scholarly journals Turbulence-parameter estimation for current-energy converters using surrogate model optimization

2021 ◽  
Vol 168 ◽  
pp. 559-567
Author(s):  
Sterling S. Olson ◽  
Jack C.P. Su ◽  
H. Silva ◽  
Chris C. Chartrand ◽  
Jesse D. Roberts
Keyword(s):  
Parameter Estimation ◽  
Surrogate Model ◽  
Model Optimization ◽  
Turbulence Parameter ◽  
Surrogate Model Optimization ◽  
Current Energy ◽  
Energy Converters

scholarly journals SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS

2021 ◽  
Vol 247 ◽  
pp. 12003
Author(s):  
Andy Whyte ◽  
Geoff Parks
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Fuel Assembly ◽  
Surrogate Model ◽  
Regression Models ◽  
Model Optimization ◽  
Learning Models ◽  
Training Set ◽  
Surrogate Model Optimization ◽  
The Cost

This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.

scholarly journals SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS

2019 ◽  
Author(s):  
Andy Whyte ◽  
Geoff Parks
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
Fuel Assembly ◽  
Surrogate Model ◽  
Regression Models ◽  
Model Optimization ◽  
Learning Models ◽  
Training Set ◽  
Surrogate Model Optimization ◽  
The Cost

This paper investigates the applicability of surrogate model optimization (SMO) usingdeep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models.

Simulating current-energy converters: SNL-EFDC model development, verification, and parameter estimation

2020 ◽  
Vol 147 ◽  
pp. 2531-2541 ◽  
Author(s):  
Scott C. James ◽  
Erick L. Johnson ◽  
Janet Barco ◽  
Jesse D. Roberts
Keyword(s):  
Parameter Estimation ◽  
Model Development ◽  
Efdc Model ◽  
Current Energy ◽  
Energy Converters

Application of the Average Rank Time Method in Parameter Estimation for Fixed-Time Censoring Experiment

2014 ◽  
Vol 556-562 ◽  
pp. 4146-4150
Author(s):  
Shu Meng ◽  
Gui Xiang Shen ◽  
Ying Zhi Zhang ◽  
Shu Guang Sun ◽  
Qi Song
Keyword(s):  
Parameter Estimation ◽  
Likelihood Function ◽  
Fixed Time ◽  
Model Optimization ◽  
Application Process ◽  
Index Method ◽  
Average Rank ◽  
Failure Data ◽  
Two Parameters ◽  
Mutually Independent

In this paper, the parameter estimation problem of products which are mutually independent and whose life belongs to two parameters Weibull distribution in fixed-time censoring experiment is discussed. And the rank of failure data is corrected by average rank time method, when the censoring experiments appeared. It is found that the method not only achieves the same effect for likelihood function theory, but also has the characters of high precision, simple process, no programming calculation, when model optimization is done by correlation index method. Finally, take field test data of a machine tool as an example to introduce the specific application process of this method, in order to verify the effectiveness and practical applicability.

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