Advertisement

Journal of Thermal Analysis and Calorimetry

, Volume 136, Issue 3, pp 1395–1414 | Cite as

Transparent open-box learning network provides auditable predictions

Pool boiling heat transfer coefficient for alumina-water-based nanofluids
  • David A. WoodEmail author
  • Abouzar Choubineh
  • Behzad Vaferi
Article

Abstract

The transparent open-box (TOB) learning network algorithm adds the useful dimensions to machine learning of auditability and interrogation of each prediction made. It achieves this by making available for instant inspection the exact calculations and relationships it applies to its prediction for each data record in a dataset. The algorithm is applied here to predict the pool boiling heat transfer coefficient (PBHTC) for alumina-water-based nanofluids from a large dataset (870 data records with PBHTC varying from 0.33 to 65.68 kW m−2 K−1). The dataset, compiled from published sources and listed in full in a supplementary file, involves four relatively easy-to-measure input variables (nanoparticle size, operating pressure, nanoparticle concentration in water, and excess temperature). These input variables involve highly fragmented and nonlinear relationships with each other and PBHTC. The TOB predicts PBHTC to high accuracy (TOB: RMSE = 1.27; R2 > 0.99) and its predictions compare favorable to the accuracy achieved by an ANN model applied to the same dataset (ANN: RMSE = 1.09; R2 > 0.9960). The TOB algorithm involves a two-stage routine (Stage 1 matching; Stage 2 optimization) with no overt or underlying correlations involved. It generates dependent-variable predictions with high degrees of accuracy for datasets with irregular, fragmented and nonlinear input variables representing complex chemical and physical systems. Its transparency also provides key insights into the underlying dataset. The TOB algorithm overcomes the black box tendencies of many machine-learning algorithms. This makes the TOB algorithm suitable for deployment in situations where each prediction needs to be verified and supported with the complete step-by-step underlying calculations involved.

Keywords

Learning network transparency Prediction performance Nanofluid pool boiling Avoiding correlations Auditing predictions 

Supplementary material

10973_2018_7722_MOESM1_ESM.xlsx (56 kb)
Supplementary material 1 (XLSX 56 kb)

