scholarly journals Effect of Pilot-Points Location on Model Calibration: Application to the Northern Karst Aquifer of Qatar

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 679 ◽  
Author(s):  
Husam Baalousha ◽  
Marwan Fahs ◽  
Fanilo Ramasomanana ◽  
Anis Younes
Keyword(s):  
Model Calibration ◽  
Karst Aquifer ◽  
Optimization Method ◽  
United States Geological Survey ◽  
Groundwater Levels ◽  
Distributed Parameters ◽  
Improve Model ◽  
Pilot Points ◽  
Parameter Values ◽  
High Uncertainty

In hydrogeological modelling, two approaches are commonly used for model calibration: zonation and the pilot-points method. Zonation assumes an abrupt spatial change in parameter values, which could be unrealistic in field applications. The pilot-points method produces smoothly distributed parameters compared to the zonation approach; however, the number and placement of pilot-points can be challenging. The main goal of this paper is to explore the effect of pilot-points number and locations on the calibrated parameters. A 3D groundwater flow model was built for the northern karst aquifer of Qatar. A conceptual model of this aquifer was developed based on MODFLOW software (United States Geological Survey). The model was calibrated using the parameter estimation and uncertainty analysis (PEST) package employing historical data of groundwater levels. The effect of the number and locations of pilot-points was examined by running the model using a variable numbers of points and several perturbations of locations. The calibration errors for all the runs (corresponding to different configurations of pilot-points) were maintained under a certain threshold. A statistical analysis of the calibrated parameters was then performed to evaluate how far these parameters are impacted by the pilot-point locations. Finally, an optimization method was proposed for pilot-points placement using recharge and observed piezometric maps. The results revealed that the pilot-points number, locations, and configurations have a significant effect on the calibrated parameter, especially in the high permeable regions corresponding to the karstic zones. The outcome of this study may help focus on areas of high uncertainty where more field data should be collected to improve model calibration. It also helps the placement of pilot-points for a robust calibration.

scholarly journals Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Chao Zhang ◽  
Ru-bin Wang ◽  
Qing-xiang Meng
Keyword(s):  
Global Optimization ◽  
Parameter Optimization ◽  
Hydrological Model ◽  
Optimization Method ◽  
Difficult Problem ◽  
Rainfall Runoff ◽  
True Parameter ◽  
Shuffled Complex Evolution ◽  
Structure Lead ◽  
Parameter Values

Parameter optimization for the conceptual rainfall-runoff (CRR) model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the Xinanjiang (XAJ) model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years) and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.

scholarly journals A Novel Objective Function DYNO for Automatic Multi-variable Calibration and Application to Assess Effects of Velocity versus Temperature Data for 3D Lake Models Calibration

2021 ◽  
Author(s):  
Wei Xia ◽  
Taimoor Akhtar ◽  
Christine A. Shoemaker
Keyword(s):  
Objective Function ◽  
Hydrodynamic Model ◽  
Model Calibration ◽  
Goodness Of Fit ◽  
Optimization Method ◽  
Parallel Optimization ◽  
Velocity Versus ◽  
Observation Data ◽  
Multiple Variables ◽  
Optimization Search

Abstract. This study introduced a novel Dynamically Normalized objective function (DYNO) for multi-variable (i.e., temperature and velocity) model calibration problems. DYNO combines the error metrics of multiple variables into a single objective function by dynamically normalizing each variable's error terms using information available during the search. DYNO is proposed to dynamically adjust the weight of the error of each variable hence balancing the calibration to each variable during optimization search. The DYNO is applied to calibrate a tropical hydrodynamic model where temperature and velocity observation data are used for model calibration simultaneously. We also investigated the efficiency of DYNO by comparing the result of using DYNO to results of calibrating to either temperature or velocity observation only. The result indicates that DYNO can balance the calibration in terms of water temperature and velocity and that calibrating to only one variable (e.g., temperature or velocity) cannot guarantee the goodness-of-fit of another variable (e.g., velocity or temperature). Our study suggested that both temperature and velocity measures should be used for hydrodynamic model calibration in real practice. Our example problems were computed with a parallel optimization method PODS but DYNO can also be easily used in serial applications.

