Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Engineering
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Faculty Publications
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared …
Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia
Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia
Faculty Publications
Predicting the glass transition temperature (Tg) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict Tg of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 Tg data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for Tg. MD simulations further unravel the fundamental roles …
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Faculty Publications
Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional …
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …