In this study, we present a new method for obtaining the parameters of the Johnson-Mehl-Avrami-Kolmogorov equation for dynamic recrystallization grain size. The method consists of finite-element analysis and optimization techniques. An optimization tool iteratively minimizes the error between experimental values and corresponding finite-element solutions. Isothermal backward extrusion of the AA6060 aluminum alloy was used to acquire the main parameters of the equation for predicting DRX grain size. We compared grain sizes predicted using optimized and reference parameters with experimental values from the literature and found better agreement when the optimized parameters were applied.
This paper is a case study conducted to present an approach to the process of designing new products using virtual prototyping. During the first stage of research a digital geometric model of the vehicle was created. Secondly it underwent a series of tests utilising the multibody system method in order to determine the forces and displacements in selected construction nodes of the vehicle during its movement on an uneven surface. In consequence the most dangerous case of loads was identified. The obtained results were used to conduct detailed strength testing of the bicycle frame and changes its geometry. For the purposes of this case study two FEA software environments (Inventor and SolidWorks) were used. It has been confirmed that using method allows to implement the process of creating a new product more effectively as well as to assess the influence of the conditions of its usage more efficiently. It was stated that using of different software environments increases the complexity of the technical process of production preparation but at the same time increases the certainty of prototype testing. The presented example of simulation calculations made for the bicycle can be considered as a useful method for calculating other prototypes with high complexity of construction due to its systematized character of chosen conditions and testing procedure. It allows to verify the correctness of construction, functionality and perform many analyses, which can contribute to the elimination of possible errors as early as at the construction stage.
An on-line optimising control strategy involving a two level extended Kalman filter (EKF) for dynamic model identification and a functional conjugate gradient method for determining optimal operating condition is proposed and applied to a biochemical reactor. The optimiser incorporates the identified model and determines the optimal operating condition while maximising the process performance. This strategy is computationally advantageous as it involves separate estimation of states and process parameters in reduced dimensions. In addition to assisting on-line dynamic optimisation, the estimated time varying uncertain process parameter information can also be useful for continuous monitoring of the process. This strategy ensures that the biochemical reactor is operated at the optimal operation while taking care of the disturbances that are encountered during operation. The simulation results demonstrate the usefulness of the two level EKF assisted dynamic optimizer for on-line optimising control of uncertain nonlinear biochemical systems.
The paper presents a simulation model of the hybrid magnetic bearing dedicated to simulations of transient state. The proposed field-circuit model is composed of two components. The first part constitutes a set of ordinary differential equations that describes electrical circuits and mechanics. The second part of the simulation model consists of parameters such as magnetic forces, dynamic inductances and velocity-induced voltages obtained from the 3D finite element analysis. The MATLAB/Simulnik softwarewas used to implement the simulation model with the required control system. The proposed field-circuit model was validated by comparison of time responses with the prototype of the hybrid magnetic bearing.