This year at Realize Live 2025, we saw once again the importance of digital transformation and how it is helping industries and companies to accelerate their design process and reduce time to market. We have heard “Time is money,” and although that is true, something that is missed by this saying is: How long will my design last?
Here is some interesting information. Jeff Morwy, Chief Information Officer from Workhorse Group Inc., reduced the cost by 50% using cloud-based solutions versus on-premises for their PLM solution. Another interesting user demonstrated a complete multiphysics system, utilizing multi-objective optimization, where they significantly reduced the weight of a satellite. We heard multiple successful stories like this, and the main takeaway is twofold. First, we observe that companies are agile in their adoption of solutions. They require on-premises solutions with specific capabilities, but also need cloud access to reduce the complexity and cost of their IT departments, while maintaining their competitive edge. Lastly, we currently have the capabilities to work with multiple systems in a tightly coupled manner, increasing the fidelity of our numerical models and digital twins.
In our view, for a digital transformation, we need to start thinking about materials not only from a mechanical standpoint but also from an electrochemical perspective. This is like a pizza, you can’t call it pizza if you don’t have cheese, and the famous “Neapolitan sauce” (my mentor will argue with me for sure). In these presentations, we saw multiple times that materials and systems were considered only at their design conditions, but in reality, “all materials degrade and become damaged”; in other words, everything corrodes. So, the puzzle still misses a piece, an electrochemical database, and we at Corrdesa are working to put it on the right place.
The workflow we have developed is compatible with your PLM solution and Siemens products, although it can also be deployed on non-Siemens platforms. Our primary fuel is our database, highly validated and compliant with MIL-STD-889D.
This year, we demonstrated our end–to–end workflow, which you can utilize at various stages of your engineering process, from design through to sustainment. The same database, managed by our Corrosion Database Management Platform, Corrosion Djinn, is available for you for multiple fidelity models. It comes with MIL-STD-889D and 3.5% NaCl environments. Corrosion Djinn helps you update the material database in Siemens NX and Simcenter STAR-CCM+ with a single click.
Figure 2 shows the entire spectrum of corrosion assessment solutions. On the left, we see our engine, which makes all these possible, Corrosion Djinn. Users can start using this early in the process, even at the conceptual stage, when they want to screen materials. We offer both a cloud version that you can access from your phone and a standalone version. This is followed by our integrated solution, Siemens NX, where you can quickly check thousands of face pairs, and it will return the corrosion risks for a full CAD assembly. On the two right solutions, we have the Corrosion Toolset CFD (Computational Fluids Dynamics) templates solutions, and the 3D full multi-physics capabilities. The former is for those who need the fidelity of CFD but are not CFD engineers. In this case, we have created templates that allow users to select from a variety of canonical geometries. The latter is the full 3D Multiphysics, where, in theory, you don’t have limits. The main advantage is that you can integrate this into your stress analysis and thermal fluid. Still, this time you will have another KPI (Key Performance Indicator) in your optimization algorithm. Do you imagine asking your algorithm to reduce weight, increase lift, increase heat transfer, while also reducing the corrosion rate? That is what we call a truly digital twin. You can also include what-if scenarios. For example, what happened to my system when coatings or paint systems were damaged?
Although the database remains the same, the numerical procedure for predicting corrosion rates differs significantly. Our faster CAE solution, NX Corrosion Risk Indicator, checks interfaces and pulls data from Corrosion Djinn. This translates into a query that takes seconds or minutes if you have thousands of interfaces. For example, check the following analysis from a landing gear. In Figure 3, we see that the Corrosion Risk Indicator shows the corrosion “Hot-Spots” while providing the galvanic corrosion, along with the MIL-STD-889D Corrosion Rating. This solution does not solve any conservation equation; therefore, its central assumption is that the anode and cathode areas are equal. Nonetheless, the level of complexity is almost null, and the only input you need is the CAD model, where you will need to assign the material/coating information.
If we used CFD, how different would be the results? Let’s find out!
Figure 4 shows the Corrosion rate prediction when using the same database, but in Simcenter Star-CCM+, a CFD solver with the Corrosion Djinn database fully integrated. The first evident difference is that the maximum corrosion rate prediction is almost twice as low in the CFD solution. The body of the Landing gear (Coated in Zn-Ni) corrodes at a rate of around 183 microns/year, according to the CFD results, while the NX Corrosion indicator shows a corrosion rate of around 429 microns/year. The reason for the difference is the Anode-Cathode area ratio. In the CFD solver, we don’t make any assumptions up front. Instead, we explicitly address those nuances, resulting in improved predictive capability. This comes with a string attached; you need to be familiar with CFD workflows, and hence the complexity behind pre-processing and meshing. However, we firmly believe this CFD workflow is an excellent option for those cases where the limitation of the Anode-Cathode ratio is substantial or where you want to predict coating degradation.
As I initially mentioned, companies are now moving at a faster pace, and the reason is the challenging and demanding market. To stay ahead of the game, you must find and utilize the right tool. We don’t think “One tool fit all”, and instead we strongly believe the only way to reduce the time-to-market cost-effectively is to embrace different fidelity solutions at the right time in the engineer’s project timeline. Ultimately, we utilize CAE models to make informed decisions, and for that, we require the right tools, along with the appropriate data.
Happy computations !! and “Muchas Gracias”
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