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Design and Optimization Strategies of a High-Performance Vented Box
Capitoli
Riepilogo June 9th, 2023
Here, we present the range analysis method to optimize the sample points generated by an orthogonal experimental design to ensure that fresh food can be stored in a vented box for a long time by regulating the airflow pattern.
Transcript
This study aim to solve the problems of air flow chaos and the poor performance in the vented box caused by the unreasonable distribution of air flow through the design of the internal structure of the vented box on the premise of constant energy consumption. An efficient and economical optimization method considering the performance of the vented box was established and it can be readily used to extend the storage time of fresh food. The object of this study is to design and optimize a high performance vented box containing arrays of pipes with zigzag holes.
There are two air inlets of equal size set parallelly at the left and the right sides of the vented box, and an outlet was set at the upper side of the vented box. The reference model has 10 pipes. The two middle pipes have 10 holes respectively, which are staggered across the pipes.
The number of holes from the middle to the outer pipe is increased by two at a time. Considering the arrays of pipes, the three dimensional bottom half and the top half of the vented box models are established by using three dimensional software and saving them as XT files. Run the simulation software and drag the mesh component from component systems to the project schematic window.
Name it as bottom. Right click geometry and click browse to import the bottom XT file. Right click geometry and click new design modeler geometry to enter into the mesh design modeler window.
Click generate to display the bottom model. Right click the upper surface and click the named selection to rename it as Vented Box Upper. Select the selection filter bodies.
Right click the bottom model to select the named selection and rename it has bottom. Select the selection filter faces and switch the select mode to the box select. Select all inner surfaces and the right click to select the named selection and rename it as inner surfaces external, defined as mesh interfaces later.
Return to the initial window. Double click the bottom's mesh. Enter into the meshing window.
Change the physical preference from mechanical to CFD. Click the update to generate the mesh model. Return to the initial window.
Drag the mesh component from component systems to the project schematic window. Name it as top. Right click geometry and click browse to import the top XT file.
Right click geometry and click new design modeler geometry to enter into the mesh design modeler window. Click generate to display the top model. Right click the lower surface and click the named selection to rename it as vented box lower.
Select the selection filter bodies. Right click the top model to select named selection and rename it as top. Select the selection filter faces.
Right click the upper surface and click the named selection to rename it as outlet. Return to the initial window. Double click top's mesh.
Enter into the meshing window. Change the physical preference from mechanical to CFD. Right click the mesh to select the sizing in the insert.
Select the selection filter bodies. Select the top model and the top 18 in element size. Click update.
Return to the initial window. Drag the mesh component from component systems to the project schematic window. Name it as pipe.
Import the pipe XT file by clicking geometry. Enter into the mesh design modeler window. The pipe model displays again by clicking generate.
Select the two end faces of the pipe and label them as inlet one and inlet two. The pipe by body selecting is labeled as pipe. All inner surfaces by box selecting are labeled as inner surfaces internal, defined as mesh interfaces later.
Return to the initial window. Double click pipe's mesh. Enter into the meshing window.
Change the physical preference from mechanical to CFD. The mesh model can be generated by clicking update. Return to the initial window.
Drag the simulation component to the project schematic window. Link three mesh components to simulation component and update to enter. Verify the quality of the mesh model.
Check whether the mesh has a negative volume. Select steady, relaxation factor, residual, and timescale factor. Select default values.
Enter into the setting interface of viscous model to select the K-epsilon model. Set the air material. Change the type of cell zone to fluid.
Convert the type of vented box upper, vented box lower, inner surfaces external, and inner surfaces internal from default wall to interface. Open the mesh interfaces and enter into the create/edit mesh interfaces window. Match inner surfaces external to inner surfaces internal.
Match vented box upper to vented box lower. Finally, the two mesh interfaces are created among the vented box and named interface one and interface two, respectively. Set the airflow velocities of all inlets as 8.9525 meters per second in the velocity inlet window.
Set the gauge pressure of outlet as zero in the pressure outlet window. Set the style of the solution initialization as standard initialization before initializing. Set number of iterations as 2000.
