Nano-crystalline metals have attracted considerable attention over the past two decades due to their increased mechanical properties as compared to their microcrystalline counterparts. However, the behaviour of nano-crystalline metals is influenced by imperfections introduced during synthesis or heat treatment. These imperfections include pores, which are mostly located in the area of grain boundaries. To study the behaviour of multiphase nano-crystalline materials, a novel fully parametric algorithm was developed. The data required for implementing the developed numerical model were the volume fraction of the alloying elements and their basic properties as well as the density and the size of randomly distributed pores. To validate the developed algorithm, the alloy composition 75 wt% tungsten and 25 wt% copper was examined experimentally under compression tests. For the investigation, two batches of specimens were used; a batch having a coarse-grained microstructure with an average grain diameter of 150 nm and a nanocrystalline batch having 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 results of quasi-static compression testing revealed that the nanocrystalline W-Cu material exhibited brittle behaviour which was characterised by an elastic deformation that led to fracture without remarkable plasticity. A compressive strength of about 1100 MPa was derived which was more than double compared to conventional W-Cu samples. Finite element simulations of the behaviour of porous nano-crystalline materials were performed and compared with the respective experimental compression tests. The numerical model and experimental observations were in good agreement.

Nanocrystalline metals have been the cause of substantial intrigue over the past two decades due to their high strength, which is highly sensitive to their microstructure. The aim of the present project is to develop a finite element two-phase model that is able to predict the elastic moduli and the yield strength of nanostructured material as functions of their microstructure. The numerical methodology uses representative volume elements (RVEs) in which the material microstructure, i.e., the grains and grain boundaries, is presented utilizing the three-dimensional (3D) Voronoi algorithm. The implementation of the 3D Voronoi particles was performed on the nanostructure investigation of ultrafine materials by SEM and TEM. Proper material properties for the grain interiors (GI) and grain boundaries (GB) were computed using the Hall-Petch equation and a dislocation-based analytical approach, respectively. The numerical outcomes show that the Young’s Modulus of nanostructured copper increased by increasing the crystallite volume fraction, while the yield strength increased by decreasing the grain size. The numerical predictions were strongly confirmed in opposition to finite element outcomes, experimental results from the open literature, and predictions from the rule of mixtures and the Mori-Tanaka analytical models.

Traditionally, a quarter-car model and a sky-hook controller are employed to derive analytical expressions that describe conditions for self-powered operation. The main contribution of this work consists in using a seven degree of freedom vehicle model to determine numerically the condition for self-powered operation of an active suspension system with electromagnetic actuators. The performance of proportional-integral-derivative, linear quadratic regulator, and fuzzy Logic suspension controllers that employ feedback information for heave, pitch, and roll motion is evaluated under selfpowered operation. An objective function consisting of a weighted sum of performance measures, including root mean square values for accelerations, road holding, actuator travel, and power regeneration capability, is used to determine equivalent actuator damping values and controller gains that enhance self-powered operation. The resulting optimal designs for each control strategies are compared by means of frequency responses to evaluate their performance and power regeneration capability, as well as to determine the effect of self-powered operation on these characteristics. This investigation shows that the performance of a self-powered active suspension systems, based on heave, pitch, and roll motion information, can be optimized to approach that of an active suspension system with external power supply; the degree of degradation depends on the particular suspension controller and the design objectives that are adopted. The performance improvement compared to a suspension system designed using a quarter car model and a sky-hook controller is also presented.

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In the present work, a numerical model is developed to predict the Young’s modulus and shear modulus of nanocrystalline materials using a Finite Element Analysis. The model is based on Representative Volume Elements (RVE) in which the microstructure of the material is described using the Voronoi tessellation algorithm. The use of the Voronoi particles was based on the observation of the morphology of nanocrystalline materials by Scanning Electron and Transmission Electron Microscopy. In each RVE, three-dimensional modelling of the grain and grain boundaries as randomly-shaped sub-volumes is performed. The developed model has been applied to pure nanocrystallline copper at grain volume fractions of 80%, 90% and 95% taking also into account the parameters of grain size and grain boundary thickness. The elastic moduli of nanocrystalline copper have been computed by loading the RVE in tension. The numerical results reveal that the elastic moduli of nanocrystalline copper increase with increasing the grain volume fraction. On the other hand, for a given grain volume fraction, the results showed no effect of the grain size. The model predictions have been validated successfully against numerical results from the literature and predictions of the Rule of Mixtures and the Mori-Tanaka analytical model.