Switching valves experience opposing fluid forces due to movement of the moving member itself, as the surrounding fluid volume must move to accommodate the movement. This movement-induced fluid force may be divided into three main components; the added mass term, the viscous term and the so-called history term. For general valve geometries there are no simple solution to either of these terms. During development and design of such switching valves, it is therefore, common practice to use simple models to describe the opposing fluid forces, neglecting all but the viscous term which is determined based on shearing areas and venting channels. For fast acting valves the opposing fluid force may retard the valve performance significantly, if appropriate measures are not taken during the valve design. Unsteady Computational Fluid Dynamics (CFD) simulations are available to simulate the total fluid force, but these models are computationally expensive and are not suitable for evaluating large numbers of different operation conditions or even design optimization. In the present paper, an effort is done to describe these fluid forces and their origin. An example of the total opposing fluid force is given using an analytically solvable example, showing the explicit form of the force terms and highlighting the significance of the added mass and history term in certain fast switching valve applications. A general approximate model for arbitrary valve geometries is then proposed with offset in the analytic model terms. The coefficients in this general model are determined based on CFD analyses, which are evaluated throughout the movement range of the moving member on an example valve geometry. The proposed model is compared to complete unsteady CFD simulations and found to generally predict the opposing fluid force well and gives accurate predictions under certain conditions. The proposed model is suitable for valve designers who need a computationally inexpensive fluid force model suitable for optimization routines or efficient dynamic models.

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