Design and Opti­miza­tion of Valves

The design and opti­miza­tion of valves is one of many appli­ca­tions where using CAESES® to robustly automate the processes of sys­tem­atic geometry vari­a­tion and analysis of the gen­er­ated variants with a suitable CFD solver can lead to sig­nif­i­cant reduc­tions in time-to-market, as well as to a truly optimal design within the spec­i­fied constraints.

Valves are devices that control, direct or regulate the flow of a fluid by opening, closing or par­tially obstruct­ing various pas­sage­ways. In an open valve, fluid flows in a direc­tion from higher to lower pressure. The primary objec­tive in the opti­miza­tion of a valve is usually the improve­ment of the flow rate through the valve at a defined pressure drop, often expressed as the so-called flow coef­fi­cient or flow factor, a relative measure of its effi­ciency at allowing fluid flow.

Example Case: Valve Opti­miza­tion with SimericsMP+

The fol­low­ing sections describe an opti­miza­tion study that was carried out to demon­strate the workflow for valve opti­miza­tion using CAESES® in com­bi­na­tion with Simer­ic­sMP+ as CFD solver. The object of this study was a four-way spool valve from Duplo­matic Motion Solu­tions, which is a pilot operated dis­trib­u­tor with solenoid or hydraulic control. In par­tic­u­lar, the shape of two ports of the valve was opti­mized in order to obtain the highest possible mass flux at an imposed pressure drop of 5 bar. For opti­miza­tion purposes, the valve was sim­u­lated with a fixed spool position so that only ports P and A (in blue in the fol­low­ing figure) were con­nected through the spool port recesses (green in the fol­low­ing figure), a typical oper­at­ing condition.

Geometry Vari­a­tion Setup

The affected ports were removed from the original CAD model and replaced with geometry parts para­me­ter­ized in CAESES®. For each of the two con­sid­ered ports, the same set of 9 para­me­ters was selected as free vari­ables for the opti­miza­tion. These para­me­ters control the shape of the dif­fer­ent form features for the internal pas­sage­ways of the ports. The fol­low­ing ani­ma­tions illus­trate the effect of the indi­vid­ual para­me­ters on the geometry model.

Simer­ic­sMP+ Automation

Simer­ic­sMP+ was inte­grated using the CAESES® Software Con­nec­tor for the analysis of the gen­er­ated geometry variants. The geometry is exported in STL Extract Colors format, where each color defined for a part of the geometry in CAESES® is exported into a separate STL file. This allows Simer­ic­sMP+ to easily identify boundary patches and keep the asso­cia­tiv­ity to related settings (e.g. mesh settings or boundary con­di­tions), allowing auto­mated mesh regen­er­a­tion. The sim­u­la­tion setup is carried out in the Simer­ic­sMP+ user inter­face once and saved in the *.spro file, which will sub­se­quently be exported by CAESES® for each variant.

On the result side, an Ensight Gold file with the complete flow field is imported, as well as a text file with the time history of inte­grated values. The latter is used to extract the objec­tive for the opti­miza­tion, the flow rate.

Opti­miza­tion Process and Results

The overall opti­miza­tion process was struc­tured in three phases. In the first step, a pre­lim­i­nary DoE that included all 9 para­me­ters for one of the two ports (A) was carried out with 100 design variants. Based on those results, the 4 most influ­en­tial para­me­ters, i.e., the para­me­ters with the strongest cor­re­la­tion to the objec­tive function (box rotation, box shift, outer radius and bottom fillet radius), were iden­ti­fied and selected for a second DoE. Here, the selected para­me­ters were applied to both ports (A and P) and 90 designs were analyzed. Finally, in the last step, a local opti­miza­tion was carried out starting from the best design of the pre­ced­ing DoE and the same set of para­me­ters and an addi­tional 50 designs.

Compared to the baseline design, the opti­mized design at the end of this process showed an improve­ment of about 9% in mass flow rate, whereby the DoE phase con­tributed 7% and the final local opti­miza­tion an addi­tional 2%. While not being a strict con­straint, the volumes of the ports were mon­i­tored during the opti­miza­tion process, but all con­sid­ered variants were well within the tol­er­ated bounds. Exper­i­men­tal tests, carried out at the Indus­trial Engi­neer­ing Depart­ment of the Uni­ver­sity of Naples, con­firmed the results of the opti­miza­tion. It is also worth noting that the design had pre­vi­ously been opti­mized with a manual iter­a­tive pro­ce­dure by the Indus­trial Engi­neer­ing Depart­ment reaching similar results, albeit within a time frame of several months vs. a few days for the auto­mated pro­ce­dure executed with CAESES®.

More infor­ma­tion

The full paper about this case study can be accessed under this link.

A further case study about the opti­miza­tion of a very large DN700 control valve using CAESES and Autodesk CFD can be found here.