Araştırma Makalesi
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Simulation and Full-Factorial Design Response Optimizer for Solar Water Heating Energy System

Yıl 2025, Sayı: 12, 74 - 88, 31.12.2025
https://doi.org/10.52693/jsas.1798525

Öz

Solar water heating systems occupy a significant place among renewable energy technologies, and the complex interactions between design and operational parameters directly influence their efficiency. This study presents a comprehensive methodological framework that integrates Full Factorial Design (FFD), simulation modeling, and Response Optimization methods for the performance optimization of a solar water heating system. A detailed simulation model was developed for the climatic conditions of Phoenix, Arizona, utilizing the System Advisor Model (SAM) software, which was developed by the National Renewable Energy Laboratory (NREL). An FFD scheme was implemented to systematically examine the individual and interactive effects of two critical system performance parameters — daily hot water usage and total pipe length — on System Energy (kWh) and Capacity Factor (%). A total of 189 different design scenarios were simulated, and Analysis of Variance (ANOVA) was performed on the resulting data. The ANOVA results revealed that daily hot water usage was the most statistically dominant factor affecting system output. At the same time, the main effect of total pipe length and the interaction between these two parameters were also statistically significant. In the following phase, Response Optimization was applied to objectively determine the optimal design conditions that simultaneously maximize both performance metrics. The optimization resulted in an optimal daily hot water usage of 312.66 kg/day and an optimal total pipe length of 46.41 meters, with a combined desirability value of 0.726. This integrated approach, which offers a more efficient, reliable, and evidence-based process compared to traditional trial-and-error methods, provides engineers and decision-makers with a quantitative guide to improve design decisions and maximize system performance in solar water heating systems.

