Using Co Optimized Machine Learned Manifolds For Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.

The current state of the art in computational models for turbulent reacting flows.

Bridging Scales in Modelling and Simulating Reacting Flows, Part I , Volume 52 presents key methods to bridge scales in the simulation of reacting single phase flows. New sections in the updated release include topics such as quadrature-based moment methods for multiphase chemically reacting flows, the collaboration of experiments and simulations for the development of predictive models, a simulation of turbulent coalescence and breakage of bubbles and droplets in the presence of surfactants, a section on salts and contaminants, and information on the numerical simulation of reactive flows.

The focus of the current research is to develop a numerical framework on the Graphic Processing Units (GPU) capable of modeling chemically reacting flow. The framework incorporates a high-order finite volume method coupled with an implicit solver for the chemical kinetics. Both the fluid solver and the kinetics solver are designed to take advantage of the GPU architecture to achieve high performance. The structure of the numerical framework is shown, detailing different aspects of the optimization implemented on the solver. The mathematical formulation of the core algorithms is presented along with a series of standard test cases, including both nonreactive and reactive flows, in order to validate the capability of the numerical solver. The performance results obtained with the current framework show the parallelization efficiency of the solver and emphasize the capability of the GPU in performing scientific calculations.

The main objective of this thesis is to study the fundamental behaviour of multi-component gas mixture flows in micro/nano-channels undergoing catalytic chemical reactions on the walls. This work is primarily focused on nano-scale reacting flows seen in related applications; especially, miniaturized energy sources such as micro-fuel cells and batteries. At these geometries, the order of the characteristic length is close to the mean free path of the flowing gas, making the flow highly rarefied. As a result, non-equilibrium conditions prevail even the bulk flow and therefore, continuum assumptions are not held anymore. Hence, discrete methods should be adopted to simulate molecular movements and interactions described by the Boltzmann equation. The Direct Simulation Monte Carlo (DSMC) method was employed for the present research due to its natural ability for simulating a broad range of rarefied gas flows, and its flexibility to incorporate surface chemical reactions. In the first step, fluid dynamics and the heat transfer of H2/N2 and H2/N2/CO2 gas mixture slip flows in a plain micro-channel are simulated. The obtained results are compared to the corresponding data achieved from Navier-Stokes equations with slip/jump boundary conditions. Generally, very good agreements are observed between the two methods. It proves the ability of DSMC in replicating the fluid properties of multi-component gas mixtures even when high mass discrepancies exist among the species. Based on this comparison, the proper parameters are set for the prepared DSMC code, and the appropriate intermolecular collision model is identified. It is also found that stream variables should be calculated more accurately at flow boundaries in order to simulate the intense upstream diffusion emerging at low velocity flows frequently seen in micro/nano-applications. Therefore, in the second step, a novel pressure boundary condition is introduced for gas mixture flows by substituting the commonly used Maxwell velocity distribution with the Chapman-Enskog distribution function. It is shown that this new method yields better results for lower velocity and higher rarefaction level cases. In the last step, a new method is proposed for coupling the flow field simulated by DSMC and surface reactions modelled by the species conservation ODE system derived from the reaction mechanism. First, a lean H2/air slip flow subjected to oxidation on platinum coated walls in a flat micro-channel 4[mu]m in height is simulated as a verification test case. The results obtained are validated against the solutions of the Navier-Stokes equations with slip/jump boundary conditions and very good conformity is achieved. Next, several cases undergoing the same reaction with Reynolds numbers ranging from 0.2 to 3.6 and Knudsen numbers ranging from 0.025 to 0.375, are simulated using the verified code to investigate the effects of the channel height ranging from 0.5[mu]m to 2[mu]m , the inlet mass flow rate ranging from 5 kg/m2.s to 25 kg/m2.s, the inlet temperature ranging from 300K to 700K, the wall temperature ranging from 300K to 1000K, and the fuel/air equivalence ratio ranging from 0.28 to 1.5. Some of the findings are as follows: (1) increasing the surface temperature from 600K to 1000K and/or the inlet temperature from 300K to 700K results in negligible enhancement of the conversion rate, (2) the optimum value of the equivalence ratio is on the fuel lean side (around 0.5), (3) the efficiency of the reactor is higher for smaller channel heights, and (4) increasing the inlet mass flux elevates the reaction rate especially for the smaller channels; this effect is not linear and is more magnified for lower mass fluxes.

Internal combustion engines still have a potential for substantial improvements, particularly with regard to fuel efficiency and environmental compatibility. These goals can be achieved with help of control systems. Modeling and Control of Internal Combustion Engines (ICE) addresses these issues by offering an introduction to cost-effective model-based control system design for ICE. The primary emphasis is put on the ICE and its auxiliary devices. Mathematical models for these processes are developed in the text and selected feedforward and feedback control problems are discussed. The appendix contains a summary of the most important controller analysis and design methods, and a case study that analyzes a simplified idle-speed control problem. The book is written for students interested in the design of classical and novel ICE control systems.

Modeling of Chemical Reactions covers detailed chemical kinetics models for chemical reactions. Including a comprehensive treatment of pressure dependent reactions, which are frequently not incorporated into detailed chemical kinetic models, and the use of modern computational quantum chemistry, which has recently become an extraordinarily useful component of the reaction kinetics toolkit. It is intended both for those who need to model complex chemical reaction processes but have little background in the area, and those who are already have experience and would benefit from having a wide range of useful material gathered in one volume. The range of subject matter is wider than that found in many previous treatments of this subject. The technical level of the material is also quite wide, so that non-experts can gain a grasp of fundamentals, and experts also can find the book useful. A solid introduction to kinetics Material on computational quantum chemistry, an important new area for kinetics Contains a chapter on construction of mechanisms, an approach only found in this book