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Chung, Wai Tong

Publications and source records attributed to Chung, Wai Tong.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Analysis of ducted fuel injection at high-pressure transcritical conditions using large-eddy simulations

Ducted fuel injection (DFI) is a proposed fuel injection concept for achieving substantial reductions in emissions. In this concept, the fuel is injected through a coannular duct, resulting in increased fuel-air mixing and minimized formation of soot and other unwanted combustion products. Apart from comprehensive experimental investigations on DFI, so far computational studies have been limited to single-point Reynolds-averaged Navier Stokes simulations. Therefore, the objective of this work is to complement these studies by performing large-eddy simulations using a diffuse-interface method to examine the physical mechanisms and combustion processes of DFI, specifically focusing on the mixing process and the effect of fuel-ducting on combustion and pollutant emissions. To this end, finite-rate chemistry simulations are performed of the DFI configuration corresponding to the Engine Combustion Network Spray A injector at transcritical conditions (n-dodecane fuel, 60 bar pressure and 1000 K temperature chamber conditions). A two-equation soot model is employed for the qualitative analysis of soot emissions. Direct comparisons of averaged and instantaneous flow field results with the Spray A configuration are performed to assess the effect of DFI on the first- and second-stage ignition and soot formation. Compared to the free-spray condition, the results show that the DFI case exhibits a combination of (i) increased mass flow rate and entrained air, (ii) larger pressure drop magnitude and flow velocity, and (iii) a closer-to-stoichiometric mixture composition (both globally and locally), each of which is conjectured to contribute toward reduced soot production.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

LES of HCCI combustion of iso-octane/air in a flat-piston rapid compression machine

Homogeneous Charge Compression Ignition (HCCI) engines promise better efficiency and cleaner emissions than conventional piston engines, but can be challenging to control. Rapid compression machines (RCM) provide a simplified configuration for investigating HCCI combustion behavior, which is necessary for effective control of engine ignition timing and peak pressures. Here, in this study, we assess the utility of large eddy simulations (LES) for predicting HCCI combustion in a 3-D configuration. To this end, LES with finite-rate chemistry employing a 99-species iso-octane/air mechanism of two RCM operating conditions are performed. The RCM configuration under consideration was designed by Strozzi et al. with a flat piston to introduce large amounts of thermal stratification representative of realistic HCCI engine conditions, through the generation of corner vortices. It is shown that the simulation provides reasonable agreement with temperature fluctuations (7% difference), as well as ignition delay in the short ignition case (1 ms difference), while the long ignition case (35 ms difference) highlights more substantial deficiencies that are still within the expected uncertainty from the employed chemical mechanism. Flame propagation modes predicted by LES agree with experimental observations: spontaneous ignition is seen in the short ignition case, while deflagration is more prominent in the long ignition case. Analysis of global and local quantities classify the short ignition case in a mixed ignition regime, and the long ignition case in the mild ignition regime. These results demonstrate the utility of FRC-LES for investigations of multimode combustion regimes of HCCI combustion in a 3-D configuration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Combustion machine learning: Principles, progress and prospects

Progress in combustion science and engineering has led to the generation of large amounts of data from large-scale simulations, high-resolution experiments, and sensors. This corpus of data offers enormous opportunities for extracting new knowledge and insights—if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While data-driven methods are utilized in various combustion areas, recent advances in algorithmic developments, the accessibility of open-source software libraries, the availability of computational resources, and the abundance of data have together rendered ML techniques ubiquitous in scientific analysis and engineering. This article examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we examine supervised, unsupervised, and semi-supervised ML methods. Various combustion examples are considered to illustrate and to evaluate these methods. Next, we review past and recent applications of ML approaches to problems in combustion, spanning fundamental combustion investigations, propulsion and energy-conversion systems, and fire and explosion hazards. Challenges unique to CombML are discussed and further opportunities are identified, focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

33 ADVANCED PROPULSION SYSTEMS↗