Comparing global sensitivity analysis for a biofilm model for two-step nitrification using the qualitative screening method of Morris or the quantitative variance-based Fourier Amplitude Sensitivity Test (FAST)

2007 ◽  
Vol 56 (8) ◽  
pp. 85-93 ◽  
Author(s):  
D. Brockmann ◽  
E. Morgenroth
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Screening Method ◽  
Biofilm Reactor ◽  
Operating Conditions ◽  
Sensitivity Analyses ◽  
Fast Method ◽  
Biofilm Thickness ◽  
Global Sensitivity ◽  
Biofilm Model

Two different methods for global sensitivity analysis were compared exemplarily for a biofilm model for two-step nitrification. Especially for biofilm models, local sensitivity analysis is not very useful as parameters can vary over a large range. Parameters that were evaluated included kinetic and stoichiometric parameters, and also biofilm parameters, such as internal and external mass transfer, the biofilm thickness, and the biomass density. Global sensitivity analyses were performed for a range of operating conditions of a biofilm reactor. The results of the qualitative screening method of Morris were compared with the results of the quantitative variance-based method FAST regarding the input parameters indicated as unimportant. Both methods resulted in similar sets of parameters with a small influence on the model output, but the screening method of Morris required a much smaller number of model evaluations to compute the sensitivity measures than the FAST method.

scholarly journals Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

2017 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Global Sensitivity Analysis ◽  
Landscape Evolution ◽  
Sensitivity Analyses ◽  
Large Set ◽  
Sediment Yields ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Morris Method

Abstract. Landscape Evolution Models have a long history of use as exploratory models, providing greater understanding of the role large scale processes have on the long-term development of the Earth’s surface. As computational power has advanced so has the development and sophistication of these models. This has seen them applied at increasingly smaller scale and shorter-term simulations at greater detail. However, this has not gone hand-in-hand with more rigorous verifications that are commonplace in the applications of other types of environmental models- for example Sensitivity Analyses. This can be attributed to a paucity of data and methods available in order to calibrate, validate and verify the models, and also to the extra complexity Landscape Evolution Models represent – without these it is not possible to produce a reliable Objective Function against which model performance can be judged. To overcome this deficiency, we present a set of Model Functions – each representing an aspect of model behaviour – and use these to assess the relative sensitivity of a Landscape Evolution Model (CAESAR-Lisflood) to a large set of parameters via a global Sensitivity Analysis using the Morris Method. This novel combination of behavioural Model Functions and the Morris Method provides insight into which parameters are the greatest source of uncertainty in the model, and which have the greatest influence over different model behaviours. The method was repeated over two different catchments, showing that across both catchments and across most model behaviours the choice of Sediment Transport formula was the dominate source of uncertainty in the CAESAR-Lisflood model, although there were some differences between the two catchments. Crucially, different parameters influenced the model behaviours in different ways, with Model Functions related to internal geomorphic changes responding in different ways to those related to sediment yields from the catchment outlet. This method of behavioural sensitivity analysis provides a useful method of assessing the performance of Landscape Evolution Models in the absence of data and methods for an Objective Function approach.

Homogeneous Sono-Fenton Process: Statistical Modeling and Global Sensitivity Analysis

2015 ◽  
Vol 16 (1) ◽  
pp. 11-21
Author(s):  
Daniela Căilean ◽  
Florina Ungureanu ◽  
Carmen Teodosiu
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Modeling ◽  
Global Sensitivity Analysis ◽  
Operating Conditions ◽  
Pollutant Concentration ◽  
Sample Mean ◽  
Global Sensitivity ◽  
Aqueous Effluents ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

AbstractThe main objective of this study is to obtain and validate a mathematical model to describe a complex homogeneous Sono-Fenton (HSF) process used for the removal of 4-chlorophenol model pollutant from aqueous effluents. The investigated process parameters (acoustic amplitude, power density depending on the surface of the tip, initial pollutant concentration and time) serve as input parameters for the statistical modeling, while the output parameters considered are the final pollutant concentration and energy delivered to the sample. The accuracy of the models is analyzed by the values of the determination coefficients and by graphical tools available in MATLAB software such as: the Kolmogorov–Smirnov test (KS test), the graphical sensitivity tools, e.g. contribution to the sample mean (CSM) and variance (CSV) plots. The robustness of the model is also analyzed by global sensitivity analysis. Furthermore, the optimum set of operating conditions are determined by using the nlintool function.

scholarly journals Global Sensitivity Analysis of the Vibration Reduction System with Seated Human Body

