scholarly journals Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

JACS Au ◽  
2021 ◽  
Author(s):  
Bjarne Kreitz ◽  
Khachik Sargsyan ◽  
Katrín Blöndal ◽  
Emily J. Mazeau ◽  
Richard H. West ◽  
...  
Keyword(s):  
Parametric Uncertainty ◽  
Co2 Hydrogenation ◽  
Automatic Mechanism ◽  
The Impact

scholarly journals Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

2021 ◽  
Author(s):  
Bjarne Kreitz ◽  
C. Franklin Goldsmith ◽  
Richard West ◽  
Emily Mazeau ◽  
Katrin Blondal ◽  
...  
Keyword(s):  
Rate Control ◽  
Parametric Uncertainty ◽  
Co2 Hydrogenation ◽  
Sensitivity Analyses ◽  
Control Analysis ◽  
Co2 Methanation ◽  
Model Predictions ◽  
Automatic Mechanism ◽  
Global Uncertainty ◽  
The Impact

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process, while considering the uncertainty in energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. 5,000 unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect 1 of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions<br>

scholarly journals Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

2021 ◽  
Author(s):  
Bjarne Kreitz ◽  
C. Franklin Goldsmith ◽  
Richard West ◽  
Emily Mazeau ◽  
Katrin Blondal ◽  
...  
Keyword(s):  
Rate Control ◽  
Parametric Uncertainty ◽  
Co2 Hydrogenation ◽  
Sensitivity Analyses ◽  
Control Analysis ◽  
Co2 Methanation ◽  
Model Predictions ◽  
Automatic Mechanism ◽  
Global Uncertainty ◽  
The Impact

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process, while considering the uncertainty in energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. 5,000 unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect 1 of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions<br>

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

Role of initial condition and parametric uncertainty in a severe hailstorm forecast

2021 ◽  
Author(s):  
Patrick Kuntze ◽  
Annette Miltenberger ◽  
Corinna Hoose ◽  
Michael Kunz
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Parametric Uncertainty ◽  
Radar Data ◽  
Cloud Microphysics ◽  
Initial Condition ◽  
Relative Contribution ◽  
Cloud Properties ◽  
Set Up ◽  
The Impact

&lt;p&gt;Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.&lt;br&gt;To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.&lt;br&gt;In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.&lt;/p&gt;

Sensitivity of automatic control of emergency manoeuvre to parametric uncertainty of the airplane model

2019 ◽  
Vol 91 (3) ◽  
pp. 407-419
Author(s):  
Jerzy Graffstein ◽  
Piotr Maslowski
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Flight Control ◽  
Parametric Uncertainty ◽  
Second Phase ◽  
Flight Control System ◽  
Content Type ◽  
Control Quality ◽  
Wide Range ◽  
The Impact

Purpose The main purpose of this work was elaboration and verification of a method of assessing the sensitivity of automatic control laws to parametric uncertainty of an airplane’s mathematical model. The linear quadratic regulator (LQR) methodology was used as an example design procedure for the automatic control of an emergency manoeuvre. Such a manoeuvre is assumed to be pre-designed for the selected airplane. Design/methodology/approach The presented method of investigating the control systems’ sensitivity comprises two main phases. The first one consists in computation of the largest variations of gain factors, defined as differences between their nominal values (defined for the assumed model) and the values obtained for the assumed range of parametric uncertainty. The second phase focuses on investigating the impact of the variations of these factors on the behaviour of automatic control in the manoeuvre considered. Findings The results obtained allow for a robustness assessment of automatic control based on an LQR design. Similar procedures can be used to assess in automatic control arrived at through varying design methods (including methods other than LQR) used to control various manoeuvres in a wide range of flight conditions. Practical implications It is expected that the presented methodology will contribute to improvement of automatic flight control quality. Moreover, such methods should reduce the costs of the mathematical nonlinear model of an airplane through determining the necessary accuracy of the model identification process, needed for assuring the assumed control quality. Originality/value The presented method allows for the investigation of the impact of the parametric uncertainty of the airplane’s model on the variations of the gain-factors of an automatic flight control system. This also allows for the observation of the effects of such variations on the course of the selected manoeuvre or phase of flight. This might be a useful tool for the design of crucial elements of an automatic flight control system.

scholarly journals Initiation of secondary ice production in clouds

2018 ◽  
Vol 18 (3) ◽  
pp. 1593-1610 ◽  
Author(s):  
Sylvia C. Sullivan ◽  
Corinna Hoose ◽  
Alexei Kiselev ◽  
Thomas Leisner ◽  
Athanasios Nenes
Keyword(s):  
Secondary Production ◽  
Parametric Uncertainty ◽  
Cloud Base ◽  
Base Temperature ◽  
Phase Partitioning ◽  
Thermodynamic Conditions ◽  
Ice Production ◽  
Cloud Ice ◽  
Droplet Shattering ◽  
The Impact

