ABSIM–modular simulation of advanced absorption systems: Recent software enhancements. (September 2018)

• Record Type: Journal Article
• Authors: Evron, Yigal; Gommed, Khaled; Grossman, Gershon
• Is Part Of: International journal of refrigeration. Volume 93(2018)
• Page Start: 79
• Page End: 84
• Publication Date: 2018-09
• Subjects: Genetic optimization -- Absorption -- Thermodynamic cycle modeling -- Simulation; Optimisation génétique -- Absorption -- Modélisation du cycle thermodynamique -- Simulation; COP Coefficient of Performance -- DOE Department Of Energy (USA) -- GA Genetic Algorithm -- GUI Graphical User Interface -- ORNL Oak Ridge National Laboratory -- PTX Pressure–Temperature - Concentration; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2018.06.011
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: Improvements to the ABSIM software code were implemented and demonstrated. Improvements include an algorithm to automate the initial guess selection for the solver. The algorithm improves reliability and reduces manual user work. The algorithm made possible the implementation of a genetic global optimization feature. Abstract: ABSIM (an acronym for ABsorption SIMulation) is a computer code for simulation of absorption systems in flexible and modular form. It may be applied to closed and/or open absorption cycles operating at steady state, as well as to vapor compression and other thermodynamic cycles. This modular code is based on unit subroutines containing the governing equations for the system's units/components and on property subroutines containing thermodynamic properties of the working fluids. The units are linked together by a main program which calls the unit subroutines according to the user's specifications to form the complete cycle. When all the equations for the entire cycle have been established, a mathematical solver routine is employed to solve them simultaneously. As the simulated system becomes more complex, it is increasingly difficult to find a suitable initial guess. If the user obtains one, there is no guarantee that this guess will be adequate for simulating a different system state, even with but a slight change in its parameters (small change in temperature, and/or flow rate, heat transfer coefficients, etc.). To deal with this problem, an automatic initial guess generating algorithm was developed. This algorithm aids the user by automatically selecting the initial guess for the solver to work with. This is to achieve convergence for different operating conditions, given that at least one good initial guess was already obtained for a certain operating point. An additional new feature of the ABSIM software is the implementation of a genetic optimization algorithm. It is enabled by the above automated initial guess algorithm.