Mechanical Characterization of Nanocrystalline Materials via a Finite Element Nanoindentation Model

The difficulty of producing sufficient quantities of nanocrystalline materials for test specimens has led to an effort to explore alternative means for the mechanical characterization of small material volumes. In the present work, a numerical model simulating a nanoindentation test was developed using Abaqus software. In order to implement the model, the principal material properties were used. The numerical nanoindentation results were converted to stress–strain curves through an inverse algorithm in order to obtain the macroscopic mechanical properties. For the validation of the developed model, nanoindentation tests were carried out in accordance with the ISO 14577. The composition of 75% wt. tungsten and 25% wt. copper was investigated by producing two batches of specimens with a coarse-grain microstructure with an average grain size of 150 nm and a nanocrystalline microstructure with a grain diameter of 100 nm, respectively. The porosity of both batches was derived to range between 9% and 10% based on X-ray diffraction analyses. The experimental nanoidentation results in terms of load–displacement curves show a good agreement with the numerical nanoindentation results. The proposed numerical technique combined with the inverse algorithm predicts the material properties of a fully dense, nanocrystalline material with very good accuracy, but it shows an appreciable deviation with the corresponding compression results, leading to the finding that the porosity effect is a crucial parameter which needs to be taken into account in the multiscale numerical methodology.

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m.

Numerical simulation for expansion of preform and optimization of preform in thermoset composites

The expansion of preform and the optimization of preform have become important steps in the molding process. At present, there are some questions in the expansion of thermoset composite material preform and precompression, for example, the inaccurate dimensions, cracks, and wrinkles. For the expansion of preform, the finite element inverse algorithm is used as the expansion algorithm, and then the initial solution is optimized by the arc length mapping method, the expansion of preform is realized by the iterative equation which is solved by the ABAQUS solver. The effectiveness of the expansion of preform is verified through the comparison between the finite element inverse algorithm with DYNAFORM. The optimization of the precompression process is researched in order to solved the problems of cracks and wrinkles in the integral precompression method of preform. Firstly, the precompression sequence is adjusted by the precompression method, and then the precompression direction is optimized by the genetic algorithm. Through numerical simulation, the maximum thinning rate is reduced to 13%, and the maximum thickening rate is reduced to 6%, which improve the problems of cracks and wrinkles of preform, and the effectiveness of the optimization method is verified.

Intelligent Wavelet Based Pre-Filtering Method for Ultrasonic Sensor Fusion Inverse Algorithm Performance Optimization

Certain obstacle mapping applications require the live evaluation of the measured data to prevent collision with obstacles. The fusion of different or similar sensors usually has a high calculation demand, which increases significantly with the area to be evaluated and the number of sensors. In the present considerations, we propose a wavelet-based adaptive optimization method, which can greatly decrease the number of grid points to be evaluated, and thus the necessary computation time. The basis of the method is to use the fact that the areas to be evaluated mostly face a rather small number of obstacles, which cover a smaller percentage of the whole environment. The first step in a pre-filtering process is the determination of the zones where no obstacles are present. This step can already result in a considerable decrease in the computation time, however with the transformation to polar coordinates, the method will not only be more fitted to the problem to be solved, but the area of the evaluation can also be increased with the same number of grid points. As a last step, we applied wavelet transformation to identify the regions of interest, where the application of a refined raster is necessary, and thus further decreasing the number of grid points where the calculation has to be carried out. We used our previously developed probability-based ultrasonic sensor fusion inverse algorithm to demonstrate the efficiency of the proposed method.