Lucyna Brzozowska

Modelling the propagation of smoke from a tanker fire in a built-up area

The paper presents the application of a Lagrangian particle model to problems connected with safety in road transport. Numerical simulations were performed for a hypothetical case of smoke emission from a tanker fire in a built-up area. Propagation of smoke was analysed for three wind directions. A diagnostic model was used to determine the air velocity field, whereas the dispersion of pollutants was analysed by means of a Lagrangian particle model (Brzozowska, 2013). The Idrisi Andes geographic information system was used to provide data on landforms and on their aerodynamic roughness. The presented results of computations and their analysis exemplify a possible application of the Lagrangian particle model: evaluation of mean (averaged over time) concentrations of pollutants and their distribution in the considered area (especially important due to the protection of people, animals and plants) and simulation of the propagation of harmful compounds in time as well as performing computations for cases of the potential effects of road incidents.

An Application of Artificial Neural Network to Diesel Engine Modelling

The paper presents an application of artificial neural network to modelling of the processes in Diesel engine cylinder. In our study six independent engine control variables have been identified and used. These parameters determine the engine behaviour (output parameters). In order to found the relations between input and output parameters the radial artificial neural network has been applied. The ANN has been trained on the base of measurement results in order to approximate measurement results of toxicity of exhaust gas components. Measurements data which have been used to training ANN cover a wide range of steady state engine operating conditions (more then 230). The ANN modelling accuracy has been controlled by optimisation method which has been used in order to find the influence of given centres of Gauss function. Outputs from the ANN for some set of control parameters are compared with measurements. A good correspondence of results has been achieved.

Modelling of Ropes with Consideration of Large Deformations and Friction by Means of the Rigid Finite Element Method

This paper presents a model of a rope, which is an example of a slender system. The method used for modelling is the modified rigid finite element method, which enables us to consider not only bending flexibility but also longitudinal flexibility. A new approach presented in the paper is validated by comparison of our own results with an analytical solution for a catenary line. For this problem the influence of both the number of elements into which the link is discretised and various values of stiffness coefficient is analysed. Numerical simulations of the dynamics of a rope of an offshore crane lifting a load from a vessel are presented.

Application of Neural Network to Control the Load Motion of an Offshore Crane

The main aim of the paper is to present the computer models, algorithms and programs which can be applied in real time control of an A-frame winch motion. This control algorithm has to stabilize load position and compensate the weaving. On the basis of the model of A-frame dynamics, the proper optimisation tasks are defined and solved. In each task the various crane and sea conditions are assumed. As a result of this process the optimal winch drive courses assuring load stabilization are defined. This approach allows the neural network to be defined and applied in real-time conditions.

Evaluation of a Diagnostic Model of an Air Velocity Field: The Must Wind Tunnel Case

The paper presents the application of a diagnostic model of an air velocity field in complex geometric settings. A variational algorithm is used to satisfy the criterion of mass balance. The formulated elliptic-type equation is solved with conformal mapping and the finite difference method for a non-equidistant discretisation net. A series of transformations sets the problem in the form of a system of algebraic linear equations which is subsequently solved with the iterative conjugate gradient method. The initial velocity field is determined by adjusting the air velocity field to the measurement data. The method comprises two stages. First, the measurement data are interpolated at a given altitude above the surface. Next, the initial vertical profile of the velocity vector is determined at the nodes of the discretisation net. Computations are performed for an area representing buildings, as in the MUST field experiment, and the results are compared to those of measurements from a wind tunnel. An analysis of the results including appropriate error measures for assessing accuracy of the model is presented.