Aerospace Engineering Commons^™

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Convolutional-Neural-Network-Based Des-Level Aerodynamic Flow Field Generation From Urans Data, John P. Romano, Oktay Baysal, Alec C. Brodeur

Mechanical & Aerospace Engineering Faculty Publications

The present paper culminates several investigations into the use of convolutional neural networks (CNNs) as a post-processing step to improve the accuracy of unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for subsonic flows over airfoils at low angles of attack. Time-averaged detached eddy simulation (DES)-generated flow fields serve as the target data for creating and training CNN models. CNN post-processing generates flow-field data comparable to DES resolution, but after using only URANS-level resources and properly training CNN models. This document outlines the underlying theory and progress toward the goal of improving URANS simulations by looking at flow predictions for a class of …

Probabilistic Short Term Solar Driver Forecasting With Neural Network Ensembles, Joshua Daniell

Graduate Theses, Dissertations, and Problem Reports (ETD)

Commonly utilized space weather indices and proxies drive predictive models for thermosphere density, directly impacting objects in low-Earth orbit (LEO) by influencing atmospheric drag forces. A set of solar proxies and indices (drivers), F_10.7, S_10.7, M_10.7, and Y_10.7, are created from a mixture of ground based radio observations and satellite instrument data. These solar drivers represent heating in various levels of the thermosphere and are used as inputs by the JB2008 empirical thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM) relies on JB2008, and …

Predicting Defects In Laser Powder Bed Fusion Using In-Situ Thermal Imaging Data And Machine Learning, Sina Malakpour Estalaki, Cody S. Lough, Robert G. Landers, Edward C. Kinzel, Tengfei Luo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. …

Additive Manufacturing Of Complexly Shaped Sic With High Density Via Extrusion-Based Technique – Effects Of Slurry Thixotropic Behavior And 3d Printing Parameters, Ruoyu Chen, Adam Bratten, Joshua Rittenhouse, Tian Huang, Wenbao Jia, Ming-Chuan Leu, Haiming Wen

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y2O3 and Al2O3 …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

A Brief Literature Review For Machine Learning In Autonomous Robotic Navigation, Jake Biddy, Jeremy Evert

Student Research

Machine learning is becoming very popular in many technological aspects worldwide, including robotic applications. One of the unique aspects of using machine learning in robotics is that it no longer requires the user to program every situation. The robotic application will be able to learn and adapt from its mistakes. In most situations, robotics using machine learning is designed to fulfill a task better than a human could, and with the machine learning aspect, it can function at the highest level of efficiency and quality. However, creating a machine learning program requires extensive coding and programming knowledge that can be …

Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros

Theses and Dissertations

No abstract provided.

Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the modern space age, private companies are crowding the already-congested low Earth orbit (LEO) regime with small satellite mega constellations. With over 25,000 objects larger than 10 cm already in LEO, this rapid expansion is forcing us towards the enterprise on Space Traffic Management (STM). STM is an operational effort that focuses on conjunction assessment and collision avoidance between objects. While the equations of motion for objects in orbit are well-known, there are many uncertain parameters that result in the uncertainty of an object's future position. The force that the atmosphere exerts on satellite - known as drag - …

Trajectory Generation For A Multibody Robotic System: Modern Methods Based On Product Of Exponentials, Aryslan Malik

Doctoral Dissertations and Master's Theses

This work presents several trajectory generation algorithms for multibody robotic systems based on the Product of Exponentials (PoE) formulation, also known as screw theory. A PoE formulation is first developed to model the kinematics and dynamics of a multibody robotic manipulator (Sawyer Robot) with 7 revolute joints and an end-effector.

In the first method, an Inverse Kinematics (IK) algorithm based on the Newton-Raphson iterative method is applied to generate constrained joint-space trajectories corresponding to straight-line and curvilinear motions of the end effector in Cartesian space with finite jerk. The second approach describes Constant Screw Axis (CSA) trajectories which are generated …

Nonlinear Intelligent Model Predictive Control Of Mobile Robots, Benjamin Albia

Theses and Dissertations

This thesis presents a framework for an artificial neural network (ANN) model-based nonlinear model predictive control of mobile ground robots. A computer vision analysis module was first developed to extract quantitative position information from onboard camera feed with respect to a prescribed path. Various strategies were developed to construct nonlinear physical plant models for model predictive control (MPC), including the physics-based model (PBM), the ANN trained on PBM-generated data, the ANN trained on test-captured data, and the ANN initially trained on PBM-generated data and then retrained with captured data. All the models predict physical states and positions of the robot …

Transport-Based Pattern Recognition Versus Deep Neural Networks In Underwater Oam Communications, Patrick L. Neary, Jonathan M. Nichols, Abbie T. Watnik, K. Peter Judd, Gustavo K. Rohde, James R. Lindle, Nicholas S. Flann

Space Dynamics Laboratory Publications

Comparisons between machine learning and optimal transport-based approaches in classifying images are made in underwater orbital angular momentum (OAM) communications. A model is derived that justifies optimal transport for use in attenuated water environments. OAM pattern demultiplexing is performed using optimal transport and deep neural networks and compared to each other. Additionally, some of the complications introduced by signal attenuation are highlighted. The Radon cumulative distribution transform (R-CDT) is applied to OAM patterns to transform them to a linear subspace. The original OAM images and the R-CDT transformed patterns are used in several classification algorithms, and results are compared. The …

Real-Time Material State Assessment Of Composites Using Artificial Intelligence And Its Challenges, Muthu Ram Prabhu Elenchezhian

