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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">kaz29</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник Казахстанско-Британского технического университета</journal-title><trans-title-group xml:lang="en"><trans-title>Herald of the Kazakh-British Technical University</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1998-6688</issn><issn pub-type="epub">2959-8109</issn><publisher><publisher-name>Казахстанско-Британский Технический Университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.55452/1998-6688-2025-22-3-110-122</article-id><article-id custom-type="elpub" pub-id-type="custom">kaz29-2109</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КОМПЬЮТЕРНЫЕ НАУКИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>COMPUTER SCIENCE</subject></subj-group></article-categories><title-group><article-title>МОДЕЛИРОВАНИЕ ДИНАМИКИ НАГРЕВА В ПОМЕЩЕНИИ С ИСПОЛЬЗОВАНИЕМ COMSOL MULTTIPHYSICS</article-title><trans-title-group xml:lang="en"><trans-title>MODELING HEATING DYNAMICS IN THE ROOM USING COMSOL MULTIPHYSICS</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0004-2313-6952</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Телгожаева</surname><given-names>Ф. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Telgozhayeva</surname><given-names>F.</given-names></name></name-alternatives><bio xml:lang="ru"><p>докторант</p><p>г. Алматы</p></bio><bio xml:lang="en"><p>PhD student</p><p>Almaty</p></bio><email xlink:type="simple">faridats@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4322-8983</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Тюлепбердинова</surname><given-names>Г. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Tyulepberdinova</surname><given-names>G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>к.ф.-м.н.</p><p>г. Алматы</p></bio><bio xml:lang="en"><p>Cand.Phys-Math.Sc.</p><p>Almaty </p></bio><email xlink:type="simple">tyulepberdinova@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5648-4476</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Кунелбаев</surname><given-names>М. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Kunelbayev</surname><given-names>M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>магистр</p><p>г. Алматы</p></bio><bio xml:lang="en"><p>Master's degree</p><p> Almaty </p></bio><email xlink:type="simple">murat7508@yandex.kz</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Казахский национальный университет им. аль-Фараби<country>Казахстан</country></aff><aff xml:lang="en">Al-Farabi Kazakh National University<country>Kazakhstan</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru">Казахский национальный университет им. аль-Фараби;&#13;
Институт информационных и вычислительных технологий<country>Казахстан</country></aff><aff xml:lang="en">Al-Farabi Kazakh National University;&#13;
Institute of Information and Computational Technologies<country>Kazakhstan</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>27</day><month>09</month><year>2025</year></pub-date><volume>22</volume><issue>3</issue><fpage>110</fpage><lpage>122</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Телгожаева Ф.С., Тюлепбердинова Г.А., Кунелбаев М.М., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Телгожаева Ф.С., Тюлепбердинова Г.А., Кунелбаев М.М.</copyright-holder><copyright-holder xml:lang="en">Telgozhayeva F., Tyulepberdinova G., Kunelbayev M.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vestnik.kbtu.edu.kz/jour/article/view/2109">https://vestnik.kbtu.edu.kz/jour/article/view/2109</self-uri><abstract><p>В данной работе была разработана физическая модель динамики изменения температуры воздуха в помещении с учетом теплопередачи и конвекции. Система была смоделирована в COMSOL Multiphysics и протестирована в MATLAB, где было проанализировано влияние внешней температуры, площади помещения, количества секций радиатора и скорости воздушного потока. Результаты показали сильную корреляцию между температурой в помещении и внешней температурой (0,92), в то же время наблюдалась более слабая зависимость от температуры радиатора (0,2), высоты (0,1) и площади помещения (0,11). Количество секций и размер радиатора оказывают наименьшее влияние на температуру в помещении (0,07). Кроме того, начальная температура помещения не имеет существенной корреляции с конечной температурой в помещении. Корреляция, наблюдаемая при моделировании, позволила разработать передаточную функцию управляемого объекта в MATLAB/Simulink. Нелинейное реле, используемое в результирующей модели, применяется для включения и выключения радиатора с целью управления температурой в помещении. Результаты исследования могут быть использованы для создания нейронной сети для моделирования динамики изменения температуры в помещении при различных начальных условиях.