This article proposes an effective methodology for the fluid-dynamic design optimization of the sliding spool of a hydraulic proportional directional valve: the goal is the minimization of the flow force at a prescribed flow rate, so as to reduce the required opening force while keeping the operation features unchanged. A full three-dimensional model of the flow field within the valve is employed to accurately predict the flow force acting on the spool. A theoretical analysis, based on both the axial momentum equation and flow simulations, is conducted to define the design parameters, which need to be properly selected in order to reduce the flow force without significantly affecting the flow rate. A genetic algorithm, coupled with a computational fluid dynamics flow solver, is employed to minimize the flow force acting on the valve spool at the maximum opening. A comparison with a typical single-objective optimization algorithm is performed to evaluate performance and effectiveness of the employed genetic algorithm. The optimized spool develops a maximum flow force which is smaller than that produced by the commercially available valve, mainly due to some major modifications occurring in the discharge section. Reducing the flow force and thus the electromagnetic force exerted by the solenoid actuators allows the operational range of direct (single-stage) driven valves to be enlarged
This paper introduces the concept of Resilience Engineering in the context of space systems design and a model of Global System Reliability and Robustness that accounts for epistemic uncertainty and imprecision. In particular, Dempster-Shafer Theory of evidence is used to model uncertainty in both system and environmental parameters. A resilience model is developed to account for the transition from functional to degraded states, and back, during the operational life and the dependency of these transitions on system level design choices and uncertainties. The resilience model is embedded in a network representation of a complex space system. This network representation, called Evidence Network Model (ENM), allows for a fast quantification of the global robustness and reliability of the system. A computational optimisation algorithm is then proposed to derive design solutions that provide an optimal compromise between resilience and performance. The result is a set of design solutions that maximise the probability of a system to recover functionalities in the case of a complete or partial failure and at the same time maximises the
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