Mechanical and Aerospace Engineering Dissertations - Archive

Over several decades of careful experimental investigation and exhaustive development of discrete damage analysis methods including integrated computational mechanics methods, our community knows a great deal about how discrete defects such as matrix cracks and defect growth (e.g. delamination) can be predicted in structural composites. For many practical situations controlled by laminated multiaxial composite structures, the loss of performance and “sudden death” end of life is controlled by defect coupling which becomes a precursor to fracture plane development. These interaction sequences are highly dependent on local details of manufacture, design configurations, and loading for a given application material and influenced …

A Study On An Accurate Underwater Location Of Hybrid Underwater Gliders Using Machine Learning, Sang-Ki Jeong, Hyeung-Sik Choi, Dea-Hyung Ji, Mai The Vu, Joon-Young Kim, Sung Min Hong, Hyun Joon Cho

Journal of Marine Science and Technology–Taiwan

A hybrid underwater glider (HUG) is marine observation equipment that consumes a small amount of energy and offers greater range and navigation times. To achieve reduced energy consumption, however, the HUG uses imprecise navigation sensors, such as mems-type GPS and AHRS, resulting in inaccurate coordination. This study makes a new attempt on the application of machine learning algorithms in a way that complements sensor data errors to improve navigation performance. The proposed algorithm was used to a simulation of the HUG’s navigation and control system, after which the updated heading angle was decided by using the previous position data and …

Satellite Constellation Deployment And Management, Joseph Ryan Kopacz

Electronic Theses and Dissertations

This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.

The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …

Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman

Master's Theses

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on …

Inverse Analysis Of Heat Generating Body For Safety Applications, Sandeep Patil

Mechanical and Aerospace Engineering Dissertations - Archive

Inverse thermal analysis and its applications have been applied to numerous fields of science and engineering. Historically during the 1950's and early 1960's, space programs played a significant role in the advancement of solution techniques for Inverse Heat transfer Problems(IHTP). It was applied to measure the surface temperature of thermal shield of a space vehicle during its re-entry into atmosphere. Inverse analysis was also used in the estimation of thermo-physical properties of the shield at high operating temperatures. Besides thermal application, inverse technique was also used in other engineering applications such as estimation of alloy specification, design of a shape …

A Two Stage Event Based Data Driven Controller For Improved Grasping Of An Artificial Hand, Christopher Abrego

Mechanical and Aerospace Engineering Dissertations - Archive

The human hand is one of the greatest (if not the greatest) tool known to mankind for grasping objects. So much so, that researchers have been investigating the development of artificial biomimetic hands in an effort to mimic their functionality and dexterity with the indent to apply the technology to various robotic platforms; ranging from end effectors for industrial pick-and-place robotics, to upper-limb prosthetics, to humanoids. There are certain features that make this endeavor challenging such as the mechanical design, actuation and sensorization, and functionality as well as the interaction from both the view point of interacting with an end …

On The Development And Integration Of Pneumatic Extrusion Module And A Methodology To Identify Process Parameters For Additive Manufacturing Using Machine Learning, Kashish Dhal

Mechanical and Aerospace Engineering Theses - Archive

Commonly used additive manufacturing platforms have a single extrusion module based on Fused Filament Fabrication (FFF) and their processing software generates G-Codes for this FFF module using defined process parameters. These platforms and software do not accommodate different processing modules such as viscous extruders or Direct Ink Writing (DIW). This research is focused on the development of a Pneumatic Extrusion Module (PEM) capable of dispensing viscous materials such as gels or slurries controlled through a digital pneumatic valve. A PEM is developed, integrated and its performance is evaluated on a multi-modality additive manufacturing platform in the MARS Lab. The operation …

Cfd Simulation And Design Optimization To Improve Cooling Performance Of Open Compute ‘Big Sur’ Server, Mangesh Mohan Dhadve

Mechanical and Aerospace Engineering Theses - Archive

In recent years, there have been a phenomenal increase in Artificial Intelligence and Machine Learning that require data collection, mining and using data sets to teach computers certain things to learn, analyze image and speech recognition. Machine Learning tasks require a lot of computing power to carry out numerous calculations. Therefore, most servers are powered by Graphics Processing Units (GPUs) instead of traditional CPUs. GPUs provide more computational throughput per dollar spent than traditional CPUs. Open Compute Servers forum has introduced the state-of-the-art machine learning servers “Big Sur” recently. Big Sur unit consists of 4OU (OpenU) chassis housing eight NVIDIA …

Human Robot Interaction Using Knowledge Base Approach, Utsav Shah

Mechanical and Aerospace Engineering Theses - Archive

This research investigated Human Robot Interaction modalities. The performance of a robotic prosthetic hand (RPH) was used as a test bed robot. A user wearable glove was fitted with sensors to provide tele-control of the RPH. An open hardware control and easily expandable research platform based on LabVIEW software and myRIO control hardware was developed. LabVIEW graphical programming platform provides the tools for the development to customized interfaces for visualization purposes which is desired in research. The research platform was used to calibrate and control the operation of the artificial hand using various modalities such as open loop, glove master-slave …

Procase: A Prototype Of Intelligent Case-Based Process Planning System With Simulation Environment, Hao Yang, Wen F. Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

An intelligent case-based process planning system with interactive graphic simulation environment, PROCASE, is developed lo demonstrate an integrated methodology of case-based process planning system. In PROCASE, both the mechanical part features and the machining operations are represented with a frame-based scheme. PROCASE contains a retriever, a modifier, a simulator and a repairer. It distinguishes itself from traditional rule-based process planning systems by representing the process planning knowledge through previous process planning cases instead of production rules. It therefore can overcome some problems in the traditional rule-based expert systems. PROCASE currently resides in IRIS Indigo workstation. With a user-friendly graphic environment, …