Kaynakça

  • [1] W. A. Fadzlin, M. Hasanuzzaman, N. A. Rahim, N. Amin, and Z. Said, “Global challenges of current building-integrated solar water heating technologies and its prospects: a comprehensive review,” Energies (Basel), vol. 15, no. 14, p. 5125, 2022.
  • [2] S. A. Kalogirou, “Solar thermal collectors and applications,” Prog Energy Combust Sci, vol. 30, no. 3, pp. 231–295, 2004, doi: 10.1016/j.pecs.2004.02.001.
  • [3] H. I. Abu-Mulaweh, “Design and development of solar water heating system experimental apparatus,” Global Journal of Engineering Education, vol. 14, no. 1, pp. 99–105, 2012.
  • [4] L. M. Shaker, A. A. Al-Amiery, M. M. Hanoon, W. K. Al-Azzawi, and A. A. H. Kadhum, “Examining the influence of thermal effects on solar cells: a comprehensive review,” Sustainable Energy Research, vol. 11, no. 1, p. 6, 2024.
  • [5] J. Baleta, H. Mikulčić, J. J. Klemeš, K. Urbaniec, and N. Duić, “Integration of energy, water and environmental systems for a sustainable development,” J Clean Prod, vol. 215, pp. 1424–1436, 2019.
  • [6] M. A. Arslan and T. Talan, “Comparative Analysis of Electricity Consumption Forecast,” Journal of Innovative Science and Engineering (JISE), vol. 9, no. 1, pp. 89–102, Jun. 2025, doi: 10.38088/jise.1619782.
  • [7] F. Calise, A. Palombo, and L. Vanoli, “Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments,” Appl Energy, vol. 87, no. 2, pp. 524–540, Feb. 2010, doi: 10.1016/j.apenergy.2009.08.033.
  • [8] M. Milousi and M. Souliotis, “A circular economy approach to residential solar thermal systems,” Renew Energy, vol. 207, pp. 242–252, May 2023, doi: 10.1016/j.renene.2023.02.109.
  • [9] E. Carnevale, L. Lombardi, and L. Zanchi, “Life Cycle Assessment of solar energy systems: Comparison of photovoltaic and water thermal heater at domestic scale,” Energy, vol. 77, pp. 434–446, Dec. 2014, doi: 10.1016/j.energy.2014.09.028.
  • [10] W. Yuan et al., “Numerical simulation and experimental validation of the solar photovoltaic/thermal system with phase change material,” Appl Energy, vol. 232, pp. 715–727, Dec. 2018, doi: 10.1016/J.APENERGY.2018.09.096.
  • [11] A. Atalan and Y. A. Atalan, “Nonlinear Optimization Models of Box-Behnken Experimental Design: Turbine Simulation for Wind Power Plant,” 3rd International Conference on Engineering and Applied Natural Sciences.
  • [12] A. Atalan, H. Şahin, and Y. A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022, doi: 10.3390/healthcare10101920.
  • [13] K. Hinkelmann and D. C. Montgomery, Design and Analysis of Experiments, 8th ed. in Wiley Series in Probability and Statistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. doi: 10.1002/9781118147634.
  • [14] G. Gujral, D. Kapoor, and M. Jaimini, “An updated review on design of experiment (DOE) in pharmaceuticals,” Journal of Drug Delivery and Therapeutics, vol. 8, no. 3, pp. 147–152, 2018.
  • [15] A. Atalan, “Application of Integer Response Optimization Models for the Healthcare Resources,” Acta Infologica, vol. 7, no. 1, pp. 81–93, Apr. 2023, doi: 10.26650/acin.1096774.
  • [16] T. Rakić, I. Kasagić-Vujanović, M. Jovanović, B. Jančić-Stojanović, and D. Ivanović, “Comparison of Full Factorial Design, Central Composite Design, and Box-Behnken Design in Chromatographic Method Development for the Determination of Fluconazole and Its Impurities,” Anal Lett, 2014, doi: 10.1080/00032719.2013.867503.
  • [17] T. Saeheaw, “Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method,” Heliyon, vol. 10, no. 1, p. e23920, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23920.
  • [18] V. B. Veljković, A. V. Veličković, J. M. Avramović, and O. S. Stamenković, “Modeling of biodiesel production: Performance comparison of Box–Behnken, face central composite and full factorial design,” Chin J Chem Eng, vol. 27, no. 7, pp. 1690–1698, Jul. 2019, doi: 10.1016/j.cjche.2018.08.002.
  • [19] A. I. Khuri and S. Mukhopadhyay, “Response Surface Methodology,” John Wiley and Sons, Inc. Wiley Online Library, vol. 2, no. 2, pp. 128–150, 2010.
  • [20] E. K. Ezeanya, G. H. Massiha, W. E. Simon, J. R. Raush, and T. L. Chambers, “System advisor model (SAM) simulation modelling of a concentrating solar thermal power plant with comparison to actual performance data,” Cogent Eng, vol. 5, no. 1, p. 1524051, Jan. 2018, doi: 10.1080/23311916.2018.1524051.
  • [21] N. Blair et al., “System advisor model, sam 2014.1. 14: General description,” National Renewable Energy Lab.(NREL), Golden, CO (United States), 2014.
  • [22] National Renewable Energy Laboratory, “System Advisor ModelTM Version 2025.4.16 (SAMTM 2025.4.16),” May 23, 2025, Golden: 2025.4.16.
  • [23] X. Chen, W. Wang, D. Luo, and C. Zhu, “Performance Evaluation and Optimization of a Building-Integrated Photovoltaic/Thermal Solar Water Heating System for Exterior Shading: A Case Study in South China,” Applied Sciences, vol. 9, no. 24, p. 5395, Dec. 2019, doi: 10.3390/app9245395.
  • [24] A. Atalan, “A cost analysis with the discrete‐event simulation application in nurse and doctor employment management,” J Nurs Manag, vol. 30, no. 3, pp. 733–741, Apr. 2022, doi: 10.1111/jonm.13547.
  • [25] D. Coakley, P. Raftery, and M. Keane, “A review of methods to match building energy simulation models to measured data,” Renewable and Sustainable Energy Reviews, vol. 37, pp. 123–141, Sep. 2014, doi: 10.1016/J.RSER.2014.05.007.
  • [26] A. T. Nguyen, S. Reiter, and P. Rigo, “A review on simulation-based optimization methods applied to building performance analysis,” Appl Energy, vol. 113, pp. 1043–1058, Jan. 2014, doi: 10.1016/J.APENERGY.2013.08.061.
  • [27] Y. Wang et al., “Experiment and simulation study on the optimization of the PV direct-coupled solar water heating system,” Energy, vol. 100, pp. 154–166, Apr. 2016, doi: 10.1016/j.energy.2016.01.022.
  • [28] E. Touloupaki and T. Theodosiou, “Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review,” Energies (Basel), vol. 10, no. 5, p. 637, May 2017, doi: 10.3390/en10050637.
  • [29] F. Calise, A. Palombo, and L. Vanoli, “Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments,” Appl Energy, vol. 87, no. 2, pp. 524–540, Feb. 2010, doi: 10.1016/j.apenergy.2009.08.033.
  • [30] N. Monazzam, A. Alinezhad, and M. A. Adibi, “Simulation-Based Optimization using DEA and DOE in Production System,” Scientia Iranica, vol. 0, no. 0, pp. 0–0, Jan. 2021, doi: 10.24200/sci.2021.55499.4253.
  • [31] T. E. Yankeelov et al., “Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success,” Ann Biomed Eng, vol. 44, no. 9, pp. 2626–2641, Sep. 2016, doi: 10.1007/s10439-016-1691-6.
  • [32] A. Jankovic, G. Chaudhary, and F. Goia, “Designing the design of experiments (DOE) – An investigation on the influence of different factorial designs on the characterization of complex systems,” Energy Build, vol. 250, p. 111298, Nov. 2021, doi: 10.1016/j.enbuild.2021.111298.

