Analysis of internal ablation for the thermal control of aerospace vehicles

A new method of thermal protection for transatmospheric vehicles is introduced. The method involves the combination of radiation, ablation and transpiration cooling. By placing an ablating material behind a fixed-shape, porous outer shield, the effectiveness of transpiration cooling is made possible while retaining the simplicity of a passive mechanism. A simplified one-dimensional approach is used to derive the governing equations. Reduction of these equations to non-dimensional form yields two parameters which characterize the thermal protection effectiveness of the shield and ablator combination for a given trajectory. The non-dimensional equations are solved numerically for a sample trajectory corresponding to glide re-entry. Four typical ablators are tested and compared with results obtained by using the thermal properties of water. For the present level of analysis, the numerical computations adequately support the analytical model

Airbreathing rotating detonation wave engine cycle analysis

A cycle analysis model for an airbreathing, rotating detonation wave engine (RDE) is presented. The engine consists of a steady inlet system with an isolator which delivers air into an annular combustor. A detonation wave continuously rotates around the combustor with side relief as the flow expands towards the nozzle. A model for the side relief is used to find the pressure distribution around the combustor. Air and fuel enter the combustor when the rarefaction wave pressure behind the detonation front drops to the inlet supply pressure. To create a stable RDE, the inlet pressure is matched in a convergence process with the average combustor pressure by increasing the annulus channel radial width with respect to the isolator channel. Performance of this engine is considered using several parametric studies and compared with rocket-mode computational results. A hydrogen–air RDE reaches a specific impulse of 3800 s and can reach a flight speed of Mach 5

High-Speed External Aerodynamic Analysis using Exergy Balance with Hypersonic Panel Code

Recently, the second law of thermodynamics has been used to develop the concept of a “common currency” for use in analysis and optimization of multidisciplinary systems. The second law approach relates the irreversibility (or entropy generation) in different subsystems or physical mechanisms to the overall system performance. Since entropy generation has the same units in every subsystem, this provides a common framework for evaluating the impact of each subsystem on the overall system performance. One difficulty in applying second law techniques to aerospace vehicles is the fact that commonly used engineering codes often do not simulate the physics necessary to directly calculate the irreversible entropy generation. An example is hypersonic aerodynamic codes based on local surface inclination (panel methods). In this paper, an enhancement to a low-fidelity hypersonic aerodynamic analysis code is developed, allowing calculation of irreversible entropy generation due to different aerodynamic mechanisms. This development is presented in context of a multidisciplinary framework for aerospace vehicle design

Analysis of Power Losses and Wake Entropy Production for Hypersonic Flight Vehicles

We examine the governing relationships between forces experienced by a vehicle, vehicle power usage, losses and entropy production. Vehicle wake losses (like losses associated with the vehicle itself) are shown to be directly related to lost force power increments on the vehicle. Conventional exergy (or availability) methodology without explicit consideration of the wake and, specifically, without consideration of vehicle flow-field/wake entropy relationships, is shown to be problematic for accurate assessment of losses. The methodology is then re-formulated with explicit consideration of the vehicle/wake entropy relationship. Lost power increments are derived, hence allowing the detailed analysis/auditing of vehicle performance. This methodology is rigorously related throughout its entire development to the concept of wake entropy production and the important impact of vehicle entropy production and characteristics on the wake entropy production. The fundamental thermodynamic linkages and relationships between force, entropy, and power for an individual streamtube are examined. These analytical results are then expanded to entire flow-fields characteristic of vehicles in flight (multiple streamtubes in and over vehicle surfaces and downstream wake equilibration of those streamtubes) in order to unify explicit force-based methodology and the entropy method. The ongoing research represented by this and previous work should ultimately enable the integration of all vehicle subsystems (not just aerodynamic and propulsive subsystems) using a synergistic currency for analysis, design, and, ultimately, optimization of aerospace vehicles

Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning

Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network models are trained with different random initializations. The ensemble of model realizations is used to assess epistemic modeling uncertainty caused due to lack of training samples. This uncertainty estimation is crucial information for successful goal-oriented adaptive learning in an aerospace system design exploration. However, the costs of training the ensemble models often become prohibitive and pose a computational challenge, especially when the models are not trained in parallel during adaptive learning. In this work, a new type of emulator embedded neural network is presented using the rapid neural network paradigm. Unlike the conventional neural network training that optimizes the weights and biases of all the network layers by using gradient-based backpropagation, rapid neural network training adjusts only the last layer connection weights by applying a linear regression technique. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy. The proposed method is demonstrated on multiple analytical examples, as well as an aerospace flight parameter study of a generic hypersonic vehicle