</p></abstract><trans-abstract xml:lang="en"><p>In this study, a physical model of indoor air temperature change dynamics has been developed, considering heat transfer and convection. The system was modeled in COMSOL Multiphysics and tested in MATLAB, where the influence of external temperature, room area, number of radiator sections and air flow velocity were analyzed. The results showed a strong correlation between room temperature and external temperature (0.92), while weaker dependence was observed on the temperature of a radiator (0.2), height (0.1) and area of the room (0.11). However, number of sections and size of the radiator have the least impact on the room temperature (0.07). Additionally, initial temperature of the room does not have any significant correlation with final room temperature. The correlation, observed in simulations enabled us to develop transfer function of controlled object in MATLAB/Simulink. Nonlinear relay, used in resultant model, is used to turn actuator on and off to control room temperature. The results of the study can be used to create neural network to simulate the physical behavior of the room temperature in different initial conditions.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>система контроля температуры в помещении</kwd><kwd>COMSOL Multiphysics</kwd><kwd>MATLAB</kwd><kwd>передаточная функция</kwd><kwd>обучение с подкреплением</kwd></kwd-group><kwd-group xml:lang="en"><kwd>room temperature control system</kwd><kwd>COMSOL Multiphysics</kwd><kwd>MATLAB</kwd><kwd>transfer function</kwd><kwd>reinforcement learning</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Halhoul Merabet, G., Essaaidi, M., Ben Haddou, M., Qolomany, B., Qadir, J., Anan, M., Al-Fuqaha, A., Riduan Abid, M., and Benhaddou, D. Intelligent building control systems for internal comfort and energyefficiency: A systematic review of artificial intelligence-assisted techniques 144, 110969 (2021).</mixed-citation><mixed-citation xml:lang="en">Halhoul Merabet, G., Essaaidi, M., Ben Haddou, M., Qolomany, B., Qadir, J., Anan, M., Al-Fuqaha, A., Riduan Abid, M., and Benhaddou, D. Intelligent building control systems for internal comfort and energyefficiency: A systematic review of artificial intelligence-assisted techniques 144, 110969 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Smith, J., and Brown, T. Smart Climate Control System: A Review. Energy Efficiency Journal, 13(5), 987–1002 (2020).</mixed-citation><mixed-citation xml:lang="en">Smith, J., and Brown, T. Smart Climate Control System: A Review. Energy Efficiency Journal, 13(5), 987–1002 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Mirnaghi, M.S., and Haghighat, F. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings, 229, 110492 (2020).</mixed-citation><mixed-citation xml:lang="en">Mirnaghi, M.S., and Haghighat, F. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings, 229, 110492 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Simpeh, E.K. et al. Improving energy efficiency of HVAC systems in buildings: A review of best practices. International Journal of Building Pathology and adaptation, 40(2), 165–182 (2022).</mixed-citation><mixed-citation xml:lang="en">Simpeh, E.K. et al. Improving energy efficiency of HVAC systems in buildings: A review of best practices. International Journal of Building Pathology and adaptation, 40(2), 165–182 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Drgona, Jan, et al. All you need to know about model predictive control for building. Annual Reviews in Control, 50, 190–232 (2020).</mixed-citation><mixed-citation xml:lang="en">Drgona, Jan, et al. All you need to know about model predictive control for building. Annual Reviews in Control, 50, 190–232 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Kusiak Andrew, Fan Tang and Guanglin Xu. Multi-objective optimization of HVAC system with evolutionary computation algorithm. Energy, 36(5), 2440–2449 (2011).</mixed-citation><mixed-citation xml:lang="en">Kusiak Andrew, Fan Tang and Guanglin Xu. Multi-objective optimization of HVAC system with evolutionary computation algorithm. Energy, 36(5), 2440–2449 (2011).</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Johnson, L. Artificial Intelligence in HVAC Systems. Journal of Sustainable Building Technology, 15(3), 245–256 (2019).</mixed-citation><mixed-citation xml:lang="en">Johnson, L. Artificial Intelligence in HVAC Systems. Journal of Sustainable Building Technology, 15(3), 245–256 (2019).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Asim, N. et al. Sustainability of heating, ventilation and air-conditioning (HVAC) systems in buildings – An overview. International journal of environment research and public health., 19(2), 1016 (2022).</mixed-citation><mixed-citation xml:lang="en">Asim, N. et al. Sustainability of heating, ventilation and air-conditioning (HVAC) systems in buildings – An overview. International journal of environment research and public health., 19(2), 1016 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Yao, Y., Shekhar, D.K. State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field. Building and Environment., 200, 107956 (2021).</mixed-citation><mixed-citation xml:lang="en">Yao, Y., Shekhar, D.K. State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field. Building and Environment., 200, 107956 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Gunay, H. Burak, William Shen, and Guy Newsham. Data analytics to improve building performance: A critical review. Automation in Construction, 97, 96–109 (2019).</mixed-citation><mixed-citation xml:lang="en">Gunay, H. Burak, William Shen, and Guy Newsham. Data analytics to improve building performance: A critical review. Automation in Construction, 97, 96–109 (2019).