Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

A Nicolle, S Deng, M Ihme… - Journal of Chemical …, 2024 - ACS Publications
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

Modeling of high-speed, methane-air, turbulent combustion, Part II: Reduced methane oxidation chemistry

R Xu, SS Dammati, X Shi, ES Genter, Z Jozefik… - Combustion and …, 2024 - Elsevier
A reduced, 12-species reaction model (FFCMy-12) is proposed for modeling high-speed
turbulent methane flames at high Karlovitz numbers. The model was derived from an early …

Foundational Fuel Chemistry Model 2–iso-Butene chemistry and application in modeling alcohol-to-jet fuel combustion

Y Zhang, W Dong, R Xu, GP Smith, H Wang - Combustion and Flame, 2024 - Elsevier
Abstract The Hybrid Chemistry (HyChem) approach is applied to model the combustion
chemistry of Gevo's alcohol-to-jet (ATJ) fuel, a conventional Jet A fuel, and their blends. The …

Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization

S Shao, Y Tian, Y Zhang - Swarm and Evolutionary Computation, 2025 - Elsevier
Expensive constrained multi-objective optimization problems (ECMOPs) exist in a wide
variety of applications from industrial processes to engineering systems. When solving …

Aspects of fundamental reaction kinetics and legacy combustion properties in data-assimilated combustion reaction model development

W Dong, Y Zhang, GP Smith, H Wang - Proceedings of the Combustion …, 2024 - Elsevier
A recently developed Foundation Fuel Chemistry Model (FFCM-2) assimilated
comprehensively over 1,000 legacy combustion property targets. This paper discusses two …

Uncertain lithium-ion cathode kinetic decomposition modeling via Bayesian chemical reaction neural networks

BC Koenig, H Chen, Q Li, P Zhao, S Deng - Proceedings of the …, 2024 - Elsevier
Lithium-ion batteries are the focus of significant recent research interest due to their use in
energy storage systems, electric vehicles, and other green technologies. Under various …

Fast QoI-Oriented Bayesian Experimental Design with Unified Neural Response Surfaces for Kinetic Uncertainty Reduction

H Chen, Q Li, S Deng - Energy & Fuels, 2024 - ACS Publications
In the realm of combustion and reacting flow modeling, the calibration of the kinetic model
parameters often relies on experimental data. However, not all data obtained under different …

Measurements of high-temperature H2 laminar flame speeds across a wide range of pressure and Ar dilution for improved comparative evaluation of chemical kinetic …

M Figueroa-Labastida, L Zheng, JW Streicher… - International Journal of …, 2025 - Elsevier
Laminar flame speeds of H 2-O 2-Ar mixtures were experimentally studied in a shock tube at
high temperatures between 501 K and 915 K. To better evaluate and constrain chemical …

Multi-target active subspaces generated using a neural network for computationally efficient turbulent combustion kinetic uncertainty quantification in the flamelet …

BC Koenig, S Deng - Combustion and Flame, 2023 - Elsevier
Propagating uncertainties in kinetic models through combustion simulations can provide
important metrics on the reliability and accuracy of a model, but remains a challenging and …

Artificial intelligence as a catalyst for combustion science and engineering

M Ihme, WT Chung - Proceedings of the Combustion Institute, 2024 - Elsevier
Combustion and energy conversion play critical roles in all facets of environmental and
technological applications, including the utilization of sustainable energy sources for power …