Modeling Corrosion, Atom by Atom

corrosion_atom_by_atomAn article by Christopher D. Taylor in the latest issue of Interface.

In the late 20th century, computer programs emerged that could solve the fundamental quantum mechanical equations that control the interactions of atoms that give rise to bonding. These tools, first applied to molecules and bulk solid materials, then began to be applied to surfaces and, in the early 21st century, to electrochemical environments. Commercial and open-source programs are now readily available and can be used on both desktop and high-performance computing platforms to solve for the electronic structure of a given configuration of atomic centers (nuclei) and, in so doing, provide the basis for determining a whole host of properties, including electronic and vibrational spectra, electrical moments such as the system dipole, and, most importantly, the energy and forces on the atoms. Other derived properties include the extent to which each atom is charged and bond-orders, although to compute these latter properties one of a variety of methods for dividing up and quantifying the electron density associated with each atom must be selected.

The physics behind these codes is complex, and, challengingly, has no rigorous analytical solution that can be obtained within a finite allotment of time. Thus, the computer programs themselves take advantage of approximations that allow for a feasible solution but, at the same time, constrain the accuracy of the result. Nonetheless, solutions can usually be reliably obtained for model systems representing materials, interfaces, or molecules that do not exceed thousands, and, more realistically, hundreds of atoms. Given that system sizes of hundreds or thousands of atoms amount to no more than the smallest nanoparticle of a substance, the question arises: What can atomistic simulations teach us about corrosion?

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corrosion_blog_interfaceAn article by Kenji Amaya, Naoki Yoneya, and Yuki Onishi published in the latest issue of Interface.

Protecting structures from corrosion is one of the most important challenges in engineering. Cathodic protection using sacrificial anodes or impressing current from electrodes is applied to many marine structures. Prediction of the corrosion rates of structures and the design of cathodic protection systems have been traditionally based on past experience with a limited number of empirical formulae.

Recently, application of numerical methods such as the boundary element method (BEM) or finite element method (FEM) to corrosion problems has been studied intensively, and these methods have become powerful tools in the study of corrosion problems.

With the progress in numerical simulations, “Inverse Problems” have received a great deal of attention. The “Inverse Problem” is a research methodology pertaining to identifying unknown information from external or indirect observation utilizing a model of the system.

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computer_simulation2An article by N.J. Laycock, D.P. Krouse, S.C. Hendy, and D.E. Williams published in the latest issue of Interface.

Stainless steels and other corrosion resistant alloys are generally protected from the environment by ultra-thin layers of surface oxides, also called passive films. Unfortunately, these films are not perfect and their Achilles’ heel is a propensity to catastrophic local breakdown, which leads to rapid corrosion of the metallic substructure. Aside from the safety and environmental hazards associated with these events, the economic impact is enormous.

In the oil and gas and petrochemical industries, it is of course usually possible to select from experience a corrosion-resistant alloy that will perform acceptably in a given service environment. This knowledge is to a large extent captured in industry or company-specific standards, such as Norsok M1.

However, these selections are typically very conservative because the limits tend to be driven by particular incidents or test results, rather than by fundamental understanding. Decision-making can be very challenging, especially in today’s mega-facilities, where the cost of production downtime is often staggeringly large. Thus significant practical benefits could be gained from reliable quantitative models for pitting corrosion of stainless steels. There have been several attempts to develop purely stochastic models of pitting corrosion.

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