An adaptive dynamic surrogate model using a constrained trust region algorithm: application to DNAPL-contaminated-groundwater-remediation design

2020 ◽  
Vol 28 (4) ◽  
pp. 1285-1298
Author(s):  
Jiannan Luo ◽  
Wenxi Lu ◽  
Qingchun Yang ◽  
Yefei Ji ◽  
Xin Xin
Keyword(s):  
Surrogate Model ◽  
Groundwater Remediation ◽  
Trust Region ◽  
Contaminated Groundwater ◽  
Trust Region Algorithm ◽  
Adaptive Dynamic ◽  
Remediation Design

scholarly journals Optimal Latin hypercube sampling-based surrogate model in NAPLs contaminated groundwater remediation optimization process

2017 ◽  
Vol 18 (1) ◽  
pp. 333-346 ◽  
Author(s):  
Jiannan Luo ◽  
Yefei Ji ◽  
Wenxi Lu ◽  
He Wang
Keyword(s):  
Surrogate Model ◽  
Groundwater Remediation ◽  
Model Performance ◽  
Latin Hypercube Sampling ◽  
Feasible Region ◽  
Contaminated Groundwater ◽  
Chance Constrained Programming ◽  
Latin Hypercube ◽  
Optimal Latin Hypercube Sampling ◽  
The Impact

Abstract A surrogate model based groundwater optimization model was developed to solve the non-aqueous phase liquids (NAPLs) contaminated groundwater remediation optimization problem. To illustrate the impact of sampling method improvement to the surrogate model performance improvement, aiming at a nitrobenzene contaminated groundwater remediation problem, optimal Latin hypercube sampling (OLHS) method was introduced to sample data in the input variables feasible region, and a radial basis function artificial neural network was used to construct a surrogate model. Considering the surrogate model's uncertainty, a chance-constrained programming (CCP) model was constructed, and it was solved by genetic algorithm. The results showed the following, for the problem considered in this study. (1) Compared with the Latin hypercube sampling (LHS) method, the OLHS method improves the space-filling degree of sample points considerably. (2) The effects of the two sampling methods on surrogate model performance were analyzed through comparison of goodness of fit, residual and uncertainty. The results indicated that the OLHS-based surrogate model performed better than the LHS-based surrogate model. (3) The optimal remediation strategies at 99%, 95%, 90%, 85%, 80% and 50% confidence levels were obtained, which showed that the remediation cost increased with the confidence level. This work would be helpful for increasing surrogate model performance and lowering the risk of a groundwater remediation strategy.

scholarly journals Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-wei Yang ◽  
Man-feng Dou ◽  
Zhi-yong Dai
Keyword(s):  
Parameter Estimation ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Estimation Method ◽  
Trust Region ◽  
Fault Tolerant Control ◽  
Trust Region Algorithm ◽  
Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

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