Sunday, July 28. 2019
Numerical Analysis of Bearing Capacity of Soil S. Harabinova, E. Panulinova, E. Kormanikova Institute of Structural Engineering, Faculty of Civil Engineering, Technical University of Kosice, 042 00 Kosice, Slovakia
Received 31 January, 2018; accepted in revised form 23 July, 2019
Abstract: The strength of soil and the bearing capacity are a keys design parameters in designing foundations and other earth structures. Proper interpretation of shear strength parameters and the application to bearing capacity problems are presented and evaluated in this paper. Theory of bearing capacity is developed, on the basis of plastic theory, by changing the shear strength parameters for cohesive soil. In foundation design, the capacity of the foundation to support footing load is given by the soil´s bearing capacity, which is a function of its strength parameters. The maximum pressure, that the soil can support at foundation level without failure depends on the bearing capacity of soil. The bearing capacity of soil in purely cohesive material changes linear with cohesion resistance. However, the bearing capacity changes exponential with the angle of internal friction of soil. By comparing obtained results it was founded, that change the angle of internal friction in cohesive soils have more effect on increasing bearing capacity.
© European Society of Computational Methods in Sciences and Engineering Keywords: bearing capacity, foundation, failure, strength of soil, cohesive soil, numerical analysis Mathematics SubjectClassification: 00A69, 49Mxx
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Friday, July 19. 2019
Evaluation of Turbulence Models for Flow Over a Thermally Loaded Hill
Ivan Kološ1, a), Lenka Lausová1, b) and Vladimíra Michalcová1, c)
1VŠB - Technical University of Ostrava, Faculty of Civil Engineering, L. Podéště 1875/17, 708 33 Ostrava-Poruba, Czech Republic
a)Corresponding author: ivan.kolos@vsb.cz, b)lenka.lausova@vsb.cz, c)vladimira.michalcova@vsb.cz
Abstract. CFD models are widely used for modelling of flow over objects, as they provide very good results of the turbulence characteristics of the flow. The aim of the paper is to analyse suitability of selected numerical models of flow in an unstable temperature-stratified boundary layer over the temperature-loaded object. A numerical analysis has been carried out using four turbulence models: Transition SST κ-ω model, LES model, SAS model and DES model. The velocity and temperature fields of a single temperature loaded hill are evaluated in horizontal profiles at four levels of the hill on both, the windward and leeward sides. © European Society of Computational Methods in Sciences and Engineering
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Wednesday, July 17. 2019
Fundamentals of Mathematical Modeling of Cognitive Digital Automata
V.V. Kozhevnikov
Technological Research Institute of Ulyanovsk State University, 1/4 Universitetskaya Naberezhnaya, Ulyanovsk, 432017, Russia
Received: 31 May 2019; accepted in revised form: 18 June 2019
Abstract An approach to the mathematical modeling of cognitive digital automata (CDA) is proposed. This approach represents a further development of the theory of digital automata and is based on the methods of mathematical modeling of conventional digital automata. At the same time, the task of formalizing the concept of cognitivity of the mathematical model of CDA comes to the fore. Cognitivity of the mathematical model, respectively, is determined by the possibility of training and generating solutions that are not provided for in the process of learning. A specific feature of the mathematical model of CDA consists in the fact that the description of the neural network (NN) structure serves as the structure diagram of digital automata, and the logical function “NOT-AND-OR” is used as the model of the neuron. In the case of the formation of feedbacks from the output to the inputs of the neurons, the model of the neuron is a binary trigger with a logical function “NOT-AND-OR” at the input. The mathematical apparatus of Petri nets (PNs) is proposed as a tool for constructing the mathematical model of CDA: marked graphs, inhibitory PNs and PNs with programmable logic. The mathematical model is constructed on the basis of the representation of CDA in the form of the state equation of PNs from the class of Murata equations (matrix equations) or a system of linear algebraic equations. The task of formalizing the concept of cognitivity is solved as a result of the synthesis of the logic of the initial CDA structure diagram or the formation of the formula of CDA. At the same time, the possibility of forming the formula of CDA depends on the critical mass of training sets and training algorithms. Hence, the task of generating the minimum training sets for a given function or experimentally determined function of CDA bears particular importance. Prediction or generation of solutions, in its turn, is performed on the basis of the mathematical model of CDA obtained in the training process. Keywords: intelligent control system, cognitive digital automata, artificial intelligence, neural networks, machine learning, cognition, Petri nets, equation of states, mathematical modeling, synthesis, generation, analysis, logic.
c 2019 European Society of Computational Methods in Sciences and Engineering
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