scholarly journals Future Frontiers in Corrosion Science and Engineering, Part III: The Next “Leap Ahead” in Corrosion Control May Be Enabled by Data Analytics and Artificial Intelligence

CORROSION ◽  
10.5006/3432 ◽  
2019 ◽  
Vol 75 (12) ◽  
pp. 1395-1397 ◽  
Author(s):  
John R. Scully ◽  
Prasanna V. Balachandran
Keyword(s):  
Artificial Intelligence ◽  
Data Analytics ◽  
Corrosion Control ◽  
Science And Engineering ◽  
Corrosion Science

Corrosion chemistry closing comments: opportunities in corrosion science facilitated by operando experimental characterization combined with multi-scale computational modelling

2015 ◽  
Vol 180 ◽  
pp. 577-593 ◽  
Author(s):  
John R. Scully
Keyword(s):  
Ad Hoc ◽  
Kinetic Modelling ◽  
Computational Modelling ◽  
Molecular Surface ◽  
Experimental Characterization ◽  
Corrosion Control ◽  
Science And Engineering ◽  
Complex Alloy ◽  
Corrosion Science ◽  
Physical And Chemical

Recent advances in characterization tools, computational capabilities, and theories have created opportunities for advancement in understanding of solid–fluid interfaces at the nanoscale in corroding metallic systems. The Faraday Discussion on Corrosion Chemistry in 2015 highlighted some of the current needs, gaps and opportunities in corrosion science. Themes were organized into several hierarchical categories that provide an organizational framework for corrosion. Opportunities to develop fundamental physical and chemical data which will enable further progress in thermodynamic and kinetic modelling of corrosion were discussed. These will enable new and better understanding of unit processes that govern corrosion at the nanoscale. Additional topics discussed included scales, films and oxides, fluid–surface and molecular–surface interactions, selected topics in corrosion science and engineering as well as corrosion control. Corrosion science and engineering topics included complex alloy dissolution, local corrosion, and modelling of specific corrosion processes that are made up of collections of temporally and spatially varying unit processes such as oxidation, ion transport, and competitive adsorption. Corrosion control and mitigation topics covered some new insights on coatings and inhibitors. Further advances inoperandoorin situexperimental characterization strategies at the nanoscale combined with computational modelling will enhance progress in the field, especially if coupling across length and time scales can be achieved incorporating the various phenomena encountered in corrosion. Readers are encouraged to not only to use thisad hocorganizational scheme to guide their immersion into the current opportunities in corrosion chemistry, but also to find value in the information presented in their own ways.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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