Güneş Enerjili Su Isıtma Enerji Sistemi için Simülasyon ve Tam Faktöriyel Tasarım Tepki Optimizasyonu

Yıl 2025, Sayı: 12, 74 - 88, 31.12.2025
https://doi.org/10.52693/jsas.1798525

Öz

Güneş enerjili su ısıtma sistemleri, yenilenebilir enerji teknolojileri arasında önemli bir yer tutmakta olup, tasarım ve işletme parametreleri arasındaki karmaşık etkileşimler verimliliklerini doğrudan etkilemektedir. Bu çalışma, bir güneş enerjili su ısıtma sisteminin performans optimizasyonu için Tam Faktöriyel Tasarım (FFD), simülasyon modellemesi ve Tepki Optimizasyonu (Response Optimization) yöntemlerini bütünleştiren kapsamlı bir metodolojik çerçeve sunmaktadır. Phoenix, Arizona iklim koşulları için Ulusal Yenilenebilir Enerji Laboratuvarı (NREL) tarafından geliştirilen System Advisor Model (SAM) yazılımı kullanılarak ayrıntılı bir simülasyon modeli geliştirilmiştir. Sistem performansına ait iki kritik parametrenin — günlük sıcak su kullanımı ve toplam boru uzunluğu — Sistem Enerjisi (kWh) ve Kapasite Faktörü (%) üzerindeki bireysel ve etkileşimli etkilerini sistematik olarak incelemek amacıyla bir FFD düzeni uygulanmıştır. Toplam 189 farklı tasarım senaryosu simüle edilmiş ve elde edilen veriler üzerinde Varyans Analizi (ANOVA) gerçekleştirilmiştir. ANOVA sonuçları, günlük sıcak su kullanımının sistem çıktısını etkileyen istatistiksel olarak en baskın faktör olduğunu ortaya koymuştur. Aynı zamanda, toplam boru uzunluğunun ana etkisi ile bu iki parametre arasındaki etkileşimin de istatistiksel olarak anlamlı olduğu belirlenmiştir. Takip eden aşamada, her iki performans ölçütünün eş zamanlı olarak maksimize edilmesi amacıyla Tepki Optimizasyonu uygulanmış ve en uygun tasarım koşulları nesnel olarak belirlenmiştir. Optimizasyon sonucunda, 312,66 kg/gün günlük sıcak su kullanımı ve 46,41 metre toplam boru uzunluğu için 0,726 birleşik arzu edilebilirlik değeri elde edilmiştir. Geleneksel deneme-yanılma yöntemlerine kıyasla daha verimli, güvenilir ve kanıta dayalı bir süreç sunan bu bütünleşik yaklaşım, güneş enerjili su ısıtma sistemlerinde tasarım kararlarının iyileştirilmesi ve sistem performansının en üst düzeye çıkarılması için mühendisler ve karar vericilere nicel bir rehber sunmaktadır.

