Multi-Objective Optimization of Heat Transfer and Entropy Generation in Bio-Inspired Tubes via Hybrid Multi-Criteria Decision-Making

Öz

Passive enhancement techniques are crucial for improving the energy efficiency of heat exchangers. In this study, the thermodynamic performance of novel bio-inspired turbulators (FishScale, DragonEgg, and WavyCell) was experimentally investigated in the turbulent flow regime (Re: 10500–15000). A novel Consensus Multi-Criteria Decision-Making (MCDM) strategy was employed to identify the robust optimal design. The MEREC method determined the criteria weights as 0.4744 for the friction factor, 0.2997 for the Nusselt number, and 0.2286 for the total entropy generation. Results revealed a 'methodological sensitivity' among VIKOR, WASPAS, and MABAC, which initially identified different optimal designs. To resolve this, a Rank Sum consensus method was implemented. The consensus analysis demonstrated that the FishScale-2 Piece design at Re=15,000 achieved the most robust performance across all methodologies. This study highlights that relying on a single MCDM method in thermodynamic optimization can lead to biased results, whereas the proposed consensus-based approach provides a definitive solution by balancing heat transfer enhancement (Nu) against hydraulic (f) and thermodynamic (Sgen,total) penalties with high mathematical reliability.

Anahtar Kelimeler

• Bio-inspired turbulators
• Heat transfer enhancement
• Entropy generation
• MCDM

Hibrit ÇKKV Yoluyla Biyo-Esinlenilmiş Borularda Isı Transferi ve Entropi Üretiminin Çok Amaçlı Optimizasyonu

Öz

Enerji verimliliğini artırmak amacıyla ısı değiştiricilerde pasif iyileştirme tekniklerinin kullanımı giderek yaygınlaşmaktadır. Bu çalışmada, Balık Pulu (FishScale), Ejderha Yumurtası (DragonEgg) ve Dalgalı Hücre (WavyCell) geometrilerine sahip yeni biyo-esinlenilmiş türbülatorlerin termodinamik performansı deneysel olarak incelenmiştir. Deneyler, türbülanslı akış rejiminde (Reynolds sayısı: 10.500 – 15.000) gerçekleştirilmiş; Nusselt sayısı, sürtünme faktörü ve toplam entropi üretimi parametreleri analiz edilmiştir. Çalışmanın özgün yanı olarak, en iyi tasarımın belirlenmesinde tek bir yöntem yerine, Hibrit Çok Kriterli Karar Verme (ÇKKV) stratejisi benimsenmiştir. Ağırlıklandırma aşamasında MEREC yöntemi; sıralama aşamasında ise VIKOR, WASPAS ve MABAC yöntemleri karşılaştırmalı olarak kullanılmıştır. Analiz sonuçları, yöntemlerin matematiksel yapısındaki farklılıklar nedeniyle VIKOR, WASPAS ve MABAC'ın sırasıyla üç farklı tasarımı (FishScale-2, DragonEgg-1 ve WavyCell-3) önerdiğini göstermiştir. Bu sıralama belirsizliğini gidermek ve en kararlı çözümü elde etmek amacıyla Sıralama Toplamı konsensus yöntemi uygulanmıştır. Konsensus analizi sonucunda, tüm yöntemlerin ortak paydada en yüksek performansı onayladığı Re=15.000 değerindeki FishScale-2 Piece tasarımı, nihai optimal konfigürasyon olarak belirlenmiştir. Bu çalışma, termodinamik optimizasyon problemlerinde tek yöntemli analizlerin yanıltıcı olabileceğini ve konsensus temelli yaklaşımların güvenilirliği artırdığını ortaya koymaktadır.

Anahtar Kelimeler

• Biyolojik ilhamlı türbülatörler
• Isı transferinin artırılması
• Entropi üretimi
• MCDM
• optimizasyon

Kaynakça

1. [1] Soumith V., Biswas A., Nath S. Optimal selection of a sustainable passive heat transfer enhancement technique for circular pipe heat exchanger using MCDM framework. Arabian Journal for Science and Engineering. 2025. 50(4), 2835-2856.
2. [2] Razera AL, Vaz IS, Fonseca RJCd, Rodrigues MK, Rocha LAO, dos Santos ED, et al. Multiobjective numerical analysis of thermal and fluid dynamics performance of a vertical helical earth–air heat exchanger using the TOPSIS method. Heat Transf. 2025;54(8):4689-4707. Available from: https://doi.org/10.1002/htj.70022
3. [3] Yıldırım O, Güven H. A hybrid CFD-RSM approach for optimizing heat transfer in channels with biomimetic S-shaped turbulators. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2026;48:73.
4. [4] Sharma A, Chauhan R, Singh T, Kumar A, Kumar R, Sethi M. Optimizing discrete V obstacle parameters using a novel Entropy-VIKOR approach in a solar air flow channel. Renew Energy. 2017;106:310-320. Available from: https://doi.org/10.1016/j.renene.2017.01.037
5. [5] Dwivedi A, Khan MM, Pali HS. Optimization of thermal performance in ceramic microchannel heat exchangers using alumina-water nanofluids: A hybrid MCDM approach. Journal of Thermal Analysis and Calorimetry. 2025. 150, 7461–7487.
6. [6] Khan F, Khan O, Pachauri P, Parvez M, Alhodaib A, Yahya Z, et al. Experimental analysis and optimization of hydrogen pre-cooling liquefaction process with composite catalyst through a hybrid priority cluster modeling approach. Sci Rep. 2025;15:31093. Available from: https://doi.org/10.1038/s41598-025-16832-6
7. [7] Dagdevir T, Keklikcioglu O, Ozceyhan V. Multi-objective optimization of operating parameters for dimpled twisted tape inserted heat exchanger tube flowing water/ethylene glycol mixture-based TiO2 nanofluid. Journal of Thermal Analysis and Calorimetry. 2025;150:11433-11450.
8. [8] Keklikcioglu O, Günes S, Senyigit E, Akcadirci E, Ozceyhan V. The optimization of the thermal and hydraulic characteristics of a tube with twisted tapes using Taguchi-based-AHP-TOPSIS approach. Journal of Thermal Analysis and Calorimetry. 2022; 147; 13711–13723.

