Surface roughness noise prediction for silent aircraft experimental design SAX-40

Surface roughness noise is a potentially important contributor to airframe noise. In this paper, noise assessment due to surface roughness is performed for a conceptual Silent Aircraft design SAX-40 by means of a prediction model developed in previous theoretical work and validated experimentally. Estimates of three idealized test cases show that surface roughness could produce a significant noise level above that due to the trailing edge at high frequencies. Roughness height and roughness density are the two most significant parameters influencing surface roughness noise, with roughness height having the dominant effect. The ratio of roughness height to boundary-layer thickness is the relevant non-dimensional parameter and this decreases in the streamwise direction. The candidate surface roughness is selected for SAX-40 to meet an aggressive noise target and keep surface roughness noise at a negligible level. Copyright © 2008 by Yu Liu and Ann P. Dowling.

The calculation of acoustic shielding of engine noise by the Silent Aircraft Airframe

The Silent Aircraft airframe has a flying wing design with a large wing planform and a propulsion system embedded in the rear of the airframe with intake on the upper surface of the wing. In the present paper, boundary element calculations are presented to evaluate acoustic shielding at low frequencies. Besides the three-dimensional geometry of the Silent Aircraft airframe, a few two-dimensional problems are considered that provide some physical insight into the shielding calculations. Mean flow refraction effects due to forward flight motion are accounted for by a simple time transformation that decouples the mean-flow and acoustic-field calculations. It is shown that significant amount of shielding can be obtained in the shadow region where there is no direct line of sight between the source and observer. The boundary element solutions are restricted to low frequencies. We have used a simple physically-based model to extend the solution to higher frequencies. Based on this model, using a monopole acoustic source, we predict at least an 18 dBA reduction in the overall sound pressure level of forward-propagating fan noise because of shielding.

Impedance control reduces instability that arises from motor noise.

There is ample evidence that humans are able to control the endpoint impedance of their arms in response to active destabilizing force fields. However, such fields are uncommon in daily life. Here, we examine whether the CNS selectively controls the endpoint impedance of the arm in the absence of active force fields but in the presence of instability arising from task geometry and signal-dependent noise (SDN) in the neuromuscular system. Subjects were required to generate forces, in two orthogonal directions, onto four differently curved rigid objects simulated by a robotic manipulandum. The endpoint stiffness of the limb was estimated for each object curvature. With increasing curvature, the endpoint stiffness increased mainly parallel to the object surface and to a lesser extent in the orthogonal direction. Therefore, the orientation of the stiffness ellipses did not orient to the direction of instability. Simulations showed that the observed stiffness geometries and their pattern of change with instability are the result of a tradeoff between maximizing the mechanical stability and minimizing the destabilizing effects of SDN. Therefore, it would have been suboptimal to align the stiffness ellipse in the direction of instability. The time course of the changes in stiffness geometry suggests that modulation takes place both within and across trials. Our results show that an increase in stiffness relative to the increase in noise can be sufficient to reduce kinematic variability, thereby allowing stiffness control to improve stability in natural tasks.

Representations of uncertainty in sensorimotor control.

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.

Understanding jet noise.

Jets are one of the most fascinating topics in fluid mechanics. For aeronautics, turbulent jet-noise modelling is particularly challenging, not only because of the poor understanding of high Reynolds number turbulence, but also because of the extremely low acoustic efficiency of high-speed jets. Turbulent jet-noise models starting from the classical Lighthill acoustic analogy to state-of-the art models were considered. No attempt was made to present any complete overview of jet-noise theories. Instead, the aim was to emphasize the importance of sound generation and mean-flow propagation effects, as well as their interference, for the understanding and prediction of jet noise.