A Simplified Distributed Parameter Model For Predicting The Productivity Index For a Complex Commingled Well

2013 ◽  
Author(s):  
Edwin Ikezue
Keyword(s):  
Productivity Index ◽  
Distributed Parameter ◽  
Distributed Parameter Model

scholarly journals Open-loop temperature control for a distributed parameter model of a pipe

2020 ◽  
Vol 53 (2) ◽  
pp. 7765-7770
Author(s):  
Simon Bachler ◽  
Jens Wurm ◽  
Frank Woittennek
Keyword(s):  
Temperature Control ◽  
Open Loop ◽  
Distributed Parameter ◽  
Distributed Parameter Model

Comparison of Modelling Methods for Hydraulic Valve Gear

1987 ◽  
Author(s):  
H. Shang ◽  
G. K. Matthew ◽  
W. Luo
Keyword(s):  
Lumped Parameter Model ◽  
Distributed Parameter ◽  
Distributed Parameter Model ◽  
Lumped Parameter ◽  
Comparison Results ◽  
Hydraulic Valve ◽  
Modelling Methods ◽  
Measured Response ◽  
Valve Gear

Abstract A combined lumped/distributed parameter model for the follower system of a hydraulically operated valve is compared to a lumped parameter model of the same system. Since previous results show excellent correspondence between the lumped/distributed parameter model and measured response, it is natural to attempt to simplify the model and to again perform a comparison. Results of several examples are shown.

scholarly journals Anaerobic digestion model No. 1-based distributed parameter model of an anaerobic reactor: II. Model validation

2008 ◽  
Vol 99 (9) ◽  
pp. 3676-3684 ◽  
Author(s):  
B. Tartakovsky ◽  
S.J. Mu ◽  
Y. Zeng ◽  
S.J. Lou ◽  
S.R. Guiot ◽  
...  
Keyword(s):  
Anaerobic Digestion ◽  
Model Validation ◽  
Distributed Parameter ◽  
Anaerobic Reactor ◽  
Distributed Parameter Model

Analysis of herbicide biosorption by means of a phenomenological mathematical distributed parameter model

2020 ◽  
pp. 1-8
Author(s):  
Gessica Wernke ◽  
Carina Contini Triques ◽  
Pedro Yahico Ramos Suzaki ◽  
Márcia Regina Fagundes-Klen ◽  
Rosângela Bergamasco
Keyword(s):  
Distributed Parameter ◽  
Distributed Parameter Model

scholarly journals Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zongfang Li ◽  
Wei Zhao ◽  
Bo He ◽  
Tong San Koh ◽  
Yanxi Li ◽  
...  
Keyword(s):  
Blood Flow ◽  
Roc Curve ◽  
Wilcoxon Test ◽  
Distributed Parameter ◽  
Idh Mutation ◽  
Weak Correlation ◽  
Dynamic Contrast Enhanced ◽  
Distributed Parameter Model ◽  
Contrast Enhanced ◽  
Enhanced Imaging

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F ( P  = 0.018), PS ( P < 0.001 ), Vp ( P < 0.001 ), E ( P < 0.001 ), and Ve ( P  = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in Ktrans ( P  = 0.013) and Ve ( P < 0.001 ) for IDH-mutant gliomas. No significant difference was observed in Kep ( P  = 0.525). The AUCs of Ktrans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001 ). Ktrans showed a weak correlation with F (r < 0.3, P  > 0.16) and a very weak correlation with PS (r < 0.06, P  > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.

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