Modeling Uncertainties of a Gas-Delivery Project

2007 ◽  
Author(s):  
Dominique F. Frizzell ◽  
Michael Sibley ◽  
Bryan Clayton Cotner ◽  
Simon Peter McCartney ◽  
George Russell Schmidt ◽  
...  
Keyword(s):  
Modeling Uncertainties

scholarly journals Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Le Liang ◽  
Yanjie Liu ◽  
Hao Xu
Keyword(s):  
Trajectory Optimization ◽  
Control Method ◽  
External Disturbance ◽  
Backstepping Control ◽  
Adaptive Robust Control ◽  
Robot Dynamics ◽  
Adaptive Backstepping ◽  
Recycling Process ◽  
Adaptive Backstepping Control ◽  
Modeling Uncertainties

Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment.

scholarly journals Modeling Northern Hemisphere Summer Heat Extreme Changes and Their Uncertainties Using a Physics Ensemble of Climate Sensitivity Experiments

2006 ◽  
Vol 19 (17) ◽  
pp. 4418-4435 ◽  
Author(s):  
Robin T. Clark ◽  
Simon J. Brown ◽  
James M. Murphy
Keyword(s):  
Structural Changes ◽  
Heat Waves ◽  
Full Range ◽  
Daily Maximum ◽  
Model Parameters ◽  
Mean Values ◽  
Summer Heat ◽  
Modeling Uncertainties ◽  
Physics Ensemble ◽  
Projected Changes

Abstract Changes in extreme daily temperature events are examined using a perturbed physics ensemble of global model simulations under present-day and doubled CO2 climates where ensemble members differ in their representation of various physical processes. Modeling uncertainties are quantified by varying poorly constrained model parameters that control atmospheric processes and feedbacks and analyzing the ensemble spread of simulated changes. In general, uncertainty is up to 50% of projected changes in extreme heat events of the type that occur only once per year. Large changes are seen in distributions of daily maximum temperatures for June, July, and August with significant shifts to warmer conditions. Changes in extremely hot days are shown to be significantly larger than changes in mean values in some regions. The intensity, duration, and frequency of summer heat waves are expected to be substantially greater over all continents. The largest changes are found over Europe, North and South America, and East Asia. Reductions in soil moisture, number of wet days, and nocturnal cooling are identified as significant factors responsible for the changes. Although uncertainty associated with the magnitude of expected changes is large in places, it does not bring into question the sign or nature of the projected changes. Even with the most conservative simulations, hot extreme events are still expected to substantially increase in intensity, duration, and frequency. This ensemble, however, does not represent the full range of uncertainty associated with future projections; for example, the effects of multiple parameter perturbations are neglected, as are the effects of structural changes to the basic nature of the parameterization schemes in the model.

scholarly journals Sensitivity of the Stress Field of the Proximal Femur Predicted by CT‐based FE Analysis to Modeling Uncertainties

2021 ◽  
Author(s):  
Sina Youssefian ◽  
Jarred A. Bressner ◽  
Mikhail Osanov ◽  
James K. Guest ◽  
Wojciech B. Zbijewski ◽  
...  
Keyword(s):  
Stress Field ◽  
Proximal Femur ◽  
Fe Analysis ◽  
Modeling Uncertainties

Modeling uncertainties in advanced technologies: Application to a coal gasification system with hot-gas cleanup

Energy ◽  
1994 ◽  
Vol 19 (4) ◽  
pp. 449-463 ◽  
Author(s):  
H.Christopher Frey ◽  
Edward S. Rubin ◽  
Urmila M. Diwekar
Keyword(s):  
Coal Gasification ◽  
Hot Gas ◽  
Advanced Technologies ◽  
Modeling Uncertainties

A finite-time H-infinite adaptive fault-tolerant controller considering time delay for flutter suppression of airfoil flutter

2019 ◽  
Vol 234 (2) ◽  
pp. 293-307
Author(s):  
Gao Ming-Zhou ◽  
Chen Xin-Yi ◽  
Han Rong ◽  
Yao Jian-Yong
Keyword(s):  
Finite Time ◽  
Control Method ◽  
Controller Design ◽  
Fault Tolerant ◽  
Linear Matrix ◽  
Control Methods ◽  
Actuator Faults ◽  
Control Delay ◽  
Modeling Uncertainties ◽  
The Stability

