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    Interactional aerodynamics and acoustics of a propeller-augmented compound coaxial helicopter

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    The aerodynamic and acoustic characteristics of a generic hingeless coaxial helicopter with a tail-mounted propulsor and stabiliser have been simulated using Brown's Vorticity Transport Model. This has been done to investigate the ability of models of this type to capture the aerodynamic interactions that are generated between the various components of realistic, complex helicopter configurations. Simulations reveal the aerodynamic environment of the coaxial main rotor of the configuration to be dominated by internal interactions that lead to high vibration and noise. The wake of the main rotor is predicted to interact strongly with the tailplane, particularly at low forward speed, to produce a strong nose-up pitching moment that must be countered by significant longitudinal cyclic input to the main rotor. The wake from the main rotor is ingested directly into the tail propulsor over a broad range of forward speeds, where it produces significant vibratory excitation of the system as well as broadband noise. The numerical calculations also suggest the possibility that poor scheduling of the partition of the propulsive force between the main rotor and propulsor as a function of forward speed may yield a situation where the propulsor produces little thrust but high vibration as a result of this interaction. Although many of the predicted effects might be ameliorated or eliminated entirely by more careful or considered design, the model captures many of the aerodynamic interactions, and the resultant effects on the loading on the system, that might be expected to characterise the dynamics of such a vehicle. It is suggested that the use of such numerical techniques might eventually allow the various aeromechanical problems that often beset new designs to be circumvented - hopefully well before they manifest on the prototype or production aircraft

    Interactional aerodynamics and acoustics of a hingeless coaxial helicopter with an auxiliary propeller in forward flight

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    The aerodynamics and acoustics of a generic coaxial helicopter with a stiff main rotor system and a tail- mounted propulsor are investigated using Brown's Vorticity Transport Model. In particular, the model is used to capture the aerodynamic interactions that arise between the various components of the configuration. By comparing the aerodynamics of the full configuration of the helicopter to the aerodynamics of various combinations of its sub-components, the influence of these aerodynamic interactions on the behaviour of the system can be isolated. Many of the interactions follow a simple relationship between cause and effect. For instance, ingestion of the main rotor wake produces a direct effect on the unsteadiness in the thrust produced by the propulsor. The causal relationship for other interdependencies within the system are found to be more obscure. For instance, a dependence of the acoustic signature of the aircraft on the tailplane design originates in the changes in loading on the main rotor that arise from the requirement to trim the load on the tailplane that is induced by its interaction with the main rotor wake. The traditional approach to the analysis of interactional effects on the performance of the helicopter relies on characterising the system in terms of a network of possible interactions between the separate components of its configuration. This approach, although conceptually appealing, may obscure the closed-loop nature of some of the aerodynamic interactions within the helicopter system. It is suggested that modern numerical simulation techniques may be ready to supplant any overt reliance on this reductionist type approach and hence may help to forestall future repetition of the long history of unforeseen, interaction-induced dynamic problems that have arisen in various new helicopter designs

    R-CNN minus R

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    Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in order to determine whether it is a necessary modelling component, carrying essential geometric information not contained in the CNN, or whether it is merely a way of accelerating detection. We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating several learning steps in a single algorithm, as well as by proposing a number of improvements that accelerate detection
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