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Investigation of the Effect of Internal Reynolds Number and Advective Capacity Ratio on Gas Turbine Film Cooling Parameters
Get PDFThe desire for more powerful engines drives higher turbine inlet temperatures and a greater need to cool them. Experimentalists attempt to match cooling effectiveness between two conditions by matching several non-dimensional parameters, one of which is the coolant warming factor. The first investigation of this study examines the effect of the internal Reynolds number and the advective capacity ratio on the coolant warming factor by making use of a computational model and four different gases. It indicates the non-negligible and opposing effects of external convection and solid conduction on the coolant warming factor at matched internal Reynolds number and advective capacity ratio. The influence of the internal Reynolds number and advective capacity ratio on the coolant warming factor prompted a second investigation into their effects on overall cooling effectiveness. This investigation examined the relative importance of matching freestream and coolant Reynolds numbers alongside advective capacity ratio on overall effectiveness. It found contradictory results indicating both the freestream and coolant Reynolds numbers are more important than the other
3- and 6-Degree-of-Freedom Investigation of Aerobraking to Support Cislunar and Planetary Operations
Get PDFThe first aerobraking experiment occurred in 1991 as part of the Hiten spacecraft’s cislunar survey mission, while the first non-Earth aerobraking experiment occurred in 1993 as part of the Magellan spacecraft’s mission to Venus. Although making only two trans-atmospheric passes to reduce its apogee altitude, the success of Hiten’s experiment led to the implementation of aerobraking by Magellan, reducing its orbit from elliptical to nearly circular over a span of 70 Earth days by leveraging aerodynamic drag to reduce its orbital energy via transiting the upper region of Venus’ atmosphere. The Magellan experiment helped prove the viability of aerobraking for planetary missions and paved the way for its implementation in Mars missions starting in the late 1990s and the 2014 Venus Express mission. This research investigates the Hiten and Magellan aerobraking experiments from the perspective of both 3- and 6-degreeof-freedom analysis with the inclusion of J4 gravitational and lunisolar perturbations, as well as multiple atmospheric density models in order to understand the complexity and sensitivity of aerobraking maneuvers in Earth’s and Venus’ atmospheres. Alternative vehicles of varying size are studied to determine their capability of maintaining the Hiten and Magellan aerobraking flight profiles. To examine a wide range of vehicle mass, drag reference area, and configuration options, the Hiten, Magellan, and Hubble Space Telescope (HST) satellites are modeled and their aerobraking performance analyzed for the hypothetical cases of cislunar and Venus orbital operations. 3DOF and 6DOF analysis yields minimal deviation when reconstructing the historical Hiten aerobraking maneuvers with the Hiten and Magellan models, and with reconstructing the historical Magellan aerobraking maneuvers with the Magellan and Hiten models, while more significant deviation occurs with the larger HST model for both historical cases. Both 3DOF and 6DOF analysis in Venusian space reveal the sensitivity of the atmospheric density model, relative to the coinciding solar cycle, and indicate a higher fidelity gravity model is necessary for Venusian analysis, more so than what is necessary for cislunar analysis. Additionally, analysis showed that large vehicles like HST require a shallower depth of perigee transit, in comparison to smaller vehicles like Magellan and Hiten, in order to achieve target post-pass apogee altitudes due to greater drag and associated losses in orbital energy during aerobraking. Overall, aerobraking is a viable cislunar maneuver option for adjusting semi-major axis if the vehicle performing the maneuver is capable of surviving deceleration loads of approximately 9 g’s and countering aerodynamic torques in the range of 2–11 N·m depending on vehicle size and initial attitude orientation. It is also a viable maneuver option for planetary operations and can save a significant amount of ∆V and, by extension, propellant and cost when imparting a large change in semi-major axis
Autonomous Experimentation for Accelerated Calibration of Fused Deposition Modeling 3D Printers
Get PDFAdditive Manufacturing (AM), also known as 3D printing, has emerged as a key component of Industry 4.0, enabling reduced cost, quick production, greater sustainability, and increased design complexity compared to its traditional manufacturing counterpart. Currently, Fused Deposition Modeling (FDM) technology dominates the AM market with respect to the number of 3D printers in use. However, the FDM process is sensitive to changes in system configuration, especially the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters, acting as a barrier to the technology.