To suppress airfoil flutter, a lot of control methods have been proposed, such as classical control methods and optimal control methods. However, these methods did not consider the influence of actuator faults and control delay. This paper proposes a new finite-time H∞ adaptive fault-tolerant flutter controller by radial basis function neural network technology and adaptive fault-tolerant control method, taking into account actuator faults, control delay, modeling uncertainties, and external disturbances. The theoretic section of this paper is about airfoil flutter dynamic modeling and adaptive fault-tolerant controller design. Lyapunov function and linear matrix inequality are employed to prove the stability of the proposed control method of this paper. The numeral simulation section further proves the effectiveness and robustness of the proposed control algorithm of this paper.

scholarly journals Microwave snow emission modeling uncertainties in boreal and subarctic environments

2015 ◽  
Vol 9 (5) ◽  
pp. 5719-5773
Author(s):  
A. Roy ◽  
A. Royer ◽  
O. St-Jean-Rondeau ◽  
B. Montpetit ◽  
G. Picard ◽  
...  
Keyword(s):  
Grain Size ◽  
Spatial Variability ◽  
Boreal Forest ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Small Scale ◽  
Radiometric Measurements ◽  
Snow Properties ◽  
Modeling Uncertainties

Abstract. This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory-Multilayer model (DMRT-ML) with in situ measurements of snow properties. We use surface-based radiometric measurements at 10.67, 19 and 37 GHz in boreal forest and subarctic environments and a new in situ dataset of measurements of snow properties (profiles of density, snow grain size and temperature, soil characterization and ice lens detection) acquired in the James Bay and Umijuaq regions of Northern Québec, Canada. A snow excavation experiment – where snow was removed from the ground to measure the microwave emission of bare frozen ground – shows that small-scale spatial variability in the emission of frozen soil is small. Hence, variability in the emission of frozen soil has a small effect on snow-covered brightness temperature (TB). Grain size and density measurement errors can explain the errors at 37 GHz, while the sensitivity of TB at 19 GHz to snow increases during the winter because of the snow grain growth that leads to scattering. Furthermore, the inclusion of observed ice lenses in DMRT-ML leads to significant improvements in the simulations at horizontal polarization (H-pol) for the three frequencies (up to 20 K of root mean square error). However, the representation of the spatial variability of TB remains poor at 10.67 and 19 GHz at H-pol given the spatial variability of ice lens characteristics and the difficulty in simulating snowpack stratigraphy related to the snow crust. The results also show that for ground-based radiometric measurements, forest emission reflected by the surface leads to TB underestimation of up to 40 K if neglected. We perform a comprehensive analysis of the components that contribute to the snow-covered microwave signal, which will help to develop DMRT-ML and to improve the required field measurements. The analysis shows that a better consideration of ice lenses and snow crusts is essential to improve TB simulations in boreal forest and subarctic environments.

scholarly journals Water discharge estimates from large radar altimetry datasets in the Amazon basin

2012 ◽  
Vol 9 (6) ◽  
pp. 7591-7611 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Data Assimilation ◽  
Amazon Basin ◽  
Water Discharge ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
Modeling Uncertainties ◽  
The Mean ◽  
Relative Errors ◽  
The Impact

Abstract. In this study, we evaluate the use of a large radar altimetry dataset as a complementary gauging network capable of providing water discharge in ungauged regions within the Amazon basin. A rating-curve-based methodology is adopted to derive water discharge from altimetric data provided by Envisat at 444 virtual stations (VS). The stage-discharge relations at VS are built based on radar altimetry and outputs from a global flow routing scheme. In order to quantify the impact of modeling uncertainties on rating-curve based discharges, another experiment is performed using simulated discharges derived from a simplified data assimilation procedure. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean relative RMS errors as high as 52% and 12% for experiments without and with assimilation, respectively. Without data assimilation, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative errors (RE) of altimetry-based discharges varied from 15% to 92% for large and small drainage areas, respectively. Rating curves produced a mean RE of 54% versus 68% from model outputs. Assimilating discharge data decreases the mean RE from 68% to 12%. These results demonstrate the feasibility of applying the proposed methodology to the regional or global scales. Also, it is shown the potential of satellite altimetry for predicting water discharge in poorly-gauged and ungauged river basins.

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