Click calculate to start the simulation and return to the initial window until the simulation end. Click the results. Enter into the CFD post window.
Click the icon of streamline in the toolbox. Select outlet in start from and backward in direction. Click apply to generate the internal flow diagram of the vented box.
Click the plane in location. Select ZX plane in method, and input value 0.6. Click apply to generate the plane 0.6 meters from the bottom surface.
Click the icon of contour in the toolbox. Select plane one in locations. Select velocity in variable.
Select local in range. Click apply to generate the velocity contour. Export the flow rate data for the plane generated above.
Acquire the standard deviation of the flow rate in Excel. Run the statistical analysis software. Click data and click generate in orthogonal design.
Enter pipe number into factor name and A in factor label. Click add and define values to set four levels for the number of pipes. Click continue and back to the generate orthogonal design window.
Enter hole number into factor name and B in factor label. Click add and define values to set four levels for the number of holes. Click continue and back to the generate orthogonal design window.
Enter cumulative number into factor name and C in factor label. Click add and define values to set four levels for the number of increments. Click continue and create new data file to generate 16 array samples.
Click variable view to select nominal in measure and input in role. Rename it as standard deviation times 100, 000. Repeat steps 1.1 to 2.5 with sample points above.
The resulting 16 standard deviations multiplied by 100, 000 are filled into the sample list for later optimization. Click analyze and click univariate in general linear model. Fill standard deviation times 100, 000 into dependent variable and fill pipe number, hole number, cumulative number, into fixed factors.
Click model and build terms. Change interaction to main effects. Fill A, B, C into model.
Click continue and back to the univariate window. Click EM means and fill A, B, C into display means for. Click continue and back to the univariate window.
Click okay and get the optimization result. The minimum value of the mean column in the table corresponds to the optimal variable. Double click on the table.
Enter into the pivot table window. Click edit and click bar in create graph to generate the histogram. As shown in figure four and figure five, the streamline of the later vented box is even messier than that of the former, due to the inner structure of the vented box.
As shown in figure six and figure seven, the flow rate inside the vented box, which is one of the models used for sensitivity analysis, is more uneven. In order to understand the streamline distribution inside the vented box more intuitively, the standard deviation is calculated by this formula. Table one shows the standard deviation of flow rates for the 10 groups of the vented box used for sensitivity analysis.
A large standard deviation represents a large difference between most flow rates and their mean flow rate. Thus, it can be seen that changing the internal structure of the vented box can change its internal flow and makes the streamline more reasonable. When designing the orthogonal experiment, there are three design variables in this article.
Each of these three variables has four levels. As shown in the table, 16 groups of experimental design points were obtained by orthogonal experimental design. The standard deviations are calculated.
In the end, the range analysis method is used as the optimization method for finding out optimal structure parameter combination. Figure eight shows the optimization result for the structural parameter about the number of pipes. From this, we can see that the minimum value is obtained when the number of pipes is 14.
Figure nine shows the optimization result for the structure parameter about the number of holes in the middle pipes. From this, we can see that the minimum value is obtained when the number of holes in the middle pipes is 14. Figure 10 shows the optimization result for the structural parameter about the number of each increment from the inside to the outside pipe.
From this, we can see that the minimum value is obtained when the number of each increment from the inside to the outside pipe is four. The above analysis shows that the optimal combination is pipe number 14, hole number 14, cumulative number four. For confirming the accuracy, the optimal case was analyzed.
Figures four and 11 show the streamline of the reference model versus the optimized model. Figures six and 12 show the flow velocity distribution inside the reference model versus the optimized model. Table three shows the comparison between the optimization model and the reference model.
It can be seen that the standard deviation calculated by the optimized model is lower compared to the standard deviation of the reference model. Table four shows the increase in the number of holes from four to six, with little change in standard deviation. In this paper, the internal environment of the vented box is improved by optimizing its structure, and the quality of its internal environment is measured by standard deviation.
The smaller the standard deviation, the more reasonable the airflow inside of the vented box, which indicates that the optimization method adopted in this work is effective and feasible.
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