On the modelling of highly elastic flows of amorphous thermoplastics

Two approaches to the kinematic structuring of constitutive models for highly elastic flows of polymer melts have been examined systematically, assuming either: (1) additivity of elastic and viscous velocity gradients or (2) multiplicability of elastic and viscous deformation gradients. A series of constitutive models were compared, with differing kinematic structure but the same linear responses in elastic and viscous limits. They were solved numerically and their predictions compared, and they were also compared to those of the Giesekus model. Several variants, previously proposed as separate models, are shown to be equivalent and qualitatively in agreement with experiment, and therefore a sound basis for construction of models. But the assignment of viscous spin is critical: if it is assumed equal to the total spin with approach (1), or equal to zero with approach (2), then unphysical viscoelastic behaviour is predicted. © 2008 Elsevier Ltd. All rights reserved

Toward a dynamical systems analysis of neuromodulation

This work presents some first steps toward a more thorough understanding of the control systems employed in evolutionary robotics. In order to choose an appropriate architecture or to construct an effective novel control system we need insights into what makes control systems successful, robust, evolvable, etc. Here we present analysis intended to shed light on this type of question as it applies to a novel class of artificial neural networks that include a neuromodulatory mechanism: GasNets. We begin by instantiating a particular GasNet subcircuit responsible for tuneable pattern generation and thought to underpin the attractive property of “temporal adaptivity”. Rather than work within the GasNet formalism, we develop an extension of the well-known FitzHugh-Nagumo equations. The continuous nature of our model allows us to conduct a thorough dynamical systems analysis and to draw parallels between this subcircuit and beating/bursting phenomena reported in the neuroscience literature. We then proceed to explore the effects of different types of parameter modulation on the system dynamics. We conclude that while there are key differences between the gain modulation used in the GasNet and alternative schemes (including threshold modulation of more traditional synaptic input), both approaches are able to produce tuneable pattern generation. While it appears, at least in this study, that the GasNet’s gain modulation may not be crucial to pattern generation , we go on to suggest some possible advantages it could confer

Farm-gate N and P balances and use efficiencies across specialist dairy farms in the Republic Ireland

working paperThis study establishes farm gate N and P balances and use efficiencies based on the average of 2 years of Teagasc National Farm Survey data in 2009 and 2010. The weighted average farm gate N surplus for this nationally representative sample of specialist dairy farms was 143.4 kg N ha-1. Average farm gate nitrogen use efficiency was 23.2%. For dairy farms operating under an EU Nitrates Derogation, the average N surplus was higher at 181.8 kg N ha-1 and averageN use efficiency was slightly lower at 22.2%. The total average farm gate P balance was 4.1 kg ha-1 in surplus, and P use efficiency averaged 83.9%. P balance ranged from -7.3 to 23.0 kg ha-1. A total of 27% had a negative P balance. The average P surplus for farms with a Nitrates Derogation was below the average of all farms at 3.5 kg P ha-1 and average P use efficiency for these Derogation farms was above the average of all farms at 90%

Technology review of flight crucial flight controls

The results of a technology survey in flight crucial flight controls conducted as a data base for planning future research and technology programs are provided. Free world countries were surveyed with primary emphasis on the United States and Western Europe because that is where the most advanced technology resides. The survey includes major contemporary systems on operational aircraft, R&D flight programs, advanced aircraft developments, and major research and technology programs. The survey was not intended to be an in-depth treatment of the technology elements, but rather a study of major trends in systems level technology. The information was collected from open literature, personal communications and a tour of several companies, government organizations and research laboratories in the United States, United Kingdom, France, and the Federal Republic of Germany

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems

Performance Regulation and Tracking via Lookahead Simulation: Preliminary Results and Validation

This paper presents an approach to target tracking that is based on a variable-gain integrator and the Newton-Raphson method for finding zeros of a function. Its underscoring idea is the determination of the feedback law by measurements of the system's output and estimation of its future state via lookahead simulation. The resulting feedback law is generally nonlinear. We first apply the proposed approach to tracking a constant reference by the output of nonlinear memoryless plants. Then we extend it in a number of directions, including the tracking of time-varying reference signals by dynamic, possibly unstable systems. The approach is new hence its analysis is preliminary, and theoretical results are derived for nonlinear memoryless plants and linear dynamic plants. However, the setting for the controller does not require the plant-system to be either linear or stable, and this is verified by simulation of an inverted pendulum tracking a time-varying signal. We also demonstrate results of laboratory experiments of controlling a platoon of mobile robots.Comment: A modified version will appear in Proc. 56th IEEE Conf. on Decision and Control, 201