623 research outputs found
Effect of nozzle and vertical-tail variables on the performance of a 3-surface F-15 model at transonic Mach numbers
An investigation was conducted in the Langley 16 foot transonic tunnel to determine the longitudinal aerodynamic characteristics of twin two dimensional nozzles and twin baseline axisymmetric nozzles installed on a fully metric 0.047 scale model of the F-15 three surface configuration (canards, wing, horizontal tails). The effects on performance of two dimensional nozzle in flight thrust reversing, locations and orientation of the vertical tails, and deflections of the horizontal tails were also determined. Test data were obtained at static conditions and at Mach numbers from 0.60 to 1.20 over an angle of attack range from -2 deg to 15 deg. Nozzle pressure ratio was varied from jet off to about 6.5
Effect of thrust reverser operation on the lateral-directional characteristics of a three-surface F-15 model at transonic speeds
An investigation was conducted in the Langley 16 Foot Transonic Tunnel to determine the lateral directional aerodynamic characteristics of a fully metric 0.04 scale model of the F-15 three surface configuration (canards, horizontal tails) with twin two dimensional nozzles and twin axisymmetric nozzles installed. The effects of two dimensional nozzle in flight thrust reversing and rudder deflection were also determined. Test data were obtained at static conditions and at Mach numbers from 0.60 to 1.20 over an angle of attack range from -2 deg to 15 deg. Reynolds number varied from 2.6 million to 3.8 million. Angle of sideslip was set at approximately 0 deg and -5 deg for all configurations and at -10 deg for selected configurations
Flow field over the wing of a delta-wing fighter model with vortex control devices at Mach 0.6 to 1.2
As part of a cooperative research program between NASA, McDonnell Douglas Corporation, and Wright Research and Development Center, a flow field investigation was conducted on a 7.52 percent scale windtunnel model of an advanced fighter aircraft design. The investigation was conducted in the Langley 16 ft Transonic Tunnel at Mach numbers of 0.6, 0.9, and 1.2. Angle of attack was varied from -4 degrees to 30 degrees and the model was tested at angles of sideslip of 0, 5, and -5 degrees. Data for the over the wing flow field were obtained at four axial survey stations by the use of six 5 hole conical probes mounted on a survey mechanism. The wing leading edge primary vortex exerted the greatest influence in terms of total pressure loss on the over the wing flow field in the area surveyed. A number of vortex control devices were also investigated. They included two different apex flaps, wing leading edge vortex flaps, and small large wing fences. The vortex flap and both apex flaps were beneficial in controlling the wing leading edge primary vortex
Parametric Study of Afterbody/nozzle Drag on Twin Two-dimensional Convergent-divergent Nozzles at Mach Numbers from 0.60 to 1.20
An investigation has been conducted in the Langley 16-Foot Transonic Tunnel to determine the effects of upper and lower external nozzle flap geometry on the external afterbody/nozzle drag of nonaxisymmetric two-dimensional convergent-divergent exhaust nozzles having parallel external sidewalls installed on a generic twin-engine, fighter-aircraft model. Tests were conducted over a Mach number range from 0.60 to 1.20 and over an angle-of-attack range from -5 to 9 deg. Nozzle pressure ratio was varied from jet off (1.0) to approximately 10.0, depending on Mach number
Building Capacity in Nonprofit Organizations
Offers a capacity building model that is based on a review of civil society, sustainable development, and organizational management literature. Reviews effective capacity building programs sponsored or operated by foundations. Includes recommendations
3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
Deformable linear objects are vastly represented in our everyday lives. It is
often challenging even for humans to visually understand them, as the same
object can be entangled so that it appears completely different. Examples of
deformable linear objects include blood vessels and wiring harnesses, vital to
the functioning of their corresponding systems, such as the human body and a
vehicle. However, no point cloud datasets exist for studying 3D deformable
linear objects. Therefore, we are introducing two point cloud datasets,
PointWire and PointVessel. We evaluated state-of-the-art methods on the
proposed large-scale 3D deformable linear object benchmarks. Finally, we
analyzed the generalization capabilities of these methods by conducting
transferability experiments on the PointWire and PointVessel datasets
Vision Language Models in Autonomous Driving and Intelligent Transportation Systems
The applications of Vision-Language Models (VLMs) in the fields of Autonomous
Driving (AD) and Intelligent Transportation Systems (ITS) have attracted
widespread attention due to their outstanding performance and the ability to
leverage Large Language Models (LLMs). By integrating language data, the
vehicles, and transportation systems are able to deeply understand real-world
environments, improving driving safety and efficiency. In this work, we present
a comprehensive survey of the advances in language models in this domain,
encompassing current models and datasets. Additionally, we explore the
potential applications and emerging research directions. Finally, we thoroughly
discuss the challenges and research gap. The paper aims to provide researchers
with the current work and future trends of VLMs in AD and ITS
RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation
Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time
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