Hydro-Acoustic and Hydro­dy­namic Opti­miza­tion of a Sub­ma­rine Bow Form

This blog post is part of our CAESES Student Award 2020 com­pe­ti­tion about the use of CAESES in academia, where we showcase exciting material sub­mit­ted to us by students who use CAESES in their research. Many students from high school to PhD make use of CAESES to reach their project goals. If you are one of them, we encour­age you to send us an article about your project and how CAESES has helped you. Inter­est­ing articles will be posted reg­u­larly in our blog, along with some infor­ma­tion about the author, and at the end of February 2021, we will select the best author, who will win some exciting prizes.

The under­wa­ter resis­tance and radiated noise char­ac­ter­is­tics of sub­marines are espe­cially impor­tant per­for­mance metrics and should be opti­mized as much as possible. The main goal of this study is to enhance hydro-acoustics and hydro­dy­nam­ics per­for­mance of a sub­ma­rine hull with bow form opti­miza­tion using high-fidelity CFD sim­u­la­tions and an auto­mated workflow.

Intro­duc­tion

The origins of sub­ma­rine self-produced noise falls under three general cat­e­gories. Pro­peller noise is the noise which orig­i­nates from the submarine’s screws when the speed is great enough to produce cav­i­ta­tion. Hydro­dy­namic noise includes all the noise sources which result from the motion of the sub­ma­rine through the water. Machin­ery noise is the noise result­ing from the propul­sion, maneu­ver­ing and aux­il­iary machin­ery onboard the sub­ma­rine. Hydro­dy­namic noise is the dominant source and is the main subject of this study. The sub­ma­rine model is based on a standard bench­mark geometry called the DARPA SUBOFF.

This study describes the devel­op­ment of an iter­a­tive design process to reduce hydro­dy­namic noise levels using a high-fidelity CFD solver STAR-CCM+ which solves the unsteady RANS for the flow behavior and the Ffowcs-William and Hawkings (FW‑H) Equa­tions for hydro-acoustics. The para­met­ric hull geometry has been created in CAESES where hull vari­a­tions can be created and utilized within the auto­mated workflow. The bow form has been para­me­ter­ized using the fol­low­ing equation which creates an axisym­met­ric curve:

The multi-objec­tive opti­miza­tion aims to reduce the total resis­tance of the hull as well as the acoustic noise gen­er­ated one meter behind the pro­peller hub. The opti­miza­tion method chosen had an initial DoE step using the Sobol algo­rithm from which the results were used as an input to build a sur­ro­gate model using the well-known open source Python library Lin­earND­In­ter­po­la­tor method. Finally, the NSGA-II algo­rithm was used to solve for the objec­tive func­tions. CAESES includes a library of algo­rithms such as Sobol and NSGA-II. However, the Lin­earND­In­ter­po­la­tor method was imple­mented through python scripts and coupled to CAESES via its con­ve­nient feature cus­tomiza­tion capability.

Numer­i­cal Model

Resis­tance and hydro-acoustics were solved with STAR-CCM+ using steady and unsteady solvers respec­tively. The SST k-[latex]\omega [/​latex] was used to model tur­bu­lence. The time depen­dent pressure data is used as the input for the FW‑H equation to predict far-field acoustics. A hexa­he­dral mesh was created around the sub­ma­rine model with trimmed cells at the hull surfaces which were locally refined to have y+ values of ~100 for all veloc­i­ties studied, as shown below.

Opti­miza­tion Workflow

The first step was to run a Design-of-exper­i­ments (DoE) using the Sobol algo­rithm avail­able in CAESES. For the resis­tance analysis 400 variants were studied, and for the hydro-acoustics analysis another 40 variants were computed. This data was used as the input to build the sur­ro­gate model in the next step.

The sur­ro­gate model was created using the Lin­earND­In­ter­po­la­tor function to create the response surface.

Finally, after creating the response surface for resis­tance and hydro-acoustics, the opti­miza­tion was per­formed using the NSGA-II algo­rithm with 10 gen­er­a­tions that have pop­u­la­tions sizes of 50 variants.

Results and Conclusions

The 21 best designs were further inves­ti­gated as a trade off between optimal resis­tance and hydro-acoustics. The fol­low­ing figure shows a com­par­i­son of the bow geometry and two of the best designs which rep­re­sent best resis­tance and best acoustics per­for­mance, respec­tively. Resis­tance per­for­mance was improved by as much as 5.6% whereas acoustic per­for­mance could be improved by up to 3.5%.

About the Author

Thanks to Buğra Uğur Yazici and his col­leagues at Istanbul Tech­ni­cal Uni­ver­sity in Istanbul, Turkey for sub­mit­ting this inter­est­ing article about sub­ma­rine hydro­dy­nam­ics and hydro-acoustics per­for­mance opti­miza­tion using the CAESES inte­gra­tion and automa­tion platform together with STAR-CCM+ for CFD simulations.

Buğra Uğur Yazıcı grad­u­ated from the Turkish Naval Academy with a major in Naval Archi­tec­ture and Marine Engi­neer­ing. After fin­ish­ing his Master Degree in the same field, he is now doing his PhD at Istanbul Tech­ni­cal Uni­ver­sity. He is simul­ta­ne­ously working for STM A.Ş. in Turkey in the Mid-Life Upgrade Project of Preveze-Class Sub­marines of the Turkish Navy. His research field is hydroa­coustic and hydro­dy­namic per­for­mance opti­miza­tion of the sub­ma­rine exostruc­ture. He is devel­op­ing algo­rithms for imple­ment­ing acoustic analo­gies to dif­fer­ent solvers, includ­ing high order spectral methods and opti­miz­ing tur­bu­lence coef­fi­cients in order to solve acoustic equa­tions efficiently.

More Infor­ma­tion

If you like to read more about this topic, you can download the full paper here.

For further ques­tions do not hesitate to contact Buğra Uğur Yazıcı directly (bugur.yazici@gmail.com).