CHAPTER 4 EXPERIMENTATION The performance of the considered alternative refrigerant mixtures is experimentally studied in comparison with R134a and HC mixture (50%R290/50%R600a) in the visi cooler of conventional R134a system operating at medium temperature application. The experimental set up and steps followed for the above study are included in this chapter. 4.1 VISI COOLER A visi cooler is a medium temperature refrigeration appliance, generally used for cooling bottles containing cool drinks and other liquids. It generally cools the liquids to about 2 0 C to 4 0 C. The evaporating temperature of the refrigeration system is -7 0 C to -10 0 C. In general visi cooler uses either R12 or R134a as refrigerants. It consists of an air cooled condenser of plate fin type, capillary tube as expansion device, a single evaporator coil and hermetically sealed compressor. Specification of the components used in the test rig is given in Appendix-1 4.2 EXPERIMENTAL SETUP A test rig is specially designed and fabricated for the experimentation purpose. It consists of two different refrigeration circuits as shown in Figure 4.1.
Figure 4.1 Photographic view of the experimental setup Cylinder1 : mixture1 : 5%R134a/47.5% R600a /47.5%R290 Cylinder2 : mixture2 : 15%R134a/42.5%R600a/42.5% R290 Cylinder3 : mixture3 : 25%R134a/37.5%R600a/37.5%R290 Cylinder4 : mixture4 : 35%R134a/32.5%R600a/32.5%R290 Cylinder5 : mixture5 : 45%R134a/27.5%R600a/27.5%R290 Can1 : R134a Can2 : HC mixture : 50%R600a/50%R290
Circuit-1 runs with R12 refrigerant and circuit-2 runs with the R134a, and in the present study experiments were carried out on R134a circuit. Instruments are placed at all important locations to record the necessary data. Pressure gauges with ±0.5% accuracy are fixed at the exit to the evaporator and inlet to the condenser. To measure the temperature at the required points PT100 RTD temperature sensors with ±0.25 0 C accuracy were placed at all the required state points. A flowmeter was placed in the circuit to get the refrigerant flow rate. To get the energy consumption, compressor was connected to a watt meter with the accuracy of ±1W. The evaporator coil was placed in the brine solution (ethylene glycol) of a calorimeter. The calorimeter is a container of 223mm diameter and 223mm height that was insulated with PUF and thermo-rex in the wall exterior to minimize the heat leakage. A heater is placed suitably inside the calorimeter to vary the temperature of the brine solution. A stirrer was provided to stabilize the temperature. To measure the temperature of the brine solution three RTD sensors were placed at different heights in order to obtain the average temperature. To measure the heater load a wattmeter was connected. Indicators were provided over the control panel to measure the pressure, temperature and flow. A pressure guage was connected to the circuit to check the leakage of the refrigerant. A sight glass was provided at the liquid line to view the condition of the refrigerant. To offset the effect of change of environment variations, the whole set up was kept inside a temperature controlled room.
The aim of the present investigation is to optimize the length of the capillary tube. In this aspect, four capillaries having 1.1mm diameter and 1.8m, 2.7m, 3.3m and 4.5m long were fixed with the gate valves for experimenting with R134a refrigerant. By using the gate valves the required capillary lengths can be added to the circuit. To optimize the capillary length for HC mixture for minimum energy consumption, it needs double the length of the capillary tube than that of the R134a due to its lower viscosity values [24, 53]. Four capillaries with fixed 1.1mm diameter and 4.5m, 5.4m, 6.3m and 7.2m long respectively were selected for experimenting with HC mixture. As the alternative mixture is a mixture of R134a and HC mixture, the expected optimum capillary length lies in between the optimum capillary lengths of R134a and HC mixture. 4.3 EXPERIMENTAL PROCEDURE The total experimentation consists of the following tests i. Optimization of refrigerant charge and capillary length ii. iii. Pull down test Actual COP, energy consumption of the compressor and refrigeration effect. 4.3.1 Optimization of Refrigerant Charge and Capillary Length As the test rig is modified to fix the measuring instruments the charge quantity specified by the manufacturer may not be sufficient, hence there was a need for charge optimization. For the alternative refrigerants to get similar pressure difference between condenser and evaporator as R134a, the throttle resistance can be increased by
increasing the capillary length, hence there is need for the capillary optimization. Optimization of refrigerant charge and of capillary length tests were carried out with the base refrigerants (R134a and HC mixture) and also for the considered alternative refrigerants. The following is the procedure for conducting the optimization of refrigerant charge and of capillary length test. 1. Conduct the energy consumption test with the selected quantity of refrigerant and capillary length 2. Without varying the quantity of the refrigerant, vary the selected capillary lengths. 3. Repeat the step 2 by varying the mass of the refrigerant. 4. Recover the refrigerant and flush the system with nitrogen gas and evacuate it with the vacuum pump for 3 hours. 5. Charge another refrigerant and repeat the steps 1 to 4 4.3.2 Pull Down Test Pull-down time is defined as the time required for changing the brine solution temperature from ambient condition (30 O C) to the desired final temperature (2 O C). This test decides the cooling rate of the system. Refrigeration cycle off time will increase with the increase of cooling rate. The calorimeter door was opened for 24 hours to attain the thermal equilibrium of brine solution with the surroundings. The doors were closed once the system reached the desired temperature and the test was started. During the pull down test temperatures were recorded at every 20 seconds until the temperature of the brine solution reaches 30 O C to 2 O C.
