Multiple grasp-specific representations of tool dynamics mediate skillful manipulation.

Skillful tool use requires knowledge of the dynamic properties of tools in order to specify the mapping between applied force and tool motion. Importantly, this mapping depends on the orientation of the tool in the hand. Here we investigate the representation of dynamics during skillful manipulation of a tool that can be grasped at different orientations. We ask whether the motor system uses a single general representation of dynamics for all grasp contexts or whether it uses multiple grasp-specific representations. Using a novel robotic interface, subjects rotated a virtual tool whose orientation relative to the hand could be varied. Subjects could immediately anticipate the force direction for each orientation of the tool based on its visual geometry, and, with experience, they learned to parameterize the force magnitude. Surprisingly, this parameterization of force magnitude showed limited generalization when the orientation of the tool changed. Had subjects parameterized a single general representation, full generalization would be expected. Thus, our results suggest that object dynamics are captured by multiple representations, each of which encodes the mapping associated with a specific grasp context. We suggest that the concept of grasp-specific representations may provide a unifying framework for interpreting previous results related to dynamics learning.

Robust control of plants with saturation nonlinearity based on coprime factor representations

We consider the robust control of plants with saturation nonlinearities from an input/output viewpoint. First, we present a parameterization for anti-windup control based on coprime factorizations of the controller. Second, we propose a synthesis method which exploits the freedom to choose a particular coprime factorization.

Model structure and uncertainty for stochastic non-point source modelling applications

The uncertainty associated with a rainfall-runoff and non-point source loading (NPS) model can be attributed to both the parameterization and model structure. An interesting implication of the areal nature of NPS models is the direct relationship between model structure (i.e. sub-watershed size) and sample size for the parameterization of spatial data. The approach of this research is to find structural limitations in scale for the use of the conceptual NPS model, then examine the scales at which suitable stochastic depictions of key parameter sets can be generated. The overlapping regions are optimal (and possibly the only suitable regions) for conducting meaningful stochastic analysis with a given NPS model. Previous work has sought to find optimal scales for deterministic analysis (where, in fact, calibration can be adjusted to compensate for sub-optimal scale selection); however, analysis of stochastic suitability and uncertainty associated with both the conceptual model and the parameter set, as presented here, is novel; as is the strategy of delineating a watershed based on the uncertainty distribution. The results of this paper demonstrate a narrow range of acceptable model structure for stochastic analysis in the chosen NPS model. In the case examined, the uncertainties associated with parameterization and parameter sensitivity are shown to be outweighed in significance by those resulting from structural and conceptual decisions. © 2011 Copyright IAHS Press.

Additive Gaussian processes

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

Toward designing with three-dimensional bumps for lift/drag improvement and buffet alleviation

The desire to design more efficient transport aircraft has led to many different attempts to minimize drag. One approach is the use of three-dimensional shock control bumps, which have gained popularity in the research community as simple, efficient and robust devices capable of reducing the wave drag of transonic wings. This paper presents a computational study of the performance of three-dimensional bumps, relating key bump design variables to the overall wing aerodynamic performance. An efficient parameterization scheme allows three-dimensional bumps to be directly compared to two-dimensional designs, indicating that two-dimensional bumps are capable of greater design point aerodynamic performance in the transonic regime. An advantage of three-dimensional bumps lies in the production of streamwise vortices, such that, while two-dimensional bumps are capable of superior performance near the design point, three-dimensional bumps are capable of breakingup regions of separated flow at high Mach numbers, suggesting improvement in terms of buffet margin. A range of bump designs are developed that exhibit a tradeoff between design point aerodynamic efficiency and improvementinbuffet margin, indicating the potential for bespoke designs to be generated for different sections of a wing based on its flow characteristics. Copyright © 2012 by Jeremy Eastwood and Jerome Jarrett.

Winglets for Improved Aerothermal Performance of High Pressure Turbines

This paper investigates the design of winglet tips for unshrouded high pressure turbine rotors, considering aerodynamic and thermal performance simultaneously. A novel parameterization method has been developed to alter the tip geometry of a rotor blade. A design survey of un-cooled, flat-tipped winglets is performed using RANS calculations for a single rotor at engine representative operating conditions. Compared to a plain tip, large efficiency gains can be realized by employing an overhang around the full perimeter of the blade, but the overall heat load rises significantly. By employing an overhang on only the early suction surface, significant efficiency improvements can be obtained without increasing the overall heat transfer to the blade. The flow physics are explored in detail to explain the results. For a plain tip, the leakage and passage vortices interact to create a three-dimensional impingement onto the blade suction surface, causing high heat transfer. The addition of an overhang on the early suction surface displaces the tip leakage vortex away from the blade, weakening the impingement effect and reducing the heat transfer on the blade. The winglets reduce the aerodynamic losses by unloading the tip section, reducing the leakage flow rate, turning the leakage flow in a more streamwise direction and reducing the interaction between the leakage fluid and endwall flows. Generally these effects are most effective close to the leading edge of the tip, where the leakage flow is subsonic.