Experimental simulation of dolomite dissolution under burial diagenesis conditions and thermodynamic interpretation

2000 ◽  
Vol 19 (1) ◽  
pp. 58-64 ◽  
Author(s):  
Sijing Huang ◽  
Linping Xiao ◽  
Junjie Yang ◽  
Wenzheng Zhang ◽  
Yueming Huang ◽  
...  
Keyword(s):  
Experimental Simulation ◽  
Burial Diagenesis ◽  
Thermodynamic Interpretation

scholarly journals Experimental simulation of chemomechanical processes during deep burial diagenesis of carbonate rocks

2014 ◽  
Vol 119 (2) ◽  
pp. 984-1007 ◽  
Author(s):  
L. Neveux ◽  
D. Grgic ◽  
C. Carpentier ◽  
J. Pironon ◽  
L. Truche ◽  
...  
Keyword(s):  
Carbonate Rocks ◽  
Experimental Simulation ◽  
Burial Diagenesis ◽  
Deep Burial

EXPERIMENTAL SIMULATION OF A HELIUM COOLING SYSTEM FOR A SUPERCONDUCTING GENERATOR

1984 ◽  
Vol 45 (C1) ◽  
pp. C1-729-C1-732 ◽  
Author(s):  
L. Intichar ◽  
C. Schnapper
Keyword(s):  
Cooling System ◽  
Experimental Simulation

Erratum: Experimental simulation of the closed deivertor system for nuclear fusion, (Vol.23, 3^|^4, pp.22-25)

2012 ◽  
Vol 23 (3+4) ◽  
pp. E1-E1
Author(s):  
Suguru TANAKA ◽  
Hiroyasu KOMUKAI ◽  
Akira TONEGAWA ◽  
Kazutaka KAWAMURA
Keyword(s):  
Nuclear Fusion ◽  
Experimental Simulation

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

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