Creative Ways to Use a Digital Twin Visualization

A digital twin visualization is a highly realistic recreation of a physical asset. Many designers and engineers use them in early product development phases, including testing various product versions before trying them in real life. Another possibility is to use the digital twin to proactively find and solve problems, thereby reducing future production costs. However, there are also some particularly forward-thinking ways to rely on digital twin visualizations. Here are some of them. 

Increase Interest in Bike-Sharing Programs

Biking is a widely recommended way for people to reduce their carbon footprints. However, not everyone has the resources to purchase bikes. Plus, some individuals don’t want to buy them until they know they’ll enjoy cycling enough to stick with it for the long term. Bike-sharing programs tackle both these issues and more. Participants can often pay flat fees for unlimited use of bicycles owned by a local authority. That makes the option affordable and easy for people to try without long commitments. 

Such arrangements can work well, but one possible downside is that someone could go to pick up a bike at a specific location and find there are no more left to use. The people organizing the bike-sharing program also need trustworthy data about which areas of a town or city are most likely to get the highest adoption rates. 

Once they have that information, bike-share participants have the best chances of arriving at a location to pick up a bicycle and finding there are plenty available. 

Using a Digital Twin to Boost Bike Availability

A team at The Norwegian University of Science and Technology was well aware that one of the biggest problems with bike-sharing programs is providing enough bicycles for users wherever they are. The main issue is that the situation is so dynamic, particularly as people move around the city and go about their business. 

Relatedly, people often pick up the bikes from different locations than where they’ll drop them off later. Things like weather or city events can also change demand, so usage may differ from week to week. 

The group attempted to solve these issues by building an optimization model and using a digital twin to test various possibilities. They believed this approach would make workers 30% more efficient in moving bikes between different locations as needed. They also anticipated a 20% bike life span improvement because of a predictive maintenance aspect. 

The optimization model provides a decision-support model to the bike-sharing program operators. It receives large amounts of current data and uses it to recommend the best actions based on present conditions. Then, the digital twin allowed testing of different suggested options to see which would get the best results. 

For example, a digital twin simulation indicated the optimization model could reduce the typical problems by 41% compared to an approach where workers don’t move bikes between locations. Plus, the model would reduce problems by 24% compared to workers’ current methods.

Once the team improved their model, the simulations showed even more potential. 

Further work resulted in a tool people could use outside of the larger optimization model. It assigned a criticality score to specific areas, alerting individuals to the stations running low on bikes and which need replenishing the soonest. 

The researchers believe this work has created a resource-efficient system that responds to users’ changing needs. 

Improve Treatment for Hay Fever Patients

Medical facilities are perpetually busy places. Decision-makers must juggle numerous variables to ensure people have the care and other resources they need. A person could use a digital twin simulation when designing a hospital, discovering they can improve traffic flow by putting an entrance in a certain place. 

However, people are also experimenting with how digital twins could enhance patient care decisions. Millions of people worldwide suffer from hay fever, making the warmer months of the year miserable for many of them. Researchers built digital twins to determine what makes specific hay fever medications effective for some patients but not others. They also hoped a digital twin simulation could suggest the right times to give medicine to patients based on their symptoms. 

The team used single-cell RNA sequencing to learn about gene activity in thousands of white blood cells. They then stimulated the white blood cells with pollen to see how it changed the interactions between genes and cell types. All this data went into digital twin models of hay fever patients. 

One of the most significant findings was that multiple proteins and signaling cascades collectively played an important role in someone’s seasonal allergies. Relatedly, the scientists tracked several changes that occur across different disease phases. They also used a digital twin simulation to identify a critical protein. Tests showed that inhibiting it caused better symptom relief than a known allergy medication that addresses a different protein. 

Experiments also suggested this approach might help doctors make better decisions about which medications to give and when to people with other diseases associated with the immune response, such as inflammatory bowel disease. 

Reducing Food Waste

Global estimates of food waste indicate 14% happens between when the consumables get harvested and sold. Then, another 17% occurs in retail stores or once households discard what they use. These problems result in 8-10% of the world’s greenhouse gas emissions. How could a digital twin simulation solve these issues? 

Researchers focused on the food loss associated with unfavorable storage conditions as goods move along supply chains. They centered their work on citrus fruits since those are highly likely to spoil before they get to consumers’ plates. Once the team created digital twins, they used temperature data to track the variables that promote or detract from the fruits’ quality. 

Experiments monitored the temperature changes in 47 container loads of citrus fruits across 30 days in transit. The results showed that 50% of the shipments were outside the optimal shipment conditions during the trip. Some only had shelf lives of a few days on arrival. Digital twin visualization data can help people make the best tweaks to ensure the fruits arrive as fresh as possible. 

Digital twins also showed that getting the best results was more complex than refrigerating the goods. The specific temperature also makes a significant difference. Chilly conditions keep fruit flies at bay, but prolonged exposure could damage citrus fruits, making them unsellable. However, people can use a digital twin visualization to identify the optimal travel conditions for delicate fruits, increasing the probability of them arriving delicious and ready to sell. 

A Digital Twin Visualization Aids Planning

When it comes to planning things such as bike utilization, medication responses or how well foods will withstand shipments, guesswork usually falls short. Fortunately, these examples show that digital twin visualizations let people run various scenarios that help them make smarter, more strategic choices about matters that would otherwise be hard to determine.