Abstract
This chapter develops a methodology for modeling of heat transfer dynamics in a building atrium via Koopman mode decomposition (KMD). We address the phenomenon of heat transfer due to movement of air inside an atrium, where the air slowly moves over the distance between individual rooms. The heat transfer is modeled as a heat advection equation with a vector-valued velocity coefficient and a nonlinear input term from heating, ventilation, and air-conditioning (HVAC) operations, which corresponds to a control variable. KMD is applied to the equation so that the heat transfer and HVAC operation are characterized in terms of frequencies and wavenumber vectors of Koopman modes (KMs). The velocity coefficient is then identified with a KM governing the heat transfer. The effectiveness of the modeling is demonstrated with measurement data on temperature field and HVAC operation of a practically used building. A possibility of how to use this modeling for control of heat transfer dynamics is discussed at the end of this chapter.
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Notes
- 1.
Although \(\mathbf {V}_\text {eff}\) and \(D_\text {eff}\) are constant in the typical averaging and homogenization frameworks, we now treat them as space dependent functions in order to represent global trend of \(\mathbf {v}(\mathbf {r},t)\) over the domain \(V_\text {c}\). The space- (and time-)dependency of \(D_\text {eff}\) and \(\mathbf {V}_\text {eff}\) is investigated in the literature: see, e.g., [27].
- 2.
KMs {10,11} also have large magnitudes of \(\Vert \mathbf {A}_m\Vert \) and \(\Vert \mathbf {B}_m\Vert \). However, they can be neglected because the original data contain no oscillatory component associated with the period \(T_{10}=6.490\) h. Indeed, we applied the discrete Fourier transform (DFT) to the original data \(\{T(\mathbf {r}_p,n\varDelta t) | n=0,1,\ldots , N\}\) for every position \(\mathbf {r}_p\), and confirmed that the values of the period of dominant components derived by DFT are different from \(T_{10}\): see [25].
References
Hensen, J.L.M., Lamberts, R. (eds.): Building Performance Simulation for Design and Operation. Spon Press, London (2011)
Zhang, W., Hiyama, K., Kato, S., Ishida, Y.: Building energy simulation considering spatial temperature distribution for nonuniform indoor environment. Build. Environ. 63, 89–96 (2013)
Osawa, S., Hara, S., Koga, K., Honda, M., Kaseda, C.: Optimal control for room air conditioning using time-space muti-scale modeling. In: Proceedings of SICE Control Division Conference (2013) (in Japanese)
Kono, Y., Susuki, Y., Hayashida, M., Mezić, I., Hikihara, T.: Multiscale modeling of in-room temperature distribution with human occupancy data: a practical case study. J. Build. Perform. Simul. 11(2), 145–163 (2018)
Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 1–6 (2010)
Veselỳ, M., Zeiler, W.: Personalized conditioning and its impact on thermal comfort and energy performance–a review. Renew. Sustain. Energy Rev. 34, 401–408 (2014)
Saxon, R.: Atrium Buildings: Development and Design. Architectural Press, London (1983)
Levermore, G.J.: Building Energy Management Systems: Applications to Low-energy HVAC and Natural Ventilation Control, 2nd edn. E & FN Spon, London (2000)
Liu, P.C., Lin, H.T., Chou, J.H.: Evaluation of buoyancy-driven ventilation in atrium buildings using computational fluid dynamics and reduced-scale air model. Build. Environ. 44(9), 1970–1979 (2009)
Ren, Z., Stewart, J.: Simulating air flow and temperature distribution inside buildings using a modified version of COMIS with sub-zonal divisions. Energy Build. 35(3), 257–271 (2003)
Megri, A.C., Haghighat, F.: Zonal modeling for simulating indoor environment of buildings: review, recent developments, and applications. HVAC&R Res. 13(6), 887–905 (2007)
Price, C.R., Rasmussen, B.P.: Effective tuning of cascaded control loops for nonlinear HVAC systems. In: Proceedings of ASME 2015 Dynamic Systems and Control Conference, vol. 2 (2015)
Pavliotis, G.A., Stuart, A.M.: Multiscale Methods: Averaging and Homogenization. Springer, Berlin (2008)
Mezić, I.: Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dyn. 41(1), 309–325 (2005)
Mezić, I.: Analysis of fluid flows via spectral properties of the Koopman operator. Annu. Rev. Fluid Mech. 45, 357–378 (2013)
Eisenhower, B., Maile, T., Fischer, M., Mezić, I.: Decomposing building system data for model validation and analysis using the Koopman operator. In: Proceedings of 4th National Conference of IBPSA-USA (SIMBUILD 2010) (2010)
Georgescu, M., Eisenhower, B., Mezić, I.: Creating zoning approximations to building energy models using the Koopman operator. In: Proceedings of 5th National Conference of IBPSA-USA (SimBuild2012), vol. 5, pp. 40–47 (2012)
Georgescu, M., Mezić, I.: Building energy modeling: a systematic approach to zoning and model reduction using Koopman mode analysis. Energy Build. 86, 794–802 (2015)
Georgescu, M., Mezić, I.: Improving HVAC performance through spatiotemporal analysis of building thermal behavior. In: Proceedings of 3rd International High Performance Buildings Conference at Purdue (2014)
Georgescu, M., Mezić, I.: Site-level energy monitoring and analysis using Koopman operator methods. In: Proceedings of 2014 ASHRAE/IBPSA-USA Building Simulation Conference (SimBuild 2014) (2014)