9. [9] Chamoli S. Preference selection index approach for optimization of V down perforated baffled roughened rectangular channel. Energy. 2015;93(2):1418-1425.
10. [10] Subasi A. Mini-channel heat exchanger optimization using genetic algorithm and multiple-criteria decision making. Journal of Thermal Engineering. 2022;8(1):29-37.
11. [11] Subasi A, Sahin B, Kaymaz I. Multi-objective optimization of a honeycomb heat sink using response surface method. International Journal of Heat and Mass Transfer. 2016;101:295-302.
12. [12] Subasi A, Erdem K. An integrated optimization methodology for heat transfer enhancement: A case study on nanofluid flow in a pipe equipped with inserts. International Journal of Heat and Mass Transfer. 2021;172:121187.
13. [13] Singh SK, Kacker R, Gautam SS, Tamang SK. Multi-objective optimization of thermo-hydraulic behavior of heat exchanger with v-cut twisted tape in axial and radial direction using NSGA-II. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2023;45(4):13057-13076.
14. [14] Sharma S, Maithani R. Multi-objective optimization of solar air heater having centerline perforated sine wave baffles using experimental and machine learning based approaches. Experimental Heat Transfer. 2026;39(1):1-23.
15. [15] Mohammadzadeh M, Anisi A, Sheikholeslami M. Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape. Applied Thermal Engineering. 2024;254:123851.
16. [16] El Gawad MSA, Aly SM, Atwa AM, Hussein MH. Multi-objective optimization for synthesis heat exchanger network using TOPSIS method. Petroleum and Coal. 2023;65(4).
17. [17] Hontoria E, López-Belchí A, Munier N, Vera-García F. A MCDM methodology to determine the most critical variables in the pressure drop and heat transfer in minichannels. Energies. 2021;14(8);2069.
18. [18] Singh T. Entropy weighted WASPAS and MACBETH approaches for optimizing the performance of solar water heating system. Case Studies in Thermal Engineering. 2024;53(4):103922.
19. [19] Raza J, Lund LA, Ashraf H, Shah Z, Alshehri MH, Vrinceanu N. Fuzzy TOPSIS optimization of MHD trihybrid nanofluid in heat pipes. Case Studies in Thermal Engineering. 2024;64:105493.
20. [20] Abouzied AS, Basem A, Ibrahim TK, Almadhor A, Almutairi K, Albalawi H, et al. Thermal performance optimization of microchannel heat sinks with triangle wave fin designs and various heat transfer fluids using GA/RSM/TOPSIS. Case Studies in Thermal Engineering. 2025;72:106407.
21. [21] Kumar R, Zunaid M, Mishra RS. Multi-objective optimization of the hydrothermal and exergetic performance of a convergent-divergent microchannel heat sink using ANN, NSGA-II, and TOPSIS algorithms. Thermal Science and Engineering Progress. 2025;104228.
22. [22] Soumith V, Biswas A, Nath S. Optimal selection of a sustainable passive heat transfer enhancement technique for circular pipe heat exchanger using MCDM framework. Arabian Journal for Science and Engineering. 2025;50(2):2835-2856.
23. [23] Montazerifar F, Akbari OA, Javadpour SM. Multi-optimization of nanofluid and fractal fin in the plate heat exchanger using RSM and TOPSIS. Energy Conversion and Management: X. 2026;29:101474.
24. [24] Yildirim O. Biomimetic design for in-tube heat transfer enhancement: Experimental evaluation and thermodynamic optimization. Experimental Heat Transfer. 2026.
25. [25] Firat I, Karagoz S, Yildirim O, Yilmaz M. Performance and entropy production analysis of angle blade turbulators used to increase heat transfer. Journal of Thermal Analysis and Calorimetry. 2023;148:7811-7828.
26. [26] Firat I, Karagoz S, Yildirim O, Sonmez F. Experimental investigation of the thermal performance effects of turbulators with different fin angles in a circular pipe. International Journal of Thermal Sciences. 2023;184:107969.
27. [27] Goh LHK, Hung YM, Chen GM, Tso CP. Entropy generation analysis of turbulent convection in a heat exchanger with self-rotating turbulator inserts. International Journal of Thermal Sciences. 2021;160:106652.
28. [28] Keshavarz-Ghorabaee M, Amiri M, Zavadskas EK, Turskis Z, Antucheviciene J. Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry. 2021;13(4):525.
29. [29] Opricovic S, Tzeng GH. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 2004;156(2):445-455.
30. [30] Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A. Optimization of weighted aggregated sum product assessment. Electronics and Electrical Engineering. 2012;122(6):3-6.
31. [31] Pamucar D, Cirovic G. The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert Systems with Applications. 2015;42(6):3016-3028.
32. [32] Spearman C. The proof and measurement of association between two things. The American Journal of Psychology. 1904;15(1):72-101.
33. [33] Shieh GS. A weighted Kendall’s tau statistic. Statistics & Probability Letters. 1998;39(1):17-24.