arXiv subjects

Zhi X. Chen

Publications and source records attributed to Zhi X. Chen.

25 records · Page 2Linked to original sources

arXiv2023

GPU-accelerated Large Eddy Simulation of turbulent stratified flames with machine learning chemistry

Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increasingly stringent environmental regulations. Numerous numerical simulations with different combustion models and mesh resolutions have been conducted on laboratory-scale flames to further understand the stratified premixed combustion. However, the trade-off between the high-fidelity and low computational cost for simulating laboratory-scale flames still remains, particularly for those combustion models involving direct coupling of chemistry and flow. In the present study, a GPU-based solver is employed to solve partial differential equations and calculate the thermal and transport properties, while an artificial neural network (ANN) is introduced to replace reaction rate calculation. Particular emphasis is placed on evaluating the proposed GPU-ANN approach through the large eddy simulation of the Cambridge stratified flame. The simulation results show good agreement for the flow and flame statistics between the GPU-ANN approach and the conventional CPU-based solver with direct integration (DI). The comparison suggests that the GPU-ANN approach can achieve the same level of accuracy as the conventional CPU-DI solver. In addition, the overall speed-up factor for the GPU-ANN approach is over two orders of magnitude. This study lays the potential groundwork for fully resolved laboratory-scale flame simulations based on detailed chemistry with much more affordable computational cost.

physics.flu-dyn↗

arXiv2023

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning

Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numerical simulations of LOX/GCH4 flame-vortex interaction under supercritical conditions. The well-established benchmark configuration of three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR) cubic equation of states are studied to reveal the real fluid effects on the TGV evolution and flame-vortex interaction. The results show intensified flame stretching and quenching arising from the intrinsic large density gradients of real gases, as compared to that for the idea gases. Furthermore, to reduce the computational cost associated with real fluid thermophysical property calculations, a machine learning-based strategy utilising deep neural networks (DNNs) is developed and then assessed using the three-dimensional reactive TGV. Generally good prediction accuracy is achieved by the DNN, meanwhile providing a computational speed-up of 13 times over the convectional approach. The profound physics involved in flame-vortex interaction under supercritical conditions demonstrated by this study provides a benchmark for future related studies, and the machine learning modelling approach proposed is promising for practical high-fidelity simulation of supercritical combustion.

physics.flu-dyn↗

arXiv2023

Direct numerical simulations of the Taylor-Green Vortex interacting with a hydrogen diffusion flame: Reynolds number and non-unity Lewis number effects

Understanding the interactions between hydrogen flame and turbulent vortices is important for developing the next-generation carbon neutral combustion systems. In the present work, we perform several direct numerical simulation (DNS) cases to study the dynamics of a hydrogen diffusion flame embedded in the Taylor-Green Vortex (TGV). The evolution of flame and vortex is investigated for a range of initial Reynolds numbers up to 3200 with different mass diffusion models. We show that the vortices dissipate rapidly in cases at low Reynolds numbers, while the consistent stretching, splitting and twisting of vortex tubes are observed in cases with evident turbulence transition at high Reynolds numbers. Regarding the interactions between the flame and vortex, it is demonstrated that the heat release generated by the flame has suppression effects on the turbulence intensity and its development of the TGV. Meanwhile, the intense turbulence provides abundant kinetic energy, accelerating the mixing of the diffusion flame with a contribution to higher strain rate and larger curvatures of the flame. Considering the effects of non-unity Lewis number, it is revealed that the flame strength is more intense in the cases with mixture averaged model. However, this effect is relatively suppressed under the impacts of the intense turbulence.

physics.flu-dyn↗

arXiv2022

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware.

physics.flu-dyn↗

arXiv2022

Numerical investigation of azimuthal thermoacoustic instability in a gas turbine model combustor

