Flow state estimation in the presence of discretization errors

2020 ◽  
Vol 890 ◽  
Author(s):  
Andre F. C. da Silva ◽  
Tim Colonius
Keyword(s):  
State Estimation ◽  
Flow State ◽  
Discretization Errors ◽  
Image Position

New Method Based on Model-Free Adaptive Control Theory and Kalman Filter for Multi-Product Unsteady Flow State Estimation

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Lei He ◽  
Jing Gong ◽  
Kai Wen ◽  
Changchun Wu ◽  
Yuan Min
Keyword(s):  
Adaptive Control ◽  
Unsteady Flow ◽  
State Estimation ◽  
Flow Model ◽  
Transient Flow ◽  
Pipeline System ◽  
Flow State ◽  
Flow Estimation ◽  
Model Based ◽  
Model Free

Abstract In this paper, a new methodology is proposed to realize real-time unsteady flow estimation for a multi-product pipeline system. Integrating transient flow model, adaptive control theory, and adaptive filter, this method is developed to solve the contradiction between the efficiency and accuracy in traditional model-based methods. In terms of improving computational efficiency, the linear flow model based on frequency response and difference transforming is established to replace the traditional nonlinear flow model for transient flow state estimation. To reduce the deviation between actual observations and linear model estimates, we first introduce a model-free adaptive control method as linear compensation of the reduced order unsteady flow state model. To overcome the interference of observation noise, the Kalman filter method is applied to the modified state space model to obtain the one-step-ahead transient flow estimation. The proposed method is applied to the transient flow state estimation of a multi-product pipeline system and compared with the model-based method and two data-driven methods. The proposed method can reduce the deviation of transient flow estimation between the reduced order linear model and the traditional nonlinear model to less than 0.5% under unforeseen conditions and shows strong robustness to noise interference and parameter drift.

Harmonic Distortion and Power Flow State Estimation for Distribution Systems Based on Evolutionary Strategies

2015 ◽  
Vol 13 (9) ◽  
pp. 3066-3071 ◽  
Author(s):  
Walace do Nascimento Sepulchro ◽  
Lucas Frizera Encarnacao ◽  
Marcelo Brunoro
Keyword(s):  
State Estimation ◽  
Power Flow ◽  
Distribution Systems ◽  
Harmonic Distortion ◽  
Evolutionary Strategies ◽  
Flow State

scholarly journals Sparsity-based error detection in DC power flow state estimation

2016 ◽  
Author(s):  
M.H. Amini ◽  
Mostafa Rahmani ◽  
Kianoosh G. Boroojeni ◽  
George Atia ◽  
S.S. Iyengar ◽  
...  
Keyword(s):  
State Estimation ◽  
Error Detection ◽  
Power Flow ◽  
Flow State ◽  
Dc Power

State estimation in wall-bounded flow systems. Part 3. The ensemble Kalman filter

2011 ◽  
Vol 682 ◽  
pp. 289-303 ◽  
Author(s):  
C. H. COLBURN ◽  
J. B. CESSNA ◽  
T. R. BEWLEY
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Channel Flow ◽  
Ensemble Kalman Filter ◽  
Accurate Estimation ◽  
Wall Region ◽  
Flow State ◽  
Model Based ◽  
Wall Flow ◽  
Near Wall

State estimation of turbulent near-wall flows based on wall measurements is one of the key pacing items in model-based flow control, with low-Re channel flow providing the canonical testbed. Model-based control formulations in such settings are often separated into two subproblems: estimation of the near-wall flow state via skin friction and pressure measurements at the wall, and (based on this estimate) control of the near-wall flow field fluctuations via actuation of the fluid velocity at the wall. In our experience, the turbulent state estimation sub-problem has consistently proven to be the more difficult of the two. Though many estimation strategies have been tested on this problem (by our group and others), none have accurately captured the turbulent flow state at the outer boundary of the buffer layer (5 ≤ y+ ≤ 30), which is deemed to be an important milestone, as this is the approximate range of the characteristic near-wall turbulent structures, the accurate estimation of which is important for the control problem. Leveraging the ensemble Kalman filter (an effective variant of the Kalman filter which scales well to high-dimensional systems), the present paper achieves at least an order of magnitude improvement (in the near-wall region) over the best results available in the published literature on the estimation of low-Reynolds number turbulent channel flow based on wall information alone.

scholarly journals CAPILLARY BLOOD FLOW STATE ESTIMATION DURING PATIENTS TREATMENT WITH ORTODENTIC MECHANICAL APPLIANCES

2015 ◽  
Vol 3 (2(9)) ◽  
pp. 52
Author(s):  
D. A. Domenyuk ◽  
A. G. Karslieva ◽  
E. G. Vedeshina ◽  
S. V. Dmitrienko ◽  
A. S. Kochkonyan ◽  
...  
Keyword(s):  
Blood Flow ◽  
State Estimation ◽  
Capillary Blood ◽  
Capillary Blood Flow ◽  
Flow State

Surface Mounted Flow State Estimation of Reduced Order POD Models: Shear Layer

2010 ◽  
Author(s):  
Casey Fagley ◽  
Mark Balas ◽  
Jurgen Seidel ◽  
Stefan Siegel ◽  
Thomas Mclaughlin
Keyword(s):  
Shear Layer ◽  
State Estimation ◽  
Flow State ◽  
Reduced Order
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