Kaynakça

  • [1] W. A. Fadzlin, M. Hasanuzzaman, N. A. Rahim, N. Amin, and Z. Said, “Global challenges of current building-integrated solar water heating technologies and its prospects: a comprehensive review,” Energies (Basel), vol. 15, no. 14, p. 5125, 2022.
  • [2] S. A. Kalogirou, “Solar thermal collectors and applications,” Prog Energy Combust Sci, vol. 30, no. 3, pp. 231–295, 2004, doi: 10.1016/j.pecs.2004.02.001.
  • [3] H. I. Abu-Mulaweh, “Design and development of solar water heating system experimental apparatus,” Global Journal of Engineering Education, vol. 14, no. 1, pp. 99–105, 2012.
  • [4] L. M. Shaker, A. A. Al-Amiery, M. M. Hanoon, W. K. Al-Azzawi, and A. A. H. Kadhum, “Examining the influence of thermal effects on solar cells: a comprehensive review,” Sustainable Energy Research, vol. 11, no. 1, p. 6, 2024.
  • [5] J. Baleta, H. Mikulčić, J. J. Klemeš, K. Urbaniec, and N. Duić, “Integration of energy, water and environmental systems for a sustainable development,” J Clean Prod, vol. 215, pp. 1424–1436, 2019.
  • [6] M. A. Arslan and T. Talan, “Comparative Analysis of Electricity Consumption Forecast,” Journal of Innovative Science and Engineering (JISE), vol. 9, no. 1, pp. 89–102, Jun. 2025, doi: 10.38088/jise.1619782.
  • [7] F. Calise, A. Palombo, and L. Vanoli, “Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments,” Appl Energy, vol. 87, no. 2, pp. 524–540, Feb. 2010, doi: 10.1016/j.apenergy.2009.08.033.
  • [8] M. Milousi and M. Souliotis, “A circular economy approach to residential solar thermal systems,” Renew Energy, vol. 207, pp. 242–252, May 2023, doi: 10.1016/j.renene.2023.02.109.
  • [9] E. Carnevale, L. Lombardi, and L. Zanchi, “Life Cycle Assessment of solar energy systems: Comparison of photovoltaic and water thermal heater at domestic scale,” Energy, vol. 77, pp. 434–446, Dec. 2014, doi: 10.1016/j.energy.2014.09.028.
  • [10] W. Yuan et al., “Numerical simulation and experimental validation of the solar photovoltaic/thermal system with phase change material,” Appl Energy, vol. 232, pp. 715–727, Dec. 2018, doi: 10.1016/J.APENERGY.2018.09.096.
  • [11] A. Atalan and Y. A. Atalan, “Nonlinear Optimization Models of Box-Behnken Experimental Design: Turbine Simulation for Wind Power Plant,” 3rd International Conference on Engineering and Applied Natural Sciences.
  • [12] A. Atalan, H. Şahin, and Y. A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022, doi: 10.3390/healthcare10101920.
  • [13] K. Hinkelmann and D. C. Montgomery, Design and Analysis of Experiments, 8th ed. in Wiley Series in Probability and Statistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. doi: 10.1002/9781118147634.
  • [14] G. Gujral, D. Kapoor, and M. Jaimini, “An updated review on design of experiment (DOE) in pharmaceuticals,” Journal of Drug Delivery and Therapeutics, vol. 8, no. 3, pp. 147–152, 2018.
  • [15] A. Atalan, “Application of Integer Response Optimization Models for the Healthcare Resources,” Acta Infologica, vol. 7, no. 1, pp. 81–93, Apr. 2023, doi: 10.26650/acin.1096774.
  • [16] T. Rakić, I. Kasagić-Vujanović, M. Jovanović, B. Jančić-Stojanović, and D. Ivanović, “Comparison of Full Factorial Design, Central Composite Design, and Box-Behnken Design in Chromatographic Method Development for the Determination of Fluconazole and Its Impurities,” Anal Lett, 2014, doi: 10.1080/00032719.2013.867503.
  • [17] T. Saeheaw, “Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method,” Heliyon, vol. 10, no. 1, p. e23920, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23920.