Self-excited spinning mode azimuthal instability in an annular combustor with non-swirling flow is investigated using large eddy simulation (LES). Compressible Navier-Stoke equations are solved with a flamelet combustion model to describe the subgrid chemistry$-$turbulence interactions. Two flamelet models, with and without heat loss effects, are compared to elucidate the non-adiabatic wall effects on the thermoacoustic instability. The azimuthal modes are captured well by both models with only marginal differences in the computed frequencies and amplitudes. By comparing with the experimental measurements, the frequencies given by the LES are approximately 10\% higher and the amplitudes are well predicted. Further analysis of the experimental and LES data shows a similar dominant anti-clockwise spinning mode, under which a good agreement is observed for the phase-averaged heat release rate fluctuations. Dynamic mode decomposition (DMD) is applied to shed more light on this spinning mode. The LES and experimental DMD modes reconstructed for their azimuthal mode frequencies agree very well for the heat release fluctuations. The DMD mode structure for the acoustic pressure from the LES shows a considerable non-zero profile at the combustor outlet, which could be essential for azimuthal modes to establish in this annular combustor. Finally, a low-order modelling study was conducted using an acoustic network combined with the flame transfer function extracted from LES. The results show that the dominant mode is associated with the plenum showing a first longitudinal and azimuthal mixed mode structure. By tuning the plenum length to match the effective volume, the predicted frequency becomes very close to the measured value.

physics.flu-dyn↗

arXiv2022

A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics

Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation standards and reproducibility. The current work aims to understand two basic questions regarding the deep neural network (DNN) method: what data the DNN needs and how general the DNN method can be. Sampling and preprocessing determine the DNN training dataset, further affect DNN prediction ability. The current work proposes using Box-Cox transformation (BCT) to preprocess the combustion data. In addition, this work compares different sampling methods with or without preprocessing, including the Monte Carlo method, manifold sampling, generative neural network method (cycle-GAN), and newly-proposed multi-scale sampling. Our results reveal that the DNN trained by the manifold data can capture the chemical kinetics in limited configurations but cannot remain robust toward perturbation, which is inevitable for the DNN coupled with the flow field. The Monte Carlo and cycle-GAN samplings can cover a wider phase space but fail to capture small-scale intermediate species, producing poor prediction results. A three-hidden-layer DNN, based on the multi-scale method without specific flame simulation data, allows predicting chemical kinetics in various scenarios and being stable during the temporal evolutions. This single DNN is readily implemented with several CFD codes and validated in various combustors, including (1). zero-dimensional autoignition, (2). one-dimensional freely propagating flame, (3). two-dimensional jet flame with triple-flame structure, and (4). three-dimensional turbulent lifted flames. The results demonstrate the satisfying accuracy and generalization ability of the pre-trained DNN. The Fortran and Python versions of DNN and example code are attached in the supplementary for reproducibility.

physics.chem-ph↗

arXiv2021

Large eddy simulation of a supersonic lifted hydrogen flame with perfectly stirred reactor model

Large Eddy Simulation with a Perfectly Stirred Reactor model (LES-PSR) is developed to simulate supersonic combustion with high-enthalpy flow conditions. The PSR model considers the viscous heating and compressibility effects on the thermo-chemical state, through correcting the chemical source term for progress variable and incorporating absolute enthalpy as the control variable for the look-up table. It is firstly validated by using a priori analysis of the viscous heating and compressibility effects. Then an auto-igniting hydrogen flame stabilized in supersonic vitiated co-flowing jet is simulated with LES-PSR method. The results show that the shock wave structure, overall flame characteristics, flame-shock interaction and lift-off height are accurately captured. Good agreements of the velocity and mixture fraction statistics with the experimental data are observed. The results also show that the LES-PSR model can predict the mean temperature and mole fractions of major species quite well in both flame induction and stabilization zones. However, there are some under-predictions of temperature RMS by about 100-150 K, which may be due to the chemical non-equilibrium in the H2/O2-enriched combustion product of the co-flowing jet. The scatter plots of two probe locations respectively from induction and flame zones show that the respective flame structures in mixture fraction space are captured well. However, the flucturations of the temperature and species mole fractions are under-predicted in the flame zone. The shock-induced auto-igniting spots are captured by the PSR model. These spots are highly unsteady and play an important role in flame stabilization. It is also shown that the intense reactions are initiated at mixture fractions around the stoichiometry or fuel-lean values, corresponding to local elevated pressure (1.5-2.0 atm) due to shock compression.

physics.flu-dyn↗