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Intelligent Corrosion Simulation and Design Tool


RT&L FOCUS AREA(S): Machine Learning/AI

TECHNOLOGY AREA(S): Ground / Sea Vehicles

OBJECTIVE: Develop an Intelligent Corrosion Simulation and Design Tool that will read Computer Aided Design (CAD) drawings, select the corrosion modes that the materials are likely to encounter, and assign the service environment to the selected computational engine.

DESCRIPTION: Currently available commercial options for computational corrosion modeling are based on modeling approaches that require detailed materials science knowledge for the end users, apply narrowly focused subset of relevant corrosion modes for materials of interest, and are not context sensitive to select which modes of corrosion are most likely for materials of interest.

The cost of corrosion to the Naval fleet exceeds $9.5B/year, with 40% of that cost avoidable with improved corrosion design. The current state of the art for computational corrosion simulations requires the end user to have advanced knowledge of materials science, service environment chemistry and corrosion countermeasure options. Designing warship subsystems for corrosion cost avoidance requires detailed knowledge of construction material performance to each mode of corrosion damage, service environments in which the materials are intended to be used, available corrosion countermeasure technologies, and ownership costs associated with these decisions.

The Navy seeks development of an intelligent modeling environment, which is agnostic to the source of materials properties data allowing user-definable corrosion materials properties, user-definable corrosion modes/mechanisms, user-definable behavioral relationships between properties & environmental stressors; and provides an integrating platform to connect these corrosion modes/mechanisms to specific materials & geometries read from Computer Aided Design (CAD) data inputs. This would create a corrosion information ecosystem allowing corrosion behavior modes/mechanism relationships to be developed under a technical community crowd-sourcing paradigm, and aid in the development of an integrated Naval corrosion simulation paradigm. The Navy intends to leverage the skills and expertise of a broad base of materials science specialists from academia, industry, and DoD subject matter experts in creating a diverse toolbox of available corrosion simulation engines.

The objective of this SBIR topic is to create an intelligent corrosion tool that can store (and retrieve) a complex dataset along with key materials information and use cases that would trigger selection of specific corrosion simulation engine. The tool would also create an interface to assemble the information from a designer’s CAD drawing/modeling environment in order to implement the proper corrosion simulation engine. Specifically, the tool must adequately incorporate modules that accommodate: (1) the materials of interest, derived from CAD packages, (2) the service environment corrosion severity, (3) mechanisms of material corrosion and driving physical parameters for such, and (4) handoff parameters for incorporating these mechanisms into external modeling codes.

Implementing advanced analytics into warship design requires simplifying access to the simulation engines that can perform these analyses. This intelligent tool will have the capacity to read a designer’s drawing and extract the key information parameters that may be required to hand over to a corrosion simulation engine. The tool will have capabilities to down select which CAD dimensions, materials, coatings, corrosion countermeasures, etc. are required to evaluate the design against a specific mode of corrosion attack. The tool will also house a cursory analysis module that allows a design engineer to evaluate which modes of corrosion attack are most likely in the specified design, prior to conducting rigorous simulations to determine their severities.

This effort will leverage the Navy-owned materials database as well as materials data or behavioral characteristics to the corrosion database from academia, industries and DoD partners through an interface provided by the developer. The intelligent tool will have clear guidelines on how the data or algorithm must be implemented to be of value to the Navy and provide a means to assess cost avoidance through improved design changes.


1) Develop a concept for an Intelligent Corrosion Tool that will develop and demonstrate a computational database architecture that can store and retrieve user-specified material properties and behavior equations for specific materials corrosion modes; and is searchable in context of the material, corrosion mode, corrosivity of the environment, and other user-definable contextual parameters.

2) Demonstrate the ability to gather key geometry and materials information from a component drawing file, reading Standard Triangle Language (STL)-based drawings designed in commercial CAD software.

3) Allow designation of a “Service Zone” or “Service Environment” based on selecting service parameters from a diagram of a ship/submarine diagram where the component is intended to operate or corrosion severity zone selection. Extract and assemble key information required to exercise corrosion simulation models.

4) Demonstrate the ability to read multiple CAD drawings, identify materials and potential corrosion modes, automatically prepare model preprocessing files, and interface files for commercial modeling tools including geometry and modeling parameters.

5) Demonstrate the ability to capture cost avoidance data from corrosion countermeasures simulation results.

6) Incorporate logic to evaluate drawings/designs against the US Navy’s Corrosion Control and Design Criteria Manual – a wide ranging design document that outlines best practices for robust designs and corrosion cost avoidance.

As part of Phase III, the products will be included in the anticipated Future Naval Capability (FNC) program as a key component that can be utilized by ship designers to enable corrosion-informed materials selection and design.

PHASE I: Develop a concept for a tool that will satisfy requirements 1, 2, and 3 in the Description.

Perform testing and certification using materials properties and drawings supplied by the Navy. Demonstration must include exercising the Intelligent Corrosion Tool against a prototypical working CAD model of a section of the ship’s hull and cathodic protection system to capture corrosion interactions between wetted materials. The Intelligent Corrosion Tool will then return this information to the user in a distilled format. Phase I Option, if exercised, would include the initial layout and capabilities description to build the unit in Phase II.

PHASE II: Based on the results of the Phase I and Phase II Statement of Work (SOW), develop the Intelligent Corrosion Tool that incorporates requirements 4, 5, and 6 in the Description.

Testing and verification for the tool will include analysis of prototypical CAD drawings and comparison against the user-provided materials properties/corrosion modes database. Successful outcomes will involve selecting multiple potential corrosion modes for the materials and geometries included in the CAD drawings, and down select the most likely corrosion mechanism occurrences in the presented scenarios based on the CAD drawings and user-supplied materials database. The tool will then compile the necessary information in order to hand off corrosion mode simulations to commercial/Navy specific analytical packages that are consumers of pre-packaged information provided by the Intelligent Corrosion Tool.

PHASE III DUAL USE APPLICATIONS: Dual Use Applications for the Intelligent Corrosion Tool will naturally evolve from a demonstrated ability to incorporate corrosion cost avoidance into design practices. Engineering design processes for naval warships are similar to engineering design processes for non-military vessels, and many partners that design/build components for the Naval fleet also design/build components for non-military customers, such as automotive, aerospace, oil & gas, and piping industries.


  1. Taylor, Christopher. “Corrosion Informatics: An Integrated Approach to Modeling Corrosion.” Corrosion Engineering, Science and Technology, 2015.  
  2. Koch, G., Ayello, F., Sridhar, N., Khare, V., Al-Mathen, A. W., and Safri, S. “Internal Corrosion Threat Assessment of Pipelines Using Bayesian Network Models.” Corrosion 2014 Conference, San Antonio, TX; 2014, NACE International.  
  3. Anderko, A. “2.38: Modeling of Aqueous Corrosion.” Shreir's Corrosion, Elsevier: 2010; pp 1585-1629.  
  4. Palani, S.; Hack, T.; Deconinck, J. and Lohner, H. “Validation of predictive model for galvanic corrosion under thin electrolyte layers: An application to aluminum 2024-CFRP material combination.” Corrosion Science 2014, 78, pp. 89-100.
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