Obstacle contained system (OCS) design method and its application in valve core orifice design of pilot-control globe valve

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With the development of the society and the times, traditional parametric design methods are witnessing a severe challenge due to the more and more complex physical systems. Thus, developing novel parametric analysis methods is very important for dealing with complex physical systems, refining useful parameters from numerous data, and proposing accurate prediction formulas. A spring slider system, a direct-current circuit system, a pipeline pressure drop system and a steady heat transfer model of flat plate system were described from the point of systemic parametric analysis method. Then, the key physical parameters in above four systems were summarized. Based on the comparative results, a novel systemic parametric design method, obstacle contained system (OCS) design method, was proposed. The OCS was made up of three elements:an obstacle element, a pass body element and a D-value element. With an abundant accurate data pole, the OCS design method could build the direct relationship of the obstacle element and the D-value element, which meant the simplification of the physical models and much easier to get relatively accurate results. Meanwhile, the design of pilot-control globe valve orifice was checked with both the OCS design method and the numerical simulation. The diameter of orifice on the valve core could influence the pressure difference and the maximum vapor rate inside pilot-control globe valves. Achieved by two different methods, the OCS design method and the numerical simulation, the results showed that the effects of orifice diameters on the pressure difference and the maximum vapor rate under different inlet velocities, were within 2% errors, which was reasonable and acceptable for the engineering application. In other words, the OCS design method was credible for parametric analysis. In future, the OCS design method has a broad application prospect to analyze various types of physical models especially in the era of big data.