Buckling behaviour of dented aluminium alloy cylindrical shell subjected to uniform axial compression

Tankozide cilindrične strukture imaju veoma visoku primenu u industriji. Pošto su obično opterećene na aksijalni pritisak, najčešći uzrok njihovog otkaza je pojava izvijanja. U ovom članku se prikazuje numerička analiza izvijanja tankozide cilindrične strukture izrađene od legure aluminijuma sa imperfekcijom u vidu udubljenja na sredini strukture i eksperimentalna verifikacija rezultata dobijenih tom analizom. Numerička analiza je odrađena pomoću softvera ANSYS 16.2, a eksperimentalna ispitivanja pomoću hidraulične prese Armavir, PSU-50, na kojoj je uzorak podvrgnut aksijalnom pritisku čiji se intenzitet postepeno povećavao do pojave izvijanja. Poređenjem rezultata eksperimenta uočeno je značajno smanjenje vrednosti kritičnog napona na izvijanje između epruveta bez imperfekcije i epruveta sa imperfekcijom od 2 mm, dok su vrednosti kritičnog napona za epruvete sa imperfekcijama od 2 mm i 4 mm približne.Thin-walled cylindrical shells are commonly used in numerous branches of industry. Since they are subjected to axial load, the most common cause of their failure is buckling. This paper provides numerical analysis and experimental verification of the buckling of the thin-walled aluminium alloy cylindrical shell with special regard to the influence of dent, positioned in the middle of the shell. Numerical simulation was performed using ANSYS 16.2, and experimental verification was performed by means of hydraulic press Armavir, PSU-50, which was used to subject the specimen to the increasing axial load until the occurrence of buckling. Comparing the results it was concluded that there is significant decrease of the buckling resistance if compared the values of the specimen with no dent, and the specimen with 2 mm deep dent. On the contrary, resistance of the 2 mm and 4 mm dented specimen is quite similar. Position and shape of the deformations occurred due to buckling are matching if experimental and numerical results are compared

Primena veštačkih neuronskih mreža za kratkoročno predviđanje i analizu sistema daljinskog grejanja

The subject of the research relates to the development and implementation of algorithms for short-term prediction of the district heating system characteristics using artificial neural networks. The research is aimed at developing algorithms for the selection of standard feedforward and recurrent artificial neural networks and their architectures, choice and adjustment their parameters, choice and definition of adequate inputs, modification of network architecture and its adaptation to meet the demands imposed by the application of artificial neural networks for short-term prediction of heat load as main characteristic od district heating system. Special attention will be devoted to a comparative analysis of proposed and adopted artificial neural networks with their different architectures to obtain optimal algorithms. An adequate heat load prediction and satisfying consumer demands with delivered heat energy in sense of control system, energy saving and environment protection, are very important preconditions for optimal adjusting of district heating system Improving quality of prediction, as one of the dissertation objective, has positive impact to control of district heating system, in general. The main focus is on adequate choice of input vector, number of input nodes and other parameters for standard types of neural networks, contrary to solutions of some authors from literature, where they are creating totally new and unique networks for solving specific problem. On that way, they are loosing possibility of generalization which is opposite to one of the dissertation objective. Specific attention is given to problem of transient regime of heating, where there are no continuation in heating during a day and defined heating period. Achieving qualitative prediction for short period is very important for decrease heat consumption and increase the coefficient of equipment exploitation. This is more important due the fact that district heating systems in Serbia are intermitted by definition which means that heating is not realized in continuation but with turning on and off in the morning and evening hours. Short term prediction is realized for prediction of selected parameters and district heating system characteristics for period of one, three and seven days. Deigned modified feedforward and recurrent neural networks satisfy needed quality of prediction for district heating systems, adequately predict peak loads in transient heating regimes and through the realization of neural networks of the same architecture on four different data heat sources, they are showing possibility of generalization on specific level

Comparative analysis of numerical computational techniques for determination of the wind turbine aerodynamic performances

The purpose of this paper is to explore and define an adequate numerical setting for the computation of aerodynamic performances of wind turbines of various shapes and sizes, which offers the possibility of choosing a suitable approach of minimal complexity for the future research. Here, mechanical power, thrust, power coefficient, thrust coefficient, pressure coefficient, pressure distribution along the blade, relative velocity contoure, at different wind speeds and streamlines were considered by two different methods: the blade element momentum and CFD, within which three different turbulence models were analyzed. The estimation of the mentioned aerodynamic performances was carried out on two different wind turbine blades. The obtained solutions were compared with the experimental and nominal (up-scaled) values, available in the literature. Although the flow was considered as steady, a satisfactory correlation between numerical and experimental results was achieved. The comparison between results also showed, the significance of selection, regarding the complexity and geometry of the analyzed wind turbine blade, the most appropriate numerical approach for computation of aerodynamic performances

