Computational Analysis of Water Braking Phenomena for High-Speed Sled and Its Machine Learning Framework
Abstract
Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses a process called ‘water braking’ as a method to bring vehicles at the test track to a stop. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange. By studying water braking using Computational Fluid Dynamics (CFD), forces acting on track vehicles can be approximated and prepared for prior to the actual test. In this study, focus will be made on the brake component of the track sled that is responsible for interacting with the water for braking. By discretizing a volume space around our brake, we accelerate water and air to relatively simulate the brake engaging. The model is a multi-phase flow that uses the governing equations of gas and liquid phases with the finite volume method, to perform 3D simulations. By adjusting the inflow velocity of air and water, it is possible to simulate HHSTT sled tests at various operational speeds. In the development of the 3D predictive model, convergence issues associated with the numerical mesh, initial/boundary conditions, and compressibility of the fluids were encountered. Once resolved, the effect of inflow velocities of water and air on the braking of the sled is studied.Improving the prediction capabilities of water braking phenomena has the potential to result in radical changes in the designs of sleds, improve rocket sled velocity-time test profile predictions, provide greater confidence of braking mechanisms, and decrease risk in the recovery of critical infrastructures. Understanding the water’s behavior with the sled is critical to predicting how the water could damage the sled, which affects the recoverability of the sled and can determine the success of a mission, and the amount of drag it will experience from the air and water. Traditionally, sled design for the test missions for water braking has been guided by empirical/hand calculations to estimate the forces on various components. The calculations involve various approximations in arriving at the force balance law and predicting the acceleration/deceleration profile. The CFD results from various geometry configurations for the sled and modeling parameters will be presented. The main goals of the CFD investigations are to improve the accuracy of the predicted profile that often depends on the complexity of the design and operating conditions.Due to CFD modeling being very computationally expensive, however, a machine learning (ML) framework is suggested to increase result turnover and fidelity. This framework consists of multiple neural networks that once trained are to be validated and coupled. In addition to the framework, a project management plan is outlined to guide the integration of computational modeling with the machine learning framework.In summary, five different geometries of the sled water braking mechanism, scoop, are simulated in a 3-dimensional space and a machine learning framework is provided to offset the CFD requirement of expensive computational resources.
Subject Area
Mechanical engineering|Systems science
Recommended Citation
Terrazas, Jose Armando, "Computational Analysis of Water Braking Phenomena for High-Speed Sled and Its Machine Learning Framework" (2023). ETD Collection for University of Texas, El Paso. AAI30494238.
https://scholarworks.utep.edu/dissertations/AAI30494238