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Igor Maciejewski ◽  
Tomasz Krzyzynski
Keyword(s):  
Sensitivity Analysis ◽  
Vibration Isolation ◽  
Global Sensitivity Analysis ◽  
Operating Conditions ◽  
Design Parameters ◽  
Global Sensitivity ◽  
Seat Suspension ◽  
Sensitivity Indices ◽  
Vibration Transmissibility ◽  
Nonlinear Force

The paper deals with the global sensitivity analysis for the purpose of shaping the vibroisolation properties of suspension systems under strictly defined operating conditions. The variance-based method is used to evaluate an influence of nonlinear force characteristics on the system dynamics. The proposed sensitivity indices provide the basis for determining the effect of key design parameters on the vibration isolation performance. The vibration transmissibility behaviour of an exemplary seat suspension system is discussed in order to illustrate the developed methodology.

scholarly journals Enhance Derivative Design Considering Global Sensitivity of Design Parameters

2013 ◽  
Author(s):  
Hyeong-UK Park ◽  
Kamran Behdinan ◽  
Joon Chung ◽  
Jae-Woo Lee
Keyword(s):  
Sensitivity Analysis ◽  
Design Process ◽  
Life Cycle Cost ◽  
Global Sensitivity Analysis ◽  
Collaborative Optimization ◽  
Design Parameters ◽  
Fast Method ◽  
Engineering Product ◽  
Global Sensitivity ◽  
Design Variables

An engineering product design considers derivatives to reduce the life cycle cost and to increase the efficiency on operation when it has new demands. The proposed design process in this study obtains derivative designs based on sensitivity of design variable. The efficiency and accuracy of the derivative design process can be enhanced by implementing global sensitivity analysis. Sensitivity analysis sensors the design variables accordingly and variables with low sensitivity for objective function can be neglected, since computational effort and time is not necessary for a design with less priority. In this research, e-FAST method code for global sensitivity analysis module was developed and implemented on Multidisciplinary Design Optimization (MDO) problem. The wing design was considered for MDO problem that used aerodynamics and structural disciplines. The global sensitivity analysis method was applied to reduce the number of design variables and Collaborative Optimization (CO) was used as MDO method. This research shows the efficiency of reduction of dimensionality of complex MDO problem by using global sensitivity analysis. In addition, this result shows important design variables for design requirement to student when they solving design problem.

Global Sensitivity Analysis of Optimal Climate Policies

2020 ◽  
Author(s):  
Alena Miftakhova
Keyword(s):  
Sensitivity Analysis ◽  
High Efficiency ◽  
Global Sensitivity Analysis ◽  
Economic Modeling ◽  
Sensitivity Analyses ◽  
Global Sensitivity ◽  
Computational Costs ◽  
Climate Policies ◽  
Careful Treatment ◽  
Polynomial Chaos Expansions

<p><span>A major tool that supports climate policy decisions, integrated assessment models are highly vulnerable to their initial assumptions and calibrations. Despite the broad literature rich in both single-model and multi-model sensitivity analyses, universal, well-established practices are still missing in this field. This paper endorses structured global sensitivity analysis (GSA) as an indispensable routine in climate–economic modeling. An application of a high-efficiency GSA method based on polynomial chaos expansions to DICE provides two insights. First, only global and comprehensive—as opposed to local or selective—sensitivity analysis delivers a trustworthy picture of the uncertainty propagated through the model. Second, careful treatment of the model’s structure throughout the analysis reconciles the results with established analytical insights—enhancing these insights with more details. The efficient GSA method provides a comprehensive decomposition of the uncertainty in a model’s output while minimizing computational costs, and is hence potentially applicable to models of higher complexity.</span></p>

Development of a simplified model for the fixed biofilm reactor

2015 ◽  
Vol 72 (9) ◽  
pp. 1601-1610
Author(s):  
Sushovan Sarkar ◽  
Debabrata Mazumder
Keyword(s):  
Liquid Interface ◽  
Biofilm Reactor ◽  
Fast Method ◽  
Biofilm Thickness ◽  
Biofilm Model ◽  
Runge Kutta Method ◽  
Average Flux ◽  
Model Equations ◽  
The Relationship ◽  
Fixed Biofilm

A simplified fixed biofilm model was developed to formulate the relationship between the substrate concentrations at both the entry and exit, at the biofilm–liquid interface and at the biofilm attached surface along with average substrate flux in the biofilm, substrate flux at the biofilm–liquid interface and effective biofilm thickness. The model considered the substrate mass transport external to the biofilm and into the biofilm as per Fick's law and the steady state substrate as well as biomass balance for attached growth microorganisms. Monod's growth kinetics has been adopted in substrate utilization, incorporating relevant boundary conditions. The numerical solution of model equations was accomplished for calculating average flux and exit substrate concentration and thereafter the Runge–Kutta method was employed for determining effective biofilm thickness. Consequently, two computer programs were developed for the purpose of rapid solution. The model was satisfactorily applied to data available from the literature for checking its accuracy and was validated with the experimental results. The model was found to be an easy, accurate and fast method that can be used for process design of a fixed biofilm reactor.

