Browsing by Author "Gilbert, John Nicholas"

Now showing 1 - 4 of 4
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    Accelerating a Coupled SPH-FEM Solver through Heterogeneous Computing for use in Fluid-Structure Interaction Problems
    Gilbert, John Nicholas (Virginia Tech, 2015-06-08)
    This work presents a partitioned approach to simulating free-surface flow interaction with hyper-elastic structures in which a smoothed particle hydrodynamics (SPH) solver is coupled with a finite-element (FEM) solver. SPH is a mesh-free, Lagrangian numerical technique frequently employed to study physical phenomena involving large deformations, such as fragmentation or breaking waves. As a mesh-free Lagrangian method, SPH makes an attractive alternative to traditional grid-based methods for modeling free-surface flows and/or problems with rapid deformations where frequent re-meshing and additional free-surface tracking algorithms are non-trivial. This work continues and extends the earlier coupled 2D SPH-FEM approach of Yang et al. [1,2] by linking a double-precision GPU implementation of a 3D weakly compressible SPH formulation [3] with the open source finite element software Code_Aster [4]. Using this approach, the fluid domain is evolved on the GPU, while the CPU updates the structural domain. Finally, the partitioned solutions are coupled using a traditional staggered algorithm.
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    The Application of Reinforcement Learning for Interceptor Guidance
    Porter, Daniel Michael (Virginia Tech, 2024-10-04)
    The progression of hypersonic vehicle research and development has presented a challenge to modern missile defenses. These attack vehicles travel at speeds of Mach 5+, have low trajectories that result in late radar detections, and can be highly maneuverable. To counter this, new interceptors must be developed. This work explores using machine learning for the guidance of these interceptors through applied steering commands, with the intent to improve upon traditional guidance methods. Specifically, proximal policy optimization (PPO) was selected as the reinforcement learning algorithm due to its advanced and efficient nature, as well as its successful use in related work. A framework was developed and tuned for the interceptor guidance problem, combining the PPO algorithm with a specialized reward shaping method and tuned parameters for the engagements of interest. Low-fidelity vehicle models were used to reduce training time and narrow the scope of work towards improving the guidance algorithms. Models were trained and tested on several case studies to understand the benefits and limitations of an intelligently guided interceptor. Performance comparisons between the trained guidance models and traditional methods of guidance were made for cases with supersonic, hypersonic, weaving, and dynamically evasive attack vehicles. The models were able to perform well with initial conditions outside of their training sets, but more significant differences in the engagements needed to be included in training. The models were therefore found to be more rigid than desired, limiting their effectiveness in new engagements. Compared to the traditional methods, the PPO-guided interceptor was able to intercept the attacker faster in most cases, and had a smaller miss distance against several evasive attackers. However, the PPO-guided interceptor had a lower percent kill against nonmaneuvering attackers, and typically required larger lateral acceleration commands than traditional methods. This work acts as a strong foundation for using machine learning for guiding missile interceptors, and presents both benefits and limitations of a current implementation. Proposals for future efforts involve increasing the fidelity and complexity of the vehicles, engagements, and guidance methods.
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    Shape Matching for Reduced Order Models of High-Speed Fluid Flows
    Dennis, Ethan James (Virginia Tech, 2024-08-30)
    While computational fluid dynamics (CFD) simulations are an indispensable tool in modern aerospace engineering design, they bear a severe computational burden in applications where simulation results must be found quickly or repeatedly. Therefore, creating computationally inexpensive models that can capture complex fluid behaviors is a long-sought-after goal. As a result, methods to construct these reduced order models (ROMs) have seen increasing research interest. Still, parameter dependent high-speed flows that contain shock waves are a particularly challenging class of problems that introduces many complications in ROM frameworks. To make approximations in a linear space, ROM techniques for these problems require that basis functions are transformed such that discontinuities are aligned into a consistent reference frame. Techniques to construct these transformations, however, fail when the topology of shocks is not consistent between data snapshots. In this work, we first identify key features of these topology changes, and how that constrains transformations of this kind. We then construct a new modeling framework that can effectively deal with shockwave interactions that are known to cause failures. The capabilities of the resulting model were evaluated by analyzing supersonic flows over a wedge and a forward-facing step. In the case of the forward-facing step, when shock topology changes with Mach number, our method exhibits significant accuracy improvements. Suggestions for further developments and improvements to our methodology are also identified and discussed
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    Variable Geometry Scramjet Combustor Cavity Multi-Dimensional Treatise for Performance Analysis
    Sorensen, Andrew Liam (Virginia Tech, 2021-11-02)
    The abilities of Scramjets and Ramjets, in their respective operating ranges, are partially bridged by dual-mode Scramjets. The limitations of operation are due to making a static motor that is designed to function in both modes resulting in low and high speed restrictions. This study covers the analysis into the ability of morphing the combustor in a Scramjet to allow for expanded operational capacities through simple mechanisms. Through the restriction and expansion of combustor cavity volume, operational capabilities of the engine can, therefore, be modified to best match scenario requirements. Due to the engine's ability to match a wide variety of scenarios the limitations seen in that of the dual-mode Scramjet are avoided through the usage of a morphing combustor. From initial findings using the quasi-1D Canonical REactor Scramjet Simulation (CReSS) solver, progress was made to confirm results through the usage of Computational Fluid Dynamics (CFD). Prior analysis of the momentum balance between stages two and four of the simulated Scramjet engines, the results showed that the variable geometry matched or outperformed the baseline HiFiRE geometry. The analysis revealed points of Mach and altitude where certain combustor volumes demonstrated greater performance. This greater performance is only gained by the ability to tune the engine in flight to react to external factors as there is no dominant geometry for a given range of Machs and altitudes. This tuning allows for the usage of performance mapping to extract the greatest performance possible over a variety of conditions. Further, it allows for the project to be continuously expanded into mapping appropriate reactions to other initial conditions and stimuli. Using CFD modeling to perform a parametric study on the prior work allows for finer control and analysis of said initial conditions and the resulting flow paths in the variety of tested combustor volumes. From this a discussion is made in regards to the effectiveness of the prior CReSS based analysis of the novel approach.