AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Fast simulation of dynamic heat transfer through building envelope via model order reduction

Qiongxiang Kong1( )Xiao He1Yaolin Jiang2
School of Human Settlement & Civil Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049,China
School of Mathematics & Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
Show Author Information

Abstract

In this paper, a fast and accurate numerical simulation method on dynamic heat transfer through building envelopes has been developed by using the Krylov subspace and the balanced truncation model order reduction (MOR) algorithms. The computational accuracy and efficiency of the two MOR algorithms are discussed through the numerical simulation on a roof heat transfer in a one-day period, and then the two verified algorithms are applied to simulate the heat transfer through a multilayer wall for a week and the two-dimensional heat transfer through an L-shape thermal bridge. The results show that the relative errors of the two algorithms to the harmonic response method or to the direct solution method are all less than 1%, and the solving time with the two MOR algorithms decreases greatly. In addition, the Krylov subspace MOR algorithm has a faster solving speed and is more suitable for solving the heat transfer through a building envelope than the balanced truncation MOR algorithm.

References

 
D Agdas, RS Srinivasan (2014). Building energy simulation and parallel computing: Opportunities and challenges. In: Proceedings of 2014 Winter Simulation Conference, Savannah, GA, USA, pp. 3167–3175.
 
AC Antoulas (2005). Approximation of Large-Scale Dynamical Systems. Philadelphia: SIAM.
 
K Arendt, M Krzaczek (2014). Co-simulation strategy of transient CFD and heat transfer in building thermal envelope based on calibrated heat transfer coefficients. International Journal of Thermal Sciences, 85: 1–11.
 
F Ascione, N Bianco, D Masi R F, et al. (2013). Simplified state space representation for evaluating thermal bridges in building: Modelling, application and validation of a methodology. Applied Thermal Engineering, 61: 344–354.
 
Z Bai (2002). Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems. Applied Numerical Mathematics, 43: 9–44.
 
CA Beattie, S Gugercin (2007). Krylov-based minimization for optimal H2 model reduction. In: Proceedings of 46th IEEE Conference on Decision and Control, pp. 4385–4390.
 
P Chen, S Cao, J Guo (1987). Air Conditioning Load Calculation Theory and Method. Shanghai: Tongji University Press. (in Chinese)
 
Chinese Meteorological Information Center (2005). Chinese building thermal environment analysis of meteorological data. Beijing: China Architecture and Building Press. (in Chinese)
 
CC Chu, MH Lai, WS Feng (2006). The multiple point global Lanczos method for multiple-inputs multiple-outputs interconnect order reductions. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E89-A: 2706–2716.
 
CC Chu, MH Lai, WS Feng (2008). Model Order reductions for MIMO systems using globa Krylov subspace methods. Mathematics and Computers in Simulation, 79: 1153–1164.
 
P Feldman, RW Freund (1995). Reduced-order modelling of large linear subcircuits via a block Lanczos algorithm. In: Proceeding of 32nd Design Automation Conference, pp. 474–479.
 
RW Freund (2000). Krylov-subspace methods for reduced-order modeling in circuit simulation. Journal of Computational and Applied Mathematics, 123: 395–421.
 
Y Gao, R Fan, QL Zhang, JJ Roux (2014). Building dynamic thermal simulation of low-order multi-dimensional heat transfer. Journal of Central South University, 1: 293–302.
 
V Garg, K Chandrasen, J Mathur, S Tetali, A Jawa (2011). Development and performance evaluation of a methodology, based on distributed computing, for speeding EnergyPlus simulation. Journal of Building Performance Simulation, 4: 257–270.
 
V Garg, A Jawa, J Mathur, A Bhatia (2014). Development and analysis of a tool for speed up of EnergyPlus through parallelization. Journal of Building Performance Simulation, 7: 179–191.
 
EJ Grimme (1997). Krylov projection methods for model reduction. PhD Thesis, University of Illinois at Urbana-Champaign, USA.
 
S Gugercin, AC Antoulas (2004). A survey of model reduction by balanced truncation and some new results. International Journal of Control, 77: 748–766.
 
X He, Q Kong, Z Xiao (2015). Fast simulation methods for dynamic heat transfer through building envelope based on model-order-reduction. In: Proceedings of 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) and 3rd International Conference on Building Energy and Environment (COBEE), Tianjin, China.
 
M Ibrahim, PH Biwole, E Wurtz, P Achard (2014). A study on the thermal performance of exterior walls covered with a recently patented silica-aerogel-based insulating coating. Building and Environment, 81: 112–122.
 
Y Jiang (2010). Model Order Reduction Method. Beijing: Science Press. (in Chinese)
 
KJ Kontoleon, ThG Theodosiou, KG Tsikaloudaki (2013). The influence of concrete density and conductivity on walls’ thermal inertia parameters under a variety of masonry and insulation placements. Applied Energy, 112: 325–337.
 
BK Koyunbaba, Z Yilmaz, K Ulgen (2013). An approach for energy modeling of a building integrated photovoltaic (BIPV) Trombe wall system. Energy and Buildings, 67: 680–688.
 
K Li, H Su, J Chu, C Xu (2013). A fast-POD model for simulation and control of indoor thermal environment of buildings. Building and Environment, 60: 150–157.
 
Y Lin, L Bao, Y Wei (2009). Order reduction of bilinear MIMO dynamical systems using new block Krylov subspaces. Computers & Mathematics with Applications, 58: 1093–1102.
 
RC Li, ZJ Bai (2005). Structure-preserving model reduction using Krylov subspace projection formulation. Communication in Mathematical Science, 3: 179–199.
 
M Manso, J Castro-Gomes (2015). Green wall systems: A review of their characteristics. Renewable and Sustainable Energy Reviews, 41: 863–871.
 
B Moore (1981). Principal component analysis in linear systems: Controllability, observability, and model reduction. IEEE Transactions on Automatic Control, 26: 17–32.
 
L Pernebo, LM Silverman (1982). Model reduction via balanced state space representations. IEEE Transactions on Automatic Control, 27: 382–387.
 
DC Sorensen, AC Antoulas (2002). The Sylvester equation and approximate balanced reduction. Linear Algebra and Its Applications, 351: 671–700.
 
X Zhou, T Hong, D Yan (2014). Comparison of HVAC system modeling in EnergyPlus, DeST and DOE-2.1E. Building Simulation, 7: 21–33.
 
D Zhu, T Hong, D Yan, C Wang (2013). A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1E. Building Simulation, 6: 323–335.
Building Simulation
Pages 419-429
Cite this article:
Kong Q, He X, Jiang Y. Fast simulation of dynamic heat transfer through building envelope via model order reduction. Building Simulation, 2017, 10(3): 419-429. https://doi.org/10.1007/s12273-016-0327-y

578

Views

9

Crossref

N/A

Web of Science

10

Scopus

0

CSCD

Altmetrics

Received: 20 January 2016
Revised: 14 September 2016
Accepted: 21 September 2016
Published: 25 October 2016
© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016
Return