To enable greater accessibility for non-expert users, this research explores techniques in autonomous experimentation to calibrate FDM 3D printers in both single and multi-objective settings without human intervention. A camera-based computer vision framework is developed and validated as a low-cost, easily deployable alternative to traditional human-in-the-loop methods for characterizing print quality. The framework is combined with combinatorial and surrogate optimization techniques to search for optimal process parameter configurations within a limited experimental budget. The results demonstrate that for the single-objective problem of optimizing print quality, process parameters can be autonomously calibrated to produce parts with greater geometric accuracy than manufacturer specified tolerances. Additionally, when optimizing for both print quality and completion time, the results show that process parameters can be calibrated to significantly outperform manufacturer defaults, achieving an average improvement of 32.2% in quality and a 31.2% reduction in completion time with respect to a popular benchmarking protocol
Hybrid Constellation Design Evaluation for Lunar Surface Navigation
No full textExcerpt: The Satellite Constellation Design Evaluation software presents a comprehensive third-party inspection of government solicitations, tailored for program managers conducting trade space analysis. Leveraging insights from DARPA’s innovative 10-year Lunar Architecture capability study (DARPA, 2023), our analysis focuses on the critical domain of Position, Navigation, and Timing requirements in support of NASA’s Lunar Augmented Navigation System
Enhanced Heuristic Algorithm for Optimal Cislunar Space Situational Awareness Architecture
No full textThe goal of this study is to utilize heuristic optimization techniques, such as Genetic Algorithms, to examine near- optimal space-based sensor architectures within the cislunar environment for the space situational awareness (SSA) mission. Specifically, this study introduces an adapted heuristic algorithm for optimizing cislunar SSA architectures. The algorithm incorporates a categorical variable for selecting cislunar families’ periodic orbits, leading to shorter chromosome lengths and enhanced satellite capacity within a single family at reduced costs. Additionally, the algorithm allows for the removal of hidden genes, facilitating calculations for set-size architectures. Evaluation of the algorithm’s efficacy will be evaluated by assessing potential benefits such as reduced runtime and improved solution quality. The optimization problem will seek to analyze the trade-offs between SSA capability, station keeping costs, and number of satellites in a given constellation. These objectives are competing for resources within the optimization problem, so a Pareto front of optimal solutions depicting the cost of favoring a given objective will be provided. Trajectory maintenance requirements in terms of stability, as well as various satellite constellation designs utilizing different periodic orbit designs (e.g., Halo, Lyapunov, distant retrograde orbits) will be investigated to provide a baseline assessment of SSA functionality in the volume of space extending from geosynchronous Earth orbit to the Moon and beyond into translunar space. A “cloud of point” target deck will be employed covering the Earth-Moon corridor and the Earth-Moon L1, L2, L4, and L5 points. Visual magnitude SSA metric will be used to evaluate the detectability of the target deck by the architectures evaluated by the Genetic Algorithm, The research will use the circular-restricted three-body problem (CR3BP) as the foundational dynamical model. The research will comprise three fundamental components: (1) generation of a catalog of three-body periodic orbits in excess of 1,000 candidate trajectories; (2) analysis of SSA models and design criteria; and (3) formulation, simulation, and evaluation of SSA architectures comprising sensors in cislunar periodic orbits utilizing the aforementioned heuristic search algorithm
Hyperspectral and Lidar Fusion and the Evaluation of Sampling Methodologies in Remote Sensing
No full textThis dissertation explores methodologies for enhancing the processing, fusion, and semantic segmentation of multimodal hyperspectral and lidar datasets using neural networks, as well as improving sampling methodologies for image-based remote sensing data. The first study introduces composite fusion-style neural network architectures that integrate both pixel- and point-based convolutional layers, facilitating joint processing of 2D hyperspectral images and 3D lidar point cloud data. This approach addresses the challenge of effectively merging diverse data types to improve model performance. The second study expands on this by exploring the direct processing of hyperspectral data in a 3D point cloud format, eliminating the need for feature projection transformations and enabling a unified point-based convolutional approach for both data modalities. The final study systematically examines sampling methods used in remote sensing, highlighting the prevalence of spatial correlation issues caused by random sampling techniques and assessing the effectiveness of various algorithms in mitigating these biases. By establishing a set of desirable characteristics for evaluating sampling methods, this study provides practical guidance to enhance the reliability of model performance assessments. Collectively, these studies offer solutions to current challenges in remote sensing data processing, advancing the field toward more accurate analytical approaches
