Narrow Results

The two most popular algorithms for dissipative particle dynamics (DPD) are critically discussed. In earlier papers, the Groot–Warren algorithm with λ = 1/2 was recommended over the original Hoogerbrugge–Koelman scheme on the basis of a marked difference in their equilibrium temperatures. We show, however, that both schemes produce identical trajectories. Expressions for the temperatures of an ideal gas and a liquid as functions of the simulation parameters are presented. Our findings indicate that the current DPD algorithms do not possess a unique temperature because of the way in which the dissipative and random forces are included. The commonly used large time steps are beyond the stability limits of the conservative force field integrator.

It is difficult to predict the dynamics of systems which are nonlinear and whose characteristic is unknown. In order to build a model of the system from input and output data without any knowledge about the system, we try automatically to build prediction model by Genetic Programming (GP).

GP has been used to discover the function that describes nonlinear system to study the effect of wavelength and temperature on the refractive index of the fiber core. The predicted distribution from the GP based model is compared with the experimental data. The discovered function of the GP model has proved to be an excellent match to the experimental data.

Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) which represents dispersion formula of optical fiber. An efficient NN has been designed by GA to simulate the dynamics of the optical fiber system which is nonlinear. Without any knowledge about the system, we have used the input and output data to build a prediction model by NN. The neural network has been trained to produce a function that describes nonlinear system which studies the dependence of the refractive index of the fiber core on the wavelength and temperature. The trained NN model shows a good performance in matching the trained distributions. The NN is then used to predict refractive index that is not presented in the training set. The predicted refractive index had been matched to the experimental data effectively.

A solid–solid contact model of a rough surface with a single peak was established to explore the thermal effect of interfacial friction. From the perspective of friction force, temperature and energy, the law of the thermal effect was revealed. The results showed that the temperature of the asperities gradually increased during the shearing process, and a stress concentration formed in the shearing zone. After contact, the asperities had undergone unrecoverable plastic deformation. At each indentation depth, as the rotation angle of the crystal increased, the friction force, average temperature, and the sum of the changes in thermal kinetic and thermal potential energy first increased and then decreased; the trends of the three parameters changing with the rotation angle of the crystal were consistent. The average decreases in the friction force, average temperature, and the sum of the changes in thermal kinetic and thermal potential energy were 52.47%, 30.91% and 56.75%, respectively, for a crystal structure with a rotation angle of 45^∘ compared to a crystal structure with a rotation angle of 0^∘. The methods used in this study provide a reference for the design of frictional pairs and the reduction of the thermal effect of interfacial friction.