</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Afram Abdul, Farrokh Janabi-Sharifi. Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment., 72, 343–355 (2014).</mixed-citation><mixed-citation xml:lang="en">Afram Abdul, Farrokh Janabi-Sharifi. Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment., 72, 343–355 (2014).</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang Tao and Khee Poh Lam. Practical implementation and evaluation of occupancy-based HVAC control for energy-efficient buildings. Building and Environment, 66, 183–193 (2013).</mixed-citation><mixed-citation xml:lang="en">Zhang Tao and Khee Poh Lam. Practical implementation and evaluation of occupancy-based HVAC control for energy-efficient buildings. Building and Environment, 66, 183–193 (2013).</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Zhao Heng and Frederic Magoules. A review on the predication of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586–3592 (2012).</mixed-citation><mixed-citation xml:lang="en">Zhao Heng and Frederic Magoules. A review on the predication of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586–3592 (2012).</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Yang Rui and Lingfeng Wang. Multi-objective optimization for decision-making of energy and comfort management in building automation and control. Sustainable Cities and Society., 2(1), 1–7 (2012).</mixed-citation><mixed-citation xml:lang="en">Yang Rui and Lingfeng Wang. Multi-objective optimization for decision-making of energy and comfort management in building automation and control. Sustainable Cities and Society., 2(1), 1–7 (2012).</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Ascione Fabrizio et al. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118, 999–1017 (2017).</mixed-citation><mixed-citation xml:lang="en">Ascione Fabrizio et al. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118, 999–1017 (2017).</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Kampf Jerome H., Darren Robinson. A hybrid CMA-ES and HDE optimization algorithm with application to solar energy potential. Applied Soft Computing., 12(1), 239–251 (2012).</mixed-citation><mixed-citation xml:lang="en">Kampf Jerome H., Darren Robinson. A hybrid CMA-ES and HDE optimization algorithm with application to solar energy potential. Applied Soft Computing., 12(1), 239–251 (2012).</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Garcia, M., Taylor, R. Automation in Critical Environment Temperature Control. Journal of Building Performance, 19(2), 334–349 (2022).</mixed-citation><mixed-citation xml:lang="en">Garcia, M., Taylor, R. Automation in Critical Environment Temperature Control. Journal of Building Performance, 19(2), 334–349 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Alawadi, S. et al. A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings. Energy System., 1–17 (2020).</mixed-citation><mixed-citation xml:lang="en">Alawadi, S. et al. A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings. Energy System., 1–17 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Lu, C., Li, S., Lu, Z. Building energy predication using artificial neural networks: A literature survey. Energy and Buildings, 262, 111718 (2022).</mixed-citation><mixed-citation xml:lang="en">19 Lu, C., Li, S., Lu, Z. Building energy predication using artificial neural networks: A literature survey. Energy and Buildings, 262, 111718 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Brandi, S. et al. Deep reinforcement learning to optimize indoor temperature control and heating energy consumption in building. Energy and Buildings., 224, 110225 (2020).</mixed-citation><mixed-citation xml:lang="en">Brandi, S. et al. Deep reinforcement learning to optimize indoor temperature control and heating energy consumption in building. Energy and Buildings., 224, 110225 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Telgozhayeva, F. et al. A mathematical Model of an Automated Control System for Heat Regulation in a Building. WSEAS Transactions on Systems and Control., 18, 231–242 (2023).</mixed-citation><mixed-citation xml:lang="en">Telgozhayeva, F. et al. A mathematical Model of an Automated Control System for Heat Regulation in a Building. WSEAS Transactions on Systems and Control., 18, 231–242 (2023).</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Elmaz, F., et al. CNN-LSTM architecture for predictive indoor temperature modeling. Building and Environment., 206, 108327 (2020).</mixed-citation><mixed-citation xml:lang="en">Elmaz, F., et al. CNN-LSTM architecture for predictive indoor temperature modeling. Building and Environment., 206, 108327 (2020).</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Tagliabue, L.C. et al. Data driven indoor air prediction in educational facilities based on IoT network. Energy and Buildings, 236, 110782 (2021).</mixed-citation><mixed-citation xml:lang="en">Tagliabue, L.C. et al. Data driven indoor air prediction in educational facilities based on IoT network. Energy and Buildings, 236, 110782 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Franceschini, P.B., Neves, L.O. A critical review on occupant behaviour modelling for building performance simulation of naturally ventilated school buildings and potential changes due to the COVID-19 pandemic. Energy and Buildings, 258, 111831 (2022).