Application of neural networks to the ranking of perinatal variables influencing birthweight.

In this paper we compare Multi-Layer Perceptrons (a neural network type) with Multivariate Linear Regression in predicting birthweight from nine perinatal variables which are thought to be related. Results show, that seven of the nine variables, i.e., gestational age, mother's body-mass index (BMI), sex of the baby, mother's height, smoking, parity and gravidity, are related to birthweight. We found no significant relationship between birthweight and each of the two variables, i.e., maternal age and social class.

Towards identification of a general model of damping

Characterization of damping forces in a vibrating structure has long been an active area of research in structural dynamics. In spite of a large amount of research, understanding of damping mechanisms is not well developed. A major reason for this is that unlike inertia and stiffness forces it is not in general clear what are the state variables that govern the damping forces. The most common approach is to use `viscous damping' where the instantaneous generalized velocities are the only relevant state variables. However, viscous damping by no means the only damping model within the scope of linear analysis. Any model which makes the energy dissipation functional non-negative is a possible candidate for a valid damping model. This paper is devoted to develop methodologies for identification of such general damping models responsible for energy dissipation in a vibrating structure. The method uses experimentally identified complex modes and complex natural frequencies and does not a-priori assume any fixed damping model (eg., viscous damping) but seeks to determine parameters of a general damping model described by the so called `relaxation function'. The proposed method and several related issues are discussed by considering a numerical example of a linear array of damped spring-mass oscillators.

On the use of predictor-corrector method for coupled Monte Carlo burnup codes

This paper investigates the effect of the burnup coupling scheme on the numerical stability and accuracy of coupled Monte-Carlo depletion calculations. We show that in some cases, even the Predictor Corrector method with relatively short time steps can be numerically unstable. In addition, we present two possible extensions to the Euler predictor-corrector (PC) method, which is typically used in coupled burnup calculations. These modifications allow using longer time steps, while maintaining numerical stability and accuracy. © 2013 Elsevier Ltd. All rights reserved.

Numerical stability of the predictor-corrector method in Monte Carlo burnup calculations of critical reactors

Monte Carlo burnup codes use various schemes to solve the coupled criticality and burnup equations. Previous studies have shown that the simplest methods, such as the beginning-of-step and middle-of-step constant flux approximations, are numerically unstable in fuel cycle calculations of critical reactors. Here we show that even the predictor-corrector methods that are implemented in established Monte Carlo burnup codes can be numerically unstable in cycle calculations of large systems. © 2013 Elsevier Ltd. All rights reserved.

Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables

The design of wind turbine blades is a true multi-objective engineering task. The aerodynamic effectiveness of the turbine needs to be balanced with the system loads introduced by the rotor. Moreover the problem is not dependent on a single geometric property, but besides other parameters on a combination of aerofoil family and various blade functions. The aim of this paper is therefore to present a tool which can help designers to get a deeper insight into the complexity of the design space and to find a blade design which is likely to have a low cost of energy. For the research we use a Computational Blade Optimisation and Load Deflation Tool (CoBOLDT) to investigate the three extreme point designs obtained from a multi-objective optimisation of turbine thrust, annual energy production as well as mass for a horizontal axis wind turbine blade. The optimisation algorithm utilised is based on Multi-Objective Tabu Search which constitutes the core of CoBOLDT. The methodology is capable to parametrise the spanning aerofoils with two-dimensional Free Form Deformation and blade functions with two tangentially connected cubic splines. After geometry generation we use a panel code to create aerofoil polars and a stationary Blade Element Momentum code to evaluate turbine performance. Finally, the obtained loads are fed into a structural layout module to estimate the mass and stiffness of the current blade by means of a fully stressed design. For the presented test case we chose post optimisation analysis with parallel coordinates to reveal geometrical features of the extreme point designs and to select a compromise design from the Pareto set. The research revealed that a blade with a feasible laminate layout can be obtained, that can increase the energy capture and lower steady state systems loads. The reduced aerofoil camber and an increased L/. D-ratio could be identified as the main drivers. This statement could not be made with other tools of the research community before. © 2013 Elsevier Ltd.

Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive allows the concurrent use of histograms and free energy gradients and can easily be extended to include further data. In Part I we review the necessary theory and test the method for one collective variable. We demonstrate improved performance with respect to the weighted histogram analysis method and obtain meaningful error bars without any significant additional computation. In Part II we consider the case of multiple collective variables and compare to a reconstruction using least squares fitting of radial basis functions. We find substantial improvements in the regimes of spatially sparse data or short sampling trajectories. A software implementation is made available on www.libatoms.org.