A multi-parameter calibration method for the numerical simulation of morphodynamic problems

Evangelista, Stefania; Giovinco, Gaspare; Kocaman, Selahattin

doi:10.1515/johh-2017-0014

Cited by 10 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation has two free parameters, the eddy exchange coefficient, λ, and erosion rate parameter, e or e 2 , embedded in E. We implemented the model in Python, and optimized the free parameters using a Nelder‐Mead approach in which the parameter space is searched using an iterative procedure to minimize the mean square error between the modeled and measured values (Evangelista et al., 2017; Nardi et al., 2013; Nelder & Mead, 1965).…”

Section: Methodsmentioning

confidence: 99%

A Morphodynamic Model to Evaluate Long‐Term Sandbar Rebuilding Using Controlled Floods in the Grand Canyon

Mueller

Grams

2021

Geophysical Research Letters

View full text Add to dashboard Cite

Controlled floods released from dams have become a common restoration strategy in river systems worldwide. Here we present a morphodynamic model of sandbar volume change for a subset of sandbars of the Colorado River in Grand Canyon National Park, where controlled floods are part of a management strategy focused on sandbar maintenance. We simulate sandbars as a triangular wedge, where deposition and erosion are modeled using physically based approaches that are driven by nearly continuous observations of flow and suspended sand concentration. We optimize an eddy exchange coefficient and erosion rate parameter by comparing model predictions to measured bar volumes. The model captures most of the variability in observed volume changes, and demonstrates the importance of flood frequency and sand concentration on average bar size. The model is easily implemented and adaptable, providing a means for predicting the future behavior of sandbars under a variety of streamflow and sediment supply scenarios.

show abstract

Section: Methodsmentioning

confidence: 99%

A Morphodynamic Model to Evaluate Long‐Term Sandbar Rebuilding Using Controlled Floods in the Grand Canyon

Mueller

Grams

2021

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…Data assimilation (DA) [3] is a powerful mathematical approach to produce trustworthy simulations, accounting for both measurements and results of physics-based numerical models, while taking into consideration their respective uncertainties. It is applied in geosciences [10], from atmospheric and oceanographic forecasting models [30,39], to fire front tracking [44], hydrodynamics [2,24,53], morphodynamics [17,46,51], etc. Interest in such methods is enhanced in a context of climate change, where new data constantly need to be accounted for [45], and standard calibrations, specifically optimal values obtained from fitting on old measurements, are not necessarily suitable to be applied for new scenarios [44].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-based variational data assimilation for tidal modelling

Mouradi,

Goeury,

Thual

et al. 2021

Preprint

View full text Add to dashboard Cite

Data assimilation (DA) is widely used to combine physical knowledge and observations. It is nowadays commonly used in geosciences to perform parametric calibration. In a context of climate change, old calibrations can not necessarily be used for new scenarios. This raises the question of DA computational cost, as costly physicsbased numerical models need to be reanalyzed. Reduction and metamodelling represent therefore interesting perspectives, for example proposed in recent contributions as hybridization between ensemble and variational methods, to combine their advantages (efficiency, non-linear framework). They are however often based on Monte Carlo (MC) type sampling, which often requires considerable increase of the ensemble size for better efficiency, therefore representing a computational burden in ensemble-based methods as well. To address these issues, two methods to replace the complex model by a surrogate are proposed and confronted : (i) PODEn3DVAR directly inspired from PODEn4DVAR, relies on an ensemble-based joint parameter-state Proper Orthogonal Decomposition (POD), which provides a linear metamodel ; (ii) POD-PCE-3DVAR, where the model states are POD reduced then learned using Polynomial Chaos Expansion (PCE), resulting in a non-linear metamodel. Both metamodels allow to write an approximate cost function whose minimum can be analytically computed, or deduced by a gradient descent at negligible cost. Furthermore, adapted metamodelling error covariance matrix is given for POD-PCE-3DVAR, allowing to substantially improve the metamodel-based DA analysis. Proposed methods are confronted on a twin experiment, and compared to classical 3DVAR on a measurement-based problem. Results are promising, in particular superior with POD-PCE-3DVAR, showing good convergence to classical 3DVAR and robustness to noise.

show abstract

“…A calibration methodology is required to develop and configure an accurate numerical simulation tool. In other words, the success of the application of any numerical model is strongly dependent on how precisely the model is calibrated [2].…”

Section: Introductionmentioning

confidence: 99%

Minimization procedure of experimental tests for calibration purposes, within HVAC&R energy efficiency framework

et al. 2019

View full text Add to dashboard Cite

Simulation models and predictive tools need to be fast, accurate and robust at the same time. The models that have to provide numerical solutions under transient conditions for a long period of time need to be simple with the aim of minimizing the time respond, without losing the accuracy. Thus, previous experimental data and a calibration methodology are necessary to assure this objective, both are strictly necessary to reproduce the behaviour with accuracy expected. Consequently, even accurate information (e.g. look-up tables) for HVAC&R components (e.g. heat exchangers, fan/compressor, auxiliary elements, etc.) is known and all coupling system is developed, the minimization of experimental tests for calibration purposed based scattered data interpolation is now an important aspect, which looks for reducing the quantity of experiments necessary to assure the accuracy expected from an optimization point of view. The present work shows an optimization procedure based on test number minimization according detailed error comparison against existing previous data. Illustrative results for a specific component are presented highlighting test number reduction without losing accuracy.

show abstract

A multi-parameter calibration method for the numerical simulation of morphodynamic problems

Cited by 10 publications

References 18 publications

A Morphodynamic Model to Evaluate Long‐Term Sandbar Rebuilding Using Controlled Floods in the Grand Canyon

A Morphodynamic Model to Evaluate Long‐Term Sandbar Rebuilding Using Controlled Floods in the Grand Canyon

Surrogate-based variational data assimilation for tidal modelling

Minimization procedure of experimental tests for calibration purposes, within HVAC&R energy efficiency framework

Contact Info

Product

Resources

About