Fast-responding pressure-sensitive paint (PSP) is required for measuring unsteady surface pressure in cryogenic wind tunnels. However, the temporal response of conventional cryogenic PSP (cryoPSP) is limited by oxygen diffusion in the polymer binder. This study presents a fast-responding cryoPSP based on a porous poly[1-(trimethylsilyl)-1-propyne] (PTMSP) binder containing hydrophobically treated colloidal silica nanoparticles. The affinity between the trimethylsilyl-modified silica particles and PTMSP enables the formation of a porous coating with improved adhesion to model surfaces, even at high particle loadings. The response under cryogenic conditions was estimated using a model that accounts for oxygen diffusion in the binder and the luminescence lifetime of Pt(II) meso-tetra(pentafluorophenyl)porphine. For a formulation containing 400 nm colloidal silica particles at 95 wt%, the model-estimated cutoff frequency was 2833 Hz at 150 K and an oxygen partial pressure of 100 Pa. A sensitivity analysis showed that the cutoff frequency remained above 1 kHz even when the diffusion time constant was increased by a factor of 2.8. Pressure and temperature sensitivities remained stable for up to 119 h after coating. These results suggest that the proposed porous cryoPSP extends the measurable frequency range for unsteady pressure measurements in cryogenic wind tunnels, although direct response-time validation under cryogenic conditions remains necessary.
Correlation-based transition transport models are widely used in Reynolds-averaged Navier–Stokes (RANS) simulations to predict laminar–turbulent transition. However, it is observed that some of these models exhibit dependency on the initial flow conditions and may not respond adequately to time-varying flowfields in unsteady computations, especially on coarser grids. The present work introduces a modification to the intermittency transport equation to address these limitations. The modified formulation is first assessed using canonical steady-state test cases covering a wide range of Reynolds numbers. The results demonstrate that the modified formulation significantly reduces the sensitivity of the model to the initial conditions and improves the consistency of the predicted transition location. The approach is then applied to two unsteady test cases. In these simulations, the modified model demonstrates an improved ability to adapt the intermittency field in response to changes in the flowfield, leading to a behavior that is more consistent with the underlying transition criterion, particularly on coarser grids.
This paper presents the principles and techniques employed to address the AIAA Second Uncertainty Quantification Challenge Problem for Aerodynamics. Two distinct model updating strategies for uncertainty quantification and reduction are investigated: a sensitivity-based approach and a data-driven diffusion-model-based approach. Both methods aim to infer the posterior distributions of key aerodynamic input parameters, which are subsequently propagated through either a surrogate model or XFOIL to obtain posterior-predictive lift, drag, and moment coefficients. The diffusion model shows consistent posterior-predictive responses across both the surrogate and XFOIL, whereas the sensitivity-based approach produces sharply concentrated parameter posteriors that may represent a more limited uncertainty range. Across multiple operating configurations, the two approaches produce coherent predictive aerodynamic responses despite their differing parameter posterior structures. The findings suggest that, in uncertainty quantification challenges of this type, it is not only essential to characterize multiple sources of uncertainty but equally, if not more, critical to employ an appropriate inverse uncertainty-reduction mechanism capable of leveraging the necessarily limited observational data. Designing such inverse processes to extract maximal information from sparse data remains a central difficulty and an important direction for future research in aerodynamic uncertainty quantification.
Composite open-shell structures manufactured using low-cost wet layup processes in electric aircraft are prone to resin unevenness and interlayer voids, resulting in multiple manufacturing defects. These defects induce localized stiffness discontinuities and complex nonlinear interactions, which pose severe challenges for accurate dynamic prediction. To address this issue, this study develops a Multi-source Data Fusion Enhanced Physics-Informed Transformer (MDFEPT) framework that integrates multisource data with physics-based stiffness boundary constraints that regularize the network and enforce physical consistency. This hybrid strategy enables accurate modeling of structures with arbitrary defect configurations. Trained on combined numerical–experimental data sets covering isolated and interacting defects, MDFEPT effectively captures nonlinear vibration features and mode coupling under varied operating conditions. The prediction accuracy exceeds 95%, and the mean square error is on the order of [Formula: see text], clearly outperforming conventional neural networks in both fidelity and stability. By embedding interpretable physical knowledge into deep learning, the framework bridges idealized models and real manufacturing scenarios, providing a robust tool for structural health monitoring and safety assurance of composite components in electric aircraft.
Understanding unsteady aerodynamic forces during gust encounters is critical for the design of aircraft, rotorcraft, and wind turbines, particularly in regimes characterized by extensive boundary-layer separation. While pitching and plunging airfoil vortex dynamics are well-studied, characterization of airfoils in periodic longitudinal gusts under massively separated flow remains limited. This study experimentally investigates vortex shedding from an SD7003 airfoil at a poststall angle of attack ([Formula: see text]) encountering harmonic longitudinal gusts, focusing on a subharmonic lock-in case ([Formula: see text], [Formula: see text], [Formula: see text]). Using phase-averaged particle image velocimetry, the relationship between vorticity generation, leading-edge vortex (LEV) roll-up, and longitudinal gust parameters is explored. Results show that LEV detachment is triggered not by the rear reattachment point reaching the trailing edge, but by the outer flow’s ability to maintain curvature around the LEV, dictated by local pressure gradients and momentum. Turbulent shear stress measurements reveal that outer-flow reattachment anchors the LEV, reducing cycle-to-cycle positional variability. Furthermore, the phase-averaged lift coefficient correlates more closely with circulation within a chord-bounded control volume than with LEV circulation alone. These findings inform the development of future low-order models and flow-control strategies for gust responses in the case of separated flow.
