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Physics
Thermodynamics

Adaptive Heat Engine

Profile image of Alexey MelkikhAlexey Melkikh

2016, Physical Review Letters

https://doi.org/10.1103/PHYSREVLETT.117.030601
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Abstract

A major limitations for many heat engines is that their functioning demands on-line control, and/or an external fitting between environmental parameters (e.g. temperatures of thermal baths) and internal parameters of the engine. We study a model for an adaptive heat engine, where-due to feedback from the functional part-the engine's structure adapts to given thermal baths. Hence no on-line control and no external fitting are needed. The engine can employ unknown resources; it can also adapt to results of its own functioning that makes the bath temperatures closer. We determine thermodynamic costs of adaptation and relate them to the prior information available about the environment. We also discuss informational constraints on the structure-function interaction that are necessary for adaptation.

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References (45)

  1. H.B. Callen, Thermodynamics and an introduction to thermostatistics (John Wiley & Sons, NY, 1985).
  2. K. Sekimoto, Stochastic Energetics (Springer-Verlag, Berlin 2010).
  3. U. Seifert, Rep. Prog. Phys. 75, 126001 (2012).
  4. G. Mahler, Quantum Thermodynamic Processes (Pan Stanford, Singapore, 2015).
  5. D. Chowdhury, Am. J. Phys. 77, 583 (2009).
  6. H.E.D. Scovil and E.O. Schultz-DuBois, Phys. Rev. Lett., 2, 262 (1959).
  7. J.E. Geusic et al., Phys. Rev., 156, 343 (1967).
  8. V.K. Konyukhov and A.M. Prokhorov, Uspekhi Fiz. Nauk, 119, 541 (1976).
  9. E. Geva and R. Kosloff, Phys. Rev. E 49, 3903 (1994).
  10. E. Boukoza and D. Tannor, Phys. Rev. Lett., 98, 240601 (2007).
  11. R. Uzdin, A. Levy, and R. Kosloff, Phys. Rev. X 5, 031044 (2015).
  12. G. Thomas and R.S. Johal, Phys. Rev. E 85, 041146 (2012).
  13. A. E. Allahverdyan, K. V. Hovhannisyan, A. V. Melkikh, and S. G. Gevorkian, Phys. Rev. Lett. 109, 248903 (2013).
  14. N. Linden, S. Popescu, and P Skrzypczyk, arxiv.1010.6029.
  15. A.S.L. Malabarba, A.J. Short, and P. Kammerlander, New J. Phys. 17, 045027 (2015).
  16. M.F. Frenzel, D. Jennings, and T. Rudolph, New J. Phys. 18, 023037 (2016).
  17. M.O. Scully, Phys. Rev. Lett. 104, 207701 (2010). M.O. Scully et al., PNAS, 108, 15097 (2011).
  18. S. Kauffman, Phil. Trans. R. Soc. A 361, 1089 (2003).
  19. E. Albarran-Zavala and F. Angulo-Brown, Entropy, 9, 152 (2007).
  20. I. Rojdestvenski, M. G. Cottam, G. Oquist, and N. Huner, Physica A 320, 318 (2003).
  21. S. Falk et al., Photosynthetic adjustment to temperature, in Photosynthesis and the environment, pp. 367-385, ed. by N.R. Baker (Springer, Netherlands, 1996).
  22. D. La Manna et al., Renewable and Sustainable Energy Reviews, 33, 412 (2014).
  23. T. Friedlander and N. Brenner, PNAS, 106, 22558 (2009).
  24. M. Inoue and K. Kaneko, Phys. Rev. E 81, 026203 (2010).
  25. G. Lan, P. Sartori, S. Neumann, V. Sourjik, Y. Tu, Na- ture Phys. 8. 422 (2012).
  26. A. E. Allahverdyan and Q.A. Wang, Phys. Rev. E 87, 032139 (2013).
  27. A. C. Barato, D. Hartich, and U. Seifert, Phys. Rev. E 87, 042104 (2013). New J. Phys. 16, 103024 (2014).
  28. S. Bo, M. Del Giudice, and A. Celani, JSTAT, P01014 (2015).
  29. P. Sartori and Y. Tu, Phys. Rev. Lett. 115, 118102 (2015).
  30. D. Markovic and C. Gros, Phys. Rev. Let. 105, 068702 (2010).
  31. S. McGregor and N. Virgo, in Advances in Artificial Life: Darwin Meets von Neumann, pp. 230-237 (Springer, Berlin, 2009).
  32. N.G. van Kampen, Stochastic Processes in Physics and Chemistry (Elsevier, Amsterdam, 2007).
  33. C.W.F. McClare, J. Theor. Biol. 35, 233 (1972). E.T. Jaynes, The muscle as an engine (1983), http://bayes.wustl.edu/etj/articles/muscle.pdf
  34. R. Zwanzig, Acc. Chem. Res. 23, 148 (1990).
  35. H. Frauenfelder, P.G. Wolynes, and R.H. Austin, Rev. Mod. Phys. 71, S419 (1999).
  36. N. Agmon and J.J. Hopfield, J. Chem. Phys. 78, 6947 (1983).
  37. M. Kurzynski, The thermodynamic machinery of life, (Springer-Verlag, Berlin 2010).
  38. L.A. Blumenfeld and A.N. Tikhonov, Biophysical Ther- modynamics of Intracellular Processes (Springer, Berlin, 1994).
  39. H.-P. Lerch et al., PNAS, 99, 15410 (2002). A.O. Gouschcha et al., Biophys. J., 79, 1237 (2000). Z.D. Nagel and J.P. Klinman, Nat. Chem. Biol. 5, 543 (2009). K.V. Shaitan and A.B. Rubin, Biophysics, 30, 517 (1984). K.V. Shaitan, Biophysics, 39, 993 (1994)
  40. L.N. Christophorov, A.R. Holzwarth, V.N. Kharkyanen, and F. van Mourik, Chemical Physics 256, 45 (2000)
  41. Yu. I. Samoilenko, Autom. Remote Control, 39, 632 (1978); ibid. 1745 (1979). A.G. Butkovskii and Yu.I. Samoilenko, Control of Quantum Mechanical Processes (Springer, Berlin, 1991).
  42. V.P. Starr, Physics of negative viscosity phenomena (McGraw-Hill, 1968).
  43. B. Cleuren and C. Van den Broeck, Phys. Rev. E. 65, 030101(R) (2002). A. Haljas, R. Mankin, A. Sauga, E. Reiter, Phys. Rev. E. 70, 041107. (2004). J. Spiechowicz, J. Luczka, P. Hanggi, JSTAT P02044 (2013).
  44. A.E. Allahverdyan and Th.M. Nieuwenhuizen, Phys. Rev. E, 62, 845 (2000).
  45. E.T. Jaynes, IEEE Trans. Syst. Science & Cyb. 4, 227 (1968).

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