352 International Journal on Advances in Systems and Measurements, vol 11 no 3 & 4, year 2018, http://www.iariajournals.org/systems_and_measurements/ 2018, © Copyright by authors, Published under agreement with IARIA - www.iaria.org Transient Analysis of a Single-stage Vapor Compression Refrigeration System Using Lumped Parameter Approaches Analysis and simulation validation based on a reduced order differential equation with few degrees of freedom Guillermo Domínguez Librado Cooling Systems Dynamics Modeling Engineering Center for Industrial Development (CIDESI) Querétaro, México e-mail: gdominguez@posgrado.cidesi.edu.mx Eloy Edmundo Rodríguez Vázquez National Research Laboratory on Cooling Technology Engineering Center for Industrial Development (CIDESI) Querétaro, México e-mail: eloy.rodriguez@cidesi.edu.mx Luis Alvaro Montoya Santiyanes Rotordynamics for Cooling Engineering Center for Industrial Development (CIDESI) Querétaro, México e-mail: lmontoya@posgrado.cidesi.edu.mx J. Hernán Pérez Vázquez Heat Interchangers with Local Compression Engineering Center for Industrial Development (CIDESI) Querétaro, México e-mail: jperez@posgrado.cidesi.edu.mx C. Alexander Nuñez Martín Nation Dynamic Optimization of Cooling Devices Engineering Center for Industrial Development (CIDESI) Querétaro, México e-mail: cnunes@posgrado.cidesi.edu.mx Abstract—Refrigeration and air conditioning systems need to have enough capacity to maintain the desired temperature at a worst-case, design load operating condition. In this paper, a dynamic analysis of a single-stage vapor-compression refrigeration system is presented. The model is constructed by applying the lumped parameter approach to each component of the refrigeration system; the first low of thermodynamic is applied to individual components to determine the mass and energy balances; then, a linear dynamical system is obtained. The model is implemented by MATLAB and simulation results are given for comparison with real values. The results of the simulation match with the manufacturer’s specifications. Keywords-Heat exchangers; Refrigerants; Dynamic Model; Household refrigeration; Transient conditions; Control volume. I. INTRODUCTION Refrigeration and air conditioning are an active and fleet developing technologies. These devices are closely related to the living standard of people and to the outdoor environment, due to ozone depletion and global warming. Mathematical modeling is the most practical way of studying the basic behavior of cooling cycle performance, the relative losses in various components and their interactions. Standard science and engineering formulations are applied to describe mathematically the basics processes occurring in the Vapor Compression Refrigeration (VCR) systems. Mathematical modeling is a step towards simulation optimization [1], [2]. Dynamic models are often classified using such terms as white box, gray box, or black box. The term white-box models refer to physics based models that are described using physical laws, such as conservation equations. These models also appear in the literature as mechanistic models or first principles models [1], [3]. At the other extreme, black-box models refer to empirical or data-driven models, where transient experimental data is used to identify a dynamic model. This process is also known as system identification or time-series analysis, and it can be used to construct models in the time or frequency domain. In black-box model one tries to estimate the functional form of relations between variables and the numerical parameters with no need of detailed information about the components of the system [1], [3]. Examples of empirical models include regression analysis, polynomial curve fits and artificial neural networks. The bulk of modeling efforts for VCR systems are most appropriately termed as gray-box, due to they are largely based on the governing physics but including semi-empirical terms, such as efficiency maps, heat transfer correlations, etcetera, that come out from experimental test. Physics-based modeling paradigms include;  lumped parameter approaches that capture the gross pressure and cooling transients qualitatively,  moving boundary approaches, which model the dynamic variations in phase transition points, and