Flow, Turbulence and Combustion最新文献

We investigate the Lagrangian rotational and deformation dynamics of long flexible fibers in a turbulent channel flow at a shear Reynolds number equal to 300, using one-way coupled Euler-Lagrange direct numerical simulations. The fibers, modeled as chains of sub-Kolmogorov rod elements with prescribed bending stiffness, span more than one order of magnitude in length and are analyzed in three wall-normal slabs covering the near-wall, log-layer and bulk regions. We compute autocorrelations of the tumbling and spinning rates, power spectra of the fiber’s end-to-end vector, and waiting-time distributions of intense tumbling events. Spinning decorrelates on a fixed multiple of the local Kolmogorov time, independent of fiber length, whereas tumbling develops a memory that grows with fiber length by factors of two to five, reversing the ordering of the two timescales between the shortest and longest fibers. Flapping spectra separate into a high-frequency regime in which all fiber lengths carry the same energy in absolute terms and a low-frequency regime in which the energy increases with fiber length. Intense tumbling exhibits approximately exponential, weakly clustered waiting-time statistics whose rescaled form is universal across slabs, fiber lengths and bending rigidities, whereas the event rate depends on both fiber length and rigidity.

In this study, we investigate the role of phase separation in transcritical oxygen-hydrogen mixing layers under liquid rocket engine-relevant conditions. High-fidelity simulations of a splitter-plate configuration are performed to compare two thermodynamic approaches, a single-phase (SP) model and a multiphase model based on the vapor–liquid equilibrium (VLE) concept. Three operating pressures are considered, spanning near-critical to highly supercritical conditions, with fixed stream Reynolds numbers so that pressure is the only varied parameter. The two models share the same overall flow topology but follow different thermodynamic paths within the two-phase region. The SP model predicts local temperatures below the oxygen injection value, a non-ideal mixing effect absent under VLE, whose mixing path increases monotonically with hydrogen content. Accounting for phase separation results in a thinner thermal mixing layer, reduced temperature fluctuations, a lower growth rate, and modified near-wake dynamics, with a longer recirculation region behind the lip. Conditional statistics of the scalar dissipation rate show that the VLE model predicts more intense molecular mixing at compositions inside the two-phase region, which encompasses the stoichiometric condition most relevant to ignition. The spectral analysis localizes the model effect at the small scales for velocity, while density is affected across all scales. All discrepancies are largest at near-critical pressure and attenuate as the operating pressure moves away from the critical point.

Accurate prediction of Flame-Wall Interactions (FWI) is crucial for the design of compact, high-efficiency combustors but remains a significant challenge for Reynolds-Averaged Navier-Stokes (RANS) simulations. This study presents the development and validation of a lightweight data-driven closure for the mean reaction rate, trained on high-fidelity Direct Numerical Simulation (DNS) data of a V-shaped turbulent premixed flame. To ensure numerical stability and physical consistency, the training methodology incorporates a logarithmic transformation of the reaction rate target and a novel error-function transformation of the wall distance, effectively localizing the model’s sensitivity to the near-wall quenching zone. A-priori validation demonstrates that the neural network accurately captures the mean reaction rate statistics and extrapolates successfully to unseen spatial regions. However, a-posteriori assessment in coupled RANS simulations reveals a critical sensitivity to input feature consistency. The full-feature model (7 input parameters), which includes turbulent kinetic energy and dissipation rate as inputs, fails to predict the correct quenching behavior due to significant discrepancies between the DNS-extracted turbulence quantities used for training and the corresponding quantities obtained from RANS. In contrast, a reduced-feature model (3 input parameters), relying exclusively on the mean progress variable, its gradient, and the transformed wall distance, demonstrates remarkable robustness. By decoupling the prediction from uncertain turbulence quantities, the model with 3 input parameters accurately reproduces the V-flame structure, achieving performance comparable to a manually tuned algebraic closure. Furthermore, the reduced model exhibits strong generalization capabilities, successfully predicting the transient physics of a Head-On Quenching (HOQ) configuration that was not present in the training dataset. These findings highlight that for data-driven RANS closures, selecting input features that remain consistent across fidelity levels, is paramount for achieving predictive stability and generalization.