Multi-Objective Engineering Design Via Computer Model Calibration

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Carl Ehrett ◽  
D. Andrew Brown ◽  
Evan Chodora ◽  
Christopher Kitchens ◽  
Sez Atamturktur
Keyword(s):  
Computer Model ◽  
Model Calibration ◽  
Material Design ◽  
Fine Tuning ◽  
Tuning Parameter ◽  
Design Problems ◽  
Multi Objective ◽  
Performance Targets ◽  
Calibration Techniques ◽  
Parameter Values

Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade.

scholarly journals Applying models of spatial interpolation for the approximation of responses in the refinement of parameters of finite element models

2018 ◽  
Vol 212 ◽  
pp. 01021
Author(s):  
Anatoly Pikhalov ◽  
Anton Zabelin
Keyword(s):  
Finite Element ◽  
Radial Basis Functions ◽  
Element Model ◽  
Optimization Method ◽  
Latin Squares ◽  
Basis Functions ◽  
Finite Element Models ◽  
Radial Basis ◽  
High Level ◽  
Parameter Values

The numerical experiment on refining the parameters of the finite element model of the beam by the method of approximating the responses is presented in the article. As mathematical models of joint-stock companies are used: linear combinations of radial-basis functions, and Kriging-models. These models are generated in the work on the basis of Latin squares and depend on the parameters to be refined (the moduli of elasticity of finite element groups of the beam). To obtain optimal values of the parameters, a genetic optimization method was used. The results of solving the optimization problem showed a high level of coincidence of the parameter values with a combination of response models obtained from dynamic and static types of calculations. It was also shown that when solving the problems of finite element models, it is sufficient to use models constructed only on the basis of radial-basis functions.

Borehole-based study of CO2-rich air transport in the vadose zone of a Mediterranean karst system (Malaga, southern Spain)

2020 ◽  
Author(s):  
Lucía Ojeda ◽  
José Benavente ◽  
Iñaki Vadillo ◽  
Cristina Liñán ◽  
Enrique P. Sanchez-Cañete
Keyword(s):  
Vadose Zone ◽  
Karst Aquifer ◽  
Field Measurements ◽  
Temporal Variations ◽  
Groundwater Levels ◽  
Sources And Sinks ◽  
Global Carbon Budget ◽  
Vertical Heterogeneity ◽  
Test Linking ◽  
Karst Systems

<p>The characterization of CO<sub>2 </sub>transport, and other C compounds (CH<sub>4</sub>, DIC, organic matter, etc.), in the vadose zone of a karst aquifer is key in order to quantify sources and sinks of carbon. In karst environments, most of the studies are focused on the dynamics of CO<sub>2</sub> in caves, but only a few studies are related to field measurements of the CO<sub>2</sub> content in boreholes, which provides direct insights about the vadose zone. Located at the east of the Nerja Cave (Malaga, Andalusia), one of the most important tourist caves in Spain, the vadose zone was accessed by 9 boreholes drilled into the vadose zone of a Triassic carbonate aquifer, with depths ranging between 15 and 30 m. The karst network in the study area is characterized by a great vertical heterogeneity, with significant cavities and voids at specific intervals. Groundwater levels at different altitudes are a consequence of this heterogeneity. Similarly, CO<sub>2</sub> distribution and transport are clearly determined by the complex karst network.</p><p>Our study aims to identify significant horizontal gradients of CO<sub>2</sub> in the karst vadose air, both spatial and temporally. We present monthly measurements of CO<sub>2</sub> concentration, relative humidity, air temperature and <sup>222</sup>Rn inside boreholes. In addition, we present CO<sub>2</sub> results from an 18 hours-atmospheric air injection test. Linking them to the geophysical knowledge of voids in the study area, the results allow us to identify lateral fluxes of CO<sub>2</sub>-rich air in the vadose zone and how these fluxes are favoured by the incidence of the main karst discontinuity orientations. We observe different ventilation patterns:  in spring the vadose air seems to be stored in specific orientations, while in summer there is a lower convective ventilation. The results contribute to explain the temporal variations of the chemical composition of recharge water in karst systems, as well as to support studies on the global carbon budget.</p>

Torque distribution of a four in-wheel motors electric vehicle based on a PMSM system model

