A systematic experimental investigation of significant parameters affecting model tire hydroplaning

The results of a comprehensive parametric study of model and small pneumatic tires operating on a wet surface are presented. Hydroplaning inception (spin down) and rolling restoration (spin up) are discussed. Conclusions indicate that hydroplaning inception occurs at a speed significantly higher than the rolling restoration speed. Hydroplaning speed increases considerably with tread depth, surface roughness and tire inflation pressure of footprint pressure, and only moderately with increased load. Water film thickness affects spin down speed only slightly. Spin down speed varies inversely as approximately the one-sixth power of film thickness. Empirical equations relating tire inflation pressure, normal load, tire diameter and water film thickness have been generated for various tire tread and surface configurations

Automatic Derivation of Statistical Algorithms: The EM Family and Beyond

Machine learning has reached a point where many probabilistic methods can be understood as variations, extensions and combinations of a much smaller set of abstract themes, e.g., as different instances of the EM algorithm. This enables the systematic derivation of algorithms customized for different models. Here, we describe the AUTOBAYES system which takes a high-level statistical model specification, uses powerful symbolic techniques based on schema-based program synthesis and computer algebra to derive an efficient specialized algorithm for learning that model, and generates executable code implementing that algorithm. This capability is far beyond that of code collections such as Matlab toolboxes or even tools for model-independent optimization such as BUGS for Gibbs sampling: complex new algorithms can be generated without new programming, algorithms can be highly specialized and tightly crafted for the exact structure of the model and data, and efficient and commented code can be generated for different languages or systems. We present automatically-derived algorithms ranging from closed-form solutions of Bayesian textbook problems to recently-proposed EM algorithms for clustering, regression, and a multinomial form of PCA

Modeling of the subgrid-scale term of the filtered magnetic field transport equation

Accurate subgrid-scale turbulence models are needed to perform realistic numerical magnetohydrodynamic (MHD) simulations of the subsurface flows of the Sun. To perform large-eddy simulations (LES) of turbulent MHD flows, three unknown terms have to be modeled. As a first step, this work proposes to use a priori tests to measure the accuracy of various models proposed to predict the SGS term appearing in the transport equation of the filtered magnetic field. It is proposed to evaluate the SGS model accuracy in term of "structural" and "functional" performance, i.e. the model capacity to locally approximate the unknown term and to reproduce its energetic action, respectively. From our tests, it appears that a mixed model based on the scale-similarity model has better performance.Comment: 10 pages, 5 figures; Center for Turbulence Research, Proceedings of the Summer Program 2010, Stanford Universit

An experimental investigation of leading-edge vortex augmentation by blowing

A wind tunnel test was conducted to determine the effects of over-the-wing blowing as a means of augmenting the leading-edge vortex flow of several pointed-tip, sharp-edged planforms. Arrow, delta, and diamond wings with leading-edge sweeps of 30 and 45 degrees were mounted on a body-of-revolution fuselage and tested in a low-speed wind tunnel at a Mach number of 0.2. Nozzle location data, pitch data, and flow-visualization pictures were obtained for a range of blowing rates. Results show pronounced increases in vortex lift due to the blowing