Performance modeling and parameter sensitivity analyses of an aluminum-air battery with dual electrolyte structure

2020 ◽  
Vol 32 ◽  
pp. 101696
Author(s):  
Ruijie Zhao ◽  
Jiadong Xie ◽  
Hejing Wen ◽  
Fei Wang ◽  
Jianhong Yang ◽  
...  
Keyword(s):  
Performance Modeling ◽  
Parameter Sensitivity ◽  
Sensitivity Analyses ◽  
Air Battery

Sensitivity Analysis of Parameters for FPSO Model Updating

2020 ◽  
Author(s):  
Qihao Wu ◽  
Min Zhang ◽  
Tian’en Yang ◽  
Nuoya Xu ◽  
Junrong Wang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Model ◽  
Model Updating ◽  
Direct Method ◽  
Unit Length ◽  
Parameter Sensitivity ◽  
Irregular Waves ◽  
Sensitivity Analyses ◽  
Mooring Line ◽  
Restoring Force

Abstract This paper presents parameter sensitivity analysis for a FPSO numerical model updating. Generally, model test data are considered a better presentation of physical phenomena than its numerical counterpart. To minimize the discrepancy, model updating is of pragmatically importance. Model updating of a certain FPSO can be achieved by specific steps. In each step, the required properties of numerical model and test results are matched by means of tuning of the related parameters. To avoid inefficiency and physical meaning loss resulting from large modification of parameters which are insensitive to objective properties, parameter sensitivity analyses using the direct method are conducted in this paper. The investigated parameters mainly are the FPSO’s mooring line length, mooring line mass per unit length, mooring line cross-sectional area, fairlead position, FPSO hydrostatic stiffness, FPSO mass properties, linear and quadratic damping coefficients. According to the different stages of FPSO model updating, the objective functions are set to be the FPSO’s mooring line pretension, mooring system horizontal restoring force, the natural periods of the FPSO’s 6 degree of freedom motions and the standard deviation of motion response spectra under irregular waves.

Parameter sensitivity analyses in seismic hazard

1996 ◽  
Vol 9 (4) ◽  
pp. 629-634
Author(s):  
Jian Wang ◽  
Meng-Tan Gao
Keyword(s):  
Seismic Hazard ◽  
Parameter Sensitivity ◽  
Sensitivity Analyses

scholarly journals Sub catchment Assessment of snowpack and snowmelt change by analyzing elevation bands and parameter sensitivity in the high Himalayas

2017 ◽  
Author(s):  
Vishal Singh ◽  
Manish Kumar Goyal ◽  
Rao Y. Surampalli ◽  
Francisco Munoz-Arriola
Keyword(s):  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Parameter Sensitivity ◽  
Snow Accumulation ◽  
Sensitivity Analyses ◽  
River Catchment ◽  
Hydrological Parameters ◽  
Streamflow Generation ◽  
Satluj River

Abstract. The present work proposes to improve estimates of how much streamflow is generated by snow in the watersheds of the steep Himalayas. Half of the earth’s glacial catchments in nonpolar areas are in the Himalayas, and they generate almost a third of the streamflows in India. In River catchments with glacier presence in the region, temporal variability in streamflow generation and the associated distribution of accumulated snow illustrate how changes in snowmelt and precipitation can affect water supplies to a growing population of 1.3 billion people. Estimations of snowpack and snowmelt in watersheds are critical for understanding streamflow generation and sources of catchments. However, estimating precipitation and snow accumulation is constrained by the difficulties complex terrain poses to data collection. The primary objective of this study is to assess the role of elevations in the computation of snowfall (snowpack) and snowmelt in sub-catchments. The study area is the Satluj River Catchment (up to Kasol gauge) with moderate (e.g., 526 m) to very high elevations (e.g., 7429 m) dominated by snow covers and glaciers. The Satluj River Catchment was divided into 14 sub-catchments. Snowpack and snowmelt variations in the sub-catchments in both historical and projected near-term (2011–2130) periods were analyzed using observed and Global Circulation Model (GCM) data sets. Both hydrological scenarios used elevation bands and parameter-sensitivity analyses built in the Soil Water Assessment Tool (SWAT) model. For model calibration/validation and parameter sensitivity analysis, an advanced optimization method — namely, Sequential Uncertainty Fitting (SUFI2) approach was used with multiple hydrological parameters. Among all parameters, the curve number (CN2) was found significantly sensitive for computations. The snowmelt hydrological parameters such as snowmelt factor maximum (SMFMX) and snow coverage (SNO50COV) significantly affected objective functions such as R2 and NSE during the model optimization process. The computed snowpack and snowmelt were found highly variable over the Himalayan sub-catchments as also reported by previous researchers in other regions. The magnitude of snowpack change consistently decreases across all the sub-catchments of the Satluj River Catchment (varying between 4 % and 42 %). The highest percentage of changes in snowpack was observed over high-elevation subcatchments.

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