We propose corrections to the diffusion equation providing solutions consistent with nano-scale and multi-scale physics. These corrections can be useful for various applications, including nano-technology, strong-gradient and rarefied transport. The problem is that the classical diffusion equation (which terms are in the core of fluid dynamics models) does not carry information about the structure of the fluid, and fails, for example, at the nano- and multi-scale. As we show here, this information was lost due to the way the original equation was derived. In this paper, we reconsidered Einstein’s derivation of the diffusion equation to eliminate truncation of higher terms. The results give the finite difference equation of mass transport where information about the fluid structure is not lost; this is because the finite-difference length is not a numerical discretization parameter but a mean free path, λ, a fluid property determined by the structure of the fluid and by molecular mechanisms of collision. For this reason, the (λ-discretized) finite difference diffusion equation becomes consistent with nano-scale and multi-scale physics. This is demonstrated by specific examples.

Surface roughness has a critical influence on the aerothermal and combustion behavior of gas turbine burners, particularly with the growing use of additively manufactured (AM) components that exhibit inherently rougher surfaces than conventionally machined parts. This study investigates the impact of surface roughness on flow and flame dynamics in a premixed jet burner (PJB) operating under both isothermal-air and reacting hydrogen-fired conditions. Experimental measurements using Laser Doppler Anemometry (LDA) and OH* chemiluminescence were complemented by Computational Fluid Dynamics (CFD) simulations employing Reynolds-Averaged Navier–Stokes (RANS) and Detached Eddy Simulation (DES) frameworks. Building upon existing equivalent sand-grain roughness (kₛ) models, a novel correlation incorporating both measured roughness parameters and bulk flow velocity was developed to improve predictive accuracy for resolved boundary layers (y⁺ ≈ 1), a regime in which k_s⁺ > y⁺ is common and standard roughness-function formulations lose validity. The new correlation demonstrated strong agreement with experimental data in isothermal simulations. When extended to reacting hydrogen–air flames, RANS simulations successfully reproduced roughness-induced flow redistribution and flame shortening, although DES tended to overpredict bluff-body effects and underperform under lean conditions. Overall, the proposed k_s correlation, validated under isothermal conditions, provides a computationally efficient framework for modelling surface-roughness effects, and its extension to reacting hydrogen flames, particularly under RANS, offers a promising basis for future development toward additively manufactured gas turbine components.

Effects of wind direction in realistic urban boundary layers remain difficult to characterize systematically because local flow patterns, and turbulent statistics within a realistic urban canopy may respond differently to changes in the approaching wind. To investigate these multi-scale directional impacts, we conducted high-resolution large-eddy simulations of the atmospheric boundary layer over the Zona Universitària Pedralbes district in Barcelona. The computational meshes employ fourth-order spectral elements, yielding pedestrian-level resolutions below 1 m and resulting in approximately (bf{5.0times 10^8}) degrees of freedom. Sixteen wind directions uniformly distributed over 360({}^circ) are simulated to capture the full directional response of the urban fabric. At pedestrian level, the flow exhibits strong directional sensitivity, with moderate rotations of the incoming wind reorganizing the dominant flow pathways from continuous accelerated corridors, when winds align with major street axes, to fragmented cellular patterns under oblique inflows. These directionally activated channeling corridors govern local ventilation and sheltering. Yet, despite this pronounced local variability, double averaged vertical profiles of mean velocity and turbulence intensity collapse remarkably across all wind directions. Two distinct inflection points are consistently identified in the mean velocity profiles: one located slightly below the average building height, (bf{H_mathrm{avg}}), confirming the persistence of a shear-driven mixing-layer regime even in highly heterogeneous urban morphology, and a second, deeper inflection point near pedestrian level, approximately 0.08–0.10 (bf{H_mathrm{avg}}) above the ground, that marks the transition between recirculating near-ground flow and more connected transport within the urban canopy. Subdomain analysis further shows that this lower inflection point is morphology- and direction-dependent, reflecting changes in the transition between sheltered near-ground recirculation and transport within the urban canopy as preferential flow routes are activated or suppressed by the incoming wind direction. It is shown that urban aerodynamic response is simultaneously robust at the neighbourhood scale, behaving quasi-isotropically in the double-averaged sense, and strongly directional at the local scale, where the activation of preferential ventilation pathways dictates the spatial distribution of momentum and turbulence.

