• Bulletin of the Korean Chemical Society
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• v.34 no.10
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• pp.3141-3142
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• 2013

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Nuclear Engineering and Technology
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• v.37 no.6
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• pp.587-594
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• 2005

In an effort to develop a thermal-hydraulic (TH) safety analysis code for Gas-cooled Reactors (GCRs), the MARS code, which was primarily developed for TH analysis of water reactor systems, has been extended here for application to GCRs. The modeling requirements of the system code were derived from a review of major processes and phenomena that are expected to occur during normal and accident conditions of GCRs. Models fur code improvement were then identified through a review of existing MARS code capability. Among these, the following priority models necessary fur the analysis of limiting high and low pressure conduction cooling events were evaluated and incorporated in MARS-GCR/V1 : 1) Helium (He) and Carbon Dioxide ($CO_2$) as main system fluids, 2) gas convection heat transfer, 3) radiation heat transfer, and 4) contact heat transfer models. Each model has been assessed using various conceptual problems for code-to-code benchmarks and it was demonstrated that MARS-GCR/V1 is capable of capturing the relevant phenomena. This paper describes the models implemented in MARS-GCR/V1 and their verification and validation results.