Questions and Answers

1. What was the major objective of your project and what was your plan to achieve it? 

The pandemic raised urgent concerns about whether air travel is safe and by what means can we increase its safety level. Airlines, aircraft manufacturers, and passengers needed answers to how we can prevent or reduce the infection of healthy passengers by unidentified infectors. My project serves to offer potential solutions to this big problem. Over 2020, I successfully developed the method to accurately simulate the airflow and evaluate the infection risk in every corner of the cabin. After MSEF 2021,  I used scientific simulations to understand Covid-19 infection risk in aircraft cabins and propose possible measures and modifications that can be implemented to improve the safety of air travel under the pandemic of COVID-19. Moreover, I developed a function that can efficiently evaluate the safety level for passengers.

       a. Was that goal the result of any specific situation, experience, or problem you encountered?  

I came up with this objective as a result of my personal experience. When the pandemic started in March 2020, my high school announced that all courses would be moved online. On the flight home, I wore clothes that almost covered all of my body, including one surgical mask plus an N95 mask. Despite these precautions, my family and I felt nervous. In the cabin, passengers are in a crowded, closed environment, and some of them even did not wear masks. Talking to other international students and observing my surroundings, I realized that everybody felt this way. I realized that science might offer us a way to solve this problem. Fortunately, I had an opportunity to meet with my advisor who is an expert on aircraft cabin environment research. Hence, I decided to perform this research under his guidance.

       b. Were you trying to solve a problem, answer a question, or test a hypothesis?

The crucial problem I aim to solve in this project is the concern of the safety of air travel under the pandemic of COVID-19.

 

2. What were the major tasks you had to perform in order to complete your project?

There were five major tasks that I performed to complete my research project. Tasks one to three were completed during 2020 and presented in MSEF 2021, and tasks four and five were completed over the last year.

The first task was to accurately simulate the non-uniform airflow field and the pollutant concentration field inside the aircraft cabin, which has a complicated geometrical structure and boundary conditions including temperature and ventilation rate.

Based on the fields calculated from the previous task, the second task was to introduce a new term, the dilution ratio, to show how much the pollutant is diluted at a certain location compared to the source. The introduction of dilution ratio makes it possible to calculate the infection risk at different locations under different circumstances.

The third task was to combine the dilution ratio with the traditional risk assessment model, which in the past could be only applied in an environment with uniform concentration fields. This innovative model was then used to quantitatively assess the infection risks inside the aircraft cabin under different conditions and with varied locations of infection sources. 

Furthermore, using precise evaluations of infection risk, the project develops a series of improvement measures based on “Combining Normal and Epidemic into One Consideration”, and in turn maximizes the safety of the passengers. The improvements proposed are cost-effective and do not require drastic changes to cabin designs.

Finally, a convenient function that can quickly evaluate the infection risk based on the distance between the infected person and the susceptible person is developed.

 

       a. For teams, describe what each member worked on.

Not applicable.

 

3. What is new or novel about your project?

The first novel aspect of my project is its approach. I developed a model that is innovative in that it shows a deep and realistic understanding of the fields inside the complex space of the aircraft cabin. Previous models overly simplified the field inside the aircraft cabin to be uniform, making it impossible to distinguish the infection risk level when sitting close to an infector or away from him/her. In my model, the airfields inside are treated as non-uniform which is closer to reality. Moreover, the approach I used is new: it uses a clear physical parameter (dilution ratio) to provide non-uniform information for calculating the infection risks at different locations in the aircraft cabin.

The second novel aspect of my project is the modification. Based on the precise simulation of the airflow and pathogen distribution within the cabin I obtained during 2020, last year I analyzed designed the most economical measures to boost the safety level for the passengers. For example, the method of distributing a hat with an exhaust vent is highly flexible that can be organized according to the real situation. 

The third novel aspect of my project is the simplified function I derived last year that can quickly calculate the infection risk using the relative distance between the infected passenger and the susceptible passenger. Moreover, my novel function is able to consider the physical settings of the cabin including the overall ventilation rate.

       a. Is there some aspect of your project's objective, or how you achieved it that you haven't done before?

I started my entire project from scratch, learning new knowledge in the process. Before doing this project I self-studied ventilation in a room at the beginning of my sophomore year. The composition of the aircraft ventilation system and how it works were totally new to me. Hence, it took me several months to study and understand this field better, especially when I learned the fundamentals of fluid mechanics and the mechanisms of droplets or aerosol transmission in the air.

       b. Is your project's objective, or the way you implemented it, different from anything you have seen?

My project differed from the past research that I reviewed in that it used a more complex distribution model that better resembled reality, whereas the previous models used an overly simplified uniform distribution model. Therefore my project was able to make quantitative assessments of infection risk under non-uniform distribution inside the aircraft cabin possible. With a more accurate evaluation of the infection risk, the modification measures were designed based on the current situation and targeted directly to solve specific problems to reduce the infection risk. For the quick evaluation function, the previous function is only capable to evaluate the probability distribution of the pathogen based on the physical mechanism of the ejected droplets, but my novel function is not only able to calculate the infection risk directly but also able to consider other parameters like the cabin ventilation rate.

       c. If you believe your work to be unique in some way, what research have you done to confirm that it is?

To test the uniqueness of my project’s approach, I conducted a literature search on Google Scholar by using such keywords as “aircraft cabin”, “infection risk.” Through my literature review, I found that past research did not consider this problem to such a sophisticated extent, possibly because there had been no situations like Covid-19 to encourage such in-depth research. Ultimately, my simulation results about the fluid field and pollutant concentration field are consistent with the testing results from the aircraft cabin mockup. The accuracy of my simulations was proved to be promising. Moreover, simulations showed that the infection risk was efficiently reduced after implementing my improving measures.

