Reaction Mechanism Reduction for Ozone-Enhanced CH4/Air Combustion by a Combination of Directed Relation Graph with Error Propagation, Sensitivity Analysis and Quasi-Steady State Assumption

In this study, an 18-steps, 22-species reduced global mechanism for ozone-enhanced CH4/air combustion processes was derived by coupling GRI-Mech 3.0 and a sub-mechanism for ozone decomposition. Three methods, namely, direct relation graphics with error propagation, (DRGRP), sensitivity analysis (SA), and quasi-steady-state assumption (QSSA), were used to downsize the detailed mechanism to the global mechanism. The verification of the accuracy of the skeletal mechanism in predicting the laminar flame speeds and distribution of the critical components showed that that the major species and the laminar flame speeds are well predicted by the skeletal mechanism. However, the pollutant NO was predicated inaccurately due to the precursors for generating NO were removed as redundant components. The laminar flame speeds calculated by the global mechanism fit the experimental data well. The comparisons of simulated results between the detailed mechanism and global mechanism were investigated and showed that the global mechanism could accurately predict the major and intermediate species and significantly reduced the time cost by 72%Peer reviewe

AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks

AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to high computational cost. Here we propose AutoTransfer, an AutoML solution that improves search efficiency by transferring the prior architectural design knowledge to the novel task of interest. Our key innovation includes a task-model bank that captures the model performance over a diverse set of GNN architectures and tasks, and a computationally efficient task embedding that can accurately measure the similarity among different tasks. Based on the task-model bank and the task embeddings, we estimate the design priors of desirable models of the novel task, by aggregating a similarity-weighted sum of the top-K design distributions on tasks that are similar to the task of interest. The computed design priors can be used with any AutoML search algorithm. We evaluate AutoTransfer on six datasets in the graph machine learning domain. Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude. Finally, we release GNN-Bank-101, a large-scale dataset of detailed GNN training information of 120,000 task-model combinations to facilitate and inspire future research.Comment: ICLR 202

Verification and Validation of a Low-Mach-Number Large-Eddy Simulation Code against Manufactured Solutions and Experimental Results

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).To investigate turbulent reacting flows, a low-Mach number large-eddy simulation (LES) code called ‘LESsCoal’ has been developed in our group. This code employs the Germano dynamic sub-grid scale (SGS) model and the steady flamelet/progress variable approach (SFPVA) on a stagger-structured grid, in both time and space. The method of manufactured solutions (MMS) is used to investigate the convergence and the order of accuracy of the code when no model is used. Finally, a Sandia non-reacting propane jet and Sandia Flame D are simulated to inspect the performance of the code under experimental setups. The results show that MMS is a promising tool for code verification and that the low-Mach-number LES code can accurately predict the non-reacting and reacting turbulent flows. The validated LES code can be used in numerical investigations on the turbulent combustion characteristics of new fuel gases in the future.Peer reviewedFinal Published versio