2017 ◽  
Vol 232 (13) ◽  
pp. 1828-1845 ◽  
Author(s):  
Dongmei Wu ◽  
Yang Li ◽  
Jianwei Zhang ◽  
Changqing Du
Keyword(s):  
Electric Vehicle ◽  
Distribution Ratio ◽  
Permanent Magnet Synchronous Motor ◽  
Optimization Method ◽  
Torque Distribution ◽  
Improve Energy Efficiency ◽  
First Case ◽  
Motor Parameter ◽  
Parameter Values ◽  
Minimum System

This study investigates a method for determining the torque distribution between front and rear in-wheel motors in an electric vehicle to improve energy efficiency. The method is based on an analytical model of the permanent magnet synchronous motor (PMSM) losses, which consist of the losses from electric motors and inverters. The loss model is used to analyze the optimal torque distribution ratio for minimum system losses. The analysis is conducted for two cases: the first is for identical motor parameter values of the front and rear wheels; the second is for different motor parameter values. For the first case, the results show that the even torque distribution between the front and rear wheels results in minimum system losses if the motor loss is a convex function of electromagnetic torque. When the motor parameters for the front and rear wheels are different, the optimal torque distribution coefficient depends on the motor parameters. To validate the analysis results, simulations of the four-motor drive system were conducted. Furthermore, the idle loss is added to the system efficiency data and a numerical optimization method is used to resolve the optimal distribution ratio. It is shown that the optimal solutions are consistent with the analytical results. Finally, the proposed method is validated through bench tests and vehicle dynamometer tests.

scholarly journals The chaos in calibrating crop models

2020 ◽  
Author(s):  
Daniel Wallach ◽  
Taru Palosuo ◽  
Peter Thorburn ◽  
Zvi Hochman ◽  
Emmanuelle Gourdain ◽  
...  
Keyword(s):  
Experimental Data ◽  
Model Calibration ◽  
Crop Model ◽  
Process Models ◽  
Model Parameters ◽  
Model Structure ◽  
Crop Models ◽  
Estimation Of Model Parameters ◽  
Parameter Values ◽  
Made In

Calibration, that is the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.

Optimal Concurrent Design Based Upon Distributed Database and Knowledge Base Modeling

2001 ◽  
Author(s):  
F. Zhang ◽  
D. Xue
Keyword(s):  
Knowledge Base ◽  
Optimal Parameter ◽  
Distributed Database ◽  
Optimization Method ◽  
Concurrent Design ◽  
Swarm Optimization ◽  
Multi Level ◽  
Realization Process ◽  
Product Realization ◽  
Parameter Values

Abstract This research introduces an optimal concurrent design approach based upon a previously developed distributed database and knowledge base modeling method. In this approach, the product realization process alternatives and relevant activities are modeled at different locations that are connected through the Internet. The optimal product realization process alternative and its parameter values are identified using a multi-level optimization method. Genetic Programming (GP) and Particle Swarm Optimization (PSO) are employed for identifying the optimal product realization process alternative and the optimal parameter values of the feasible alternatives, respectively.

Robust Adaptable Design of Mechanical Products

2013 ◽  
Author(s):  
Jian Zhang ◽  
Yongliang Chen ◽  
Deyi Xue ◽  
Peihua Gu
Keyword(s):  
Optimal Design ◽  
Functional Performance ◽  
Optimal Parameter ◽  
Design Method ◽  
Optimization Method ◽  
Adaptable Design ◽  
Operation Stage ◽  
Mechanical Products ◽  
Tree Product ◽  
Parameter Values

For an adaptable product, both configuration and parameter values associated with the configuration can be adapted in the product operation stage to satisfy different requirements. This research aims at developing a new design approach to identify the adaptable product whose functional performance is the least sensitive to parameter variations caused by uncertainties. First different configuration candidates in design and different product configurations in operation stage to satisfy design requirements are modeled by a novel hybrid AND-OR tree. Product/operating parameters associated with configurations are also modeled. A two-level optimization method is developed for identifying the optimal design configuration and the parameter values: design configuration optimization for identifying the optimal design configuration and parameter optimization for identifying the optimal parameter values associated with this design configuration. Case study of an adaptable vibratory feeder is developed to demonstrate the effectiveness of the newly developed robust adaptable design method.

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