This work presents the ideal adiabatic model of the stirling engine which was combined with the dynamic model of the piston-crankshaft system with three degrees of freedom. On the basis of the conducted thermodynamic ...
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This work presents the ideal adiabatic model of the stirling engine which was combined with the dynamic model of the piston-crankshaft system with three degrees of freedom. On the basis of the conducted thermodynamic analysis for the working space in the stirling engine, and on the basis of the physical model of the working mechanism (constructed with the assumption of the static mass reduction), a multidomain simulation model has been developed, using the Matlab&Simulink software. On the basis of the derived equations of energy conservation for the thermodynamic model, as well as equations of motion for the mechanical model, the influence of chosen thermodynamic and mechanical parameters have been analysed, among others: influence of the temperature of the upper heat source on the theoretical work and power gain, of the temperature changes in the compression and expansion space in the course of the working cycle on the simulation model behaviour, whose input parameters correspond to the real object - the stirling engine. As a result of the performed simulations, additionally: the flow of the working gas mass at the control borders, and the influence of the transient states on the pressure curve were presented among others. The results discussed in this work convey practical information about the thermodynamic and dynamic properties of the simulated real object.
The work presents a dynamic model of the stirling engine with a free piston, which was combined with a thermodynamic model taking into account isothermal heat exchange in the compression and expansion spaces. On the b...
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The work presents a dynamic model of the stirling engine with a free piston, which was combined with a thermodynamic model taking into account isothermal heat exchange in the compression and expansion spaces. On the basis of the performed thermodynamic analysis for the stirling engine working space, and of the physical model of the system with a free piston, a multidomain simulation model was developed, using the Matlab&Simulink software. On the basis of the derived equations of energy conservation for the thermodynamic model and equations of motion for the mechanical submodel, the influence of the selected thermodynamic and mechanical parameters, including: the influence of the mass of the displacer and piston on the p(V) closed-loop diagram was analysed and the influence of the thermodynamic parameters, including: temperature of the upper heat source on the theoretical work gain and the theoretical work. As a result of the conducted simulations, flow of the working gas mass at the control boundaries and also the curves of the heat fluxes, among other things, were additionally presented. The results presented in this work convey practical information about the thermodynamic and dynamic properties of the simulated object represented by the stirling engine with a free piston.
This paper presents modelling of a post-combustion CO 2 capture process using bootstrap aggregated extreme learning machine. Extreme learning machine (ELM) randomly assigns the weights between input and hidden layers...
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This paper presents modelling of a post-combustion CO 2 capture process using bootstrap aggregated extreme learning machine. Extreme learning machine (ELM) randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This paper proposes using principal component regression to obtain the weights between the hidden and output layers. Due to the weights between input and hidden layers are randomly assigned, ELM could have variations in performance. This paper proposes combining multiple ELMs to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, seven parameters in the process were regarded as input variables: inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flowrate, lean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine (BA-ELM) can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.
Simulation in real-time is a very useful tool because of didactical and practical benefits. Very important benefit of real-time simulation is a fact that operator's decision can be taken into account in the same t...
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Simulation in real-time is a very useful tool because of didactical and practical benefits. Very important benefit of real-time simulation is a fact that operator's decision can be taken into account in the same time scale as the real system would work. This enables construction of simulators, and opportunity to test control algorithms in Hardware in The Loop scheme using target industrial equipment. Professional real-time environments like Simulink Desktop Real-Time (formerly known as Real-Time Windows Target) and Simulink Real-Time (formerly known as xPC) are devoted rather to research than dissemination purposes. Real-time simulator for a mass audience must be made with widely accessible environment e.g. Windows OS or Web browser. So we have here a paradoxical expectation: real-time simulator in non real-time environment. Authors developed soft real-time solver that is able to adapt its time step to actual computational effort of the system. Approach presented in the paper is based on the idea of following after the real-time when the computed time step is larger than a priori assumed global time step of the simulation. Derivative functionality of this adaptive solver is the ability to determine the number of realtime violations during the simulation horizon. Execution of a priori simulation benchmark allows to determine which step of the simulation will meet the requirements of real-time simulation. Based on reference data it can be determined which step of simulation will provide results with smallest error.
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