TOWARD A SMART ECOSYSTEM WITH AUTOMATED SERVICES

New ICT architectures enable a better response to constant pressure on the industry and services to improve their business performance and productivity, especially in data processing. At the same time, due to the growing number of sensor modules, the amount of data that needs to be processed, in real time, is growing. Delays in communication with the cloud environment can lead to poor management decisions or user dissatisfaction. In automation and services, one of the new ICT architectures is Edge computing in the data processing. Edge computing is a networking architecture that brings computing close to the source of data in order to reduce latency and bandwidth use. Edge computing brings new power to data processing and the ability to process large amounts of data in real time. This is essential for predicting the behavior of machines, systems, or customers in order to detect errors or provide personalized service as in the case of smart vending machines. In that way, Edge computing enables taking steps toward establishing a smart ecosystem in automation and services

ARTIFICIAL NEURAL NETWORK APPLICATION FOR THE TEMPORAL PROPERTIES OF ACOUSTIC PERCEPTION

Though acoustic perception is well established in literature, it seems to be insufficiently implemented in practice. Experimental results are excellent but a lot of issues arise when it comes to the application in real conditions. Using artificial neural networks makes acoustic signal processing very comfortable from the mathematical point of view. However, a great job has to be done in order to make it possible. The procedure includes data acquisition, filtering, feature extraction and selection. These techniques require much more resources than mere artificial neural networks and this represents a limiting factor for the implementation. The paper investigates the complete procedure of acoustic perception, in terms of time, in order to identify limitations

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

INPUT VECTOR IMPACT ON SHORT-TERM HEAT LOAD PREDICTION OF SMALL DISTRICT HEATING SYSTEM

Short-term load prediction is very important for advanced decision making in district heating systems. The idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. The common thing for each way of prediction is usage of historical data for certain last period which makes possible development of many methodologies for adequate prediction and control. In this paper, application of feedforward artificial neural network for short-term load prediction for period of 1, 3 and 7 days, of one small district heating system, is presented. Three different input vectors are implemented and their impact on quality of prediction discussed. The simulation results are compared and detailed analysis is done where operation in transient regime is of special importance. Satisfied prediction average error is obtained

Vision-Based Inspection of Tyre Tread Depth

In this paper, an approach for visual, non-contact automatic inspection of tyre tread depth based on existing image processing techniques is presented. Histograms of oriented gradient are used for feature extraction from images. In order to analyse which set of features gives the best classification results, a linear support-vector machine classifier was trained and tested using different numbers of pixels and numbers of cells per block. The obtained processing and experimental results are presented in this paper

Comparative analysis of numerical computational techniques for determination of the wind turbine aerodynamic performances

The purpose of this paper is to explore and define an adequate numerical setting for the computation of aerodynamic performances of wind turbines of various shapes and sizes, which offers the possibility of choosing a suitable approach of minimal complexity for the future research. Here, mechanical power, thrust, power coefficient, thrust coefficient, pressure coefficient, pressure distribution along the blade, relative velocity contoure at different wind speeds and streamlines were considered by two different methods: the blade element momentum (BEM) and computational fluid dynamics (CFD), within which three different turbulence models were analyzed. The estimation of the mentioned aerodynamic performances was carried out on two different wind turbine blades. The obtained solutions were compared with the experimental and nominal (up-scaled) values, available in the literature. Although the flow was considered as steady, a satisfactory correlation between numerical and experimental results was achieved. The comparison between results also showed, the significance of selection, regarding the complexity and geometry of the analyzed wind turbine blade, the most appropriate numerical approach for computation of aerodynamic performances

FUZZY CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT FOR MOVING TARGET TRACKING

Tracking of moving objects, including humans has important role in mobile robotics. In this paper, the hierarchical control structure for target/human tracking consisted of high and low level control was presented. The low level subsystem deals with the control of the linear and angular velocities using multivariable PD controller whose parameters are obtained by Particle swarm optimization. The position control of the mobile robot represents the high level control, where we use two fuzzy logic Mamdani controllers for distance and angle control. In order to test the effectiveness of the proposed control scheme a simulation was performed. Two cases, when the mobile robot pursues a target moving along a circular path and when the mobile robot pursues a target moving along a rectangle path, were simulated