scholarly journals A physics-informed data-driven low order model for the wind velocity deficit at the wake of isolated buildings

2021 ◽  
Author(s):  
Dimitrios Fytanidis ◽  
Romit Maulik ◽  
Ramesh Balakrishnan ◽  
Rao Kotamarthi
Keyword(s):  
Wind Velocity ◽  
Data Driven ◽  
Order Model ◽  
Velocity Deficit ◽  
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.

An efficient Cholesky decomposition and applications for the simulation of large-scale random wind velocity fields

2018 ◽  
Vol 22 (6) ◽  
pp. 1255-1265 ◽  
Author(s):  
Yongle Li ◽  
Chuanjin Yu ◽  
Xingyu Chen ◽  
Xinyu Xu ◽  
Koffi Togbenou ◽  
...  
Keyword(s):  
Wind Velocity ◽  
Large Scale ◽  
Spectral Representation ◽  
Cholesky Decomposition ◽  
Velocity Fields ◽  
Data Driven ◽  
Long Span Bridges ◽  
Long Span ◽  
Spectral Representation Method ◽  
Representation Method

A growing number of long-span bridges are under construction across straits or through valleys, where the wind characteristics are complex and inhomogeneous. The simulation of inhomogeneous random wind velocity fields on such long-span bridges with the spectral representation method will require significant computation resources due to the time-consuming issues associated with the Cholesky decomposition of the power spectrum density matrixes. In order to improve the efficiency of the decomposition, a novel and efficient formulation of the Cholesky decomposition, called “Band-Limited Cholesky decomposition,” is proposed and corresponding simulation schemes are suggested. The key idea is to convert the coherence matrixes into band matrixes whose decomposition requires less computational cost and storage. Subsequently, each decomposed coherence matrix is also a band matrix with high sparsity. As the zero-valued elements have no contribution to the simulation calculation, the proposed method is further expedited by limiting the calculation to the non-zero elements only. The proposed methods are data-driven ones, which can be applicable broadly for simulating many complicated large-scale random wind velocity fields, especially for the inhomogeneous ones. Through the data-driven strategies presented in the study, a numerical example involving inhomogeneous random wind velocity field simulation on a long-span bridge is performed. Compared to the traditional spectral representation method, the simulation results are with high accuracy and the entire simulation procedure is about 2.5 times faster by the proposed method for the simulation of one hundred wind velocity processes.

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

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.

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