 

4. What was the most challenging part of completing your project?

The most challenging part of completing this project was how to combine existing scientific knowledge with a new approach. As a high school student, most of the knowledge needed was new to me, so I had to grasp it within a short time. Meanwhile, there were also knowledge gaps that I needed to fill. For example, the traditional risk assessment model (Wells-Riley equation) is only applicable to a space whose air is uniformly mixed. This is clearly not the case in an aircraft cabin so I had to develop a new approach. Through this rigorous training, I gained a much better understanding of scientific research and truly enjoyed this process.

      a. What problems did you encounter, and how did you overcome them?

There were challenges in each of the three tasks I conducted.

For Task 1: I needed to make sure the simulation was consistent with the field conditions and basic physics. I spent several weeks figuring out how to model the thermal plume from humans and how to set the correct boundary conditions.

For Task 2: I came up with a new concept called the dilution ratio, based also on the physics of and the insight into the non-uniform nature of the cabin environment. This was key to solving the risk assessment in a non-uniform environment like an aircraft cabin.

For Task 3: After simulating the distribution of the infection risk through the interior of the cabin, I needed to calculate the infection risk for each passenger. However, it was inaccurate to use any single point to represent the infection risk for passengers. Thus, by reading papers about the human respiration range, I drew a box surrounding the oral-nasal region and calculated the average of infection risk in the space within the box to represent the risk of the corresponding passenger.

For Task 4: I needed to suggest rational ways for passengers to reduce the likelihood of infection. For instance, I could not increase the ventilation rate of the ventilation system. I could not ask people to breathe slower. However, I could suggest they wear facial masks and demonstrate how beneficial it was to both themselves and others.

For Task 5: To derive the quick-evaluation function, I need to modify the Wells-Riley equation. Through analyzing the droplets mechanism, the equation that describes the distribution of the probability of the existence of droplets can be obtained, but how to combine this probability with the Wells-Riley equation is hard since the equation itself utilizes quanta—a specific characteristic of diseases. Eventually, I compared the CFD simulation results under different scenarios with the risk calculated by the function, and I matched the data up by multiplying a factor to the probability after putting it into the Wells-Riley equation.

      b. What did you learn from overcoming these problems?

Through solving the above challenges, I learned how to conduct rigorous scientific research to solve pressing problems. First, grasping solid scientific knowledge and research skills was crucial. I also learned that existing knowledge might not be sufficient to solve novel problems, so I had to learn to develop new knowledge and fill in the gaps in the existing research. I realized that a successful researcher should always be ready to face new challenges and find ways to solve them.

 

5. If you were going to do this project again, are there any things you would you do differently the next time?

For the most part, I would do the project the same, because I learned a lot along my research path. I began with an initial study on relevant topics such as fluid dynamics, computational fluid dynamics (CFD) simulations, and aircraft ventilation systems. After getting familiar with basic knowledge, I started my project and ultimately got useful results. Later I analyzed my entire research process and felt what I did was actually a good way of mastering the skills of scientific research. 

One area I would improve on is that I would pay more attention to the organization and planning of my research, especially its challenging points. Since this my first formal science research project, I sometimes underestimated the difficulty of the project which caused mistakes. If I can do this project again, I would make sure to understand the functions in Fluent better, especially for their applied range before starting to simulate the fields. By doing so I wouldn’t have gotten an inaccurate result in the beginning, and have to spend a lot of time seeking ways to modify it.

 

6. Did working on this project give you any ideas for other projects? 

The project I thought of is the investigation of how particles interact with airflow. A better understanding of this problem will not only help us gain a better sense of the movement of particles but also understand the fate of virus-laden particles, which will allow us to better control the spread of infectious diseases in enclosed spaces and improve public safety accordingly. This problem arose when I was using the Lagrange method to calculate the flow field and see how particles are moving. I realized that many forces act on a single particle, which makes particles very complicated. Due to this complicated situation, I want to look for a simplification of or a more general solution for particle interactions with airflow. 

While there were more variants of COVID-19 that still act as a threat to human society, it is crucial to maintain a high level of accuracy while monitoring the infection risk for them. To achieve so, continuously investigating the quanta value for the new variants is important. As the only factor in the Wells-Riley equation that describes the infection characteristic of the disease, quanta is specific to its corresponding disease. Thus, utilizing a more accurate quanta value for the new variants can make the overall simulation results more precise. 

 

7. How did COVID-19 affect the completion of your project?

COVID-19 prevented me from doing the field test on the aircraft cabin mockup in my supervisor’s laboratory. After I worked on computer simulations, I planned to do a field test in the aircraft cabin mockup to verify the simulation results. I had prepared the lab skills that I need to conduct the tests. However, COVID-19 started to spread weeks before the day of the scheduled experiment, so I was not able to go to the university to do the field test. It was a pity, but I kept in close communication with my supervisor and his team. Later I received the experimental results and used them to verify my simulation model. By doing so, I ultimately made sure that my simulations were accurate and consistent with the field test results. Hence I found an improvisation that helped me get around the complications caused by COVID-19.

During the summer of 2021, I was originally planning to come to the US and work with my professor at the University of Colorado; however, the factors caused by the pandemic hindered my plan, so I had to work in Beijing and communicate with my mentor through the internet. When I first received 60+ research papers from my mentor for review, I was lost. Due to the time difference between my mentor and me, I had to some my confusions by myself. I was overwhelmed by the intricate terms in the beginning, but at the same time, I started to read more articles and papers to understand my confusion. In the end, I read much more papers than my mentor provided. I later developed a function that can quickly calculate the infection risk with knowledge ranging from physical characteristics of droplets, Wells-Riley equation, and statistics–all learned while reading papers.