Abstract. Disparities between the measured concentrations of ice-nucleating particles (INPs) and in-cloud ice crystal number concentrations (ICNCs) have led to the hypothesis that mechanisms other than primary nucleation form ice in the atmosphere. Here, we model three of these secondary production mechanisms – rime splintering, frozen droplet shattering, and ice–ice collisional breakup – with a six-hydrometeor-class parcel model. We perform three sets of simulations to understand temporal evolution of ice hydrometeor number (Nice), thermodynamic limitations, and the impact of parametric uncertainty when secondary production is active. Output is assessed in terms of the number of primarily nucleated ice crystals that must exist before secondary production initiates (NINP(lim)) as well as the ICNC enhancement from secondary production and the timing of a 100-fold enhancement. Nice evolution can be understood in terms of collision-based nonlinearity and the “phasedness” of the process, i.e., whether it involves ice hydrometeors, liquid ones, or both. Ice–ice collisional breakup is the only process for which a meaningful NINP(lim) exists (0.002 up to 0.15 L−1). For droplet shattering and rime splintering, a warm enough cloud base temperature and modest updraft are the more important criteria for initiation. The low values of NINP(lim) here suggest that, under appropriate thermodynamic conditions for secondary ice production, perturbations in cloud concentration nuclei concentrations are more influential in mixed-phase partitioning than those in INP concentrations.

scholarly journals Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect

2010 ◽  
Vol 10 (23) ◽  
pp. 11373-11383 ◽  
Author(s):  
U. Lohmann ◽  
S. Ferrachat
Keyword(s):  
Climate Modeling ◽  
Global Climate ◽  
Parametric Uncertainty ◽  
Radiation Balance ◽  
Anthropogenic Aerosol ◽  
Uncertainty Range ◽  
Ice Cloud ◽  
Cloud Parameters ◽  
Aerosol Effect ◽  
The Impact

Abstract. Clouds constitute a large uncertainty in global climate modeling and climate change projections as many clouds are smaller than the size of a model grid box. Some processes, such as the rates of rain and snow formation that have a large impact on climate, cannot be observed. The uncertain parameters in the representation of these processes are therefore adjusted in order to achieve radiation balance. Here we systematically investigate the impact of key tunable parameters within the convective and stratiform cloud schemes and of the ice cloud optical properties on the present-day climate in terms of clouds, radiation and precipitation. The total anthropogenic aerosol effect between pre-industrial and present-day times amounts to −1.00 W m−2 obtained as an average over all simulations as compared to −1.02 W m−2 from those simulations where the global annual mean top-of-the atmosphere radiation balance is within ±1 W m−2. Thus tuning of the present-day climate does not seem to have an influence on the total anthropogenic aerosol effect. The parametric uncertainty regarding the above mentioned cloud parameters has an uncertainty range of 25% between the minimum and maximum value when taking all simulations into account. It is reduced to 11% when only the simulations with a balanced top-of-the atmosphere radiation are considered.

scholarly journals Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect

2010 ◽  
Vol 10 (8) ◽  
pp. 19195-19217 ◽  
Author(s):  
U. Lohmann ◽  
S. Ferrachat
Keyword(s):  
Climate Modeling ◽  
Global Climate ◽  
Parametric Uncertainty ◽  
Radiation Balance ◽  
Anthropogenic Aerosol ◽  
Uncertainty Range ◽  
Tuning Parameters ◽  
Ice Cloud ◽  
Aerosol Effect ◽  
The Impact

Abstract. Clouds constitute a large uncertainty in global climate modeling and climate change projections as many clouds are smaller than the size of a model grid box. Some processes, such as the rates of rain and snow formation that have a large impact on climate, cannot be observed. These processes are thus used as tuning parameters in order to achieve radiation balance. Here we systematically investigate the impact of various tunable parameters within the convective and stratiform cloud schemes and of the ice cloud optical properties on the present-day climate in terms of clouds, radiation and precipitation. The total anthropogenic aerosol effect between pre-industrial and present-day times amounts to −1.00 W m−2 obtained as an average over all simulations as compared to −1.02 W m−2 from those simulations where the global annual mean top-of-the atmosphere radiation balance is within ±1 W m−2. The parametric uncertainty when taking all simulations into account has an uncertainty range of 25% between the minimum and maximum value. It is reduced to 11% when only the simulations with a balanced top-of-the atmosphere radiation are considered.

scholarly journals Stability Analysis of Rotor-Bearing Systems under the Influence of Misalignment and Parameter Uncertainty

2021 ◽  
Vol 11 (17) ◽  
pp. 7918
Author(s):  
Xiaodong Sun ◽  
Kian K. Sepahvand ◽  
Steffen Marburg
Keyword(s):  
Computation Time ◽  
Parametric Uncertainty ◽  
Parameter Uncertainties ◽  
Misalignment Effect ◽  
Stability Threshold ◽  
Rotating Systems ◽  
Rotor Bearing ◽  
Run Up ◽  
The Stability ◽  
The Impact

Stability is a well-known challenge for rotating systems supported by hydrodynamic bearings (HDBs), particularly for the condition where the misalignment effect and the parametric uncertainty are considered. This study investigates the impact of misalignment and inherent uncertainties in bearings on the stability of a rotor-bearing system. The misalignment effect is approximately described by introducing two misaligned angles. The characteristics of an HDB, such as pressure distribution and dynamic coefficients, are calculated by the finite difference method (FDM). The stability threshold is evaluated as the intersection of run-up curve and borderline. Viscosity and clearance are considered as uncertain parameters. The generalized polynomial chaos (gPC) expansion is adopted to quantify the uncertainty in parameters by evaluating unknown coefficients. The unknown gPC coefficients are obtained by using the collocation method. The results obtained by the gPC expansion are compared with those of the Monte Carlo (MC) simulation. The results show that the characteristics of the HDB and the stability threshold are affected by misalignment and parameter uncertainties. As the uncertainty analysis using the gPC expansion is performed on a relatively small number of predefined collocation points compared with the large number of MC samples, the method is very efficient in terms of computation time.

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