A low-pressure-loss short afterburner for sea-level thrust augmentation
Low-pressure-loss short afterburner design for sea level thrust augmentation of axial flow turbojet engine
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Low-pressure-loss short afterburner design for sea level thrust augmentation of axial flow turbojet engine
Simultaneous linear equation solution of rocket thrust augmentation problem
Turbojet thrust augmentation with fuel-rich afterburning of hydrogen, diborane, and hydrazine
Light aircraft lateral stability augmentation system for wing leveling, noting operation and pilot performance
Full scale wind tunnel investigation of VTOL AIRCRAFT with jet ejector system for lift augmentation
Lateral stability augmentation of supersonic aircraft using linear multichannel state-vector optimal feedback control
Augmented Reality is an emerging field in technology, and encompasses Head Mounted Displays, smartphone apps, and even projected images. HMDs include the Meta 2, Magic Leap, Avegant Light Field, and the Microsoft HoloLens, which is evaluated specifically. The Microsoft HoloLens is designed to be used as an AR personal computer, and is being optimized with that goal in mind. Microsoft allied with the Unity3D game engine to create an SDK for interested application developers that can be used in the Unity environment.
As augmented and virtual reality grows in popularity, and more researchers focus on its development, other fields of technology have grown in the hopes of integrating with the up-and-coming hardware currently on the market. Namely, there has been a focus on how to make an intuitive, hands-free human-computer interaction (HCI) utilizing AR and VR that allows users to control their technology with little to no physical interaction with hardware. Computer vision, which is utilized in devices such as the Microsoft Kinect, webcams and other similar hardware has shown potential in assisting with the development of a HCI system that requires next to no human interaction with computing hardware and software. Object and facial recognition are two subsets of computer vision, both of which can be applied to HCI systems in the fields of medicine, security, industrial development and other similar areas.
The objective of this dissertation is to characterize augmented spark impinging (torch) igniters. Torch igniters inject propellants into a small prechamber where a spark igniter imparts the activation energy to initiate combustion. The flame stabilizes within the prechamber and exits into the main combustion chamber where it ignites the main flow directly or by mixing with a coaxial flow to create a secondary flame. The underlying phenomena that promote reliable ignition are multifaceted and required a broad scope of investigations. Initial efforts focused on spark discharges across an annular geometry. Gaseous propellent projects the electrical arc toward the prechamber, where the arc and the thermal energy is imparted to the surrounding fluid then meets with a combustible mixture. Discharges are parametrically examined against pressure, spark-gap size, and exciter types to emulate transient engine startup. The energy imparted to the flow, approximate velocity, and distance traveled by the exhaust plume are determined. Spark quenching (no energy deposition) and its effects on perceived versus actual measurements are shown. Results outline the prechamber target zone where a combustible mixture is necessary for ignition. Next, three-dimensional, time-accurate, and nonreacting computational fluid dynamics simulations assess effects of geometric and mass flow differences on the size, location, and composition of a combustible mixture. Fluid dynamic phenomena are elucidated. Correlations between nondimensional variables are found. Results are coupled with a conservation-law analysis to produce novel objective functions. Lastly, a full-scale, modular torch igniter is tested. Injector sizes, mixture ratios, and injector momentum ratios are systematically varied to map ignition probability and ignition delay. Results show the importance of local mixture ratios, provide injector momentum ratios guidelines, and demonstrate reliable ignition of core mixture ratios typically outside flammability limits. The deliverable of this project is not a torch igniter or derivative thereof. Instead, this dissertation produces novel objective-functions for igniter design. These equations are functions of variables that may be constrained by engine-level requirements, physics, and experimental results from this work. They provide a foundation for future igniter design and will minimize iterative processes from the design cycle for next-generation ignition hardware.
Broadcast messages of the FAA's Wide Area Augmentation System (WAAS) include the grid ionosphere vertical error (GIVE).
The objective of Wireless Augmented Reality Prototype (WARP) effort is to develop and integrate advanced technologies for real-time personal display of information relevant to the health and safety of space station/shuttle personnel.
