E. Martínez-Pañeda

R. Das, B. Bezensek, E. Martínez-Pañeda

Repurposing natural gas pipelines for hydrogen transport requires understanding how external defects, like dents and gouges, affect structural integrity under H$_2$ exposure. To address this, we combine experiments with a new hydrogen embrittlement model aimed at large plastic straining scenarios, which integrates: (i) multi-trap hydrogen transport, (ii) finite-strain plasticity, and (iii) a hydrogen- and triaxiality-dependent damage law. Each constituent of the model is validated with experiments on X65 pipeline steel: (i) hydrogen permeation, (ii) full-scale pipe-indentation, and (iii) mechanical testing at different hydrogen and triaxiality levels. The validated model is used to study \textit{passive} (indent before H$_2$ exposure) and \textit{active} (indent with H$_2$) dents and gouges. Results reveal that hydrogen does not significantly increase the damage severity of those defects, unless hydrogen egress is completely precluded at the outer surface of a pipeline that is being pressurised internally and contains a pre-existing \textit{passive} dent with a gouge.

J. C. García-Merino, C. Calvo-Jurado, E. Martínez-Pañeda et al.

This paper presents a surrogate modelling technique based on domain partitioning for Bayesian parameter inference of highly nonlinear engineering models. In order to alleviate the computational burden typically involved in Bayesian inference applications, a multielement Polynomial Chaos Expansion based Kriging metamodel is proposed. The developed surrogate model combines in a piecewise function an array of local Polynomial Chaos based Kriging metamodels constructed on a finite set of non-overlapping subdomains of the stochastic input space. Therewith, the presence of non-smoothness in the response of the forward model (e.g.~ nonlinearities and sparseness) can be reproduced by the proposed metamodel with minimum computational costs owing to its local adaptation capabilities. The model parameter inference is conducted through a Markov chain Monte Carlo approach comprising adaptive exploration and delayed rejection. The efficiency and accuracy of the proposed approach are validated through two case studies, including an analytical benchmark and a numerical case study. The latter relates the partial differential equation governing the hydrogen diffusion phenomenon of metallic materials in Thermal Desorption Spectroscopy tests.

S. Luza-Vega, Y. Zhao, E. Martínez-Pañeda

Cathode particle fracture is widely recognised as a major degradation mechanism in lithium-ion batteries, yet cracking also permits electrolyte wetting of newly exposed internal surfaces, modifying interfacial reaction pathways. The mechanistic role of electrolyte wetting in redistributing reactions within cracked particles remains unclear. Here, we isolate this effect through a controlled comparison between (i) a fully coupled electro-chemo-mechanical model resolving lithium concentration, electrostatic potential, and stress fields in both the active material and the electrolyte inside and outside cracks, and (ii) a single-particle chemo-mechanical model employing the conventional uniform flux assumption. The coupled model predicts strong spatial heterogeneity in interfacial reaction rates, with flux amplification approximately 8x relative to the imposed uniform flux at the crack tip. Reaction redistribution, and thus lithium flux, is governed predominantly by local solid-state lithium concentration and stress variations, while electrolyte potential gradients inside cracks remain secondary under the conditions considered. Uniform flux models can underpredict delivered capacity by 25% at 1C-rate; this discrepancy increases at higher rates. They also underestimate tensile stresses throughout the delithiation process by 10%, directly affecting crack driving conditions. These results demonstrate that neglecting crack-electrolyte coupling leads to systematic underestimation of both utilisation limits and fatigue-relevant stress histories.

N. Marrani, T. Hageman, E. Martínez-Pañeda

The hydrogen trapping behaviour of metallic alloys is generally characterised using Thermal Desorption Spectroscopy (TDS). However, as an indirect method, extracting key parameters (trap binding energies and densities) remains a significant challenge. To address these limitations, this work introduces a machine learning-based scheme for parameter identification from TDS spectra. A multi-Neural Network (NN) model is developed and trained exclusively on synthetic data to predict trapping parameters directly from experimental data. The model comprises two multi-layer, fully connected, feed-forward NNs trained with backpropagation. The first network (classification model) predicts the number of distinct trap types. The second network (regression model) then predicts the corresponding trap densities and binding energies. The NN architectures, hyperparameters, and data pre-processing were optimised to minimise the amount of training data. The proposed model demonstrated strong predictive capabilities when applied to three tempered martensitic steels of different compositions. The code developed is freely provided.