V.B. Nguyen

Analyzing the transition pressure and viscosity limit of a hydroelastic microfluidic oscillator

We analyze the transition of a hydroelastic microfluidic oscillator from steady laminar flow to oscillatory flow. Results show that the transition pressure is influenced by both the fluid viscosity and the device geometry, which can be explained through the negative differential resistance effects. Due to the deflection of the elastic diaphragm, the flow resistance increases with the driving pressure (P0), and this increase is more significant at higher viscosities. At the critical transition point, further increase of P0 will cause a reduction in the flow rate and trigger the oscillation. This provides an alternative point of view to the occurrence of flow induced vibrations. We also demonstrate that through optimization of the design, the current device is capable of working in high-viscosity environments of up to 89 cP.

Model reduction for reacting flow applications

A model reduction approach based on Galerkin projection, proper orthogonal decomposition (POD), and the discrete empirical interpolation method (DEIM) is developed for chemically reacting flow applications. Such applications are challenging for model reduction due to the strong coupling between fluid dynamics and chemical kinetics, a wide range of temporal and spatial scales, highly nonlinear chemical kinetics, and long simulation run-times. In our approach, the POD technique combined with Galerkin projection reduces the dimension of the state (unknown chemical concentrations over the spatial domain), while the DEIM approximates the nonlinear chemical source term. The combined method provides an efficient offline–online solution strategy that enables rapid solution of the reduced-order models. Application of the approach to an ignition model of a premixed H2/O2/Ar mixture with 19 reversible chemical reactions and 9 species leads to reduced-order models with state dimension several orders of magnitude smaller than the original system. For example, a reduced-order model with state dimension of 60 accurately approximates a full model with a dimension of 91,809. This accelerates the simulation of the chemical kinetics by more than two orders of magnitude. When combined with the full-order flow solver, this results in a reduction of the overall computational time by a factor of approximately 10. The reduced-order models are used to analyse the sensitivity of outputs of interest with respect to uncertain input parameters describing the reaction kinetics.

On the Investigation of Detonation Re-initiation Mechanisms and the Influences of the Geometry Confinements and Mixture Properties

The topic of detonation re-initiation is studied through both experimental measurements and numerical simulations using a bifurcation channel and the detonation research facilities in Temasek Laboratories. The main objective is to understand the re-initiation mechanisms through shock reflections, and investigate the performance of detonation re-initiation at different test conditions. Stable and unstable detonation waves are both taken into consideration. It is found that the re-initiation through shock reflection is mainly achieved through the interactions of the multiple transverse waves. The details of the generation and evolution of the transverse waves are also clarified. The influence of the geometry confinement to detonation re-initiation is investigated. It is found that the length of the bifurcation channel can affect the re-initiation results by limiting the shock reflection times, which is discovered to be the main reason leading to the discrepancies between the previous similar studies. The width of the bifurcation channel is also critical as it can directly affect the induction length during detonation diffraction which determines the shock reflection strength. The differences of re-initiation using various mixture properties are also addressed, and a sudden transitional behavior of detonation re-initiation is found between stable and unstable detonation waves. Regarding the reason why a certain number of shock reflections are required before successful re-initiation, it can be explained using the relative relation between the shock reflection strength and the corresponding marginal solution curve of a quasi-steady detonation.

Effect of ethylene fuel/air equivalence ratio on the dynamics of deflagration-to-detonation transition and detonation propagation process

ABSTRACT During the operation, the pulse detonation engine (PDE) may have to work with different fuel/air equivalence ratios (from a lean to rich fuel mixture) as in the cold start-up operation, which would strongly affect the engine performance characteristics and the outputs of interest. For a better operational control, it is necessary to gain understanding of the effect of the equivalence ratio on the dynamics of the processes of PDE. Thus, in this study, numerical simulations are performed for different ethylene fuel/air equivalence ratios to study its effect on the dynamics of the deflagration-to-detonation transition (DDT) and detonation processes. In particular, the density-based solver with a shock-capturing scheme is employed to solve for the viscous, compressible, and reacting flows governed by reacting Navier–Stokes equations. The computed flame propagation speed, run-up distance, and Chapman–Jouguet detonation velocity are comparable to the current experimental results. In addition, the numerical results show that the minimum values of the run-up distance and run-up time, as well as the maximum value of the detonation velocity occur at the equivalence ratio of about 1.1. Analysis of the computed results associate these findings to the firm correlation of the flame speed with equivalence ratio, which is in turn function of the temperature, pressure, and mixture composition. The shifting of the outputs of interest to the richer fuel side from the stoichiometric point can be attributed to the combustion product dissociation, mixture heat capacity, and oxidizer enrichment.

Numerical Simulation of Combustion Process for Two-Phase Fuel Flows Related to Pulse Detonation Engines

In this study, combustion process is simulated for two-phase fuel flows (droplets of the liquid fuel and gas mixture of the fuel vapor and oxygen) using a combination of Lagrangian–Eulerian approaches. The Lagrangian is used to track for the liquid fuel droplets in the gas mixture region, while the Eulerian is used to gas mixture features accordingly. A two-way coupling is employed to take into account the interaction between gas mixture and liquid droplets. Evaporation process is followed D2-law with satisfaction of mass conservation. A reduced chemical kinetic model is employed instead of the detailed chemistry of fuel. The obtained numerical results are used to study and analyze the combustion process and physical insights. The effects of droplet size on the detonation characteristics are briefly discussed.

Model Order Reduction for Reacting Flows: Laminar Gaussian Flame Applications

A model reduction technique based on Galerkin projection, proper orthogonal decomposition (POD), and the discrete empirical interpolation method (DEIM) is developed for chemically reacting flow applications. These applications are challenging problems which involve a strong coupling between fluid dynamics and chemical kinetics, a wide range of temporal and spatial scales, and highly nonlinear chemical kinetics. These problems often require a very long simulation time. In this study, the POD technique combined with Galerkin projection reduces the dimension of unknown chemical concentrations over the spatial domain, while the DEIM approximates the nonlinear chemical source term at interpolation points. The combined method provides an efficient offline–online solution strategy that enables rapid solution of the reduced-order model. Application of the technique to a premixed Gaussian flame leads to a reduced-order model with state dimension several orders of magnitude smaller than the original system. In this case, a reduced-order model with state dimension of 60 accurately approximates a full model with a dimension of about 100,000. This accelerates the simulation of the chemical kinetics by more than two orders of magnitude. The reduced-order model is used to analyse the sensitivity of outputs of interest with respect to uncertain input parameters describing the reaction kinetics.