The United States Navy’s stated force-structure goal is a 500-ship fleet by 2045, a significant build-up from today’s 293 ships. The expanded surface fleet will be designed to meet this goal with operational capabilities, and within operational environments, that have evolved since the last surge of naval shipbuilding in the 1980s. These new challenges will likely drive increased diversity in the design characteristics of future surface combatants—a diversity requiring tools able to rapidly inform set-based designs with high-confidence predictions. This research provides such a tool. Utilizing machine learning, the residual resistance coefficients of a new hull form, across a desired range of speed-to-length ratios, are accurately predicted. With parametric, nondimensional hull form coefficients and ratios from historically tested ship hull methodical series, parametric, non-parametric, and ensemble method algorithms were trained, validated, and tested. The research analyzes the necessity of first classifying a hull design to an established methodical series prior to performing regression efforts to predict resistance. Conversely, the research also analyzes the benefit of machine learning regression algorithms to predict resistance from an entire hull design data set, without first classifying. Finally, the research demonstrates the power of machine learning in quickly adapting to small amounts of additional training data to accurately refine the predictions. The intersection of machine learning and hydrodynamics is a little-explored frontier that, even in its infancy, provides dynamic resistance predictions that are more accurate than Holtrop’s statistical method with a speed dependent form factor. Through the establishment of a shared hull form geometry and performance repository, the true power of machine learning may be unleashed, removing the most challenging of obstacles associated with preliminary hull form design.

Machine Learning in the Prediction of Displacement Hull Form Resistance

**Moderator:** [Jennifer Williams](https://www.underline.io/speakers/80155-jennifer-williams)

**Speakers:**

[Christopher Hevey](https://www.underline.io/speakers/77541-christopher-hevey)

[Talya Stehley](https://www.underline.io/speakers/79709-talya-stehley)

[Jeff Byers](https://www.underline.io/speakers/77554-jeff-byers)

[Maurice Ayache](https://www.underline.io/speakers/77569-maurice-ayache)

[Adam Moses](https://www.underline.io/speakers/77534-adam-moses)

Modeling of Physical Phenomena

technical paper

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Christopher Hevey

1

SHORT BIO

Presentations

Machine Learning in the Prediction of Displacement Hull Form Resistance

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES