Design for Additive Manufacturing: Valves Without Moving Parts

Abstract

This work presents an innovative design method to obtain valves without moving parts that can be built using additive manufacturing and applied to gas turbines. Additive manufacturing offers more flexibility than traditional manufacturing methods, which implies less constraints on the manufacture of engineering parts and it is possible to build complex geometries like the Tesla valve. The Tesla valve is a duct that shows a diodicity behavior: it allows a fluid to flow in one direction with lower losses than in the other one. Unfortunately the design of the Tesla valve is two dimensional and it relies on the designer experience to obtain good performance. The method presented here allows the automatic generation of valves similar to the Tesla one, obtained automatically by a topology optimization algorithm. It is the first time that a three dimensional method is presented, the available algorithms in the open literature works in two dimensions. A fluid sedimentation process enables the creation of a new geometry optimized to meet a prescribed set of performance, such as pressure losses. The steepest descent method is used to approximate the integrals met during the calculation process. The optimizer is used to obtain three dimensional geometries for different multi-objective functions. The geometry is compared to an existing similar solution proposed in the open literature and validated. The results are compared to a Tesla valve to show the performance of the optimized geometries. The advantage of the proposed solution is the possibility to apply the design method with any spatial constraints and for a wide range of mass flow.

Towards digital design of gas turbines

“…Ducts with a diodicity behaviour (valves without parts) 3. Heat transfer and pressure losses (multi-objective) Detailed explanations of these application are shown in (Gaymann et al, 2017;Pietropaoli et al, 2018) and a brief overview will be presented here. For stagnation pressure loss optimization, the return channel used by (Vestraete et al, 2013) is used.…”

Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

“…The structure design is optimized according to the fluid path which drives the solution and performance. The objective of a FSTO is to build for example channels with low pressure losses and high heat transfer [3][4][5] .…”

“…Among the different TO mathematical approaches developed, two methods have stand out and are currently attracting research interest in the domain of FSTO: the Level-Set Method (LSM) 6 and the Adjoint based method (ABM) 3 .…”

“…To overcome these limitations, adjoint based methods are becoming more popular. Adjoint formulation relies on the definition of the derivatives of the dependence of a specific performance indicator (such as efficiency) to a geometrical variation 3 . The advantage of such formulation is the ability to deal with a very high number of design parameters.…”

“…Some examples can be found by the recent work of 10 , who minimized the pressure losses. Other examples using LSM include 11,12 while an adjoint formulation has been used by [3][4][5] .…”