To ensure that fruit stays fresh after harvest, it must be cooled evenly during transport. In practice, however, this is not so easy, because a fully loaded refrigerated truck is a complex system: Cool air must circulate between tightly stacked cartons and pallets. How well this works depends on the airflow velocity, the packaging, and the arrangement of the pallets. Often, however, the temperature is monitored using only a single sensor. While this sensor records a temperature curve over the entire trip, it measures only at its specific location. Empa researchers therefore aim to use a virtual refrigerated truck to visualize potential hot spots and their impact on the transported goods. “Our digital twin simulates air circulation and heat transfer throughout the entire cargo compartment. This allows us to estimate the temperatures at various points within the load,” says Empa researcher Elisabeth Tobler.


A virtual refrigerated truck

For instance, the Empa researchers digitally recreated a refrigerated truck loaded with oranges and lemons. Their physics-based model calculates how the cooled air flows through the different types of packaging and how heat is transferred between the air and the cargo in the process. It also takes into account the temperatures of the fruit at the start of the trip as well as the small amount of heat they generate themselves through respiration. To test the digital twin of the refrigerated truck, the researchers fed it with real-world data on the cargo, the initial temperatures, and the refrigerated air during the trip. They used a commercial shipment from Nafplion, Greece, to Pratteln, Switzerland, for this purpose. The 31 pallets of oranges and lemons were pre-cooled for about 24 hours and then transported for four days at a temperature of four to six degrees.

The researchers then compared the temperatures calculated by the digital twin with the actual measurements. During the fruit’s packaging, they had placed a total of 108 sensors at various points throughout the cargo. 97 of these sensors recorded the fruit temperatures, while the remaining ones measured, among other things, the air temperature as well as the supply and return air of the refrigeration unit. “This was a challenging test for our digital twin,” says Tobler. “Not all the fruit had been cooled down evenly at the start, plus the oranges and lemons were in different types of packaging. Nevertheless, the model was able to accurately reproduce the key temperature trends at most locations.”


Warm pallets affect the load

However, the measurements also revealed some discrepancies. During the trip, the oranges in the middle of the truck warmed up more than the Empa model had predicted: Their temperature rose from about 7.7 to as high as 10.8 degrees, even though the cooling air was significantly colder. “We suspected that, in addition to the monitored pallets, there were insufficiently pre-cooled pallets that had not been equipped with sensors,” explains Tobler.

In a second calculation, the researchers therefore increased their assumed initial temperature from about 5.4 to 10 degrees. As a result, the simulated temperature trend matched the actual measurements much more closely. “In the simulation, we were able to track how heat was transferred from one pallet to the next and carried further by the airflow. The model thus supported our hypothesis,” said the Empa researcher.

It also became apparent that a truck’s refrigeration system has only limited capacity to cool down fruit that is (too) warm inside a densely loaded pallet. Refrigeration units are primarily designed to maintain the temperature of pre-cooled goods. If fruit is loaded too warm, it also affects neighboring pallets during transport.

Packaging also plays a crucial role. In the shipment studied, the lemon crates had ventilation openings on several sides, while the short sides of the orange cartons were closed. “When it comes to packaging, it’s not just about how it looks or how sturdy it is,” says Tobler. “It must also allow cool air to reach the produce. It’s equally important that the pallets are aligned evenly and that the openings aren’t blocked by adjacent boxes.”


A view of the entire load

The digital twin can initially be used as a simulation tool. Logistics companies and producers could use it, for example, to examine how new packaging, a changed pallet arrangement, or air circulation affects temperature distribution. According to Elisabeth Tobler, companies with standardized shipments that regularly use the same trucks, packaging, and loading patterns would benefit particularly.

In the future, the model should also complement monitoring systems. To do this, it will be linked to real-time sensor data from an ongoing shipment. The sensor provides the connection to reality, while the model uses this data to deduce the conditions throughout the entire cargo area. “Much of the necessary data is already being collected. The key is to bring it together and use it in the right place at the right time,” says Tobler. This would allow importers to identify, even before a shipment arrives, which pallets were exposed to higher temperatures and should be inspected more closely. Goods with a shorter expected shelf life could be brought to market earlier in a targeted manner.

“In the long term, the goal is data-driven monitoring of the supply chain,” adds Empa researcher Thijs Defraeye. “The digital twin is not only intended to display temperatures but also to help companies manage their shipments and the handling of incoming goods more effectively, thereby reducing food waste.”