Calculation of Critical Clearing Time of Embedded Generation for Relay Setting Based on Optimisation Method

his paper proposes an optimisation-based method to calculate the critical slip (speed) of dynamic stability and critical clearing time (CCT) of a self-excited induction generator (SEIG). A simple case study using the Matlab/Simulink environment has been included to exemplify the optimisation method. Relationships between terminal voltage, critical slip and reactance of transmission line, CCT and inertial constant have been determined, based on which analysis of impact on relaying setting has been further conducted for another simulation case.

An optimisation of Gaussian mixture models for integer processing units

This paper investigates sub-integer implementations of the adaptive Gaussian mixture model (GMM) for background/foreground segmentation to allow the deployment of the method on low cost/low power processors that lack Floating Point Unit (FPU). We propose two novel integer computer arithmetic techniques to update Gaussian parameters. Specifically, the mean value and the variance of each Gaussian are updated by a redefined and generalised "round'' operation that emulates the original updating rules for a large set of learning rates. Weights are represented by counters that are updated following stochastic rules to allow a wider range of learning rates and the weight trend is approximated by a line or a staircase. We demonstrate that the memory footprint and computational cost of GMM are significantly reduced, without significantly affecting the performance of background/foreground segmentation.

An application of bi-directional evolutionary structural optimisation for optimising energy absorbing structures using a material damage model

The Bi-directional Evolutionary Structural Optimisation (BESO) method is a numerical topology optimisation method developed for use in finite element analysis. This paper presents a particular application of the BESO method to optimise the energy absorbing capability of metallic structures. The optimisation objective is to evolve a structural geometry of minimum mass while ensuring that the kinetic energy of an impacting projectile is reduced to a level which prevents perforation. Individual elements in a finite element mesh are deleted when a prescribed damage criterion is exceeded. An energy absorbing structure subjected to projectile impact will fail once the level of damage results in a critical perforation size. It is therefore necessary to constrain an optimisation algorithm from producing such candidate solutions. An algorithm to detect perforation was implemented within a BESO framework which incorporated a ductile material damage model.

Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Pavement surface profiles induce dynamic ride responses in vehicles which can potentially be used to classify road surface roughness. A novel method is proposed for the characterisation of pavement roughness through an analysis of vehicle accelerations. A combinatorial optimisation technique is applied to the determination of pavement profile heights based on measured accelerations at and above the vehicle axle. Such an approach, using low-cost inertial sensors, would provide an inexpensive alternative to the costly laser-based profile measurement vehicles. The concept is numerically validated using a half-car roll dynamic model to infer measurements of road profiles in both the left and right wheel paths.

Optimisation of Environment-friendly SCFRC mixes use in precast Concrete Industry

The concept of green concrete has been progressively introduced in concrete technology. At the same time, new generations of superplasticisers have become widely available and self-compacting concrete is being increasingly implemented. The aim of this research is to study the impact that different sustainable materials have on both fresh and hardened properties of Self-Compacting Fibre Reinforced Concrete (SCFRC) in order to implement their use in a precast concrete company. Different combinations of cement, mineral additions (active and inert), polypropylene fibres, superplasticisers, and aggregates have been considered. Fresh state performance has been assessed by means of: slump flow test, V-funnel, and J-ring. Concrete compressive strength values at different ages have been retained as representative of the material's performance in its hardened state. All these properties have been correlated with SCFRC proportioning parameters. The importance of interactions between mineral additions and between these and superplasticiser is emphasised, as well as the different consequences of using powders as cement replacement or as mineral additions.

Optimisation of Environment-friendly SCFRC mixes use in precast Concrete Industry (PDF Download Available). Available from: http://www.researchgate.net/publication/263304799_Optimisation_of_Environment-friendly_SCFRC_mixes_use_in_precast_Concrete_Industry [accessed Jun 5, 2015].

Taguchi optimisation approach for production of activated carbon from phosphoric acid impregnated palm kernel shell by microwave heating

Taguchi method was applied to investigate the optimal operating conditions in the preparation of activated carbon using palm kernel shell with quadruple control factors: irradiation time, microwave power, concentration of phosphoric acid as impregnation substance and impregnation ratio between acid and palm kernel shell. The best combination of the control factors as obtained by applying Taguchi method was microwave power of 800 W, irradiation time of 17 min, impregnation ratio of 2, and acid concentration of 85%. The noise factor (particle size of raw material) was considered in a separate outer array, which had no effect on the quality of the activated carbon as confirmed by t-test. Activated carbon prepared at optimum combination of control factors had high BET surface area of 1,473.55 m² g-1 and high porosity. The adsorption equilibrium and kinetic data can satisfactorily be described by the Langmuir isotherm and a pseudo-second-order kinetic model, respectively. The maximum adsorbing capacity suggested by the Langmuir model was 1000 mg g-1.

Aerofoil Optimisation Using CST Parameterisation in SU2

This paper describes an implementation of the popular method of Class-Shape Transformation for aerofoil design within SU2 software framework. To exploit the adjoint based methods for aerodynamic optimisation within the SU2, a formulation to obtain geometric sensitivities from the new parameterisation is introduced, enabling the calculation of gradients with respect to new design variables. To assess the accuracy and efficiency of the alternative approach, two transonic optimisation problems are investigated: an inviscid problem with multiple constraints and a viscous problems without any constraints. Results show the new parameterisation obtaining reliable optimums, with similar levels of
performance of the software native parameterisations.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.