We study the viscous spatial linear stability characteristics of the time-averaged flow in turbulent subsonic jets issuing from serrated (chevroned) nozzles, and compare them to analogous round jet results. Linear parabolized stability equations (PSE) are used in the calculations to account for the non-parallel base flow. By exploiting the symmetries of the mean flow due to the regular arrangement of serrations, we obtain a series of coupled two-dimensional PSE problems from the original three-dimensional problem. This reduces the solution cost and manifests the symmetries of the stability modes. In the parallel-flow linear stability theory (LST) calculations that are performed near the nozzle to initiate the PSE, we find that the serrated nozzle reduces the growth rates of the most unstable eigenmodes of the jet, but their phase speeds are approximately similar. We obtain encouraging validation of our linear PSE instability wave results vis-à-vis near-field hydrodynamic pressure data acquired on a phased microphone array in experiments, after filtering the latter with proper orthogonal decomposition (POD) to extract the energetically dominant coherent part. Additionally, a large-eddy simulation database of the same serrated jet is investigated, and its POD-filtered pressure field is found to compare favourably with the corresponding PSE solution within the jet plume. We conclude that the coherent hydrodynamic pressure fluctuations of jets from both round and serrated nozzles are reasonably consistent with the linear instability modes of the turbulent mean flow.

This article reports on the development of a fast method for generating the aerodynamic database for subsonic flow over a missile. At present, this is typically achieved using RANS-based CFD, which is expensive for the complex missile geometries and the multiplicity of operating conditions to be evaluated. The presented reduced-order model (ROM) provides a reasonably accurate prediction of the aerodynamic coefficients of the missile (and, in fact, the full flow field around it) within half a minute. In particular, in the interpolative regime, prediction errors for all coefficients are typically less than 1% of their respective maximum values encountered in the database, with extrapolation incurring more error. The empirical approach ‘learns’ from the CFD solutions calculated for a few operating conditions, and then is able to make predictions for any other condition within a feasible set. The learning employs proper orthogonal decomposition (POD) to characterise the most important features of the flow. The prediction is posed as an optimisation problem that aims to find the flow solution as a linear combination of the above POD modes that minimises the residual of the governing equations. Innovations on the prevailing POD-ROM approach include novel implementation of boundary conditions, simplified computation of the aerodynamic coefficients, and a procedure for choosing modelling parameters based on extensive cross-validation. Challenges overcome in application to a problem of industrial relevance are discussed.