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Tytuł artykułu

Impact of input parameters on numerical calculations optimized by swarming algorithms during computer simulations of the heat conduction phenomenon

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents the application of swarming algorithms in heat conduction, taking into account the continuity of the boundary condition (type IV). The influence of the input parameters of the bee and ant algorithm and tessellation on the selection of the heat conduction coefficient between the casting mold and the casting in computer simulations was presented. The results were compared for two different finite element grids, a different number of individuals, and a different number of iterations. The study also considered the magnitude of the reference temperature disturbance as the input temperature for numerical calculations. The analysis showed that the relative error of reproducing the value of the thermal conductivity coefficient in the continuity condition did not exceed 1.5% of the reference value of this coefficient.
Rocznik
Strony
107--118
Opis fizyczny
Bibliogr.21 poz., rys., tab.
Twórcy
autor
  • Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology Czestochowa, Poland
  • Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology Czestochowa, Poland
autor
  • Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology Czestochowa, Poland
  • Faculty of Mechanical Engineering, Poznan University of Technology Poznan, Poland
Bibliografia
  • [1] Rutkowski, L. (2009). Metody i techniki sztucznej inteligencji. Seria Informatyka – Zastosowania, Warszawa: Wydawnictwo Naukowe PWN.
  • [2] Słota, D. (2011). Rozwiazywanie odwrotnych zagadnien krzepniecia z wykorzystaniem algorytmów genetycznych. Gliwice: Wydawnictwo Politechniki Śląskiej.
  • [3] Changwei, M., Guangzhu, Ch., Chengliang, Y., & Yuanyuan, W. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm, (vol. 156), Computers and Industrial Engineering, doi:https://doi.org/10.1016/j.cie.2021.107230.
  • [4] Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42, 21-57, https://doi.org/10.1007/s10462-012-9328-0.
  • [5] Komar, D. (2013). Nowa implementacja algorytmu mrówkowego wykorzystująca technologie przetwarzania wieloprocesorowego i rozproszonego w systemie nawigacji. Biuletyn Naukowy Wrocławskiej Wyższej Szkoły Informatyki Stosowanej. Informatyka, (vol. 3), 17-22, YADDA:bwmeta1.element.baztech-36e2fd37-410b-4109-8dec-b755222a1b89.
  • [6] Sharma, A.S., & Kim D.S. (2021). Energy efficient multipath ant colony based routing algorithm for mobile ad hoc networks. Ad Hoc Networks, 113, doi:https://doi.org/10.1016/j.adhoc.2020.102396.
  • [7] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report. ID: 8215393.
  • [8] Gerardo, B., & Wang, J. (1989). Swarm Intelligence. Proceedings of the Seventh Annual Meeting of the Robotic Society of Japan. RSJ Press, 425-428.
  • [9] Hackwood S., & Beni G. (1992). Self-organization of sensors for swarm intelligence. Proceedings of IEEE International Conference on Robotics and Automation, 819-829.
  • [10] Hetmaniok, E., Słota, D., & Zielonka, A. (2012). Application of the Ant Colony Optimization Algorithm for Reconstruction of the Thermal Conductivity Coefficient. Swarm and Evolutionary Computation, Proceedings. Zakopane, I, 240-244.
  • [11] Hetmaniok, E., Słota, D., & Zielonka, A. (2013). Application of the Swarm Intelligence Algorithm for Investigating the Inverse Continuous Casting Problem. Contemporary Challenges and Solutions in Applied Artificial Intelligence, Springer International Publishing, 157-162.
  • [12] Gorecki, J. (2021). Preliminary analysis of the sensitivity of the FEM model of the process of dry ice extrusion in the die with a circularly converging channel on the changing its geometrical parameters. IOP Conference Series: Materials Science and Engineering, IOP Conf. Series: Materials Science and Engineering, 1199, 012006, DOI: 10.1088/1757-899X/1199/1/012006.
  • [13] Berdychowski, M., Gorecki, J., Biszczanik, A., & Wałesa, K. (2022). Numerical simulation of dry ice compaction process: Comparison of Drucker-Prager/cap and cam clay models with experimental results. Materials, 15, DOI: 10.3390/ma15165771.
  • [14] Zheng, Q., Xiao, Y., Zhang, T., Zhu, P., Ma, W., & Liu, J. 2020. Numerical simulation of latent heat of solidification for low pressure casting of aluminum alloy wheels. Metals, 10, doi:10.3390/met10081024.
  • [15] Wisniewski, S., & Wisniewski. T.S. (2012). Wymiana ciepła. Warszawa: WNT.
  • [16] Sczygiol, N. (2000). Modelowanie numeryczne zjawisk termomechanicznych w krzepnacym odlewie i formie odlewniczej. Częstochowa: Wydawnictwo Politechniki Czestochowskiej.
  • [17] Karaboga, D. & Basturk, B. (2007). Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. Berlin Heidelberg: Springer-Verlag, 789-798.
  • [18] Dyja, R. & Grosser, A. (2012). Obliczanie równoległe w symulacji krzepnięcia wykorzystującej model pośredni narastania fazy stałej. Applications of Physics in Mechanical and Material Engineering, 42, 1896-771Z, DOI: 10.1016/j.xxx.2012.08.007.
  • [19] Dyja, R., Gawronska, E., Grosser, A., Jeruszka, P., & Sczygiol, N. (2016). Estimate the impact of different heat capacity approximation methods on the numerical results during computer simulation of solidification. Engineering Letters.
  • [20] Dyja, R. (2021). Comparison of results from in-house solidification convection model with standard benchmark. Applications of Physics in Mechanical and Material Engineering, DOI:10.12693/APhysPolA.XX.TEMP-9502.
  • [21] Gawronska, G., Dyja, R., Zych, M., & Domek, G. (2022). Selection of the heat transfer coefficient using swarming algorithms. Acta Mechanica et Automatica, 16, 4, Special Issue ”Machine Modeling and Simulations 2022”, DOI: 10.2478/ama-2022-0039.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bec489c6-f2be-4590-979e-458f07f47d0d
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