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.
When jet-based flow control, such as stall control and fluidic thrust vectoring, is employed, jet nozzles are commonly installed near a convex-curved surface. Numerous researchers have investigated the most fundamental curved-wall continuous jet. For synthetic jets, although numerous applied studies exist, fundamental investigations on cylindrical walls remain scarce. Synthetic jets have zero net mass flux; the vortex pair generated during the expulsion phase dominates the suction phase and establishes a time-averaged flow downstream. This study clarifies how a cylindrical surface influences the vortex pair and the resulting mean flow. We performed flow visualization using Particle Image Velocimetry and surface-pressure measurements for a synthetic jet issuing tangentially over a cylindrical surface. We examined the effects of stroke length (the distance fluid particle travels at the representative velocity during one oscillation period) and radius of curvature on the flow. The boundary layer on the wall organized into discrete vortices linked to the dimensionless stroke length, and both the time-averaged pressure distribution and separation point on the cylindrical surface depended on it. Moreover, the vortex street and time-averaged-flow characteristics can be parameterized using a nominal dimensionless stroke based on the radius of curvature rather than the slot width typically used as the representative length.
Jet impingement cooling is a traditional method that provides higher heat transfer rates for cooling components in many engineering applications to increase the life and efficiency of the components. The present work focuses on the experimental measurement of heat transfer on a flat plate, cooled by an obliquely impinging low-speed jet using a recently developed green-spectrum-based temperature-sensitive paint (TSP). The TSP is used to measure temperature distributions on the hot plate (at 333 K) when subjected to a relatively cold jet (298 K). Surface temperature data are extracted from the TSP luminescence emission captured by a charge-coupled device camera. The experiments are performed for different impingement angles and jet exit Reynolds numbers. Heat flux is calculated using the modified Cook-Felderman method, which employs a semi-infinite and finite thickness model for the hot plate. The obtained heat flux is compared with the commercially available differential thermophile heat flux (point) sensor. Temporal self-similarity in heat flux is observed. The corresponding normalized heat flux is identified. A feedforward artificial neural network is trained to link the normalized heat flux distribution with the experimental parameters.
Flow-induced vibrations in wall-bounded flows remain a major challenge in many engineering applications. While fully predictive turbulence-induced vibration simulations generally require scale-resolving approaches, unsteady Reynolds-averaged Navier–Stokes (URANS) methods are employed in practice due to their favorable computational cost. However, their predictive reliability is limited by uncertainty in turbulence-model parameters, structural model–form error, and deficiencies in representing turbulent fluctuations. This work presents a surrogate-based Bayesian calibration framework for the SST k – ω model, incorporating anisotropic pressure-fluctuation reconstruction to quantify and reduce model uncertainty. Global Sobol’ sensitivity analysis, surrogate modeling, and Bayesian inference are combined to identify influential parameters and assimilate high-fidelity reference data. The framework is applied to turbulent channel flow, turbulent annular flow, and a blunt-end cantilevered-rod configuration. Across all cases, the specific dissipation-rate coefficient α_ω1 and the turbulent kinetic-energy dissipation coefficient β^* consistently dominate variability in mean-flow quantities and reconstructed fluctuation statistics. Bayesian calibration constrains these parameters to consistent regions of the admissible space, while less influential coefficients remain weakly informed. Gaussian process regression and polynomial chaos expansion surrogates yield nearly identical posterior and predictive distributions, demonstrating robustness to surrogate choice. Remaining discrepancies with reference data are attributed primarily to structural turbulence model limitations rather than parametric uncertainty.