4.3.3 Energy Consumption of the Compressor, Refrigeration Effect and Actual COP To measure the energy consumption of the compressor, the test rig was kept inside a test room. The performance tests were conducted at 32 0 C ambient temperature. All the observations were taken after four hours at a steady state condition. The energy consumption was recorded with an energymeter with ±1W accuracy. To compute the actual COP and refrigeration effect at a particular calorimeter temperature, the brine solution calorimeter is used. To find actual refrigeration effect and power of compressor for various calorimeter temperatures (temperature of brine solution) at 2 0 C, 5 0 C and 8 0 C were selected, and the heater load was in tune by a dimmer stat. The system was allowed to run at balance condition for any chosen calorimeter temperature. The balance state was ascertained by ensuring that the calorimeter shell temperature, power consumption of the compressor and heater remained unchanged for atleast four hours. At this state, heater load and compressor power were noted. The heater load was the refrigeration effect at balance condition. The above procedure was repeated twice, and the average has been calculated. The flow chart for the experimentation is shown in Figure 4.2
Experimentation R134a HC mix mix 1 mix 2 mix 3 mix 4 mix 5 Investigated Refrigerants L1 L2 L3 L4 L3 L4 L5 L6 L7 L3 L4 L5 L6 Optimization of Capillary Mass Mass Mass Optimization of Mass Temp Temp Temp Different Freezer Temperatur Pull down test at the optimum settings Energy consumption test Refrigeration effect COP OUT PUT Figure 4.2 Flow chart for the experimentation
4.4 DESIGN OF EXPERIMENTS (DOE) USING TAGUCHI METHOD Design of Experiments is a systematic procedure to layout the factors and conditions of an experiment in standardized specific partial factorial arrangements (Orthogonal Array, OA) to determine the optimum design. DOE is the technique of defining and investigating all possible conditions in experiments involving multiple factors or variables or parameters. DOE must satisfy the following two objectives, firstly, the number of trials must be determined and secondly, the conditions for each trial must be specified. Taguchi method is based upon an approach, which is completely different from the conventional practices of quality engineering. Taguchi methodology emphasizes designing the quality into the product or process, whereas the more usual practice relies upon inspection. In his quality improvement practices, Taguchi essentially used the conventional statistical tools and simplified them by identifying the set of sequence guidelines for experiment layout and the analysis of results with the least number of tests or experiments. His method actually introduces quality improvement through design optimization. His technique has commonly been applied to what he classifies as offline quality control, which refers to quality improvement efforts in activities before production (improve the quality through prevention before production rather than correction after production). Thus Taguchi has introduced a parameter design which seeks to determine the factor levels that produce the best performance of the product or process
under study. His approach attempts to improve quality, which he defines as the consistency of performance. Consistency is achieved where variation is reduced. Variation in the output of a process produces non-uniformity in the product. To achieve better quality, a product must perform optimally and should have less variation around the optimum performance. This can be done by moving the mean performance to the target value (a value that a product is expected to posses) simultaneously reducing variations around the target value. This is known as robust design, which describes a condition where a product or process is least influenced by the variation of individual factors. To become robust is to become less sensitive to variations. Simply, robust design can make a product perform in the best manner most of the time (less deviation from the target value). Make all products perform on a similar level (less variation between the products). Multiple factors involved are selected in a very appropriate condition, by referring to reference manuals, handbooks or from the previous experience, so that the best results can be achieved [68]. 4.4.1 Steps in Taguchi Technique The following are the sixteen steps involved in DOE using Taguchi method 1. Design of experiments and the Taguchi approach A quick understanding of the Taguchi method based DOE is essential. The purpose here is to have a clear understanding of DOE and to know the standardization procedure for the experiment design process.
