scholarly journals Modification of an Ecosystem Model for Carrying Capacity of Shellfish System -I . Validation and Sensitivity Analysis-

2002 ◽  
Vol 35 (4) ◽  
pp. 386-394
Author(s):  
Won Chan Lee ◽  
Hyung Chul Kim ◽  
Woo Jeung Choi ◽  
Pil Yong Lee ◽  
Jun Ho Koo ◽  
...  
2021 ◽  
Vol 1035 ◽  
pp. 813-818
Author(s):  
Zheng Long Li ◽  
Lin Chen ◽  
Zhi Hong Li ◽  
Guo Shuai Yan ◽  
Wei Li

In order to study the pressure carrying capacity of X80 pipe with metal loss defect on the girth weld the water-pressure blasting test of the pipe with metal loss defect was analyzed by experiment and finite element simulation. Based on this, the sensitivity analysis of the factors affecting the pressure carrying of the pipeline, such as the circular size, the axial size, and the depth of the metal loss defect, was carried out. The research results show that the circular size of the metal loss defect on the girth weld had little impact to the pressure carrying capacity of the pipe while it reduced with the increasing of the axial size and the depth of the metal loss defect.


2019 ◽  
Vol 944 ◽  
pp. 835-840
Author(s):  
Peng Song ◽  
Zheng Long Li ◽  
Yu Ran Fan ◽  
Lei Guo ◽  
Xi Xi Zhang ◽  
...  

In order to study the pressure carrying capacity of X80 pipe with plain dents, the formation process and the hydraulic test were analyzed by finite element simulation. Based on this, the sensitivity analysis of the factors affecting the pressure carrying capacity of the pipeline, such as the internal pressure, the confinement state and the material performance, is carried out. Research results show that springback amount of the pipeline decreases due to the initial internal pressure, and constraint state has little effect on the pressure carrying capacity while increases with the increasing of the material tensile properties. When the depth of the dent is less than 6% pipe diameter or the strain of the dent is less than 6%, the dent has little impact to the pressure carrying capacity of the pipe.


2013 ◽  
Vol 118 (2) ◽  
pp. 505-528 ◽  
Author(s):  
Christoforos Pappas ◽  
Simone Fatichi ◽  
Sebastian Leuzinger ◽  
Annett Wolf ◽  
Paolo Burlando

1981 ◽  
Vol 12 (3) ◽  
pp. 173-190 ◽  
Author(s):  
R.H. Gardner ◽  
R.V. O'Neill ◽  
J.B. Mankin ◽  
J.H. Carney

2015 ◽  
Vol 8 (7) ◽  
pp. 2231-2262 ◽  
Author(s):  
T. R. Anderson ◽  
W. C. Gentleman ◽  
A. Yool

Abstract. Modelling marine ecosystems requires insight and judgement when it comes to deciding upon appropriate model structure, equations and parameterisation. Many processes are relatively poorly understood and tough decisions must be made as to how to mathematically simplify the real world. Here, we present an efficient plankton modelling testbed, EMPOWER-1.0 (Efficient Model of Planktonic ecOsystems WrittEn in R), coded in the freely available language R. The testbed uses simple two-layer "slab" physics whereby a seasonally varying mixed layer which contains the planktonic marine ecosystem is positioned above a deep layer that contains only nutrient. As such, EMPOWER-1.0 provides a readily available and easy to use tool for evaluating model structure, formulations and parameterisation. The code is transparent and modular such that modifications and changes to model formulation are easily implemented allowing users to investigate and familiarise themselves with the inner workings of their models. It can be used either for preliminary model testing to set the stage for further work, e.g. coupling the ecosystem model to 1-D or 3-D physics, or for undertaking front line research in its own right. EMPOWER-1.0 also serves as an ideal teaching tool. In order to demonstrate the utility of EMPOWER-1.0, we implemented a simple nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model and carried out both a parameter tuning exercise and structural sensitivity analysis. Parameter tuning was demonstrated for four contrasting ocean sites, focusing on station BIOTRANS in the North Atlantic (47° N, 20° W), highlighting both the utility of undertaking a planned sensitivity analysis for this purpose, yet also the subjectivity which nevertheless surrounds the choice of which parameters to tune. Structural sensitivity tests were then performed comparing different equations for calculating daily depth-integrated photosynthesis, as well as mortality terms for both phytoplankton and zooplankton. Regarding the calculation of daily photosynthesis, for example, results indicated that the model was relatively insensitive to the choice of photosynthesis–irradiance curve, but markedly sensitive to the method of calculating light attenuation in the water column. The work highlights the utility of EMPOWER-1.0 as a means of comprehending, diagnosing and formulating equations for the dynamics of marine ecosystems.


2020 ◽  
Vol 13 (10) ◽  
pp. 4691-4712
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton–ecosystem model (OPEM), implemented in the University of Victoria Earth System Climate Model (UVic-ESCM), using a Latin hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3-, PO43-, O2, and surface chlorophyll a concentrations. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3- inventory. Global O2 varies by a factor of 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.


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