This work uses Arizona State University’s (ASU) newly developed high-speed vehicle stability and control screening methodologies to reverse-engineer famous United States Air Force (USAF) flight tests from the 1950s and 1960s. This thesis analyzes the root cause of Chuck Yeager's fateful 1953 supersonic spin in the Bell X-1A to become the "Fastest Man Alive". This thesis then takes a look back at Neil Armstrong's inadvertent atmospheric skip in the North American X-15 and his subsequent hypersonic flight months later. The fundamental flying qualities assessment shown in this work begins with calculating rigid-body frequencies and damping ratios of an aircraft to Military Standard (MIL) requirements, and uses these to create a full, classical stability and control analysis of a high-speed vehicle. Through reverse engineering the flight envelopes and missions for the above aircraft, it appears that the near-disasters of each flight were due to a confluence of then overlooked, yet fundamental, aerodynamic instabilities.

The Vortex-lattice method has been utilized throughout history to both design and analyze the aerodynamic performance characteristics of flight vehicles. There are numerous different programs utilizing this method, each of which has its own set of assumptions and performance limitations. This thesis highlights VORLAX, one such solver, and details its historic and modernized performance characteristics through a series of code improvements and optimizations. With VORLAX, rapid synthesis and verification of aircraft performance data related to wing pressure distributions, stability and control, and Federal Regulation compliance can be quickly and accurately obtained. As such, VORLAX represents a class of efficient yet largely forgotten computational techniques that allow users to explore numerous design solutions in a fraction of the time that would be needed to use more complex, full-fledged engineering tools. In the age of modern computers, one hypothesis is that VORLAX and similar “lean” computational fluid dynamics (CFD) solvers have preferential performance characteristics relative to expensive, volume grid CFD suites, such as ANSYS Fluent. By utilizing these types of programs, tasks such as pre- and post-processing become trivially simple with basic scripting languages such as Visual Basic for Applications or Python. Thus, lean engineering programs and methodologies deserve their place in modern engineering, despite their wrongfully decreasing prevalence.

Autonomic closure is a recently-proposed subgrid closure methodology for large eddy simulation (LES) that replaces the prescribed subgrid models used in traditional LES closure with highly generalized representations of subgrid terms and solution of a local system identification problem that allows the simulation itself to determine the local relation between each subgrid term and the resolved variables at every point and time. The present study demonstrates, for the first time, practical LES based on fully dynamic implementation of autonomic closure for the subgrid stress and the subgrid scalar flux. It leverages the inherent computational efficiency of tensorally-correct generalized representations in terms of parametric quantities, and uses the fundamental representation theory of Smith (1971) to develop complete and minimal tensorally-correct representations for the subgrid stress and scalar flux. It then assesses the accuracy of these representations via a priori tests, and compares with the corresponding accuracy from nonparametric representations and from traditional prescribed subgrid models. It then assesses the computational stability of autonomic closure with these tensorally-correct parametric representations, via forward simulations with a high-order pseudo-spectral code, including the extent to which any added stabilization is needed to ensure computational stability, and compares with the added stabilization needed in traditional closure with prescribed subgrid models. Further, it conducts a posteriori tests based on forward simulations of turbulent conserved scalar mixing with the same pseudo-spectral code, in which velocity and scalar statistics from autonomic closure with these representations are compared with corresponding statistics from traditional closure using prescribed models, and with corresponding statistics of filtered fields from direct numerical simulation (DNS). These comparisons show substantially greater accuracy from autonomic closure than from traditional closure. This study demonstrates that fully dynamic autonomic closure is a practical approach for LES that requires accuracy even at the smallest resolved scales.

Autonomic closure is a new general methodology for subgrid closures in large eddy simulations that circumvents the need to specify fixed closure models and instead allows a fully- adaptive self-optimizing closure. The closure is autonomic in the sense that the simulation itself determines the optimal relation at each point and time between any subgrid term and the variables in the simulation, through the solution of a local system identification problem. It is based on highly generalized representations of subgrid terms having degrees of freedom that are determined dynamically at each point and time in the simulation. This can be regarded as a very high-dimensional generalization of the dynamic approach used with some traditional prescribed closure models, or as a type of “data-driven” turbulence closure in which machine- learning methods are used with internal training data obtained at a test-filter scale at each point and time in the simulation to discover the local closure representation.

In this study, a priori tests were performed to develop accurate and efficient implementations of autonomic closure based on particular generalized representations and parameters associated with the local system identification of the turbulence state. These included the relative number of training points and bounding box size, which impact computational cost and generalizability of coefficients in the representation from the test scale to the LES scale. The focus was on studying impacts of these factors on the resulting accuracy and efficiency of autonomic closure for the subgrid stress. Particular attention was paid to the associated subgrid production field, including its structural features in which large forward and backward energy transfer are concentrated.

More than five orders of magnitude reduction in computational cost of autonomic closure was achieved in this study with essentially no loss of accuracy, primarily by using efficient frame-invariant forms for generalized representations that greatly reduce the number of degrees of freedom. The recommended form is a 28-coefficient representation that provides subgrid stress and production fields that are far more accurate in terms of structure and statistics than are traditional prescribed closure models.

Reynolds-averaged Navier-Stokes (RANS) simulation is the industry standard for computing practical turbulent flows -- since large eddy simulation (LES) and direct numerical simulation (DNS) require comparatively massive computational power to simulate even relatively simple flows. RANS, like LES, requires that a user specify a “closure model” for the underlying turbulence physics. However, despite more than 60 years of research into turbulence modeling, current models remain largely unable to accurately predict key aspects of the complex turbulent flows frequently encountered in practical engineering applications. Recently a new approach, termed “autonomic closure”, has been developed for LES that avoids the need to specify any prescribed turbulence model. Autonomic closure is a fully-adaptive, self-optimizing approach to the closure problem, in which the simulation itself determines the optimal local, instantaneous relation between any unclosed term and the simulation variables via solution of a nonlinear, nonparametric system identification problem. In principle, it should be possible to extend autonomic closure from LES to RANS simulations, and this thesis is the initial exploration of such an extension. A RANS implementation of autonomic closure would have far-reaching impacts on the ability to simulate practical engineering applications that involve turbulent flows. This thesis has developed the formal connection between autonomic closure for LES and its counterpart for RANS simulations, and provides a priori results from FLUENT simulations of the turbulent flow over a backward-facing step to evaluate the performance of an initial implementation of autonomic closure for RANS. Key aspects of these results lay the groundwork on which future efforts to extend autonomic closure to RANS simulations can be based.