Upscaling Polymer Flooding in Heterogeneous Reservoirs

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Class number one criterion for some non-normal totally real cubic fields

Let ${\{K_m\}_{m\geq 4}}$ be the family of non-normal totally real cubic number fields defined by the irreducible cubic polynomial $f_m(x)=x^3-mx^2-(m+1)x-1$, where $m$ is an integer with $m\geq 4$. In this paper, we will give a class number one criterion for $K_m$.Comment: 9 page

METCAN simulation of candidate metal matrix composites for high temperature applications

The METCAN (Metal Matrix Composite Analyzer) computer code is used to simulate the nonlinear behavior of select metal matrix composites in order to assess their potential for high temperature structural applications. Material properties for seven composites are generated at a fiber volume ratio of 0.33 for two bonding conditions (a perfect bond and a weak interphase case) at various temperatures. A comparison of the two bonding conditions studied shows a general reduction in value of all properties (except CTE) for the weak interphase case from the perfect bond case. However, in the weak interphase case, the residual stresses that develop are considerably less than those that form in the perfect bond case. Results of the computational simulation indicate that among the metal matrix composites examined, SiC/NiAl is the best candidate for high temperature applications at the given fiber volume ratio

Review of Literature and Curricula in Smart Supply Chain & Transportation

This study provides a review of existing smart supply chain management (SCM) literature and current course offerings in order to identify unexplored implications of smart SCM. Specifically, the study focuses on curricula within the state of California to derive potential opportunities for the relevant practitioners in the Bay Area. In addition, the study further extends curriculum review to other well-recognized SCM programs around the U.S. By exploring current relevant course offerings from different academic institutions for higher education (i.e., universities), this research aims to deliver general ideas useful to knowledge practitioners in fields concerning SCM. Finally, the research illustrates a conceptual framework aimed at fostering familiarity with the necessary research topics for the evolving smart SCM

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states