Littooy, B., Loire, S., Georgescu, M., Mezić, I.: Pattern recognition and classification of HVAC rule-based faults in commercial buildings. In: Proceedings of 2016 IEEE International Conference on Big Data (Big Data), pp. 1412–1421 (2016)
Georgescu, M., Loire, S., Kasper, D., Mezić, I.: Whole-building fault detection: a scalable approach using spectral methods. In: Proceedings of the 2017 ASHRAE Winter Meeting (2017)
Kono, Y., Susuki, Y., Hayashida, M., Hikihara, T.: Modeling of effective heat diffusion in a building atrium via Koopman mode decomposition. In: Proceedings of 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA2016), pp. 362–365 (2016)
Kono, Y., Susuki, Y., Hayashida, M., Hikihara, T.: Applications of Koopman mode decomposition to modeling of heat transfer dynamics in building atriums–I–effective heat diffusion by small-scale air movement. Trans. Soc. Instrum. Control Eng. 53(2), 123–133 (2017) (in Japanese)
Kono, Y., Susuki, Y., Hikihara, T.: Applications of Koopman mode decomposition to modeling of heat transfer dynamics in building atriums–II–advection by large-scale air movement. Trans. Soc. Instrum. Control Eng. 53(2), 188–197 (2017) (in Japanese)
Gao, N.P., Niu, J.L.: CFD study of the thermal environment around a human body: a review. Indoor Built Environ. 14(1), 5–16 (2005)
Pavliotis, G.A.: Homogenization theory for advection–diffusion equations with mean flow. PhD thesis, Rensselaer Polytechnic Institute (2002)
Sharma, A.S., Mezić, I., McKeon, B.J.: Correspondence between Koopman mode decomposition, resolvent mode decomposition, and invariant solutions of the Navier-Stokes equations. Phys. Rev. Fluids 1(3), 032402 (2016)
Temam, R.: Infinite-Dimensional Dynamical Systems in Mechanics and Physics, vol. 68. Springer Science & Business Media, New York (2012)
Arnol’d, V.I., Avez, A.: Ergodic Problems of Classical Mechanics. WA Benjamin, New York (1968)
Koopman, B.O.: Hamiltonian systems and transformations in Hilbert space. Proc. Natl. Acad. Sci. USA 17, 315–318 (1931)
Okochi, G.S., Yao, Y.: A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems. Renew. Sustain. Energy Rev. 59, 784–817 (2016)
Rowley, C.W., Mezić, I., Bagheri, S., Schlatter, P., Henningson, D.S.: Spectral analysis of nonlinear flows. J. Fluid Mech. 641, 115–127 (2009)
Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010)
Tu, J.H., Rowley, C.W., Luchtenburg, D.M., Brunton, S.L., Kutz, J.N.: On dynamic mode decomposition: theory and application. J. Comput. Dyn. 1(2), 391–421 (2015)
Williams, M.O., Kevrekidis, I.G., Rowley, C.W.: A data-driven approximation of the Koopman operator: extending dynamic mode decomposition. J. Nonlinear Sci. 25(6), 1307–1346 (2015)
Susuki, Y., Mezić, I.: A prony approximation of Koopman mode decomposition. In: Proceedings of 54th IEEE Conference on Decision and Control, pp. 7022–7027 (2015)
Raak, F., Susuki, Y., Mezić, I., Hikihara, T.: On Koopman and dynamic mode decompositions for application to dynamic data with low spatial dimension. In: Proceedings of 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 6485–6491 (2016)
Pope, S.B.: Turbulent Flows. Cambridge University Press, Cambridge (2000)
Gouda, M.M., Danaher, S., Underwood, C.P.: Building thermal model reduction using nonlinear constrained optimization. Build. Environ. 37(12), 1255–1265 (2002)
Chintala, R., Price, C., Rasmussen, B.: Identification and elimination of hunting behavior in HVAC systems. ASHRAE Trans. 121, 294–305 (2015)
Hiramatsu, N., Susuki, Y., Ishigame, A.: An estimation of in-room temperature gradient using Koopman mode decomposition. In: Proceedings of SICE Annual Conference 2018 (SICE2018), pp. 78–82 (2018)
Camacho, E.F., Bordons, C.: Model Predictive Control, 2nd edn. Springer, London (2007)
Ma, Y., Anderson, G., Borrelli, F.: A distributed predictive control approach to building temperature regulation. In: Proceedings of the 2011 American Control Conference, pp. 2089–2094 (2011)
Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems-a review of model predictive control (MPC). Build. Environ. 72, 343–355 (2014)
Korda, M., Mezić, I.: Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. Automatica 93, 149–160 (2018)
Mauroy, A., Goncalves, J.: Linear identification of nonlinear systems: a lifting technique based on the Koopman operator. In: Proceedings of 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 6500–6505 (2016)
Mauroy, A., Goncalves, J.: Koopman-based lifting techniques for nonlinear systems identification (2017). arXiv:1709.02003
Acknowledgements
The authors appreciate Mr. Chosei Kaseda, Mr. Junnya Nishiguchi, and Mr. Kei Koga (Azbil Corporation) for the provision of the photographs and measurement data, and insightful discussion. This work was partly supported by JST, CREST #JPMJCR15K3.
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Kono, Y., Susuki, Y., Hikihara, T. (2020). Modeling of Advective Heat Transfer in a Practical Building Atrium via Koopman Mode Decomposition. In: Mauroy, A., Mezić, I., Susuki, Y. (eds) The Koopman Operator in Systems and Control. Lecture Notes in Control and Information Sciences, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-030-35713-9_18
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