127 research outputs found
A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation
The high cost of high-resolution computational fluid/flame dynamics (CFD) has
hindered its application in combustion related design, research and
optimization. In this study, we propose a new framework for turbulent
combustion simulation based on the deep learning approach. An optimized deep
convolutional neural network (CNN) inspired from a U-Net architecture and
inception module is designed for constructing the framework of the deep
learning solver, named CFDNN. CFDNN is then trained on the simulation results
of hydrogen combustion in a cavity with different inlet velocities. After
training, CFDNN can not only accurately predict the flow and combustion fields
within the range of the training set, but also shows an extrapolation ability
for prediction outside the training set. The results from CFDNN solver show
excellent consistency with the conventional CFD results in terms of both
predicted spatial distributions and temporal dynamics. Meanwhile, two orders of
magnitude of acceleration is achieved by using CFDNN solver compared to the
conventional CFD solver. The successful development of such a deep
learning-based solver opens up new possibilities of low-cost, high-accuracy
simulations, fast prototyping, design optimization and real-time control of
combustion systems such as gas turbines and scramjets
Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion
To effectively simulate the combustion of hydrogen/hydrocarbon-fueled supersonic engines, such as scramjet and rocket-based combined cycle (RBCC) engines, a detailed mechanism for chemistry is usually required but computationally prohibitive. In order to accelerate chemistry calculation, an artificial neural network (ANN) based methodology was introduced in this study. This methodology consists of two different layers: self-organizing map (SOM) and back-propagation neural network (BPNN). The SOM is for clustering the dataset into subsets to reduce the nonlinearity, while the BPNN is for regression for each subset. Compared with previous studies, the chemical reaction mechanism involved in this study is more complex, therefore, the particle swarm optimization (PSO) method is employed for accelerating training process in this study. Then we were committed to constructing an ANN-based mechanism of hydrogen and kerosene for supersonic turbulent combustion and verifying it in a practical RBCC combustion chamber. The training data was generated by RANS simulations of the RBCC combustion chamber, and then fed into the SOM-BPNN with six different topologies (three different SOM topologies and two different BPNN topologies). Through LES simulation of the Rocket-Based Combined Cycle (RBCC) combustor, the six ANN-based mechanisms were verified. By comparing the predicted results of six cases with those of the conventional ODE solver, it is found that if the topology is properly designed, high-precision results in terms of ignition, quenching and mass fraction prediction can be achieved. As for efficiency, 8~20 times speedup of the chemical system integration was achieved, which will greatly improve the computational efficiency of combustion simulation of hydrogen/carbon monoxide/kerosene mixture
Machine Learning and Its Application to Reacting Flows
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
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
Complete computation of turbulent combustion flow involves two separate
steps: mapping reaction kinetics to low-dimensional manifolds and looking-up
this approximate manifold during CFD run-time to estimate the thermo-chemical
state variables. In our previous work, we showed that using a deep architecture
to learn the two steps jointly, instead of separately, is 73% more accurate at
estimating the source energy, a key state variable, compared to benchmarks and
can be integrated within a DNS turbulent combustion framework. In their natural
form, such deep architectures do not allow for uncertainty quantification of
the quantities of interest: the source energy and key species source terms. In
this paper, we expand on such architectures, specifically ChemTab, by
introducing deep ensembles to approximate the posterior distribution of the
quantities of interest. We investigate two strategies of creating these
ensemble models: one that keeps the flamelet origin information (Flamelets
strategy) and one that ignores the origin and considers all the data
independently (Points strategy). To train these models we used flamelet data
generated by the GRI--Mech 3.0 methane mechanism, which consists of 53 chemical
species and 325 reactions. Our results demonstrate that the Flamelets strategy
is superior in terms of the absolute prediction error for the quantities of
interest, but is reliant on the types of flamelets used to train the ensemble.
The Points strategy is best at capturing the variability of the quantities of
interest, independent of the flamelet types. We conclude that, overall, ChemTab
Deep Ensembles allows for a more accurate representation of the source energy
and key species source terms, compared to the model without these
modifications
A feasibility study on the use of low-dimensional simulations for database generation in adaptive chemistry approaches
LES/PDF approaches can be used for simulating challenging turbulent
combustion configurations with strong turbulence chemistry interactions.
Transported PDF methods are computationally expensive compared to flamelet-like
turbulent combustion models. The pre-partitioned adaptive chemistry (PPAC)
methodology was developed to address this cost differential. PPAC entails an
offline preprocessing stage, where a set of reduced models are generated
starting from an initial database of representative compositions. At runtime,
this set of reduced models are dynamically utilized during the reaction
fractional step leading to computational savings. We have recently combined
PPAC with in-situ adaptive tabulation (ISAT) to further reduce the
computational cost. We have shown that the combined method reduced the average
wall-clock time per time step of large-scale LES/particle PDF simulations of
turbulent combustion by 39\%. A key assumption in PPAC is that the initial
database used in the offline stage is representative of the compositions
encountered at runtime. In our previous study this assumption was trivially
satisfied as the initial database consisted of compositions extracted from the
turbulent combustion simulation itself. Consequently, a key open question
remains as to whether such databases can be generated without having access to
the turbulent combustion simulation. Towards answering this question, in the
current work, we explore whether the compositions for forming such a database
can be extracted from computationally-efficient low-dimensional simulations
such as 1D counterflow flames and partially stirred reactors. We show that a
database generated using compositions extracted from a partially stirred
reactor configuration leads to performance comparable to the optimal case,
wherein the database is comprised of compositions extracted directly from the
LES/PDF simulation itself
Machine Learning and Its Application to Reacting Flows
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
- …