Shock-Induced Multiphase Instability in a High Volume Fraction Finite-Thickness Particle Layer

We study the interaction of a planar air shock with a perturbed, monodispersed, particle curtain using point-particle simulations. In this Eulerian-Lagrangian approach, equations of motion are solved to track the position, momentum, and energy of the computational particles while the carrier fluid flow is computed in the Eulerian frame of reference. In contrast with many Shock-Driven Multiphase Instability (SDMI) studies, we investigate a configuration with an initially high particle volume fraction, which produces a strongly two-way coupled flow in the early moments following the shock-solid phase interaction. In the present study, the curtain is about 4 mm in thickness and has a peak volume fraction of about 26%. It is composed of spherical particles of d = 115μm in diameter and a density of 2500 kg.m−3, thus replicating glass particles commonly used in multiphase shock tube experiments or multiphase explosive experiments. We characterize both the evolution of the perturbed particle curtain and the gas initially trapped inside the particle curtain in our planar three-dimensional numerical shock tube. Control parameters such as the shock strength, the particle curtain perturbation wavelength and particle volume fraction peak-to-trough amplitude are varied to quantify their influence on the evolution of the particle cloud and the initially trapped gas. We also analyze the vortical motion in the flow field. Our results indicate that the shock strength is the primary contributor to the cloud particle width. Also, a classic Richtmyer-Meshkov instability mixes the gas initially trapped in the particle curtain and the surrounding gas. Finally, we observe that the particle cloud contribute to the formation of longitudinal vortices in the downstream flow.

Monte Carlo Predictions of Aero-Engine Performance Degradation Due to Particle Ingestion

Aero-engines, which encounter clouds of airborne particulate, experience reduced performance due to the deposition of particles on their high-pressure turbine nozzle guide vanes. The rate of this degradation depends on particle properties, engine operating state and the duration of exposure to the particle cloud, variables that are often unknown or poorly constrained, leading to uncertainty in model predictions. A novel method coupling one-dimensional gas turbine performance analysis with generalised predictions of particle deposition is developed and applied through the use of Monte Carlo simulations to better predict high-pressure turbine degradation. This enables a statistical analysis of deterioration from which mean performance losses and confidence intervals can be defined, allowing reductions in engine life and increased operational risk to be quantified. The method is demonstrated by replicating two particle cloud encounter events for the Rolls-Royce RB211-524C engine and is used to predict empirical particle properties by correlating measured engine performance data with Monte Carlo model inputs. Potential improvements in the confidence of these predictions due to more tightly constrained input and validation data are also demonstrated. Finally, the potential combination of the Monte Carlo coupled degradation model with in-service engine performance data and particle properties determined through remote or in situ sensing is outlined and its role in a digital twin to enable a predictive approach to operational capability is discussed.

Initial Moving Mechanism of Densely-Packed Particles Driven by a Planar Shock Wave

The initial moving mechanism of densely packed particles driven by shock waves is unclear but vital for the next accurate calculation of the problem. Here, the initial motion details are investigated experimentally and numerically. We found that before particles show notable motion, shock waves complete reflection and transmission, and stress waves propagate downstream on particle skeleton. Due to the particle stress wave, particles successively accelerate and obtain an axial velocity of 6–8 m/s. Then, the blocked gas pushes the upstream particles integrally to move downstream, while the gas flow in the pores drags the downstream particles to separate dramatically and accelerate to the velocity of 60–70 m/s. This gas push-drag dual mechanism transforms densely packed particles into a dense gas-particle cloud, which behaves as the expansion phenomena of the dense particles.