Aerospace structures increasingly use advanced, anisotropic, and heterogeneous materials whose behavior spans multiple length scales—demanding rigorous multiscale modeling for credible design and analysis. The Mechanics of Structure Genome (MSG) offers a paradigm shift in this arena: it minimizes information loss, overcomes limitations of traditional modeling approaches, and confines all approximations to the constitutive modeling step. In doing so, MSG links microstructural features directly to structural performance while remaining compatible with mainstream aerospace design tools, enabling rapid insertion of new materials early in the design cycle.

We have modeled turbulence for a century, and this entire journey has been guided by data and empiricism. Today we have more access to information, and at scale. The peculiar nature and demands of the turbulence modeling challenge, however, mean that one cannot completely bypass mechanistic modeling; data-driven elements must be cast as consistent and constrained augmentations of established RANS/LES frameworks. A brief survey will be provided on the progress over the past decade in embedding learned model augmentations. Particular emphasis will be paid to the balance between parsimony and constraint: Use of small set of physically-inspired, locally non-dimensional, bounded features that promote a near one-to-one map from features to an identifiable augmentation, and keeping training and prediction environments consistent by learning to work with operational variables. Pitfalls are subtle, not dramatic—apparent success on curated benchmarks masks brittleness at the point of use. Candid challenges remain: predictive capabilities across geometries and flow regimes, encapsulating nonlocality, identifiability from finite data, and numerical robustness. The thesis is that generality must be earned—by constraining what the particular modeling scenario (rather than purely physical considerations) demands, limiting capacity to what the data can identify, and aligning training with how we actually compute, and what we seek to predict—so data-driven closures become dependable, auditable components of mission-critical CFD. Success in this endeavor also demands principled design of experiments for information-efficient and active data acquisition, and rigorous uncertainty quantification to certify predictions and define operating envelopes.

Based on our experience and passion for the subject, we attempt a lucid description of the activity called Turbulence Modeling, its motivation, its rules, its unwritten principles, its sources of information, and sadly its many fallacies. Modeling is an essential enabler for CFD, and most of the time the model constitutes a gift to the community by the individuals and their institutions, whether government, industry, or university. Prestige is a motivation, but progress is its own reward. We contrast Intuitive and Constructed models. Rules begin with dimensional analysis, but include proper behavior at the edge of a turbulent region. Debatable guiding principles include the requirement for the Partial Differential Equations to be local, and the relationship with the exact Reynolds-Stress transport equations. Information comes from experiments and Direct Numerical Simulations, in both simple and complex flows. All the classical models are shown to suffer a "Structural Limitation" expressed by a General Law of the Wall which is physically incorrect. Fallacies include the idea that the Boussinesq approximation returns an isotropic stress tensor, the importance of realizability, the distinction between normal and off-diagonal Reynolds stresses, and of course the idea that one-equation models don't work.

Reduced-complexity models have proven valuable for analyzing, understanding, and controlling fluid flows. Often, these models require data as an a priori input or as an a posteriori point of comparison. We present a publicly accessible database specifically designed to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The database contains time-resolved data for six distinct datasets: a large eddy simulation of a turbulent jet, direct numerical simulations of a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry measurements for the same boundary layer at several Reynolds numbers, direct numerical simulations of laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force measurements of an airfoil encountering a gust, and a large eddy simulation of the separated, turbulent flow over an airfoil. For each dataset, we describe the flow setup and computational/experimental methods, catalog the data available in the database, and provide examples of how these data can be used for reduced-complexity modeling. All data can be downloaded using a browser interface or Globus. We envision that the common testbed provided by the database will help the fluid mechanics community clarify the distinct capabilities of new and existing reduced-complexity modeling methods. To this end, a subset of the data will serve as the starting point for a new series of challenges posed to the community in data-driven forecasting, sensing, and compression.

This seminar presents a series of interpretable and generalizable data-driven methods for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering, but their turbulence models often struggle with separated flows. While recent advances in data-driven techniques have improved model accuracy in complex separated flows, these models frequently lack interpretability and generalizability, sometimes even reducing accuracy in simple wall-attached flows. Our first approach integrates symbolic regression (SR) with the field inversion and machine learning (FIML) framework to derive an interpretable analytical expression for the correction term β using field inversion data. This expression demonstrates generalizability across diverse cases, including 3D separated flows not included in the training set. However, it occasionally fails to consistently maintain the baseline model's accuracy in wall-attached flows. To address this limitation, we introduce a conditioned field inversion approach that confines corrections to regions outside the attached boundary layer. The resulting SR-CND model retains the capability of correcting separated flows comparably to classical field inversion, while preserving accuracy in attached boundary layers - a feature lacking in the classical method. This model has been validated through several 2D and 3D cases, including configurations from the high-lift prediction workshops (HPWs). Results demonstrate that the flow separation prediction accuracy is superior to that of the baseline model. Finally, we propose a non-local modeling approach to further enhance generalizability. By constructing a transport equation for the correction term β and calibrating its parameters for separated flows using data-driven methods, this model shows improved accuracy in various separated flows compared to the SR-CND model.

