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EN
With the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of charging behaviour such as the starting time and charging duration are analysed. The results show that charging distribution is in line with a logarithmic normal distribution. An EV charging behaviour model is established, and error calibration is carried out. The result shows that the error is within its permitted scope. The daily EV charge load is obtained by using the Latin hypercube Monte Carlo statistical method. Genetic particle swarm optimization (PSO) is proposed to optimize the proportion of AC 1, AC 2 and DC charging equipment, and the optimal solution can not only meet the needs of users but also reduce equipment investment and the EV peak valley difference, so the effectiveness of the method is verified.
EN
The main objective of the presented study is an evaluation of the effectiveness of various methods for estimating statistics of rotor-shaft vibration responses. The computational effectiveness as well as the accuracy of statistical moment estimation are essential for e?cient robust design optimization of the rotor-shaft systems. The compared methods include sampling techniques, the perturbation approach, the dimension reduction method and the polynomial chaos expansion method. For comparison, two problems of the rotor-shaft vibration analysis are considered: a typical single-span rotor-shaft of the eight-stage centrifugal compressor driven by the electric motor and a large multi-bearing rotor-shaft system of the steam turbo-generator. The most important reason for the observed scatter of the rotor-shaft vibration responses is the inherently random nature of residual unbalances as well as stiffeness and damping properties of the journal bearings. A proper representation of these uncertain parameters leads to multidimensional stochastic models. It was found that methods that provide a satisfactory balance between the estimation accuracy and computational effectiveness are sampling techniques. On the other hand, methods based on Taylor series expansion in most of the analyzed cases fail to approximate the rotor-shaft response statistics.
3
Content available remote Numerical studies on size effects in concrete beams
EN
The numerical FE investigations of a deterministic and stochastic size effect in unnotched concrete beams of similar geometry under three point bending were performed within elasto-plasticity with non-local softening. Deterministic calculations were performed with the uniform distribution of a tensile strength. In turn, in stochastic calculations, the tensile strength took the form of spatially correlated random fields described by a truncated Gaussian distribution. In order to reduce the number of stochastic realizations without losing the calculation correctness, Latin hypercube sampling was applied. The numerical outcomes were compared with the size effect laws by Bazant.
PL
Przeprowadzono analizę numeryczną MES deterministycznego i statystycznego efektu skali w belkach betonowych geometrycznie podobnych poddanych 3-punktowemu zginaniu. Zastosowano sprężysto-plastyczny model betonu z nielokalnym osłabieniem. Obliczenia deterministyczne wykonano z jednorodnym rozkładem wytrzymałości na rozciąganie. Symulacje stochastyczne przeprowadzono z wykorzystaniem przestrzennie skorelowanych pól losowych opisanych obciętym rozkładem Gaussa. W celu zredukowania liczby symulacji stochastycznych, przy zachowaniu poprawności obliczeniowej, zastosowano metodę próbkowania typu sześcianu łacińskiego. Wyniki numeryczne zostały porównane z prawem efektu skali wg Bazanta.
EN
Numerical FE investigations of a deterministic and statistical size effect in notched concrete beams of a similar geometry under three-point bending were performed. The FE analyses were carried out with four different beam sizes. Deterministic calculations were performed assuming constant values of tensile strength. In turn, in statistical calculations, the tensile strength took the form of random spatial fields described by a truncated Gaussian random distribution. In order to reduce the number of stochastic realizations without loosing the accuracy of the calculations, Latin hypercube sampling was applied. The numerical results were compared with the corresponding laboratory tests. The numerical outcomes show that the bearing capacity of beams and their ductility increase with decreasing specimen size. If the distribution of the tensile strength is stochastically distributed, the mean beam strength is always smaller than the deterministic value.
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