{"id":2806,"date":"2021-08-25T13:47:52","date_gmt":"2021-08-25T13:47:52","guid":{"rendered":"http:\/\/www.energia.bme.hu\/?page_id=2806"},"modified":"2022-01-28T12:42:46","modified_gmt":"2022-01-28T12:42:46","slug":"reszletesfeladatok","status":"publish","type":"page","link":"https:\/\/labor.energia.bme.hu\/en\/reszletesfeladatok\/","title":{"rendered":"FELADATOK"},"content":{"rendered":"
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PROJECT ANNOUNCEMENTS<\/h3>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Life cycle assessment<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Renewables<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Thermodynamic modeling<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Combustion<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Combustion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Optimization of surrogate fuel composition for numerical heat and mass transfer analysis
Supervisor: \u00a0D\u00e1vid Csem\u00e1ny<\/a><\/h4>

Commercially used liquid fuels typically have a high number of components. This encumbers the implementation of liquid fuels into modeling environments for burner design and combustion simulations. Surrogate fuel possesses physical and chemical properties identical to the real fuel, however, it contains significantly fewer components, enabling practical application. In this project, the composition of the surrogate mixture is optimized according to measured and calculated material properties of the investigated fuel sample regarding the original composition. The objective is to determine a surrogate mixture composed of a few components but following the thermophysical properties of the real fuel sample to model atomization, evaporation, and heat release related phenomena.<\/p>

Reference:
Huang Z, Xia J, Ju D, Lu X, Han D, Qiao X, et al. A six-component surrogate of diesel from direct coal liquefaction for spray analysis. Fuel 2018;234:1259\u201368. doi: https:\/\/doi.org\/10.1016\/j.fuel.2018.07.138<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Experimental investigation on material properties of renewable liquid fuels
Supervisor: \u00a0D\u00e1vid Csem\u00e1ny<\/a><\/h4>

Thermophysical properties of liquid fuels significantly affect heat and mass transfer before the flame front and chemical reactions in the combustion chamber. Information on material properties is crucial for burner design and in case of fuel change for operating heat engines. However, reliable reference data on a broad parameter range is scarce in the literature, especially for renewable fuels. In this project, different fuel samples are investigated with standardized methods. The measured properties are density, kinematic viscosity, surface tension, atmospheric distillation curve, and flash point.<\/p>

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Reference:<\/div>

Hidegh G, Csem\u00e1ny D, V\u00e1mos J, Kavas L, J\u00f3zsa V. Mixture Temperature-Controlled combustion of different biodiesels and conventional fuels. Energy 2021;234:121219. doi: https:\/\/doi.org\/10.1016\/j.energy.2021.121219<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Experimental investigation of the volatility characteristics of multicomponent liquid fuels
Supervisor: \u00a0D\u00e1vid Csem\u00e1ny<\/a><\/h4>

Volatility characteristics of liquid fuels affect the geometry and operating parameters for burner design and operation. Droplets in fuel spray have to evaporate and mix with combustion air before reaching the flame front in premixed burners. Atmospheric distillation curve can characterize volatility properties. In this project, the volatility characteristics of different fuel samples are measured and evaluated with a modified atmospheric distillation apparatus. Renewable fuels and conventional fuels blended with renewables are investigated.<\/p>

Reference:
Ferris AM, Rothamer DA. Methodology for the experimental measurement of vapor\u2013liquid equilibrium distillation curves using a modified ASTM D86 setup. Fuel 2016;182:467\u201379. doi: https:\/\/doi.org\/10.1016\/j.fuel.2016.05.099<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Evaluation of material property estimating methods for liquid fuels
Supervisor: \u00a0D\u00e1vid Csem\u00e1ny<\/a><\/h4>

Reliable material property databases on a broad parameter range for liquid fuels are scarce in the literature, especially for alternative fuels. Estimating methods can be applied if no measurement data is available. However, their accuracy is often unknown for the investigated materials. In this project, estimating methods for thermophysical properties affecting heat and mass transfer and atomization characteristics are compared to reference data. The evaluation can highlight the applicability of the models for the different fuel types.<\/p>

Reference:
Csem\u00e1ny D, Guj\u00e1s I, Chong CT, J\u00f3zsa V. Evaluation of material property estimating methods for n-alkanes, 1-alcohols, and methyl esters for droplet evaporation calculations. Heat Mass Transf 2021. doi: https:\/\/doi.org\/10.1007\/s00231-021-03059-0<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Numerical modelling of single droplet evaporation
Supervisor: \u00a0D\u00e1vid Csem\u00e1ny<\/a><\/h4>

The single droplet measurement method is usually applied for the measurement of liquid fuel evaporation instead of investigating the whole spray. In this method, the droplet is suspended to a thin fiber. However, this measurement method has several biases and uncertainties affecting the intensity of vaporization. In this project, one-dimensional numerical modeling is performed with the finite difference method. The objective is to identify parameters of the measurement which can minimize the bias on droplet evaporation.<\/p>

Reference:
Csem\u00e1ny D, J\u00f3zsa V. Uncertainty of droplet evaporation measurements and its effect on model validation. In: Costa M, Raba\u00e7al M, Fernandes E, Pires J, Coelho P, editors. Proc. 9th Eur. Combust. Meet., Lisbon: 2019, p. 6.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Evaluation of spray evaporation in a swirl burner
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