Hydroxyl tagging velocimetry (HTV) involves tagging a flow by “writing” a line of OH molecules using a laser beam to dissociate H2O molecules and capturing an image of the line after a short delay using laser-induced fluorescence. Velocity is obtained by a time-of-flight analysis of the data. In this effort, HTV was used for obtaining both instantaneous and average velocity profiles in the flow of an augmented spark igniter. Two modes of camera readout were investigated, called conventional full frame mode and dual image mode feature (DIF) mode. In DIF mode, two images are captured in quick succession and therefore the measurement is immune to vibration effects. Measurement uncertainty for the DIF case varied from 3% at the centerline to 10% at the edges of the profile in a 1000-m/s flow. For the full-frame case, measurement uncertainty varied from 3% at the centerline to 7% at the edges. This demonstration provides evidence that the HTV technique is well suited for obtaining velocity profiles in the challenging environment of either a rocket engine or rocket engine igniter.
Integration of electric machines with the shafts of gas turbine engines is implied in various electrified aircraft propulsion concepts. This includes implementation of the Turbine Electrified Energy Management (TEEM) concept, a motivator for the Versatile Electrically Augmented Turbine Engine (VEATE) gearbox. The VEATE gearbox is a mechanical power transmission concept that seeks to interface electric machines with a gas turbine engine in a synergistic manner. It enables a hybrid electro-mechanical approach for managing power in a gas turbine engine. It is hypothesized that the mechanical design of the VEATE gearbox could be leveraged to enhance the versatility of the present electrical hardware. The VEATE gearbox concept is first introduced and applied to an electrified two-spool advanced geared turbofan meant for powering a single-aisle commercial aircraft. A modeling approach for the gearbox is presented and studies are conducted to investigate the potential application to TEEM and power extraction. There is evidence that the VEATE gearbox could help to reduce the size of the TEEM power system, provide flexibility in power extraction implementation, and exhibit fail-safe design attributes.
Integration of electric machines with the shafts of gas turbine engines is implied in various electrified aircraft propulsion concepts. This includes implementation of the Turbine Electrified Energy Management (TEEM) concept, a motivator for the Versatile Electrically Augmented Turbine Engine (VEATE) gearbox. The VEATE gearbox is a mechanical power transmission concept that seeks to interface electric machines with a gas turbine engine in a synergistic manner. It enables a hybrid electro-mechanical approach for managing power in a gas turbine engine. It is hypothesized that the mechanical design of the VEATE gearbox could be leveraged to enhance the versatility of the present electrical hardware. The VEATE gearbox concept is first introduced and applied to an electrified two-spool advanced geared turbofan meant for powering a single-aisle commercial aircraft. A modeling approach for the gearbox is presented and studies are conducted to investigate the potential application to TEEM and power extraction. There is evidence that the VEATE gearbox could help to reduce the size of the TEEM power system, provide flexibility in power extraction implementation, and exhibit fail-safe design attributes.
Challenge - Electric air taxi batteries may not have sufficient capacity for flight operations - Can microwave power beaming augment battery power during takeoff, landing, and holding operations? - Is microwave power beaming safe to conduct to a AAM vehicle with human passengers and crew?
This paper introduces an innovative training system for the Renishaw AM400 metal printer, leveraging the synergy of the advanced Vision Language Model (VLM) with Augmented Reality (AR) within the Digital Twins (DT) framework. Aimed at overcoming the limitations of conventional training methods in metal additive manufacturing (AM), our system integrates AR to provide an immersive learning environment, enhancing the real-world experience with interactive digital overlays. The core of the system lies in its use of VLM, which, pre-trained on diverse datasets, excels in processing multi-modal data, thereby offering nuanced and contextually relevant guidance for trainees. Key experiments demonstrate the system’s effectiveness, particularly highlighting the usage of VLM as an Artificial Intelligence (AI) agent to integrate external tools like YOLO-v7 for valve state classification and CRAFT for control panel text recognition. This approach significantly improves recognition accuracy, operational understanding, and human–machine interaction, especially for non-expert users, making complex metal AM operations more accessible. The research not only showcases the potential of AR and VLM in industrial training but also sets a new standard for smart manufacturing practices, indicating broader applications in various industrial domains.
Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.
Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.