Deterministic Global 3D Fractal Cloud Model for Synthetic Scene Generation
Get PDFThis paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives
Investigating Regional Communication Network Robustness of an Asymmetric String-of-Pearls Satellite Constellation Design Framework
No full textAppropriate selection of a satellite constellation design framework for a particular mission set requires a priori knowledge about the relative merits and shortcomings of different frameworks. Symmetric satellite constellation frameworks exhibit good properties for missions requiring continuous global coverage, whereas asymmetric satellite constellation frameworks benefit missions focusing on regional coverage. This research compares the performance of an asymmetric string-of-pearls common repeating ground track constellation design framework against a Walker constellation design framework for maintaining continuous connectivity between regions of interest. Several examples illustrate that the asymmetric string-of-pearls constellation framework appears to require an average of approximately 1.25 fewer satellites than the Walker constellation, whereas the Walker constellation appears approximately 7.86% more robust to satellite failures
Through-the-wall Object Reconstruction via Reinforcement Learning
No full textThis paper addresses the problem of characterizing and localizing objects via through-the-wall radar imaging. We consider two separate problems. First, we assume a single object is located in a room and we use a convolutional neural network (CNN) to classify the shape of the object. Second, we assume multiple objects are located in a room and use a U-net CNN to determine the location of the object via pixel-by-pixel classification. For both problems, we use numerical methods to simulate the electromagnetic field assuming known room parameters and object location. The simulated data is used to train and evaluate both the CNN and U-net CNN. In the case of single objects, we achieve 90% accuracy in classifying the shape of the object. In the case of multiple objects, we show that the U-Net outputs an image segmentation heat map of the domain space, enabling visual analysis to identify the characteristics of multiple unknown objects. Given sufficient data, the U-net heat map highlights object pixels which provide the location and shape of the unknown objects, with precision and recall accuracy exceeding 80%
Regulating the Rebound Effect in the Traveling Purchaser Problem
No full textDespite engineers’ best intentions, technological innovations intended to reduce resource consumption are not assured to achieve their desired effects. As self-interested agents utilize the technological innovation, theretofore unprofitable activities may be rendered profitable, leading to a disparity between the expected reduction in resource consumption and what is actually achieved. This rebound effect is particularly salient given the current global climate crisis. In practice, many national governments are promoting fuel efficiency gains via new, cutting-edge technologies. However, to maximize the effectiveness of such technological innovations, additional regulation is likely required to avoid unfavorable rebound effects. To further study the dynamics of this setting, we set forth a logistics-based Stackelberg game underpinned by the Traveling Purchaser Problem. More specifically, we develop a bilevel programming formulation wherein the upper-level player is a regulator and the lower-level player is the purchaser. The regulator encounters a multi-objective problem and desires to reduce resource consumption while minimally disrupting commerce on the network. Mathematical conditions are derived and proved to determine when the null regulator action is optimal. These results are leveraged to develop a preprocessing algorithm and customized heuristic solution methodology. Extensive empirical testing is conducted on these methods and their results are analyzed using statistical techniques to quantitatively characterize their behavior. Analysis confirms the supposition that regulation and technological innovations are often required in tandem to achieve a regulator’s aims; however, it also reveals that, under select conditions, the regulator is better served by not intervening