This numerical study, based on steady RANS simulations, investigates the effects of passive, active, and combined flow control techniques on a simplified vehicle model (Ahmed body) under highway-relevant configurations, including platooning and overtaking, at 70 mph (31.3 m/s). The study primarily focuses on an innovative passive flow control method, Rear Linking Tunnels (RLTs), previously introduced by the authors. It also examines the performance of the Single Dielectric Barrier Discharge Actuator (SDBD) as an active flow control strategy and evaluates its combined effects with RLTs on both the target vehicle and surrounding vehicles during platooning and overtaking maneuvers. The results highlight notable variations in drag and lift coefficients, showing that the effectiveness of these flow control methods differs significantly between platooning/overtaking scenarios and single-vehicle cases.

This study investigates the scale effect in hydrogen-air explosions using high-fidelity 2D and 3D numerical simulations with detailed chemistry. Simulations are conducted in an obstacle-laden channel, varying the geometric scaling factor, hydrogen mole fraction and dimensionality. Results show that flame acceleration increases significantly with geometric scale, primarily due to the enhanced growth of the turbulent flame surface area. Additionally, the local burning rate per unit flame area exhibits a complex dependency on scale and mixture composition, characterized by flame stretch and Markstein numbers, particularly in lean mixtures. It is found that the normalized critical distance to reach a specific velocity decreases as a power-law with increasing scale, confirming that explosions accelerate more effectively over shorter relative distances in larger facilities. While 2D simulations suggest that the corresponding power-law exponents are consistent across a wide range of equivalence ratios, 3D results reveal a critical deviation. The scale effect is significantly more pronounced in 3D than in 2D, where the flame acceleration is found to be significantly stronger and this holds particularly for lean mixtures. The findings are consistent with scale-dependent experimental results obtained in the laboratory-scale MiniRUT and the industrial-scale RUT facilities. They further demonstrate that two-dimensional approximations may substantially underpredict the scale sensitivity of lean hydrogen explosions. In this case, 3D effects are identified as crucial for accurately predicting the severity of hydrogen explosions.

The interaction between spray dynamics, turbulence, and combustion strongly influences the performance of compression-ignition engines operating with alternative fuels. This study employs a validated three-dimensional computational fluid dynamics (CFD) model to investigate the effects of diesel–methanol–ammonia ternary blends on in-cylinder flow, turbulent mixing, combustion characteristics, and emissions in a heavy-duty diesel engine. The simulations show that increasing ammonia content prolongs spray penetration, sustains higher turbulence kinetic energy around top dead center, and delays ignition because of its high latent heat of vaporization and low reactivity. In contrast, methanol enhances atomization, accelerates evaporation, and intensifies premixed combustion, resulting in a 7.9% increase in peak firing pressure relative to diesel. The combined action of methanol and ammonia produces more stable combustion than ammonia-only blends by moderating the pressure-rise rate while maintaining favorable heat-release characteristics. A specifically identified ternary blends improve the conventional NOₓ–soot trade-off, achieving 16.5% lower NOₓ emissions together with 3.5% lower soot emissions than neat diesel, whereas high-ammonia formulations substantially suppress soot because of their carbon-free chemistry but increase CO and unburned hydrocarbon emissions owing to slower oxidation kinetics. These results demonstrate that appropriate diesel–methanol–ammonia blending effectively tailors spray evolution, turbulence development, combustion phasing, and emission characteristics, providing valuable guidance for the design of stable and cleaner low-carbon compression-ignition combustion systems.

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