arXiv subjects

Colin Weaver

Publications and source records attributed to Colin Weaver.

2 recordsLinked to original sources

arXiv2026

SHIELD: A Reference Gas-Driven Permeation Platform for Hydrogen Permeation Studies

A gas-driven permeation (GDP) platform, SHIELD (Salt-compatible Hydrogen barrier Investigation and EvaLuation for fusion Devices), has been developed to measure hydrogen transport properties in structural materials under controlled thermal and pressure conditions. The system is designed to minimise experimental uncertainties associated with leaks, temperature instability, and pressure measurement, while providing reproducible conditions for permeation experiments. The rig operates in a static GDP configuration with independent upstream and downstream volumes, enabling precise control of driving pressure and accurate measurement of downstream pressure rise. An openly documented data acquisition and processing framework is implemented to ensure data traceability and reproducibility. The platform's performance is demonstrated by hydrogen permeation measurements on 316 stainless steel and AISI 1018 low-carbon steel over the temperature range of \SIrange{100}{600}{\degreeCelsius}. Steady-state permeation fluxes are extracted from linear downstream pressure rise and used to determine permeability. The measured permeability exhibits Arrhenius behaviour and agrees well with published literature data for both materials. Permeability measurements are shown to be robust and reproducible. These results demonstrate that SHIELD provides a reliable reference platform for hydrogen permeation measurements and is well-suited to evaluating permeation barrier coatings and advanced materials for fusion applications.

physics.ins-det↗

arXiv2022

FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames

This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant bicycle frame design and demonstrate across-the-board dominance by AutoML in regression and classification surrogate modeling tasks. We also introduce FRAMED -- a parametric dataset of 4500 bicycle frames based on bicycles designed by practitioners and enthusiasts worldwide. Accompanying these frame designs, we provide ten structural performance values such as weight, displacements under load, and safety factors computed using finite element simulations for all the bicycle frame designs. We formulate two challenging test problems: a performance-prediction regression problem and a feasibility-prediction classification problem. We then systematically search for optimal surrogate models using Bayesian hyperparameter tuning and neural architecture search. Finally, we show how a state-of-the-art AutoML method can be effective for both regression and classification problems. We demonstrate that the proposed AutoML models outperform the strongest gradient boosting and neural network surrogates identified through Bayesian optimization by an improved F1 score of 24\% for classification and reduced mean absolute error by 12.5\% for regression. Our work introduces a dataset for bicycle design practitioners, provides two benchmark problems for surrogate modeling researchers, and demonstrates the advantages of AutoML in machine learning tasks. The dataset and code are provided at \url{http://decode.mit.edu/projects/framed/}.

cs.LG↗