  • [18] V. B. Veljković, A. V. Veličković, J. M. Avramović, and O. S. Stamenković, “Modeling of biodiesel production: Performance comparison of Box–Behnken, face central composite and full factorial design,” Chin J Chem Eng, vol. 27, no. 7, pp. 1690–1698, Jul. 2019, doi: 10.1016/j.cjche.2018.08.002.
  • [19] A. I. Khuri and S. Mukhopadhyay, “Response Surface Methodology,” John Wiley and Sons, Inc. Wiley Online Library, vol. 2, no. 2, pp. 128–150, 2010.
  • [20] E. K. Ezeanya, G. H. Massiha, W. E. Simon, J. R. Raush, and T. L. Chambers, “System advisor model (SAM) simulation modelling of a concentrating solar thermal power plant with comparison to actual performance data,” Cogent Eng, vol. 5, no. 1, p. 1524051, Jan. 2018, doi: 10.1080/23311916.2018.1524051.
  • [21] N. Blair et al., “System advisor model, sam 2014.1. 14: General description,” National Renewable Energy Lab.(NREL), Golden, CO (United States), 2014.
  • [22] National Renewable Energy Laboratory, “System Advisor ModelTM Version 2025.4.16 (SAMTM 2025.4.16),” May 23, 2025, Golden: 2025.4.16.
  • [23] X. Chen, W. Wang, D. Luo, and C. Zhu, “Performance Evaluation and Optimization of a Building-Integrated Photovoltaic/Thermal Solar Water Heating System for Exterior Shading: A Case Study in South China,” Applied Sciences, vol. 9, no. 24, p. 5395, Dec. 2019, doi: 10.3390/app9245395.
  • [24] A. Atalan, “A cost analysis with the discrete‐event simulation application in nurse and doctor employment management,” J Nurs Manag, vol. 30, no. 3, pp. 733–741, Apr. 2022, doi: 10.1111/jonm.13547.
  • [25] D. Coakley, P. Raftery, and M. Keane, “A review of methods to match building energy simulation models to measured data,” Renewable and Sustainable Energy Reviews, vol. 37, pp. 123–141, Sep. 2014, doi: 10.1016/J.RSER.2014.05.007.
  • [26] A. T. Nguyen, S. Reiter, and P. Rigo, “A review on simulation-based optimization methods applied to building performance analysis,” Appl Energy, vol. 113, pp. 1043–1058, Jan. 2014, doi: 10.1016/J.APENERGY.2013.08.061.
  • [27] Y. Wang et al., “Experiment and simulation study on the optimization of the PV direct-coupled solar water heating system,” Energy, vol. 100, pp. 154–166, Apr. 2016, doi: 10.1016/j.energy.2016.01.022.
  • [28] E. Touloupaki and T. Theodosiou, “Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review,” Energies (Basel), vol. 10, no. 5, p. 637, May 2017, doi: 10.3390/en10050637.
  • [29] F. Calise, A. Palombo, and L. Vanoli, “Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments,” Appl Energy, vol. 87, no. 2, pp. 524–540, Feb. 2010, doi: 10.1016/j.apenergy.2009.08.033.
  • [30] N. Monazzam, A. Alinezhad, and M. A. Adibi, “Simulation-Based Optimization using DEA and DOE in Production System,” Scientia Iranica, vol. 0, no. 0, pp. 0–0, Jan. 2021, doi: 10.24200/sci.2021.55499.4253.
  • [31] T. E. Yankeelov et al., “Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success,” Ann Biomed Eng, vol. 44, no. 9, pp. 2626–2641, Sep. 2016, doi: 10.1007/s10439-016-1691-6.
  • [32] A. Jankovic, G. Chaudhary, and F. Goia, “Designing the design of experiments (DOE) – An investigation on the influence of different factorial designs on the characterization of complex systems,” Energy Build, vol. 250, p. 111298, Nov. 2021, doi: 10.1016/j.enbuild.2021.111298.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistiksel Deney Tasarımı
Bölüm Araştırma Makalesi
Yazarlar

Yasemin Ayaz Atalan 0000-0001-7767-0342

Gönderilme Tarihi 7 Ekim 2025
Kabul Tarihi 5 Aralık 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 12

Kaynak Göster

IEEE Y. Ayaz Atalan, “Simulation and Full-Factorial Design Response Optimizer for Solar Water Heating Energy System”, JSAS, sy. 12, ss. 74–88, Aralık2025, doi: 10.52693/jsas.1798525.