Abstract
Thermal simulation systems in buildings, contribute to the design of energy-efficient structures; however, a significant amount of computational time is required in order to obtain the results of the simulation process. The main focus of this paper is examining the possibility of reducing time required for thermal simulation systems calculations. More specifically, the paper focuses on one of the major processes that requires computing resources at solving the system of equations obtained as a result of the thermal modeling of a building. This research was undertaken in order to determine the performance of Java library methods: Matrix Tool for Java (MTJ) for solving systems of linear equations, and identifying which of these was the optimum in terms of computation time. For this purpose, tests for the iterative methods combined with pre-conditioners were conducted. The tests of the direct method were done through the development of a software implemented in two case studies of buildings, and that was modeled with the parameters of the thermal simulation software called JEner, from the Thermal Engineering Research Group from the University of Cadiz in Spain.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Blázquez, J.L.F.: Development and implementation of a simulation model of thermal installations in buildings. Doctoral Dissertation, University of Cadiz (2014)
TIOBE: TIOBE Index for May 2018 Headline: Scala cracks top 20 (2018). https://www.tiobe.com/tiobe-index/
IEEE SPECTRUM: The 2017 Top Programming Languages (2017). https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages
HEED: HEED Home Energy Efficient Design (2010). www.aud.ucla.edu/heed
Frenkel, J., Kunze, G., Fritzson, P.: Survey of appropriate matching algorithms for large scale systems of differential algebraic equations. In: Proceedings of the 9th International MODELICA Conference, Munich, Germany, vol. 076, pp. 433–442. Linköping University Electronic Press (2012)
Pothen, A., Fan, C.J.: Computing the block triangular form of a sparse matrix. ACM Trans. Math. Softw. (TOMS) 16(4), 303–324 (1990)
Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)
Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)
Magnier, L., Haghighat, F.: Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Build. Environ. 45(3), 739–746 (2010)
Bolívar, A.: Development of a Library of Java Classes to Solve Computation and Matrix Algebra Problems Using Two-Dimensional Arrays. University of Carabobo, Carabobo (2013)
García León, M.D.: Strategies for solving large systems of linear equations. Modified quasi-minimal waste methods. Doctoral Dissertation, University of Las Palmas de Gran Canaria (2003)
Guerrero, A.M., Gonzalo, N., Luque, J.S.: Simulator extension for operation as a liquid manifold-distributor using two free surface equations. Ciencia e Ingeniería 28(2), 103–109 (2007)
Maestre, I.R., Blázquez, J.L.F., Gallero, F.J.G., Cubillas, P.R.: Influence of selected solar positions for shading device calculations in building energy performance simulations. Energy Build. 101, 144–152 (2015)
Di Mare, A.: Introduction to the use of algebra libraries for engineering students. In: Eighth LACCEI Latin American and Caribbean Conference for Engineering and Technology (LACCEI) (2010)
Javadoc.IO: Matrix Toolkits for Java 1.0.4 API. http://www.javadoc.io/doc/com.googlecode.matrix-toolkits-java/mtj/1.0.4
swMATH: MTJ. https://www.swmath.org/software/22708
Knoll, D.A., Keyes, D.E.: Jacobian-free Newton-Krylov methods: a survey of approaches and applications. J. Comput. Phys. 193(2), 357–397 (2004)
Stanimirović, P.S., Pappas, D., Katsikis, V.N., Stanimirović, I.P.: Full-rank representations of outer inverses based on the QR decomposition. Appl. Math. Comput. 218(20), 10321–10333 (2012)
Sharma, J.R., Arora, H., Petković, M.S.: An efficient derivative free family of fourth order methods for solving systems of nonlinear equations. Appl. Math. Comput. 235, 383–393 (2014)
Jain, P.: Steffensen type methods for solving non-linear equations. Appl. Math. Comput. 194(2), 527–533 (2007)
Dewilde, P.: On the LU factorization of infinite systems of semi-separable equations. Indagationes Mathematicae 23(4), 1028–1052 (2012)
Wright, S.J.: Modified Cholesky factorizations in interior-point algorithms for linear programming. SIAM J. Optim. 9(4), 1159–1191 (1999)
Joly, P., Eymard, R.: Preconditioned biconjugate gradient methods for numerical reservoir simulation. J. Comput. Phys. 91(2), 298–309 (1990)
Hestenes, M.R. and Stiefel, E.: Methods of conjugate gradients for solving linear systems, vol. 49, nº 1. NBS, Washington, DC (1952)
Saad, Y., Schultz, M.H.: GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 7(3), 856–869 (1986)
Monga Made, M.M., Van der Vorst, H.A.: A generalized domain decomposition paradigm for parallel incomplete LU factorization preconditionings. Future Gener. Comput. Syst. 17(8), 925–932 (2001)
Li, D., Zhang, C.: Split Newton iterative algorithm and its application. Appl. Math. Comput. 217(5), 2260–2265 (2010)
Slaybaugh, R.N., Evans, T.M., Davidson, G.G., Wilson, P.P.: Multigrid in energy preconditioner for Krylov solvers. J. Comput. Phys. 242, 405–419 (2013)
National Institute of Standards and Technology: JAMA: A Java Matrix Package (2012). https://math.nist.gov/javanumerics/jama/#Authors
Kostюkov, V.: Linear Algebra for Java (la4j) (2012). https://code.google.com/archive/p/la4j/
CERN: The Colt Project, European Organization for Nuclear Research (2004). http://dst.lbl.gov/ACSSoftware/colt/
GitHub: Matrix Toolkit for Java, GitHub, Inc. (2016). https://github.com/fommil/matrix-toolkits-java
Apache Commons: Linear Algebra, Apache Commons Math (2016). http://commons.apache.org/proper/commons-math/userguide/linear.html
Abeles, P.: Efficient Java Matrix Library, Google Código (2015). https://code.google.com/p/efficient-java-matrix-library/
Braun, M.L., Schaback, J., Jugel, M.L., Oury, N.: JBLAS Linear Algebra for Java, Mikiobraun (2010). http://mikiobraun.github.io/jblas/
Dautelle, J.M.: Java Tools and Libraries for the Advancement of Sciences, JScience (2014). http://jscience.org/
Optimatika: oj! Algoritmos 2017. http://ojalgo.org/
Wendykier, P.: Parallel Colt (2010). https://sites.google.com/site/piotrwendykier/software/parallelcolt
Arndt, H., Bundschus, M., Nägele, A., Huso, R., Carlsen, F.: Universal Java Matrix Package, Holger Arndt (2018). https://ujmp.org/
Abeles, P.: Java Matrix Bench-mark, Google Código (2010). https://code.google.com/archive/p/java-matrix-benchmark/
McMahan, H.B., Gordon, G.J.: Fast exact planning in Markov decision processes. In: ICAPS, pp. 151–160 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Antón Cedeño, C.R. et al. (2018). Evaluation of the Computation Times for Direct and Iterative Resolution Methods of MTJ Library Matrices Applied in a Thermal Simulation System. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2018. Communications in Computer and Information Science, vol 883. Springer, Cham. https://doi.org/10.1007/978-3-030-00940-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-00940-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00939-7
Online ISBN: 978-3-030-00940-3
eBook Packages: Computer ScienceComputer Science (R0)