Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

The implementation of advanced low-emission aircraft engine technologies and the reduction of noise from airframe, fan, and jet exhaust have made noise contributions from an engine core increasingly important. Therefore, meeting future ambitious noise-reduction goals requires the consideration of engine-core noise. This article reviews progress on the fundamental understanding, experimental analysis, and modeling of engine-core noise; addresses limitations of current techniques; and identifies opportunities for future research. After identifying core-noise contributions from the combustor, turbomachinery, nozzles, and jet exhaust, they are examined in detail. Contributions from direct combustion noise, originating from unsteady combustion, and indirect combustion noise, resulting from the interaction of flow-field perturbations with mean-flow variations in turbine stages and nozzles, are analyzed. A new indirect noise-source contribution arising from mixture inhomogeneities is identified by extending the theory. Although typically omitted in core-noise analysis, the impact of mean-flow variations and nozzle-upstream perturbations on the jet-noise modulation is examined, providing potential avenues for future core-noise mitigation.

Recent experiments on pure fluids have identified distinct liquid-like and gas-like regimes even under supercritical conditions. The supercritical liquid-gas transition is marked by maxima in response functions that define a line emanating from the critical point, referred to as Widom line. However, the structure of analogous state transitions in mixtures of supercritical fluids has not been determined, and it is not clear whether a Widom line can be identified for binary mixtures. Here, we present first evidence for the existence of multiple Widom lines in binary mixtures from molecular dynamics simulations. By considering mixtures of noble gases, we show that, depending on the phase behavior, mixtures transition from a liquid-like to a gas-like regime via distinctly different pathways, leading to phase relationships of surprising complexity and variety. Specifically, we show that miscible binary mixtures have behavior analogous to a pure fluid and the supercritical state space is characterized by a single liquid-gas transition. In contrast, immiscible binary mixture undergo a phase separation in which the clusters transition separately at different temperatures, resulting in multiple distinct Widom lines. The presence of this unique transition behavior emphasizes the complexity of the supercritical state to be expected in high-order mixtures of practical relevance.

A major barrier toward the practical application of lithium-oxygen batteries is the high overpotential caused by the precipitation of oxygen-reduction products at the cathode, resulting in poor cyclability. By combining first-principle calculations and reactive molecular dynamics simulations, we show that surface functionalization of 2D MXene nanosheets offers a high degree of tunability of the catalytic activity for oxygen-reduction and oxygen-evolution reactions (ORR/OER). We show that the controlled creation of active vacancy sites on the MXene surface enhances ORR in excess of a factor of 60 compared to graphene-based cathode materials. Furthermore, we find that increasing the ratio of fluorine vs. oxygen termination of the functionalized Ti₄N₃-MXene catalyst reduces the charge overpotential by up to 70% and 80% compared with commercial platinum-on-carbon and graphene catalysts, respectively. These results provide direct guidance toward the rational design of functionalized 2D materials for modulating the catalytic activity for a wide range of electrocatalytic applications.

Capturing OH(H ₃ O ⁺ ) in ionized water: Recent advances in liquid-phase ultrafast electron diffraction techniques make it possible to observe what has only been theoretically presumed to occur at short times upon interaction of ionizing radiation with liquid water. Lin et al . provide direct evidence for capturing the short-lived radical-cation pair OH(H ₃ O ⁺ ), which has been hypothesized for years to form after ionization of liquid water but was not structurally resolved previously (see the Perspective by Cao et al .). The authors trace dissociation and subsequent nonradiative structural relaxation around the ionization center. —YS