Next Generation Safety Analysis Methods for SFRs—(6) SCARABEE BE+3 Analysis With SIMMER-III and COMPASS Codes Featuring Duct-Wall Failure

2009 ◽  
Author(s):  
Yasushi Uehara ◽  
Noriyuki Shirakawa ◽  
Masanori Naitoh ◽  
Hidetoshi Okada ◽  
Hidemasa Yamano ◽  
...  
Keyword(s):  
Mechanistic Model ◽  
Model Development ◽  
Fiscal Year ◽  
Technological Basis ◽  
Systematic Assessment ◽  
Duct Wall ◽  
Complementary Role ◽  
Fuel Pin ◽  
Spatial Window ◽  
Development And Validation

Governing key phenomena in core disruptive accidents (CDAs) in sodium-cooled fast reactors (SFRs) are supposed to be (1) fuel pin failure and disruption, (2) molten pool boiling, (3) melt freezing and blockage formation, (4) duct wall failure, (5) low-energy disruptive core motion, (6) debris-bed coolability, and (7) metal-fuel pin failure with eutectics between fuel and steel [1]. Although the systematic assessment program for SIMMER-III [4–7] has provided a technological basis that SIMMER-III is practically applicable to integral reactor safety analyses, further model development and validation efforts should be made to make future reactor calculations more reliable and rational. For mechanistic model development, a mesoscopic approach with the COMPASS code [1, 2, 3] is expected to advance the understanding of these key phenomena during event progression in CDAs. The COMPASS code has been developed since FY2005 (Japanese Fiscal Year, hereafter) to play a complementary role to SIMMER-III. In this paper, the overall analysis of SCARABEE-BE+3 test with the SIMMER-III and those with COMPASS, focusing the duct wall failure in a small temporal and spatial window cut from the SIMMER-III analysis results of the test, are described.

122 A Mechanistic Model of Growth and Amino Acid Deposition in the Pregnant Sow: Model Development, Evaluation, and Application

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 55-56
Author(s):  
Christian D Ramirez-Camba ◽  
Crystal L Levesque
Keyword(s):  
Weight Gain ◽  
Amino Acid ◽  
Mechanistic Model ◽  
Model Development ◽  
Allantoic Fluid ◽  
Late Gestation ◽  
Maternal Body ◽  
Proposed Model ◽  
Development Evaluation ◽  
Model Weight

Abstract A mechanistic model was developed with the objective to characterize weight gain and essential amino acid (EAA) deposition in the different tissue pools that make up the pregnant sow: placenta, allantoic fluid, amniotic fluid, fetus, uterus, mammary gland, and maternal body were considered. The data used in this modelling approach were obtained from published scientific articles reporting weights, crude protein (CP), and EAA composition in the previously mentioned tissues; studies reporting not less than 5 datapoints across gestation were considered. A total of 12 scientific articles published between 1977 and 2020 were selected for the development of the model and the model was validated using 11 separate scientific papers. The model consists of three connected sub-models: protein deposition (Pd) model, weight gain model, and EAA deposition model. Weight gain, Pd, and EAA deposition curves were developed with nonparametric statistics using splines regression. The validation of the model showed a strong agreement between observed and predicted growth (r2 = 0.92, root mean square error = 3%). The proposed model also offered descriptive insights into the weight gain and Pd during gestation. The model suggests that the definition of time-dependent Pd is more accurately described as an increase in fluid deposition during mid-gestation coinciding with a reduction in Pd. In addition, due to differences in CP composition between pregnancy-related tissues and maternal body, Pd by itself may not be the best measurement criteria for the estimation of EAA requirement in pregnant sows. The proposed model also captures the negative maternal Pd that occurs in late gestation and indicates that litter size influences maternal tissue mobilization more than parity. The model predicts that the EAA requirements in early and mid-gestation are 75, 55 and 50% lower for primiparous sows than parity 2, 3 and 4+ sows, respectively, which suggest the potential benefits of parity segregated feeding.

scholarly journals Viscosity Model for Nanoparticulate Suspensions Based on Surface Interactions

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2752
Author(s):  
Benedikt Finke ◽  
Clara Sangrós Sangrós Giménez ◽  
Arno Kwade ◽  
Carsten Schilde
Keyword(s):  
Mechanistic Model ◽  
Model Development ◽  
Rheological Behaviour ◽  
Hybrid Modelling ◽  
Shear Rates ◽  
Drag Forces ◽  
Boehmite Nanoparticles ◽  
Data Points ◽  
Two Parameters ◽  
Physical Phenomena

In this paper, a widely mechanistic model was developed to depict the rheological behaviour of nanoparticulate suspensions with solids contents up to 20 wt.%, based on the increase in shear stress caused by surface interaction forces among particles. The rheological behaviour is connected to drag forces arising from an altered particle movement with respect to the surrounding fluid. In order to represent this relationship and to model the viscosity, a hybrid modelling approach was followed, in which mechanistic relationships were paired with heuristic expressions. A genetic algorithm was utilized during model development, by enabling the algorithm to choose among several hard-to-assess model options. By the combination of the newly developed model with existing models for the various physical phenomena affecting viscosity, it can be applied to model the viscosity over a broad range of solids contents, shear rates, temperatures and particle sizes. Due to its mechanistic nature, the model even allows an extrapolation beyond the limits of the data points used for calibration, allowing a prediction of the viscosity in this area. Only two parameters are required for this purpose. Experimental data of an epoxy resin filled with boehmite nanoparticles were used for calibration and comparison with modelled values.

Development and validation of the Systematic Assessment of Book Reading (SABR-2.2)

2021 ◽  
Vol 55 ◽  
pp. 201-213
Author(s):  
Jill M. Pentimonti ◽  
Ryan P. Bowles ◽  
Tricia A. Zucker ◽  
Sherine R. Tambyraja ◽  
Laura M. Justice
Keyword(s):  
Book Reading ◽  
Systematic Assessment ◽  
Development And Validation
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