2. Definition and measurement of improvement No experiment that lacks the means to measure its results is complete or useful. A clear definition of the aim and measurement methods allows us to compare two individual performances, but a separate yardstick is needed to compare performances of one population with another. In general, individual performance parameters are different for different experiments, but consistency is the means by which population performance is measured. Consistent performance produces less variation around the target and results in reduction of losses in all forms. In this step, the population performances are measured and compared. 3. Common experiments and analysis methods A common practice for studying single or multiple factors is to test with one factor at a time while holding all others fixed. This practice is found suitable, as it is simple. However, the results are often misleading and fail to reproduce conclusions drawn from such an experiment. A more effective method for these conditions is to study their effect all together by setting up experiments based on DOE technique. This step should lead to some understanding of basic DOE principles. 4. Designing experiments using Orthogonal Arrays (OAs) The word "Design" in "DOE" implies a structured layout of the experiments that contains information about how many experiments are to be conducted and the combination of factors included in the study. Once the objective of the experiment is identified, the factors and
their levels are determined by following a discussion in a brainstorming session. The best method to structure the experiments is sorted out from all possible ways to lay the design of the experiment. A number of standard orthogonal arrays have been structured to facilitate DOE. Each of these orthogonal arrays can be used to design experiments to suit several experimental conditions. 5. Designing experiments with two-level factors Experiments that involve studies of process parameters with two levels are quite simple and common. There are a set of OAs (designated as L-4, L-8, L-12, L-16, L-32, L-64, etc.) created specifically for twolevel factors. Experiments of all sizes can be designed using these OAs, as long as all factors are tested at two levels. By completing this step, one can understand how quickly experiments involving two-level factors can be designed and analyzed using the standard OAs. 6. Designing experiments with three-level and four-level factors When two levels of factors are studied, the factors' behaviour is necessarily assumed to be linear. When nonlinear effects are expected, more than two levels of the factors are to be studied. Although many larger two-level OAs can be modified to accommodate three-level and four-level factors, a set of standard OAs such as L-9, L-18, L-27, modified L-16 and modified L-32 are also available. This step helps in learning the design and analysis of more complex experiments. 7. Analysis of variance (ANOVA) Calculations of output performance averages and averages for factor-level effects, which involve simple arithmetic operations, produce
answers to major questions that were unconfirmed in the earlier steps of the experiment. However, the influence of factors on the variation of results in terms of discrete proportion can be obtained by performing analysis of variance. This step illustrates the calculation part of ANalysis Of VAriance (ANOVA) terms and helps to build the confidence in interpreting the experimental results. 8. Designing experiments to study interactions between factors Interaction among factors, which is one factor's effect on another, is quite common in industrial experiments. When an experiment with factors does not produce acceptable results, or when interactions among factors are not as per the expectations, the experiment must accommodate interaction analysis. In this step, the objective will be to learn how to design experiments to include interaction and how to analyze the results to determine if interaction is present. It also helps in determining the most desirable condition in cases in which interaction is found to be significant. 9. Experiments with mixed-level factors Experiment designs with all the factors at one level are easily handled using one of the available standard OAs. But these standard arrays cannot always accommodate many mixed-factor situations that might be found in industrial settings. Most mixed-level designs, however, can be accomplished by altering the standard OAs. The goal is to learn the procedure by which columns of an array are modified to upgrade and downgrade the number of levels in creating a new column. A two-level orthogonal array can be modified to have three-level and
four-level columns. Also, to accommodate a factor with a lesser number of levels, a three-level column can be reduced to a two-level and a three-level column to two-level, by a procedure known as "dummy treatment" method. 10. Combination designs For some applications, the factors and levels are such that standard use of the OA does not produce an economical experimental strategy. In such conditions, a special experimental design such as a combination design may offer a considerable savings in number of samples. This step helps in familiarizing the necessary assumptions that must be made in order to lay out experiments using combination design. With this technique, two two-level factors are analyzed by assigning them to a three-level column. 11. Robust design strategy Deviations among parts manufactured by the same specifications are common even when efforts are made to keep all factors at their desired levels. Variation reduction is the ultimate goal. When performance is consistently on-target, the customer perceived quality of the product is favorably affected. Variation is most often due to uncontrollable factors that are too expensive to control, called the "noise factors". In robust design methodology, the approach is not to control the noise factors, but to reduce their influence by adjusting the controllable factors that are included in the experimentation. This strategy, promoted by Taguchi, reduces variation without actually removing the cause of variation.
