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A query-based approach for adaptively retraining and restructuring a two-hidden-layer artificial neural network (ANN) has been developed for the speedy prediction of the fundamental mode eigenvalue of the neutron diffusion equation, a standard nuclear reactor core design calculation which normally requires the iterative solution of a large-scale system of nonlinear partial differential equations (PDE's). The approach developed focuses primarily upon the adaptive selection of training and cross-validation data and on artificial neural-network (ANN) architecture adjustments, with the objective of improving the accuracy and generalization properties of ANN-based neutron diffusion eigenvalue predictions. For illustration, the performance of a 'bare bones' feedforward multilayer perceptron (MLP) is upgraded through a variety of techniques; namely, nonrandom initial training set selection, adjoint function input weighting, teacher-student membership and equivalence queries for generation of appropriate training data, and a dynamic node architecture (DNA) implementation. The global methodology is flexible in that it can 'wrap around' any specific training algorithm selected for the static calculations (i.e., training iterations with a fixed training set and architecture). Finally, the improvements obtained are carefully contrasted against past works reported in the literature. Index Terms - ANN, DNA, eigenvalues, membership and equivalence queries, MLP, neutron diffusion.

An analysis of the normal and shear stress distribution on the rake face of an orthogonal cutting tool is presented. A research-licensed version of DYNA3D finite element package was used to model the cutting process based upon mechanistic experiments that provided the empirical parameters of the Johnson–Cook constitutive model. The Johnson–Cook model uses strain, strain rate, and temperature to predict the material’s stress response. The computer model gave predictions for the normal and shear stresses on the rake lace that qualitatively agree with the experimentally determined stress distributions determined by other researchers. [Copyright &y& Elsevier]