References

  1. 1.
    Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.CrossRefGoogle Scholar
  2. 2.
    Heinert M. Artificial neural networks–how to open the black boxes. In: Proceedings of application of artificial intelligence in engineering geodesy (AIEG); 2008. p. 42–62.Google Scholar
  3. 3.
    Wood DA. A transparent open-box learning network provides insight to complex systems and a performance benchmark for more-opaque machine learning algorithms. Adv Geo-Energy Res. 2018;2(2):148–62.CrossRefGoogle Scholar
  4. 4.
    Elkatatny S, Tariq Z, Mahmoud M. Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). J Pet Sci Eng. 2016;146:1202–10.CrossRefGoogle Scholar
  5. 5.
    Neto AR, Oliveira JL, Passos JC. Heat transfer coefficient and critical heat flux during nucleate pool boiling of water in the presence of nanoparticles of alumina, maghemite and CNTs. Appl Therm Eng. 2017;111:1493–506.CrossRefGoogle Scholar
  6. 6.
    Niu G, Li J. Comparative studies of pool boiling heat transfer with nano-fluids on porous surface. Heat Mass Transf. 2015;51(12):1769–77.CrossRefGoogle Scholar
  7. 7.
    Vafaei S. Nanofluid pool boiling heat transfer phenomenon. Powder Technol. 2015;277:181–92.CrossRefGoogle Scholar
  8. 8.
    Moreno G, Oldenburg SJ, You SM, Kim JH. Pool boiling heat transfer of alumina-water, zinc oxide-water and alumina-water + ethylene glycol nanofluids. In: ASME summer heat transfer conference collocated with the ASME Pacific Rim technical conference and exhibition on integration and packaging of MEMS, NEMS, and electronic systems; 2005. p. 625–32.Google Scholar
  9. 9.
    You SM, Kim JH, Kim KH. Effect of nanoparticles on critical heat flux of water in pool boiling heat transfer. Appl Phys Lett. 2003;83(16):3374–6.CrossRefGoogle Scholar
  10. 10.
    Kwark SM, Kumar R, Moreno G, Yoo J, You SM. Pool boiling characteristics of low concentration nanofluids. Int J Heat Mass Transf. 2010;53(5–6):972–81.CrossRefGoogle Scholar
  11. 11.
    Manetti LL, Stephen MT, Beck PA, Cardoso EM. Evaluation of the heat transfer enhancement during pool boiling using low concentrations of Al2O3-water based nanofluid. Exp Therm Fluid Sci. 2017;87:191–200.CrossRefGoogle Scholar
  12. 12.
    Kim JH, Kim KH, You SM. Pool boiling heat transfer in saturated nanofluids. In: ASME international mechanical engineering congress and exposition; 2004. p. 621–8.Google Scholar
  13. 13.
    Shoghl SN, Bahrami M, Jamialahmadi M. The boiling performance of ZnO, α-Al2O3 and MWCNTs/water nanofluids: an experimental study. Exp Therm Fluid Sci. 2017;80:27–39.CrossRefGoogle Scholar
  14. 14.
    Kim SJ, Bang IC, Buongiorno J, Hu LW. Surface wettability change during pool boiling of nanofluids and its effect on critical heat flux. Int J Heat Mass Transf. 2007;50(19–20):4105–16.CrossRefGoogle Scholar
  15. 15.
    Soltani S, Etemad SG, Thibault J. Pool boiling heat transfer performance of Newtonian nanofluids. Heat Mass Transf. 2009;45(12):1555–60.CrossRefGoogle Scholar
  16. 16.
    Wen D, Ding Y. Experimental investigation into the pool boiling heat transfer of aqueous based γ-alumina nanofluids. J Nano Res. 2005;7(2–3):265–74.CrossRefGoogle Scholar
  17. 17.
    Xiao B, Jiang G, Chen L. A fractal study for nucleate pool boiling heat transfer of nanofluids. Sci China Phys Mech Astron. 2010;53(1):30–7.CrossRefGoogle Scholar
  18. 18.
    Shahmoradi Z, Etesami N, Esfahany MN. Pool boiling characteristics of nanofluid on flat plate based on heater surface analysis. Int Commun Heat Mass Transf. 2013;47:113–20.CrossRefGoogle Scholar
  19. 19.
    Cieśliński J, Kaczmarczyk T. The effect of pressure on heat transfer during pool boiling of water-Al2O3 and water-Cu nanofluids on stainless steel smooth tube. Chem Proc Eng. 2011;32(4):321–32.Google Scholar
  20. 20.
    Bang IC, Chang SH. Boiling heat transfer performance and phenomena of Al2O3–water nano-fluids from a plain surface in a pool. Int J Heat Mass Transf. 2005;48(12):2407–19.CrossRefGoogle Scholar
  21. 21.
    Frontline Solvers. Standard excel solver—limitations of nonlinear optimization. https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization. Accessed May 2018.
  22. 22.
    Mitrovic J. Nucleate boiling of refrigerant–oil mixtures: bubble equilibrium and oil enrichment at the interface of a growing vapour bubble. Int J Heat Mass Transf. 1998;41(22):3451–67.CrossRefGoogle Scholar
  23. 23.
    Kharangate CR, Mudawar I. Review of computational studies on boiling and condensation. Int J Heat Mass Transf. 2017;108:1164–96.CrossRefGoogle Scholar
  24. 24.
    Hassanpour M, Vaferi B, Masoumi ME. Estimation of pool boiling heat transfer coefficient of alumina water-based nanofluids by various artificial intelligence (AI) approaches. Appl Therm Eng. 2018;128:1208–22.CrossRefGoogle Scholar
  25. 25.
    Goshayeshi HR, Safaei MR, Goodarzi M, Dahari M. Particle size and type effects on heat transfer enhancement of ferro-nanofluids in a pulsating heat pipe. Powder Technol. 2016;301:1218–26.CrossRefGoogle Scholar
  26. 26.
    Ariana MA, Vaferi B, Karimi G. Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks. Powder Technol. 2015;278:1–10.CrossRefGoogle Scholar
  27. 27.
    Das SK, Putra N, Roetzel W. Pool boiling characteristics of nano-fluids. Int J Heat Mass Transf. 2003;46(5):851–62.CrossRefGoogle Scholar
  28. 28.
    Bose NK, Liang P. Neural network fundamentals with graphs, algorithms, and applications. New York: McGraw Hill; 1996.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  • David A. Wood
    • 1
    Email author
  • Abouzar Choubineh
    • 2
  • Behzad Vaferi
    • 3
  1. 1.DWA Energy LimitedLincolnUK
  2. 2.Petroleum University of TechnologyAhwazIran
  3. 3.Young Researchers and Elite Club, Shiraz BranchIslamic Azad UniversityShirazIran

Personalised recommendations