</mixed-citation><mixed-citation xml:lang="en">Franceschini, P.B., Neves, L.O. A critical review on occupant behaviour modelling for building performance simulation of naturally ventilated school buildings and potential changes due to the COVID-19 pandemic. Energy and Buildings, 258, 111831 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Yu, J., Chang, W.S., Dong, Y. Building energy prediction models and related uncertainties: A review. Buildings, 12(8), 1284 (2022).</mixed-citation><mixed-citation xml:lang="en">Yu, J., Chang, W.S., Dong, Y. Building energy prediction models and related uncertainties: A review. Buildings, 12(8), 1284 (2022).</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Afram, A., Janabi-Sharifi, F. Review of modeling methods for HVAC systems. Applied thermal engineering, 67(1–2), 507–519 (2014).</mixed-citation><mixed-citation xml:lang="en">Afram, A., Janabi-Sharifi, F. Review of modeling methods for HVAC systems. Applied thermal engineering, 67(1–2), 507–519 (2014).</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Amasyali, K., El-Gohary, N.M. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205 (2018).</mixed-citation><mixed-citation xml:lang="en">Amasyali, K., El-Gohary, N.M. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205 (2018).</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Gunay, B., Shen, W., Newsham, G. Inverse blackbox modeling of the heating and cooling load in office buildings. Energy and Buildings, 142, 200–210 (2017).</mixed-citation><mixed-citation xml:lang="en">Gunay, B., Shen, W., Newsham, G. Inverse blackbox modeling of the heating and cooling load in office buildings. Energy and Buildings, 142, 200–210 (2017).</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Hamdaoui, M.A. et al. A review on physical and data-driven modeling of buildings hygrothermal behavior: Models, approaches and simulation tools. Energy and Buildings, 251, 111343 (2021).</mixed-citation><mixed-citation xml:lang="en">Hamdaoui, M.A. et al. A review on physical and data-driven modeling of buildings hygrothermal behavior: Models, approaches and simulation tools. Energy and Buildings, 251, 111343 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Tian, W., De Wilde, P. Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study. Automation in construction, 20 (8), 1096–1109 (2011).</mixed-citation><mixed-citation xml:lang="en">Tian, W., De Wilde, P. Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study. Automation in construction, 20 (8), 1096–1109 (2011).</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Liu, X. et al. Hourly occupant clothing decisions in residential HVAC energy management. Journal of Building Engineering, 40, 102708 (2021).</mixed-citation><mixed-citation xml:lang="en">Liu, X. et al. Hourly occupant clothing decisions in residential HVAC energy management. Journal of Building Engineering, 40, 102708 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Lui, H. et al. Study on ventilation on indoor substation main transformer room on COMSOL software. 1st International Conference on Electrical Materials and Power Equipment (ICEMPE). IEEE, pp. 296–300 (2017).</mixed-citation><mixed-citation xml:lang="en">Lui, H. et al. Study on ventilation on indoor substation main transformer room on COMSOL software. 1st International Conference on Electrical Materials and Power Equipment (ICEMPE). IEEE, pp. 296–300 (2017).</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Maliki, M. et al. Two-dimensional transient modeling of energy and mass transfer in porous building components using COMSOL Multiphysics. Journal of Applied Fluid Mechanics, 10(1), 319–328 (2017).</mixed-citation><mixed-citation xml:lang="en">Maliki, M. et al. Two-dimensional transient modeling of energy and mass transfer in porous building components using COMSOL Multiphysics. Journal of Applied Fluid Mechanics, 10(1), 319–328 (2017).</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Charvatova, H., Prochazka, A., Zalesak, M. Computer simulation of temperature distribution during of the thermally insulated room. Energies., 11(11), 3205 (2018).</mixed-citation><mixed-citation xml:lang="en">Charvatova, H., Prochazka, A., Zalesak, M. Computer simulation of temperature distribution during of the thermally insulated room. Energies., 11(11), 3205 (2018).</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Gerlich, V. Modeling of heat transfer in buildings. ECMS, pp. 244–248 (2021).</mixed-citation><mixed-citation xml:lang="en">Gerlich, V. Modeling of heat transfer in buildings. ECMS, pp. 244–248 (2021).</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Charraou, A. et al. Experimental study and numerical simulation of a floor heating system in a threedimensional model: Parametric study and improvement. Applied Thermal Engineering, 233, 121151 (2023).</mixed-citation><mixed-citation xml:lang="en">Charraou, A. et al. Experimental study and numerical simulation of a floor heating system in a threedimensional model: Parametric study and improvement. Applied Thermal Engineering, 233, 121151 (2023).</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
