The fractional scaling methodology (FSM) Part 1. methodology development

Full text of publication follows: a quantitative methodology is developed, based on the concepts of hierarchy and synthesis, to integrate and organize information and data. The methodology uses scaling to synthesize experimental data and analytical results, and to provide quantitative criteria for evaluating the effects of various design and operating parameters that influence processes in a complex system such as a nuclear power plant or a related test facility. Synthesis and scaling are performed on three hierarchical levels: the process, component and system levels. Scaling on the process level determines the effect of a selected process on a particular state variable during a selected scenario. At the component level this scaling determines the effects various processes have on a state variable, and it ranks the processes according to their importance by the magnitude of the fractional change they cause on that state variable. At the system level the scaling determines the governing processes and corresponding components, ranking these in the order of importance according to their effect on the fractional change of system-wide state variables. The scaling methodology reveals on all levels the fractional change of state variables and is called therefore the Fractional Scaling Methodology (FSM). FSM synthesizes process parameters and assigns to each thermohydraulic process a dimensionless effect metric Ω = ωt, that is the product of the specific rate of fractional change ω and the characteristic time t. The rate of fractional change ω is the ratio of process transport rate over content of a preserved quantity in a component. The effect metric Ω quantifies the contribution of the process to the fractional change of a state variable in a given component. Ordering of a component effect metrics provides the hierarchy of processes in a component, then in all components and the system. FSM separates quantitatively dominant from minor processes and components and thereby justifies resource allocations and permissible simplifications for experiments and computer code development. It provides defensible criteria for test design and operation. It helps to reduce the number of necessary test or computer runs and facilitates storage, transmission and retrieval of test results by data synthesis. Synthesis of scaled experimental data from different test facilities reveals common facility characteristics as well as scale distortions. (authors)