12. Analysis using signal-to-noise (S/N) ratios The conventional method of calculating average factor effects and thereby determining the desirable factor levels (optimum condition) is to look at the simple averages of the results. The best way to compare the population behavior is to use the mean-squared deviation (MSD) that combines effects of both average and standard deviation of the results. For convenience of linearity and to accommodate wide-ranging data, a logarithmic transformation of MSD (called the signal-to-noise ratio) is suggested for the analysis of the results. This step shows how MSD is calculated for different quality characteristics and how the analysis using S/N ratios differs from the standard practice. When the S/N ratio is used for results analysis, the best condition identified from such analysis is more likely to produce consistent performance. 13. Results analysis using multiple evaluation criteria Often, a product is expected to satisfy number of objectives. The result in this case includes multiple evaluation criteria, which represents performance in each of the objectives. It is common to analyze only one criterion at a time because different objectives are likely to be evaluated by various criterions, each of which has different units of measurement and relative weightages. When the results are analyzed separately for different criteria and the desirable design conditions are determined, there is no assurance that the factor combination will all be alike. An objective way to study the results is to combine the multiple evaluations into a single criterion, which
accommodates the units of measurements and the relative weights of the overall criterion of evaluation. 14. Quantification of variation reduction and performance improvement Most of the DOE applications allow determining optimum design that is expected to produce an overall better performance. The improvement in performance means that either the average or the variations (or both) have improved. When the new/improved design is put into practice (i.e., the recommended design is incorporated), it's expected to reduce scrap and other costs. In turn, this reduction more than offsets the cost of the new design. The expected monetary benefits from the improved design can be calculated by using Taguchi's loss function. By this step, one can learn how to estimate the expected savings from the improvement predicted by the experimental results. Further, it is also possible to learn how the expected improvement in performance from the new design is expressed in terms of capability improvement indices such as Cp and Cpk. 15. Effective experiment planning As far as the benefits from the technique are concerned, experiment planning is the most important criterion among the different application activities. Therefore, it is a required first and necessary step in the application process. Planning for DOE/Taguchi requires structured brainstorming with project team members. The nature of discussions in the planning session is likely to vary from
project to project and is best facilitated by one who is well versed in the technique [71, 72]. 16. Review of example case studies The application knowledge gained in steps 1-15 could be overwhelming if immediate projects on which it is not practiced. One way to build more confidence and extend application expertise is by familiarizing with numerous types of case studies with complete experiment design and results analysis. Complete case studies should contain discussions under most of the following topics: Problem definition Aim of the project (or)project objective(s) Evaluation criteria and quality characteristic Identified factors and levels and those that are included in the experiment Suspected interactions and those that are selected for the initial experiment Uncontrollable factors (noise factors) and how they were treated Sequence of running of the experimental conditions Measured results, which represent evaluation of different objectives Main effects indicating the trend of factors' influence Analysis of variance for relative influence of the factor to the variation of results
Optimum condition and the expected performance Improvement and expected monetary savings Graphical representation of variation reduction expected from the improved design 4.4.2 Orthogonal Arrays (OAs) OA Orthogonal Arrays, a set of tables used to determine the least number of experiments and their conditions. Factors are placed in column and experiment trials are placed in a row. The intersection of column and the row represents levels of each factor. The word orthogonal means balance. The experimental layout using orthogonal array L18, calculations of means, S/N ratios and contribution of each factor are discussed in results and discussion chapter.