Written by Megan Jenkins on June 19, 2019
January 17th, 2019
approx reading time
Imagine a world where you are limited to food that is grown locally. Until the late 1800s—before the invention of the refrigerator truck—products couldn’t travel far before spoiling. Sophistication is an integral part of today’s transport refrigeration units, which are shipping over 36 million loads of refrigerated products annually, worldwide. To preserve perishable goods on the long haul, companies in the pharmaceutical, medical, and food industries are increasingly relying on the “cold chain”—a temperature-controlled logistical supply chain. To ensure that the cold chain is not interrupted, a reliable and secure insulated shipping box is indispensable.
The design of any product is a highly complex process, and designing refrigeration units is a good example of that. In order to keep the cold chain uninterrupted, the insulated shipping box design needs to satisfy multiple objectives, requirements, and constraints. In the traditional design process, the only way to ensure the durability of such a product is to perform a high number of design iterations until all criteria are met. That means a high number of physical prototypes and a time-consuming and expensive physical testing process.
In addition to the number of design iterations, the stage at which design changes need to be implemented is equally important; the earlier in the overall process, the cheaper a design change can be realized. This drastically narrows down the scope of possible design changes, making only small, incremental design modifications possible at a later stage.
Of course, design iterations and physical testing cannot (and should not) be entirely eliminated from the product design process. However, with computer-aided engineering (CAE) the days, weeks or months of physical testing are replaced with hours or sometimes even minutes of a simulation run.
So how exactly would that apply to the engineering problem at hand? Here are a few compelling reasons to use simulation in the design of an insulated shipping box and predict its performance:
The benefits listed above beg the question: Why aren’t all designers using simulation yet? Several barriers have been preventing a more widespread adoption of simulation tools by engineers and designers—and here’s how SimScale is aiming to challenge this status quo:
Cold chain systems are crucial to the growth of global trade in perishable products and the worldwide availability of food and health supplies. Global losses in the food industry due to poorly designed cold chain systems total more than $750 billion annually . These losses primarily result from lack of proper facilities, improper food safety handling procedures, and insufficient training for that personnel working in the cold chain. The World Economic Forum lists food crises as fourth in its ranking of the top global risks for the next 10 years , and globally, billions of dollars are spent on improving agricultural processes to create higher food yields, but the fact that nearly half of all food never makes it to a consumer’s plate is largely ignored .
Let’s consider how simulation can help engineers tackle this problem by improving the insulated shipping box design for the cold chain.
The purpose of this project is to perform an FEA (finite element analysis) thermal simulation on an insulated shipping box, with no conjugate heat transfer analysis necessary. We will also showcase the capabilities of SimScale with respect to heat transfer and thermal management.
The simulation project “Thermal Packaging for Cold Chain” is part of our Public Projects Library and is freely available to view, copy, and modify. A realistic insulated shipping box design is considered for the simulation, as shown in the figure below:
The CAD geometry was designed using the Onshape platform and consists of:
In this insulated shipping box design, the temperature is passively controlled by pre-conditioned frozen (-20°C) and liquid (5°C) refrigerants in combination with an insulated polyurethane (PUR) foam container (with inner product box).
For the simulation setup, a nonlinear transient heat transfer analysis type is selected to compute the temperature distribution on the entire body. The detailed procedure for the setup is as follows:
|Component||Material||Density kg/m3||Specific heat [J/(kg K)]||Conductivity [W/(m K)]|
|Base, lid and inner Bbx||PUR||55.97 (adjusted)||1500||0.025|
|Refrigerant bricks at 5°C||Water||941.14||CSV file||CSV file|
|Refrigerant bricks at -20°C||Water||619.41||CSV file||CSV file|
The simulation results are visualized below:
The change in temperature for three internal payload points:
From the simulation results, we can see that the temperature monitored inside the payload area remained between 2°C and 8°C, which was the main goal of this analysis.
If we analyze the final temperatures at each point, we see that they are really close, especially the top and bottom ones. This is due to the design. We have a -20°C refrigerant brick between the inner box and the base on the bottom. As the side -20°C refrigerants don’t reach the bottom of the inner box, it is important to have this -20°C brick as a barrier for the rising temperature from the bottom. We don’t need it on the top as the -20°C refrigerants surround the top side of the inner box.
The minimum and final values of the temperature are shown in the table below.
|Point Data||Min. temperature [K]||Temperature at the end [K]|
This shows that this insulated shipping box design holds the temperature inside the payload area between 2°C to 8°C for the entire time profile of 96 hs. A new analysis can be done for a winter temperature profile simply by uploading a new temperature curve.
The simulation has shown that, with the given 96 hours profile, the temperature profile of an insulated shipping box can be accurately simulated using a conduction approach. This case study illustrated how simulation can be useful for a quick evaluation of the initial design concepts, as well as subsequent design optimization. Predictive modeling can save both time and money on insulated shipping box design and development, and deliver the final product to the market more quickly.
In this case, we investigated making use of FEA to design thermal packaging for cold chain systems—but this is just one example of how designers and engineers can apply simulation tools in the product development process. The SimScale Public Projects Library has a wide selection of templates simulating various applications of FEA and thermal analysis across many industries, including automotive and transportation, manufacturing, and industrial equipment.
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