Flow, Turbulence and Combustion最新文献

Noise analysis is one of the most efficient and newest methods to investigate dynamic behaviors of any system. In this study, hydrodynamic noises of a single bubble are scrutinized by applying Curle's acoustic analogy as implemented in OpenFoam® v2012. Meanwhile, a new solver (interAcousticFoam) is developed to hydroacoustically evaluate the noise sources. A three-dimensional transient incompressible two-phase flow model is simulated based on the hybrid method (the volume of fluid (VOF) method and Curle’s analogy method) to predict the acoustic emission characteristics of the single bubble. The pressure fluctuations are measured by adding the scale adaptive simulation (SAS) concept to the unsteady reynolds-averaged Navier–Stokes (URANS) simulation, which resulted in precise extraction of the flow fluctuations and thus the accurate simulation of the acoustic pressure fluctuations is achieved. Additionally, the analysis of the noise production mechanism is developed by implementing the Acoustic Perturbation Equations (APE) in the new solver. An alternative version of the acoustic technique is proposed to estimate the acoustic pressure fluctuations during the bubble rising process at an orifice submerged in water. The dynamic responses and the time–frequency analyses of the bubble indicate that the numerical simulation covers the main features of the principal acoustic components and can successfully predict the natural frequency of the bubble’s dynamic behaviors.

The aerodynamic breakup physics of water droplets in shock–laden flows are investigated in this study. One-dimensional numerical simulations based on a hybrid Eulerian–Lagrangian approach are performed to study the interactions between propagating shock waves and monodispersed evaporating water droplets with breakup. Two-way coupling for the interphase exchanges of mass, momentum, and energy is considered for the two-phase gas–droplet flows. Parametric study on the droplet evaporation, motion, heating, and breakup dynamics is performed, through considering initial droplet diameters of 20–80 μm and incident shock Mach numbers (({M}_{0})) of 1.3–4.0. The resultant initial droplet Weber numbers range between 10.0 and 4758.3, which cover the bag, bag-and-stamen, sheet stripping, and wave crest stripping breakup modes. The distance for breakup completion behind the transmitted shock and the resultant diameter all decrease with increased incident shock Mach number. When ({M}_{0}) ≥ 2.1, shock attenuation is also intensified with droplet diameter besides volume fraction under fixed droplet mass loading. Furthermore, net momentum transfer from the droplets to carrier gas (instead of in the opposite direction as extensively observed) occurs when ({M}_{0}) ≥ 2.1, mainly caused by the high temperature of post-shock gas and small diameter of broken droplets under strong incident shocks. A scale analysis shows that the momentum and energy transfer rates because of droplet evaporation have comparable magnitudes respectively to the counterparts from drag force and convective heat transfer. This is particularly true in the regions far off the shock front when ({M}_{0}) ≥ 2.1.

This study investigates laser-induced ignition in a model-rocket combustor through computational simulations. The primary focus is on characterizing successful and unsuccessful ignition scenarios and elucidating the underlying physical mechanisms. Large Eddy simulations (LESs) are utilized to explore laser-based forced ignition in a methane–oxygen combustor, with attention given to the intricate interplay of factors such as initial condition variability and turbulent flow field. Perturbations in laser parameters and initial flow conditions introduce stochastic behavior, revealing critical insights into ignition location relative to the fuel-oxidizer mixture. A significant methodological innovation lies in the adaptation of established image analysis techniques to track and monitor the transport of hot packets within the flow field. By extending these tools, the study provides insights into the interaction between ignition kernels and flammable gases, offering a more comprehensive understanding of the phenomenon. Results highlight the interplay between hydrodynamic ejections from the laser spark and turbulent fluctuations in the background flow. Indeed, the hydrodynamic ejection emanating from the laser spark, which typically plays a central role for isolated kernels in quiescent flows, competes with the entrainment velocity if its values are within the same order of magnitude and if the laser focal location is particularly close to the shear layer’s edge.

Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework by Weatheritt and Sandberg (J Comput Phys 325:22–37, 2016a) to develop advanced physics closure models. Adaptive symbols utilize gradient information to learn locally optimal numerical constants during model training, for which we investigate two types of nonlinear optimization algorithms. The second contribution of this work is implementing two regularization techniques to incentivize the development of implementable and interpretable closure models. We apply (L_2) regularization to ensure small magnitude numerical constants and devise a novel complexity metric that supports the development of low complexity models via custom symbol complexities and multi-objective optimization. This extended framework is employed to four use cases, namely rediscovering Sutherland’s viscosity law, developing laminar flame speed combustion models and training two types of fluid dynamics turbulence models. The model prediction accuracy and the convergence speed of training are improved significantly across all of the more and less complex use cases, respectively. The two regularization methods are essential for developing implementable closure models and we demonstrate that the developed turbulence models substantially improve simulations over state-of-the-art models.

A high-order computational fluid dynamics code was developed to simulate the compressible Taylor–Green vortex problem by means of large-eddy (LES) and direct numerical simulations. The code, BASIC, uses explicit central-differencing to compute the spatial derivatives and explicit low storage Runge–Kutta methods for the temporal discretization. Central-differencing schemes were combined with relaxation filtering or with splitting formulas to discretize convective derivative operators. The application of split forms to convective derivatives generally guarantees good stability properties with marginal dissipation. However, these types of schemes were found to be unsuited to perform implicit large-eddy simulations (ILES). The minimally dissipative schemes showed acceptance performance, only when combined with a sub-grid scale model. The relaxation-filtering strategy, on the other hand, although more dissipative, was proven to be more adequate to perform ILES. We showed that reducing the filter dissipation, by optimizing its damping function, has a positive impact in the flow solution. When performing ILES, the utilization of split formulas in conjunction with relaxation filtering has equally yielded promising results. This combined approach enhances numerical stability while preserving low levels of numerical dissipation.

The Benchmarck on the Aerodynamics of a Rectangular 5:1 Cylinder is studied using a data-driven technique which bridges numerical simulation and available experimental results. Because of intrinsic features of the tools used for investigation, in particular in terms of set-up and boundary conditions, significant discrepancies have been observed in the literature when comparing experimental and numerical results. An approach based on the Ensemble Kalman Filter is here used to optimize a synthetic turbulent inlet used as boundary condition in the numerical calculation, in order to reduce the discrepancy with the available experiments. The data-driven method successfully optimizes the boundary condition features, which produce a significant improvement of the accuracy in the prediction of the flow. These findings open perspectives of application towards the analysis of realistic cases, where boundary conditions are complex and usually unknown.