Prediction of hopper discharge rate using combined discrete element method and artificial neural network

Precise prediction of launch speed for athletes in the aerials event of freestyle skiing based on deep transfer learning

Automatically obtaining the launch speed are powerful guarantees for athletes in the aerials event of freestyle skiing to achieve good results. In most of the published studies describing athletes getting high scores, the assisting sliding distance depends entirely on the coach and even the athlete's own experience, which may not be optimal. The main goal of the present paper is to use an acquisition system and develop an artificial neural network (ANN) model to automatically obtain the corresponding relationship between assisting sliding distance and speed. The influence of snow friction coefficient, wind speed, wind direction, slope, height and weight can be simulated in the Unity3D engine. The influence of temperature, humidity and tilt angle needs to be measured in real world by professional testers which is strenuous. The neural network is first trained by sufficient simulation data to obtain the encoded feature. Then, the information learned in simulation environment is transferred to another network. The second network uses the data taken from twenty professional testers. Compared with the model without transfer learning, the performance of proposed method has significant improvement. The mean squared error for the testing set is 0.692. It is observed that the speed predicted by the designed deep transfer learning (DTL) model is in good agreement with the experimental measurement results. The results indicate that the proposed transfer learning method is an efficient model to be used as a tool for predicting the assisting sliding distance and launch speed for athletes in the aerials event of freestyle skiing.

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress-strain curves were fitted to Duncan-Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.

Optimal sizing of roof gutters and hopper for rainwater harvesting

Bernoulli's equation was applied to a section of hopper collector to determine the appropriate dimensions of the hopper for rainwater harvesting. Also, the hopper surface area (SFA) for a given volume was minimized by differentiating it in relation to the slant angle (SA). Combining the rational formula-Manning's equation and the best hydraulic section criteria-expressions were obtained for the optimum sizes of rectangular and circular gutters. Minimum hopper SFA for a given volume was found to occur at an optimum hopper SA of 35.282°. With the optimum conditions, design charts were produced for the hopper, circular, and rectangular roof gutters. The ratio of hopper larger radius to the smaller radius designated as (α) gave hopper dimensions with excessively wide upper radius for values of 0.1 ≤ α ≤ 0.2. Alpha (α) values of 0.8 and 0.9 gave values of R and Z which are almost too close to be distinguished. The valid range of α for hopper design was found to be 0.292 ≤ α ≤ 0.8. The study revealed that roof plan area has more effect on hopper dimensions than gutter slope. In addition, the case of excessive long gutters can be addressed by placing the hopper at the lateral epicenter of the eaves. In this regard, regions with abundant rainfall can solve water scarcity issues if proper design parameters of RWH components are considered in order to avoid waste of water through the overflow of water collection systems.