Numerical investigation of switching features of a hydraulic seat valve with annular flow geometry

Bender, Niels Christian; Pedersen, Henrik C.; Winkler, Bernd; Plöckinger, Andreas

doi:10.1080/14399776.2018.1491755

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…This is of particular importance when predicting the motion near mechanical impact that is correlated to durability. The static evaluations are within reasonable accuracy and in Bender et al (2018) it was demonstrated that the dynamic forces (F M I ) can also be represented in the LPM. Therefore, it is of interest to check how this model holds up against dynamic CFD simulations with different flow conditions than those validated in Figure 8. The severity of the introduced simplifications is determined based on the discrepancy in the velocity profile of the plunger.…”

Section: Model Predictionsmentioning

confidence: 84%

“…However, as the plunger moves in the fluid this interaction will in-duce flow in various directions. This phenomenon was studied by CFD in Bender et al (2018) which quantified this Movement-Induced Flow (MIF). The MIF is added by superposition and the total flow is thus…”

Section: Valve Fluid Flowmentioning

confidence: 99%

“…Therefore, a trigonometric relation between z and w is introduced to create a non-linear jet angle function. The exact shape of this function can be changed by tuning the coefficients c 1 • • • c 3 scaling the importance of z and w. The form of F s was also investigated in Bender et al (2018) with an attempt to describe the jet angle as the angle between the vectors of z and w (only z was varied in that study). In this work a slightly different model form is proposed due to a more comprehensive use of static CFD simulations with more flow conditions and design points.…”

Section: Valve Fluid Forcesmentioning

confidence: 99%

“…This can be studied by a transient and dynamic CFD framework, where various flow conditions can be tested while switching with some actuating force. This framework is elaborated in Bender et al (2018) and the results of one switching instant is shown in Figure 8 where dynamic CFD and the force components of the LPM are compared.…”

Section: Switching Dynamicsmentioning

confidence: 99%

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Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

Bender¹,

Andersen²,

Pedersen³

2019

MIC

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This paper studies reduced-order-models for the fluid flow problem of a digital valve, and whether it may efficiently be formulated by a deep Artificial Neural Network (ANN) to model e.g. the valve flow, flowinduced force, stiction phenomena and steep local pressure gradients that arise before plunger impact, which may otherwise require CFD to be accurately modeled. Several methodologies are investigated to evaluate both the required computation time and the accuracy. The accuracy is benchmarked against CFD solutions of flows and forces. As basis for comparison an analytical model is proposed where some fitting parameters are allowed, and the equation is tested outside its fitting range. A similar model is built as a deep ANN which is trained with data from the analytical model to investigate the amount of data required for an ANN and its fitting capabilities. The results show that in higher dimensions the required training data can be maintained low if data is structured by a Latin Hypercube, otherwise the amount becomes infeasible. This makes an ANN surrogate feasible when compared to a look-up table, and may be expanded to higher dimension where dynamical effects are included. However, the required data and computational cost for this is too extensive for the valve design considered as basis for the analysis. Instead, for this specific problem, the derived analytical model is sufficient to describe the valve dynamics and reduces the computation time significantly.

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Section: Model Predictionsmentioning

confidence: 84%

Section: Valve Fluid Flowmentioning

confidence: 99%

Section: Valve Fluid Forcesmentioning

confidence: 99%

Section: Switching Dynamicsmentioning

confidence: 99%

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Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

Bender¹,

Andersen²,

Pedersen³

2019

MIC

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show abstract

“…Generally the system is described by complex models using e.g., Navier-Stoke's equations, Maxwell's equations, etc., which are used in the detailed simulation of the system. However, to understand how different parameters are related to the design parameters, simplified models are presented in the following, based on previous studies of the valve [22,25]. These models are used to identify the output parameters considered when investigating various designs parameters sensitivity, cf.…”

Section: Lumped Parameter Model Backgroundmentioning

confidence: 99%

Investigating the Design Parameters’ Influence in a Fast Switching Valve – An Approach to Simplify the Design Process

Pedersen

Bender²,

Andersen

2023

TJFP

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Designing Fast Switching Valves (FSVs) for digital displacement units is a complicated process pushing the technology to the limit. The system dynamics and the interaction of the fluid, mechanical structure, actuator and control hence calls for advanced modelling, including CFD and FEA, to capture, e.g. fluid stiction effects, end-damping and impact contact stresses. Unfortunately, this essentially renders optimization processes infeasible due to the computational burden involved, although this is precisely what is required for this type of complex multi-domain problem. Therefore, the focus of the current article is on how a complex mechatronic design problem, like designing an FSV, may be aided by considering decomposing and simplification through sensitivity analysis and analyzing correlations between the design and output parameters. This is done to significantly reduce the original design problem without compromising the investigated design space. The paper focuses specifically on the results related to an FSV and the flow delivering part of this, showing the influence of the various design parameters. However, the approach and considerations may be generalized to an other areas as well.

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Study on the high-pressure hydrogen gas flow characteristics of the needle valve with different spool shapes

Cui

Hua

et al. 2023

International Journal of Hydrogen Energy

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Numerical investigation of switching features of a hydraulic seat valve with annular flow geometry

Cited by 4 publications

References 17 publications

Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics

Investigating the Design Parameters’ Influence in a Fast Switching Valve – An Approach to Simplify the Design Process

Study on the high-pressure hydrogen gas flow characteristics of the needle valve with different spool shapes

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