Predictive model of explosive detonation parameters from an equation of state based on detonation velocity.

This article describes a predictive model of explosive detonation velocity and pressure based on first-order approximation of the detonation velocity equation. Detonation pressure was calculated from equations derived from the ideal detonation theory since that pressure is functionally related to detonation velocity. In the model calibration process, several product formation hierarchies were explored, with the best results yielded by the Kamlet and Jacobs (KJ) hierarchy. The predictive capacity of our model (labelled DEoS) was tested using different experimental databases, and was compared with predictions by thermochemical models (BKW-RR, JCZ3-J and JCZS) and by the empirical KJ method. The prediction values obtained using an experimental database of 238 explosive substances (75 singles and 163 composites), for a range of densities (1 g cc-1 to 2 g cc-1), were excellent in terms of both velocity and pressure, with root mean square error values of 1.7% (519 data items) and 6.0% (263 data items), respectively. We analysed results, broken down by explosive type, in detail, finding that the model residuals did not correlate with the predictor variables and also that the model predicts reasonable values for other parameters in the detonation state, such as density, the Jones parameter, and the Grüneisen parameter.

Learning machines applied to potential forest distribution.

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.