Towards an Analytical, Computational and Experimental Framework for Predicting Aging of Cathodic Surfaces

2017 ◽  
Author(s):  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
John C. Steuben ◽  
Virginia DeGiorgi
Keyword(s):  
First Principles ◽  
Thermodynamic Theory ◽  
Data Driven ◽  
Far Field ◽  
Experimental Task ◽  
Computational Framework ◽  
Impressed Current ◽  
Aging Models ◽  
Impressed Current Cathodic Protection ◽  
Cathodic Surface

The present work introduces the motivation, architecture and preliminary analytical and computational framework that will eventually lead to aging predictions of cathodic surfaces along with its implications on impressed current cathodic protection (ICCP) systems. This is necessary for adjusting ICCP systems in a manner that reflects the dissipative nature of cathodic surface assemblies while at the same time enabling potential electric far field requirements. We describe various approaches for developing Cathodic Surface Aging Models (CSAMs) based on both data-driven and first principles based methodologies. A computational ICCP framework is implemented to account for cathodic aging in a manner that allows the utilization of various CSAMs. An application of this framework demonstrates the applicability of the implications of the variability of the polarization curves as it is associated with cathodic surface aging. In addition to a data-driven CSAM based on a loft-surface approximation we also introduce a first principles thermodynamic theory for aging and the design of a systematic experimental task for validating and calibrating this theory.

scholarly journals On the Multiphysics Modeling of Surface Aging Under Cathodic Protection

2018 ◽  
Vol 18 (3) ◽  
Author(s):  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
John C. Steuben ◽  
Virginia DeGiorgi
Keyword(s):  
Cathodic Protection ◽  
First Principles ◽  
Data Driven ◽  
Multiphysics Modeling ◽  
Cathodic Materials ◽  
Impressed Current ◽  
Aging Models ◽  
Near Future ◽  
Impressed Current Cathodic Protection ◽  
Cathodic Surface

In order to account and compensate for the dissipative processes contributing to the aging of cathodic surfaces protected by impressed current cathodic protection (ICCP) systems, it is necessary to develop the proper modeling and numerical infrastructure that can predict aging associated with quantities affecting the controller of these systems. In the present work, we describe various approaches for developing cathodic surface aging models (CSAMs) based on both data-driven and first principles-based methodologies. A computational ICCP framework is implemented in a manner that enables the simulation of the effects of cathodic aging in a manner that allows the utilization of various CSAMs that affect the relevant potentiodynamic polarization curves of the cathodic materials. An application of this framework demonstrates the capabilities of this system. We introduce a data-driven CSAM based on a loft-surface approximation, and in response to the limitations of this approach, we also formulate a first principles-based multiphysics and thermodynamic theory for aging. Furthermore, we discuss the design of a systematic experimental task for validating and calibrating this theory in the near future.

Towards a Computational Multiphysics Framework for Modeling Antibiofouling Processes

2014 ◽  
Author(s):  
Athanasios Iliopoulos ◽  
John G. Michopoulos ◽  
Virginia DeGiorgi ◽  
Steven Policastro
Keyword(s):  
Design Methodology ◽  
Field Experiments ◽  
Hydrodynamic Drag ◽  
Computational Framework ◽  
Actual Field ◽  
Cleaning Procedures ◽  
Impressed Current ◽  
Spatio Temporal ◽  
Cathodic Protection System ◽  
Impressed Current Cathodic Protection

Biofouling is a process of major concern on naval vessels because it considerably affects their performance, maintenance and operational costs due to the fact that induces an increased hydrodynamic drag that leads to higher fuel consumption that in turn demands expensive cleaning procedures. A possible antibiofouling system can be designed by enhancing an existing impressed current cathodic protection system and taking advantage of the chlorine oxidants produced during its operation. In this work we present a design methodology for such a system, together with the associated multiphysics formulation framework based on a coupled chemical reactions — electric currents, species mass transport and electromigration model. This framework predicts the spatio-temporal distributions of the Chlorine species concentration that tend to inhibit the biofouling formations. We also demonstrate the applicability of the computational framework on a number of platforms ranging from simple panels up to a full scale boat. The computational results are compared with the actual field experiments.

scholarly journals A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weijian Ge ◽  
Vito L. Tagarielli
Keyword(s):  
Constitutive Models ◽  
Computational Procedure ◽  
Data Driven ◽  
Dissipated Energy ◽  
Computational Framework ◽  
Heterogeneous Solids ◽  
Non Linear ◽  
Recorded Data ◽  
Path Dependent ◽  
Computational Homogenisation

AbstractWe propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.

scholarly journals Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

2021 ◽  
Vol 7 (15) ◽  
pp. eabe4166
Author(s):  
Philippe Schwaller ◽  
Benjamin Hoover ◽  
Jean-Louis Reymond ◽  
Hendrik Strobelt ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Neural Networks ◽  
Chemical Synthesis ◽  
Unsupervised Learning ◽  
Chemical Reactions ◽  
Data Driven ◽  
Experimental Task ◽  
Rule Based ◽  
Atom Mapping ◽  
Mapping Information

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

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