Renewable energy allocation based on maximum flow modelling within a microgrid

This paper designs an energy allocation scheme based on maximum flow modeling for a microgrid containing renewable energy generators and consumer facilities. Basically, the flow graph consists of a set of nodes representing consumers or generators as well as a set of weighted links representing the amount of energy generation, consumer-side demand, and transmission cable capacity. The main idea lies in that a special node is added to account for the interaction with the main grid and that two-pass allocation is executed. In the first pass, the maximum flow solver decides the amount of the insufficiency and thus how much to purchase from the main grid. The second pass runs the flow solver again to fill the energy lack and calculates the surplus of renewable energy generation. The performance measurement result obtained from a prototype implementation shows that the generated energy is stably distributed over multiple consumers until the energy generation reaches the maximum link capacity.

Identification of crowd behaviour patterns using stability analysis

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.

The Hypotheses Testing Method for Evaluation of Startup Projects

This paper suggests new perspective to evaluating innovation projects and understanding the nature of startup risks. Author consider five principal hypotheses that underlie every innovative project that comprise a bunch of respective assumptions to manage startup risks in a proactive manner. Suggested approach spots the light on a project’s uncertainties and risks, embedded investment and managerial options, and enables more comprehensive and accurate evaluation of innovation. The Hypotheses Testing Method enables to estimate risks and attractiveness of a startup project in a clear and fast manner. It replaces unclear traditional techniques like NPV and DCF, avoiding heavy cash flow modelling.

Simulation of debris flows in the watershed of the Putih River, Indonesia using SIMLAR 2.1

In 2010, the eruption of Mount Merapi produced a huge volcanic material for debris flows. One area affected by the debris flows is the watershed of Putih River. To predict the impact caused by debris flows can be done by using software such as the Simulation Lahar (SIMLAR) 2.1. In this paper, debris flow modelling will be carried out using SIMLAR 2.1 in conditions without sabo dams and using sabo dams. This simulation aims to determine the effectiveness of the sabo dams in reducing the impact of debris flows. The data used are rainfall data, DEM and sediment data in Putih River. The results show that the sabo dam building can slow down the velocity of debris flow. In addition, sabo dams also function as a barrier to riverbed erosion in the Putih River watershed. Based on the results above, it can be concluded that SIMLAR 2.1 can predict the impact of debris flows in the Putih River watershed.

Mathematical modelling of fluid flow in electromagnetically stirred weld pool

In this paper, a mathematical model has been proposed to study the relationship between electromagnetic stirring (EMS) weld parameters and the mode of fluid flow on grain refinement of AA 6060 weldments. For this purpose, fluid flow modelling using Navier-Stokes equation is described first, and then, the proposed mathematical approach has been discussed in detail. For demonstration, calculations to determine the fluid velocity in the weld pool of thin plate AA6060 were performed. The application of the model on the experimental results indicates that the best grain refinement is achieved at a transition mode from laminar to turbulent fluid flow.

CFD modelling of the condensation, evaporation, coagulation and crystallization processes in the cooling tower emissions

The aim of the paper is to develop the CFD model for the environmental impact assessment of the cooling tower. The methods applied for this problem are the single-phase turbulent multispecies flow modelling with the DPM Lagrangian particle tracking. The simulations have been carried out in the steady state SIMPLE solver using the ANSYS Fluent software. User Defined Functions have been defined to enhance the accuracy and versatility of the modelling approach in terms of turbulence, fog formation, evaporation, coagulation and crystallization modelling. The Chalk Point cooling tower experiment, laboratory tests with freezing droplets and analytical correlations are used to verify the customized parts of the new CFD model. The arbitrary small-town geometry is used to demonstrate the simulation capabilities of the fog and drift deposition as well as the temperature and relative humidity values near ground and buildings. The results indicate that the new CFD model is able to predict the cooling tower plume parameters, icing and salt contamination risks as well as drift deposition.