Learning Curve Characterization Within Complex Low-Rate Production Environments

Traditional and current learning curve approaches, methods, and theories were deficient when addressing complex low-rate production systems. The purpose of this research was to address this problem and develop a learning curve approach that characterizes learning in low-rate production environments such as naval ship construction. This research identified the principal aspects that influence learning within this environment and developed a learning characterization more reflective of this environment. There obviously exists a large body of knowledge covering learning. However, the research contained herein addresses learning as it relates to learning curves in low-rate production environments, such as naval shipbuilding. The various theories impacting learning curves has been explored in detailed as part of this research. Through the completed literature review, the researcher has confirmed that there is gap in the body of knowledge associated with learning curves specifically addressing the low-rate production of naval ships. The results of this research have addressed this gap in knowledge accordingly. The research completed has a significant impact not only on the body of knowledge involving learning curves, but also on the expectations associated with the design, production, test, and delivery of complex naval ships. In addition, the results of the research were also a concise assessment of learning curve theories, their applicability, and the fact that, until now, there has not been published research addressing learning curves associated with the low-rate production environments. The results of the completed research also identified the principal factors associated with learning curves in low-rate production environments. These principal aspects formed the basis of the development of a characterization of learning in low-rate production environments, which the researcher has developed the terminology of overall learning curve characterization (OLCC) defined by stability (S), procurement strategy (P), industrial and organizational culture (I), knowledge management (K), and demographic environment (E), which the researcher has also referred to this characterization as SPIKE. The results developed by this research was also generalizable to other low-rate production complex systems such as one-of-a-kind systems like the space program, oil well platforms, and other low production rate industries

Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps