Flow, Turbulence and Combustion最新文献

Quantifying the similarity of velocity vector fields is a critical task across numerous applications within fluid mechanics research, such as computational fluid dynamics validation and quantifying the levels of variability in a flow field. However, this task remains challenging for widely used vector comparison metrics at present. Traditional metrics include the Relevance Index (RI) and Magnitude Similarity Index (MSI) as well as their local versions, Local Structural Index (LSI) and Local Magnitude Index (LMI). These metrics, however, are often sensitive to low-velocity magnitude areas, which can distort the results. To address this, improved metrics like the Weighted Relevance Index (WRI), the Weighted Magnitude Index (WMI), and their amalgamated Combined Magnitude And Relevance Index (CMRI), have been introduced in the literature. Despite having reduced sensitivity to low-velocity areas, CMRI in its original form does not equally consider the significance of WRI and WMI, and introduces a degree of subjectivity. In the present work, we propose two enhanced metrics to address this problem: the modified CMRI for one-by-one flow field comparison, and the ensemble CMRI for comparing collections of vector fields. We compare their properties to the previously developed CMRI and spatially averaged CMRI, and investigate their usage in an applied example for quantifying cyclic variations in a flow from a combustion engine cylinder. The newly proposed metrics were found to more robustly isolate the effects of discrepant vector magnitudes and directions, leading to improved diagnostics of in-cylinder flow fields. In particular, the modified CMRI, which ensures equal treatment of WMI and WRI, can serve as a baseline for flow field comparison, providing a more objective target for quantifying flow similarity.

The work constructs the Gas kinetic solver (GKUA) to solve the Boltzmann model equation. Then the solver is respectively confirmed by NS, DSMC and experiments in typical conditions during reentry. Furthermore, the Maxwellian gas-surface interaction model is utilized to study the effects of reflected gas molecules state ((alpha_{e})) on flow field and aerodynamic properties at various extent of gas rarefaction. Results reveal the temperature is more susceptible to the state of reflected gas molecules compared with pressure. And the larger gas rarefaction tends to weaken the effects. As for surface heat flux, it just increases with (alpha_{e}) in lower gas rarefaction, while it behaves as the opposite trend with larger gas rarefaction. Freestream condition (H = 50km,Ma = 8.0,AOA = 60^{o}) is set for booster model in practical application. We experience the shrinks of aerodynamic pitch moment coefficient with more (alpha_{e}). These results are valuable for the construction of expired spacecraft forecasting platform which integrates exterior ballistics with aerothermodynamic computations to obtain tracks of spacecraft fragments in advance.

Experimental studies in industrial-relevant geometries are of great value for validating computational fluid dynamics (CFD). This study provides such data using Magnetic Resonance Velocimetry (MRV) in a replica of the single-phase and isothermal OECD/NEA-KAERI rod bundle benchmark exercise based on the MATiS-H test facility at the Korea Atomic Energy Research Institute (KAERI). The geometry is a 5 × 5 nuclear fuel assembly model of a pressurized water reactor with a split-type mixing grid inducing a swirling flow in each sub-channel. The Reynolds number based on the hydraulic diameter is 50,250. Recent studies demonstrated that MRV enables a comprehensive validation of CFD results in industrial-relevant test cases by providing time-averaged, three-dimensional measurement data from complex opaque structures. Nevertheless, there was still some potential left to improve the accuracy of the measurement. This study uses a newly developed MRV method to accurately measure the mean velocity vectors and the Reynolds stress tensor in three dimensions. The measurement volume reaches from shortly upstream to 10 times the hydraulic diameter downstream of the mixing grid. The estimated mean measurement uncertainty of the velocity data is 1.9% based on the bulk velocity of 1.72 m/s. In the case of the Reynolds stress data, the estimated mean uncertainty for each component is between 0.7 and 1.8% based on the square of the bulk velocity. The comparison to previously published Laser Doppler velocimetry measurements confirms the high accuracy of the reported 3D MRV data. The study includes a detailed description of the technique and boundary conditions. The measurement data is available to interested parties upon request.

Direct numerical simulations (DNS) have been utilised to investigate the impact of different thermal wall boundary conditions on premixed V-flames interacting with walls in a turbulent channel flow configuration. Two boundary conditions are considered: isothermal walls, where the wall temperature is set either equal to the unburned mixture temperature or an elevated temperature, and adiabatic walls. An increase in wall temperature has been found to decrease the minimum flame quenching distance and increase the maximum wall heat flux magnitude. The analysis reveals notable differences in mean behaviours of the progress variable and non-dimensional temperature in response to thermal boundary conditions. At the upstream of the flame–wall interaction location, higher mean friction velocity values are observed for the case with elevated wall temperature compared to the other cases. However, during flame–wall interaction, friction velocity values decrease for isothermal walls but initially rise before decreasing for adiabatic walls, persisting at levels surpassing isothermal conditions. For all thermal wall boundary conditions, the mean scalar dissipation rates of the progress variable and non-dimensional temperature exhibit a decreasing trend towards the wall. Notably, in the case of isothermal wall boundary condition, a higher scalar dissipation rate for the non-dimensional temperature is observed in comparison to the scalar dissipation rate for the progress variable. Thermal boundary condition also has a significant impact on Reynolds stress components, turbulent kinetic energy, and dissipation rates, showing the highest magnitudes with isothermal case with elevated wall temperature and the lowest magnitude for the isothermal wall with unburned gas temperature. The findings of the current analysis suggest that thermal boundary conditions can potentially significantly affect trubulence closures in the context of Reynolds averaged Navier–Stokes simulations of premixed flame–wall interaction.

The statistical behaviours of wall heat flux and wall shear stress and their interdependence during unsteady head-on quenching of statistically planar turbulent premixed flames within turbulent boundary layers due to heat loss through the cold wall have been analysed using three-dimensional Direct Numerical Simulation data with friction Reynolds numbers of (Re_tau =110) and 180. In both cases, the mean wall shear stress decreases during flame-wall interaction, whereas the mean wall heat flux magnitude increases with time as the flame approaches the wall and eventually assumes a maximum value before decreasing with the progress of flame quenching. The integral length scales of wall heat flux in both streamwise and spanwise directions have been found to grow with time after the maximum mean heat flux magnitude is obtained for the two (Re_tau) cases considered. However, the integral length scale of wall shear stress in the streamwise direction grows but the integral length scale of wall shear stress in the spanwise direction decreases with time after the maximum mean heat flux magnitude is reached. Moreover, the correlation coefficient between the wall heat flux magnitude and wall shear stress becomes increasingly negative while the mean wall heat flux increases with time, but this negative correlation weakens with the progress of flame quenching. The first few (i.e., most energetic) Proper Orthogonal Decomposition (POD) modes of wall shear stress and the wall heat flux magnitude have been found to capture the qualitative nature of the correlation between these quantities and their spatial variations. It is found that tens of most energetic POD modes are needed to capture the mean and variances of wall heat flux and wall shear stress. The number of most energetic modes, which contribute significantly to the statistics of both wall heat flux and wall shear stress, decreases with decreasing (Re_tau) and also with the progress of flame quenching due to the weakening of turbulence effects.

Accurately predicting turbulent combustion processes is fundamental for optimizing efficiency, reducing pollutant emissions, and ensuring operational safety in combustion systems. To this purpose, computational fluid dynamics (CFD) simulations are widely employed. In particular, large eddy simulations (LES) balance prediction accuracy with computational efficiency by resolving only the most energy-containing scales of turbulence and rely on modeling the turbulence-chemistry interactions (TCI) occurring at the smallest scales. Among the existing closures, the partially stirred reactor (PaSR) model incorporates finite-rate chemistry and estimates a cell reacting fraction based on the local Damköhler number to account for the subfilter-scale TCI. Although widely validated in CFD computations, the PaSR model was found limited by the way it computes the cell reacting fraction. To tackle this point, our study proposes a machine learning (ML) enhanced partially stirred reactor model for LES. A fully connected neural network is trained on direct numerical simulation (DNS) data of turbulent premixed jet flames to compute a correction coefficient for the cell reacting fraction. Maintaining the original model shape, this ML-enhanced closure aims at bridging the gap between physics-based models and advanced data-driven techniques. The proposed formulation not only improves the prediction accuracy of quantities of interest such as the heat release rate but also features computational feasibility and generalisation capabilities over a large range of LES grid refinement. This demonstrates the significant potential of ML-aided TCI closures in future applications of combustion engineering.

