–14 times faster) and higher accuracy especially for test data compared to the artificial neural network model. A control-oriented model is then developed to predict the dynamic behavior of the system. Finally, the performance of the low-order model and high-order model is evaluated for different rising and falling input transients at four different engine speeds. The transient test results validate the high accuracy of the high-order model and the acceptable accuracy of low-order model for both [Formula: see text] and break mean effective pressure. The high-order model is proposed as an accurate virtual plant while the low-order model is suitable for model-based controller design.

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