DNA - An Integrated Open-Source Optimization Platform for Thermo-Fluid Systems

This paper presents developments and new features added to the simulation tool Dynamic Network Analysis. This open-source software is the result of ongoing development at the Department of Mechanical Engineering, Technical University of Denmark since 1988. Ever since, it has been employed to model dynamic and steady-state energy systems and is now available for the most common operating systems (Windows, Mac OS and Linux). Emerging interest in novel plant technologies, high-temperature heat pumps, refrigeration absorption modules, and in energy system optimization has stressed the necessity to extend the capabilities of the software, while at the same time decreasing computational time. Dynamic Network Analysis can now solve non-convex optimization problems by virtue of the fully-embedded genetic algorithm. Moreover, the thermophysical fluid property library has been extended with more than 110 fluids by interfacing CoolProp, a highaccuracy open-source property package for pure and pseudo-pure fluids, as well as humid air. The new features are unveiled in one case study where the optimization of an air bottoming cycle unit recuperating the exhaust heat from an offshore power system is performed by taking advantage of CoolProp’s table-based property interpolation scheme.

[1]  Kenneth A. De Jong,et al.  An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms , 1990, PPSN.

[2]  Olav Bolland,et al.  Air Bottoming Cycle: Use of Gas Turbine Waste Heat for Power Generation , 1996 .

[3]  Brian Elmegaard,et al.  DNA – A General Energy System Simulation Tool , 2005 .

[4]  R. Keys Cubic convolution interpolation for digital image processing , 1981 .

[5]  R. M. Manglik,et al.  Heat transfer and pressure drop correlations for the rectangular offset strip fin compact heat exchanger , 1995 .

[6]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[7]  W. Wagner,et al.  The IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use , 2002 .

[8]  Rasul Enayatifar,et al.  Optimal design of plate-fin heat exchangers by a hybrid evolutionary algorithm , 2012 .

[9]  Ali Saadat,et al.  HelmholtzMedia — A Fluid Properties Library , 2012 .

[10]  Charles L. Lawson,et al.  Basic Linear Algebra Subprograms for Fortran Usage , 1979, TOMS.

[11]  Fredrik Haglind,et al.  Optimization of Advanced Liquid Natural Gas-Fuelled Combined Cycle Machinery Systems for a High-Speed Ferry , 2012 .

[12]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[13]  D. P. Sekulic,et al.  Fundamentals of Heat Exchanger Design , 2003 .

[14]  M. McLinden,et al.  NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties-REFPROP, Version 8.0 , 2007 .

[15]  P. G. Hill,et al.  Rapid and Accurate Calculation of Water and Steam Properties Using the Tabular Taylor Series Expansion Method , 2001 .

[16]  J. Pátek,et al.  A computationally effective formulation of the thermodynamic properties of LiBr-H2O solutions from 273 to 500 K over full composition range , 2006 .

[17]  Roland Span,et al.  Multiparameter equations of state — recent trends and future challenges , 2001 .

[18]  Vincent Lemort,et al.  Pure and Pseudo-pure Fluid Thermophysical Property Evaluation and the Open-Source Thermophysical Property Library CoolProp , 2014, Industrial & engineering chemistry research.

[19]  Andreas Poullikkas,et al.  An overview of current and future sustainable gas turbine technologies , 2005 .

[20]  Jack Dongarra,et al.  LAPACK Users' Guide, 3rd ed. , 1999 .

[21]  Axel Vodder Ohrt Johansen Numerical study of evaporators in power plants for improved dynamic fl exibility , 2013 .

[22]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[23]  A. London,et al.  Compact heat exchangers , 1960 .

[24]  Fredrik Haglind,et al.  A review on the use of gas and steam turbine combined cycles as prime movers for large ships, Part III: Fuels and emissions , 2008 .

[25]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.