Benefits of Global Sensitivity Analysis and Reduced Order Modeling for Basin-Scale Process Simulations

2021 ◽  
Author(s):  
Denise Degen ◽  
Mauro Cacace ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Florian Wellmann
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Rock Properties ◽  
Reduced Basis ◽  
Global Sensitivity ◽  
Temporal Domain ◽  
Basin Scale ◽  
Process Simulations ◽  
Basis Method

<p>Geophysical process simulations pose several challenges including the determination of i) the rock properties, ii) the underlying physical process, and iii) the spatial and temporal domain that needs to be considered.</p><p>Often it is not feasible or impossible to include the entire complexity of the given application. Hence, we need to evaluate the consequences of neglecting certain processes, properties, etc. by using, for instance, sensitivity analyses. However, this evaluation is for basin-scale application non-trivial due to the high computational costs associated with them. These high costs arise from the high-dimensional character of basin-scale applications in the parameter, spatial, and temporal domain.</p><p>Therefore, this evaluation is often not performed or via computationally fast algorithms as, for example, the local sensitivity analysis. The problem with local sensitivity analyses is that they cannot account for parameter correlations. Thus, a global sensitivity analysis is preferential. Unfortunately, global sensitivity analyses are computationally demanding.</p><p>To allow the usage of global sensitivity analysis for a better evaluation of the changes in the influencing parameters, we construct in this work a surrogate model via the reduced basis method.</p><p>The reduced basis method is a model order reduction technique that is physics-preserving.  Hence, we are able to retrieve the entire state variable (i.e. temperature) instead of being restricted to the observation space.</p><p>To showcase the benefits of this methodology, we demonstrate with the Central European Basin System how the influences of the thermal rock properties change when moving from a steady-state to a transient system.</p><p>Furthermore, we use the case study of the Alpine Region to highlight the influences of the spatial distribution of measurements on the model response. This latter aspect is especially important since measurements are often used to calibrate and validate a given geological model. Thus, it is crucial to determine which amount of bias is introduced through our commonly unequal data distribution.</p>

scholarly journals Emulator-based global sensitivity analysis for flow-like landslide run-out models

Landslides ◽  
2021 ◽  
Author(s):  
Hu Zhao ◽  
Florian Amann ◽  
Julia Kowalski
Keyword(s):  
Sensitivity Analysis ◽  
Friction Coefficient ◽  
Global Sensitivity Analysis ◽  
Maximum Flow ◽  
Sensitivity Analyses ◽  
Strong Interactions ◽  
Order Effects ◽  
Turbulent Friction ◽  
Global Sensitivity ◽  
Landslide Event

AbstractLandslide run-out modeling involves various uncertainties originating from model input data. It is therefore desirable to assess the model’s sensitivity to these uncertain inputs. A global sensitivity analysis that is capable of exploring the entire input space and accounts for all interactions often remains limited due to computational challenges resulting from a large number of necessary model runs. We address this research gap by integrating Gaussian process emulation into landslide run-out modeling and apply it to the open-source simulation tool r.avaflow. The feasibility and efficiency of our approach is illustrated based on the 2017 Bondo landslide event. The sensitivity of aggregated model outputs, such as the angle of reach, impact area, and spatially resolved maximum flow height and velocity, to the dry-Coulomb friction coefficient, turbulent friction coefficient, and the release volume is studied. The results of first-order effects are consistent with previous results of common one-at-a-time sensitivity analyses. In addition to that, our approach allows us to rigorously investigate interactions. Strong interactions are detected on the margins of the flow path where the expectation and variation of maximum flow height and velocity are small. The interactions generally become weak with an increasing variation of maximum flow height and velocity. Besides, there are stronger interactions between the two friction coefficients than between the release volume and each friction coefficient. In the future, it is promising to extend the approach for other computationally expensive tasks like uncertainty quantification, model calibration, and smart early warning.

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