scholarly journals mumpce_py: A Python Implementation of the Method of Uncertainty Minimization Using Polynomial Chaos Expansions

2017 ◽  
Vol 122 ◽  
Author(s):  
David A. Sheen
Keyword(s):  
Measurement Uncertainty ◽  
First Principles ◽  
Polynomial Chaos ◽  
Software Tool ◽  
Physical Models ◽  
Physical Parameters ◽  
Experimental Measurements ◽  
Uncertainty Minimization ◽  
Polynomial Chaos Expansions ◽  
Parameter Values

The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.

Design calculation model of oil fluid property parameter knowledge database for production logging

2020 ◽  
Vol 20 (3) ◽  
pp. 951-958
Author(s):  
Wenguang Song ◽  
Qiongqin Jiang
Keyword(s):  
Crude Oil ◽  
Expert Knowledge ◽  
Calculation Model ◽  
Physical Parameters ◽  
Base System ◽  
Design Calculation ◽  
Calculation Methods ◽  
Fluid Property ◽  
Knowledge Base System ◽  
Parameter Values

The fluid property parameter calculation affects the accuracy of the interpretation the accuracy, in the interpretation of the liquid production profile. Therefore, it is particularly important to accurately calculate the physical property parameter values, in the establishment of the fluid property parameter expert knowledge base system. The main physical parameters include the following calculation methods of the oil. The oil property parameter conversion formula mainly studies the formulas such as bubble point pressure, dissolved gas-oil ratio, crude oil volume coefficient, crude oil density, crude oil viscosity, and crude oil compression coefficient. Design expert knowledge base system, it is based on the calculation methods of these physical parameters. A computational fluid property parameter model is constructed by training production log sample data. Finally, the interactive and friendly product interpretation software model was developed in 9 wells’ data. The design calculation model can increase the accuracy to achieve 95% of oil fluid property parameter. Accurately calculate fluid property parameter values.

A Dynamic Model for the Design of Methanol to Hydrogen Steam Reformers for Transportation Applications

2004 ◽  
Vol 126 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Gregory L. Ohl ◽  
Jeffrey L. Stein ◽  
Gene E. Smith
Keyword(s):  
Response Time ◽  
Dynamic Model ◽  
First Principles ◽  
Initial Conditions ◽  
Design Parameters ◽  
Physical Parameters ◽  
Steam Reformer ◽  
Powerful Means ◽  
Steam Reformers ◽  
Transportation Applications

As an aid to improving the dynamic response of the steam reformer, a dynamic model is developed to provide preliminary characterizations of the major constraints that limit the ability of a reformer to respond to the varying output requirements occurring in vehicular applications. This model is a first principles model that identifies important physical parameters in the steam reformer. The model is then incorporated into a design optimization process, where minimum steam reformer response time is specified as the objective function. This tool is shown to have the potential to be a powerful means of determining the values of the steam reformer design parameters that yield the fastest response time to a step input in hydrogen demand for a given set of initial conditions. A more extensive application of this methodology, yielding steam reformer design recommendations, is contained in a related publication.

ELECTRONIC STRUCTURE AND OPTICAL PROPERTIES OF CRYSTALLINE C60

1992 ◽  
Vol 06 (06) ◽  
pp. 309-321 ◽  
Author(s):  
W.Y. CHING ◽  
MING-ZHU HUANG ◽  
YONG-NIAN XU ◽  
FANQI GAN
Keyword(s):  
Optical Properties ◽  
Electronic Structure ◽  
First Principles ◽  
Optical Spectrum ◽  
Local Density ◽  
Low Dielectric Constant ◽  
Experimental Measurements ◽  
Absorption Bands ◽  
Low Dielectric ◽  
Good Agreement

The electronic structure and optical properties of crystalline C 60 and their pressure dependence have been studied by first-principles local density calculations. It is shown that fcc C 60 has a low dielectric constant and an optical spectrum rich in structures. The spectrum shows five disconnected absorption bands in the 1.4 to 7.0 eV region with sharp structures in each band that can be attributed to critical point transitions. This is a manifestation of the localized molecular structure coupled with long range crystalline order unique to the C 60 crystal. At a sufficient high pressure, the structures in the optical spectrum start to merge due to the merging of the bands. These results are in good agreement with some recent experimental measurements.

Modeling of Stochastic EMC Problems with Anisotropic Polynomial Chaos Expansions

2021 ◽  
Vol 10 (2) ◽  
pp. 70-79
Author(s):  
Theodoros Zygiridis ◽  
Georgios Kommatas ◽  
Aristeides Papadopoulos ◽  
Nikolaos Kantartzis
Keyword(s):  
Polynomial Chaos ◽  
Polynomial Chaos Expansions

Validating and Updating Finite Element Models Using Experimental Measurements of Dynamics

1990 ◽  
Vol 204 (1) ◽  
pp. 45-50
Author(s):  
C F McCulloch ◽  
P Vanhonacker ◽  
E Dascotte
Keyword(s):  
Finite Element ◽  
Structural Dynamics ◽  
Model Updating ◽  
Measured Data ◽  
Physical Parameters ◽  
Experimental Measurements ◽  
Finite Element Models ◽  
Data Sets ◽  
Modal Data ◽  
Modelling Assumptions

A method is proposed for updating finite element models of structural dynamics using the results of experimental modal analysis, based on the sensitivities to changes in physical parameters. The method avoids many of the problems of incompatibility and inconsistency between the experimental and analytical modal data sets and enables the user to express confidence in measured data and modelling assumptions, allowing flexible but automated model updating.

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