Self-stabilizing somersaults

We investigate the open-loop stability of a planar biped robot performing a periodic motion of forward somersaults with alternating single-leg contacts. The robot has a trunk and two actuated telescopic legs with point feet which are coupled to the trunk by actuated hinges. There is compliance and damping in the hip and in the legs. The concept of open-loop control implies that all actuators of the system receive predetermined inputs that are never altered by any feedback interference. Only with the right choice of model parameters and actuator inputs is it possible to create such self-stabilizing motions exploiting the natural stability properties of the system. These unknowns have been determined using special-purpose stability-optimization methods. The resulting motion is not only stable, but also a more efficient form of forward motion than running for the investigated robot.     

Elongational viscosity of LDPEs and polystyrenes using entrance loss data

For two low‐density polyethylenes and two polystyrenes, axisymmetric and planar elongational viscosities are estimated using entrance loss data from capillary and slit rheometers, respectively. The elongational viscosity is estimated by optimizing the values of various parameters in the Sarkar–Gupta elongational viscosity model such that the entrance loss predicted by a finite element simulation agrees with the corresponding experimental data. The predicted entrance loss is in good agreement with the experimental data at high flow rates. The difference in the experimental and predicted entrance loss at lower flow rates might have been caused by large error in the experimental data in this range. POLYM. ENG. SCI., 2008. © 2007 Society of Plastics Engineers     

Structural system identification: from reality to models

The paper is an expository contribution on the subject of structural system identification, measured signal processing and their applications to model-based structural health detection. The materials covered in this paper are by and large extracted from the three theses of the first three authors. The paper focuses on the state-space oriented system identification theory as specialized to structural dynamics governing equations of motion, a judicious use of wavelet transformation techniques for extracting impulse response functions, various input-output combinations for multi-input and multi-output problems, robust ways of identifying both proportional and non-proportional damping parameters, and the use of localized identification theory for damage detection from measured response data. The authors then offer several outstanding challenges in structural system identification theory and their applications.     

On Optimal Signal–Filter Pairs

We extend the notion of an optimal signal–filter pair to the case where n-convolution is used instead of cyclic convolution. We construct n-optimal n-adic signals for all n = 2, 3, . . ..     

Unsteady free surface flow induced by a line sink in a fluid of finite depth

The unsteady withdrawal of fluid from a reservoir of finite depth with a free surface through a line sink is considered. A numerical method is used to investigate the evolution of a free surface in several different geometric and initial configurations, and it is shown that there are some critical flow parameters at which the flow changes its nature. The existence of steady states is also considered. Results both with and without surface tension are included and placed in context with previous work. Inclusion of surface tension reveals that solutions either evolve to a steady state or drawdown. As the sink is moved off the base, the behaviour becomes consistent with flows in a fluid of infinite depth, and consequently flows can be classified as either “shallow” or “deep” depending on their drawdown behaviour.     

Construction of a prediction model for the structural stability of a surface grinder using backpropagation neural network

Learning and prediction capability of the backpropagation neural network (BPNN) have been used to build the prediction model for the structural stability of a surface grinder. The Lagrange energy method is applied to derive the dynamic equation of the lumped parameter model of the surface grinder. The major factors influencing the structural stability of the system can be determined after the ratio of kinetic energy of the sub-structure and the ratio of potential energy of the sub-structure interface are obtained. An orthogonal rotatable central composite design is adopted to dispose the treatment combinations of the major factors. The BPNN model is constructed by the treatment combinations of the training patterns and verified by the treatment combinations of the test patterns. In this paper, a 3-layer BPNN model with a 10-neuron hidden layer which converged after 4,072 learning cycles is selected to predict the structural stability of a surface grinder within the planned ranges. The percentage residuals of both training patterns and test patterns are all within 3.41%, thus the prediction accuracy of the BPNN model is excellent so that the engineering demands are well satisfied.