Control of chaotic systems has far-reaching implications in engineering, including fluid-based energy and transport systems, among many other fields. In real-world applications, control algorithms typically operate only with partial information about the system (partial observability) due to limited sensing, which leads to sub-optimal performance when compared to the case where a controller has access to the full system state (full observability). While it is well-known that the effect of partial observability can be mediated by introducing a memory component, which allows the controller to keep track of the system’s partial state history, the effect of the type of memory on performance in chaotic regimes is poorly understood. In this study we investigate the use of reinforcement learning for controlling chaotic flows using only partial observations. We use the chaotic Kuramoto–Sivashinsky equation with a forcing term as a model system. In contrast to previous studies, we consider the flow in a variety of dynamic regimes, ranging from mildly to strongly chaotic. We evaluate the loss of performance as the number of sensors available to the controller decreases. We then compare two different frameworks to incorporate memory into the controller, one based on recurrent neural networks and another novel mechanism based on transformers. We demonstrate that the attention-based framework robustly outperforms the alternatives in a range of dynamic regimes. In particular, our method yields improved control in highly chaotic environments, suggesting that attention-based mechanisms may be better suited to the control of chaotic systems.

A comprehensive understanding of the mechanisms of intrinsic flame instability, including hydrodynamic and thermodiffusive instabilities, is becoming more important with the move towards greater reliance on hydrogen as a zero-carbon fuel. While intrinsic flame instabilities have been studied extensively both numerically and experimentally, certain important features, including their onset, have been defined mainly by qualitative measures. This work proposes a quantitative marker to identify the onset of intrinsic flame instabilities derived from the statistics of the entropy equation. Direct numerical simulations were carried out for two-dimensional laminar premixed planar methane-air flames, with varying amounts of hydrogen addition up to 100% by volume. Entropy generation mechanisms were analysed based on contributions resulting from heat conduction, viscous dissipation, mass diffusion, and chemical reaction. Instability onset was shown to be characterised by increased data dispersion in all entropy generation terms. The dispersion was quantified by the statistical range, which increased for all locations within the flame as the flame transitioned into instability. Increasing hydrogen addition resulted in a delayed instability onset attributed to the decreasing hydrodynamic instability growth rate. The entropy generation rate due to viscous dissipation was found to be smaller in magnitude compared to other mechanisms, but it was found to be the most sensitive indicator of instability onset. This quantity is readily computed using data from numerical simulations and can be estimated from experimental data, suggesting its potential use as a marker of intrinsic flame instability.

Hydrogen may play an important role in gas turbine engines for achieving carbon neutrality and performing high-altitude missions. Hydrogen influence on the flame speed of aviation kerosene at low pressures was investigated using a constant-volume bomb. The laminar flame speed of aviation kerosene at atmospheric pressure exhibited a linear relationship with increasing hydrogen mass fraction, with a more pronounced promoting effect under fuel-rich conditions. Hydrogen promotion effects on normalized kerosene laminar flame speed are weaker at low pressures than those at atmospheric pressures. The addition of hydrogen and low pressure suppresses flame instability of aviation kerosene especially under fuel-rich conditions, thereby reducing the promoting effect of turbulence on fuel-rich flame propagation. A scaling law that accounted for the influence of flame stability was successfully constructed to characterize the turbulent flame speed of hydrogen-rich aviation kerosene under different conditions.

This study is concerned with Large Eddy Simulation of liquid jet atomization using a two-way coupled Eulerian-Lagrangian multiscale approach. The proposed framework combines Volume-of-Fluid interface capturing with Lagrangian Particle Tracking. The former is used to compute the core jet and large liquid elements in the near-nozzle region, whereas the latter is used to track the large number of small droplets in the dilute downstream region of the spray. The convective and surface tension sub-grid scale terms arising in the context of two-phase flow LES are closed using suitable models, and secondary atomization is considered by employing a modified version of the Taylor Analogy Breakup model. The introduced framework is used to simulate an oil-in-air atomization as well as the Diesel-like Spray A test case of the Engine Combustion Network. Compared to previous studies based on Eulerian-Lagrangian methods, the present work stands out for the high-fidelity numerical approach, the complex test cases and the detailed comparison of the results to experimental data, which indicates a promising performance.