Evaporation of liquid droplets can be found in several applications, from which liquid fuel combustion is one of them. The spray measurement should be performed without disturbing the flow due to the small sizes (micrometer range). From the optical techniques, the most suitable one is the Phase Doppler Anemometer, which was used for performing measurements at characteristic points of the spray in the combustion test system of our department. The project goal is processing the measurement data and characterizing spray evaporation. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
Lefebvre AH, McDonell VG. Atomization and Sprays. Second. Boca Raton, FL, FL: CRC Press; 2017.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Characterization of velocity distribution of high-velocity atomization using non-dimensional numbers
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

High-velocity atomization is present in numerous fields, from medicine to combustion. The characterization of spray is often performed by using empirical formulas and thumb rules. A previously measured, comprehensive Phase Doppler Anemometer database will be evaluated during the project. From the non-dimensional numbers, principally, Reynolds and Stokes numbers can be used to characterize droplet motion. The goal of the project is the determination of the gas phase velocity, which can be estimated only by using the entrained droplet data. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
Urb\u00e1n A, Mal\u00fd M, J\u00f3zsa V, Jedelsk\u00fd J. Effect of liquid preheating on high-velocity airblast atomization: From water to crude rapeseed oil. Exp Therm Fluid Sci 2019;102:137\u201351. doi: https:\/\/doi.org\/10.1016\/j.expthermflusci.2018.11.006<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Comparison of the evaporation of various fuels in a swirl burner
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

The will to reduce our dependence on fossil fuels is continuously intensified, however, there is no current alternative for conventional motors for numerous industries. To mitigate the situation, various alternative fuels have emerged, which show notably different both physical and chemical behaviors. During this project work, measured Phase Doppler Anemometer data sets of fuels with various volatility are compared. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
Hidegh G, Csem\u00e1ny D, V\u00e1mos J, Kavas L, J\u00f3zsa V. Mixture Temperature-Controlled combustion of different biodiesels and conventional fuels. Energy 2021;234. doi: https:\/\/doi.org\/10.1016\/j.energy.2021.121219<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Evaluation of natural gas combustion by high-speed Schlieren technique
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

Natural gas utilization will evidently determine our current century since it has the lowest specific CO2 emission among fossil fuels. In combustion, we use swirl burners for low emissions. Our team has performed a series of high-speed Schlieren imaging measurements on such a burner. The aim of the project is to characterize the flame by image processing at various setups. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
Lefebvre AH, Ballal DR. Gas turbine combustion. third. Boca Raton: CRC Press; 2010. doi: https:\/\/doi.org\/10.1002\/1521-3773<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Evaluation of various flame shapes by high-speed Schlieren technique
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

Pollutant emissions are strongly affected by the flame shape in combustion systems, which is determined by the local mixture quality. The most favorable state is distributed combustion when the fuel is almost perfectly mixed with air before ignition. The new combustion concept, developed at our department, allows this, which is the subject of the present project proposal. The goal is the comparison of this with other stable flame shapes by processing available high-speed Schlieren images. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
V. J\u00f3zsa, \u201cMixture temperature-controlled combustion: A revolutionary concept for ultra-low NOx emission,\u201d Fuel, vol. 291, no. May, p. 120200, May 2021, https:\/\/doi.org\/10.1016\/j.fuel.2021.120200<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Spectral analysis of a turbulent flame based on Schlieren imaging technique
Supervisor:\u00a0 J\u00f3zsa Viktor<\/a><\/h4>

The fluctuation of turbulent flames may damage the combustion chamber in unfavorable cases, and may lead to complete destruction in extreme situations. To prevent these events, the spectra of combustion noise and the heat realease are commonly evaluated. The aim of this project is to analyze flame regions by Fourier transform based on available high-speed Schlieren images. The recommended software environment for the project is matlab, in which we have pre-written codes; there is no need for previous software knowledge.<\/p>

Reference:
S. Candel, D. Durox, T. Schuller, J.-F. Bourgouin, and J. P. Moeck, \u201cDynamics of Swirling Flames,\u201d Annu. Rev. Fluid Mech., vol. 46, no. 1, pp. 147\u2013173, 2014, https:\/\/doi.org\/10.1146\/annurev-fluid-010313-141300<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Thermodynamic modeling<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Development of Cycle Tempo thermodynamic performance monitoring software in Fortran\/Matlab environment
Supervisor:\u00a0 Groniewsky Axel<\/a><\/h4>

Cycle Tempo is a software for modelling thermodynamic processes and systems. The task is to create an add-in to the software. (http:\/\/www.asimptote.nl\/software\/cycle-tempo\/)<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Development of a working fluid selection mechanism based on artificial neural network (ANN) for a given organic rankine cycle (ORC)
Supervisor:\u00a0 Groniewsky Axel<\/a><\/h4>

Development of a thermodynamic model of an ORC operating under given temperature limits with different working fluids; neural network is trained on these results; efficiency is estimated.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Development of a Machine Learning (ML) based (Random forest, k-mean clastering) working fluid selection mechanism for existing Organic Rankine Cycle
Supervisor:\u00a0 Groniewsky Axel<\/a><\/h4>

Designing a thermodynamic model of an ORC operating within given temperature limits for various working fluids; processing results using Machine Learning techniques that allow estimating the influence of parameters affecting the results.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Prediction of power plant behaviour using neural networks
Supervisor:\u00a0 Groniewsky Axel<\/a><\/h4>
Estimation of power plant behavior (power, efficiency) from historical data. The historical data must first be filtered based on stationarity. Then key parameters must be identified and used when the neural network (ANN) is generated.<\/div>