Applications of machine learning in thermochemical conversion of biomass-A review

CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane, also known as HNIW) is one of the most powerful energetic materials. However, its high sensitivity to environmental stimuli greatly reduces its safety and severely limits its application. In this work, ab initio based neural network potential (NNP) energy surfaces for both β-CL-20 and CL-20/TNT co-crystals were constructed. To accurately simulate the thermal decomposition processes of these two crystal systems, reactive molecular dynamics simulations based on the NNPs were performed. Many important intermediate species and their associated reaction paths during the decomposition had been identified in the simulations and the direct results on detonation temperatures of both systems were provided. The simulations also showed clearly that 2,4,6-trinitrotoluene (TNT) molecules in the co-crystal act as a buffer to slow down the chain reactions triggered by nitrogen dioxide and this effect is more significant at lower temperatures. Specifically, the addition of TNT molecules in the CL-20/TNT co-crystal introduces intermolecular hydrogen bonds between CL-20 and TNT molecules in the system, thereby increasing the thermal stability of the co-crystal. The current reactive molecular dynamics simulation is performed based on the NNP which helps in accelerating the speed of ab initio molecular dynamics (AIMD) simulation by more than 3 orders of magnitude while preserving the accuracy of density functional theory (DFT) calculations. This enabled us to perform longer-time simulations at more realistic temperatures that traditional AIMD methods cannot achieve. With the advantage of the NNP in its powerful fitting ability and transferability, the NNP-based MD simulation can be widely applied to energetic material systems.

Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT.

Interpretable data-driven methods for subgrid-scale closure in LES for transcritical LOX/GCH4 combustion

• An advanced design paradigm is necessary to achieve reactor intensification. • Multiscale Modeling provides in-depth understanding and guides the advanced design. • Hybrid Machine Learning can assist reactor design discovering new configurations. • Additive Manufacturing is able to produce unconventional reactor geometries. The intensification of catalytic reactors is expected to play a crucial role to address the challenges that the chemical industry is facing in the transition to more sustainable productions. An advanced design paradigm is necessary to develop customized and process-tailored reactor solutions able to provide the optimal operating conditions, transport properties and geometry. This can be achieved by a detailed understanding of the catalyst functionality in the reactive environment. Multiscale Modeling provides such in-depth insights into the complex physical-chemical phenomena enabling to achieve a first-principles-based understanding and design of the most suitable reactor geometry and configuration. To overcome the intrinsic complexity of the approach, Machine Learning can be synergically employed to reduce the computational cost fostering the inclusion of detailed numerical simulations since the early stage of the design process. Moreover, hybrid machine learning models trained with the data and enforced by the physics are envisioned to assist the work of designers facilitating the development of disruptive intensified solutions. The manufacturing of these unconventional systems requires adequate techniques. Additive Manufacturing is showing enormous potential in this direction and their future developments are expected to make it possible to routinely fabricate intensified reactors. 

Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing

Analysis of phase change material thermal effects in large-scale proton-exchange membrane fuel cell based on open-source computational fluid dynamics

Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to studying combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview of studies on the applications of machine learning in combustion science fields over the past several decades. We introduce the fundamentals of machine learning and its usage in aiding chemical reactions, combustion modeling, combustion measurement, engine performance prediction and optimization, and fuel design. The opportunities and limitations of using machine learning in combustion studies are also discussed. This paper aims to provide readers with a portrait of what and how machine learning can be used in combustion research and to inspire researchers in their ongoing studies. Machine learning techniques are rapidly advancing in this era of big data, and there is high potential for exploring the combination between machine learning and combustion research and achieving remarkable results.

Monica Carrer

Papers

Machine learning for combustion

Machine learning for combustion

Ethanol blending effects on auto-ignition and reaction wave propagation under engine-relevant conditions