Large-eddy simulations were carried out to describe the subsonic flow over a wing section, using Coanda-jet circulation control to augment lift. The computations correspond to an experimental investigation, where the section geometry consisted of a modified supercritical airfoil, with a jet that is blown over a one quarter circular cylindrical trailing-edge. Because the configuration does not include a slotted trailing-edge flap, the mechanical complexity and system weight may be reduced. As the experimental data consisted of only force measurements, computations were performed to augment the experiment and describe unsteady features of the flow. High-fidelity solutions were obtained to the unsteady three-dimensional Navier-Stokes equations, at a chord-based Reynolds of 475,000 and freestream Mach number of 0.1. Details of the numerical approach are outlined. For the baseline flow without control, several angles of attack were considered, and a grid-resolution study was carried out to assure numerical accuracy. Comparisons are made between the respective cases and with the available experimental data. In control cases, several different forms of control were considered. These consisted of: a) constant blowing across the entire span of the wing configuration, b) the use of a segmented-nozzle arrangement with an oscillating jet, and c) pulsed blowing of the Coanda jet. The use of a segmented nozzle and pulsed blowing were employed to reduce jet-mass flow requirements. Results of the various cases are compared and the effectiveness of control is quantified. Frequencies of flowfield unsteadiness is characterized, and physical features of the flowfields are elucidated. Videos are shown to illustrate unsteady features of the flowfields.

Variational quantum algorithms are particularly promising early applications of quantum computers since they are comparatively noise tolerant and aim to achieve a quantum advantage with only a few hundred qubits. They are applicable to a wide range of optimization problems arising throughout the natural sciences and industry. To demonstrate the possibilities for the aeroscience community, we describe how variational quantum algorithms can be utilized in computational fluid dynamics.

Bryan Kelchner, VP of Engineering at Teknicare, Inc., a company that specializes in directed-energy (DE) systems engineering, design, development, integration, and testing of beam control and laser systems for DoD; Victor Beazel, Senior Dynamics and Controls Engineer at Teknicare; and Steven Griffin, Boeing Senior Technical Fellow will talk about challenges of airborne and space-based high-energy laser systems’ line-of-sight control.

Large transient excursions in angle of attack promote unsteady separation and dynamic stall over a wing, allowing it to briefly exceed static-stall conditions but also inducing potentially undesirable variations in aerodynamic loading or structural response. This phenomenon is prevalent in a number of engineering applications, including helicopter rotors, maneuvering aircraft, wind turbines, and wing–gust/wing-wake encounters. Thus, its prediction and control are of great interest. Our group at AFRL has performed a large computational campaign over recent years coving a broad range of flow conditions, planforms, and kinematics to uncover the viscous mechanisms preceding stall onset. In these studies, we have documented the dominant role of a small-scale laminar separation bubble (LSB) on the initiation of dynamic stall, which guided the development of a novel low amplitude, high-frequency control strategy to exploit the LSB dynamics for transient stall suppression. Although originally designed for nominally 2D wing sections, the control technique was successfully extended to finite wings because by exploiting the mostly spanwise-uniform LSB prior to stall. Swept wings exhibit a similar LSB stall precursor state, yet their eventual stall behavior (transient tip stall) is drastically different than the dynamic center-wing stall of its straight wing counterpart. Despite this, the targeted, high-frequency control is also remarkably successful at also suppressing tip stall, demonstrating the dominant and somewhat universal nature of the LSB across relevant configurations and its potential for manipulation. The presentation will provide an overview of the swept-wing separation control, while also highlighting a promising passive control strategy through micro-cavity actuation.

A detonation is an explosion driven by energy released behind a leading shock wave traveling through a background of energetic material. It is the strongest form of combustion wave, travels orders of magnitude faster than a flame, and once it is started, it is difficult to impossible to contain until all of the fuel is consumed.