The objective of this project is to design an indraft supersonic wind tunnel that is safe and comparatively simple to construct. The processes and methodology of design are discussed. As with every supersonic wind tunnel, the critical components are the nozzle, diffuser, and the means of achieving the pressure differential which drives the flow. The nozzle was designed using method of characteristics (MOC) and a boundary layer analysis experimental proven on supersonic wind tunnels [5]. The diffuser was designed using the unique design features of this wind tunnel in combination with equations from Pope [7]. The pressure differential is achieved via a vacuum chamber behind the diffuser creating a pressure differential between the ambient air and the low pressure in the tank. The run time of the wind tunnel depends on the initial pressure of the vacuum tank and the volume. However, the volume of the tank has a greater influence on the run time. The volume of the tank is not specified as the largest tank feasible should be used to allow the longest run time. The run time for different volumes is given. Another method of extending the run duration is added vacuum pumps to the vacuum chamber. If these pumps can move a sufficient mass out of the vacuum chamber, the run time can be significantly extended. The mounting design addresses the loading requirements which is closely related to the accuracy of the data. The mounting mechanism is attached to the rear of the model to minimize shockwave interference and maximize the structural integrity along the direction with the highest loading. This mechanism is then mounted to the bottom of the wind tunnel for structural rigidity and ease of access.

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.