Analysis of Boeing 737 MAX 9 Door Plug Accident

I follow aviation accidents pretty closely. For as long as I can remember, I have been fascinated with air and space travel and the associated risks. I even took a flying lesson at Teterboro airport 15 years ago or so, but I never got a chance to get certified.

The latest Boeing 737 Max 9 accident1 resulted in no loss of life, but the idea of a door, even a fake door, blowing out mid-flight warrants a little analysis. Some questions come to mind: How could this happen, why it happened, under what conditions (the proximal cause), and what could have happened under slightly different conditions? First, a warning: this is not an analysis of how the defect was introduced during manufacturing and assembly; I know nothing about that. This is an analysis of the conditions under which a loose door (I will call that, even though it was technically a door plug) could separate from the fuselage.

The Facts

On January 5, 2024, at 5:00 p.m. local time, Alaska Airlines Flight 1282 took off from Portland International Airport en route to Ontario, California. The sunset was at 5:25 p.m., so it was already fairly dark. When the aircraft reached approximately 16,300 feet (about 5,000 meters), the passengers heard a loud boom as the door blew out from the side of the aircraft.

Everyone on board survived, and the aircraft landed in Portland shortly after, but I am sure most passengers will have a different experience with air travel from that point forward. Many years ago, I was in a much less dangerous but scary landing that did not go as planned (no, not the runway miss with a fly-around, which I experienced twice and is not a big deal), but something more extreme. Since then, I have never been entirely comfortable during unexpected turbulence.

Conditions at 16,300 Feet and Above

The air temperature at this altitude is approximately 0 F (-17 C), and outside air pressure is about 54 kPa (kilo-Paskals) or about 8 PSI (pounds per square inch). For reference, sea-level air pressure is about 101 kPa or 15 PSI. This pressure is the column of air pushing on you as you stand on the ground, and by convention, this gives us yet another unit of pressure — 1 atm (atmosphere). Pressure is related to the number of air molecules available for each breath since the pressure is directly proportional to the density (this comes from the ideal gas law, which I will touch on later). At the cruising altitude of 33,000 feet (about 10,000 meters), the air temperature is about -50 (here, C and F units are close to each other), and air pressure is about 19.3 kPA.

At 8,000 feet and higher, there are not enough air molecules for most people to breathe, so modern aircraft are pressurized (sealed with cabin pressure controlled by the Environmental Control System).

Ideal gas law and what happens during the change in altitude

The fact the door blew out during the ascent was no accident, pardon the pun, and can be explained by the ideal gas law.

\[
pV = nRT
\]

The key quantities are Pressure \(p\), Volume \(V\), and Temperature \(T\). The other quantities are constants, so we can write:

\[
pV \propto T
\]

From the ideal gas law, pressure is inversely proportional to volume—as pressure decreases, the volume of gas increases, and vice versa. This makes some intuitive sense, as you can imagine what happens when you reduce the volume of a sealed container; the pressure inside goes up and vice versa.

When the pressure outside the sealed container rises, say during a submerging process, the walls experience an inward pressure due to a pressure differential, and when the outside pressure falls, say during an ascent, the wall experiences an outward pressure.

This is why, during a scuba lesson, you are told to keep your mouth open on the ascent so that expanding air doesn’t damage your lungs. For the same reason, when a flight attendant hands you a bag of chips at 30,000 feet, the bag appears inflated2.

This outward pressure on the fuselage caused the blowout, and we will now try to estimate how much pressure it took for the door to separate.

Computing the pressure on the fuselage

As a statistician, it always blows my mind how much we can learn from n=1 “experiments” if we are willing to bring some background knowledge, the ideal gas law in this case, to bear on the problem. Physicists would not be impressed, as to them, this is par for the course.

Anyway, back to our problem. To make the calculations, we need to make a couple of key assumptions, namely the pressure outside the aircraft as it climbs and the pressure inside the cabin. The drop in atmospheric pressure with altitude is well-known and follows an exponential decay according to the Barometric formula. This is the blue line in the following diagram.

The green and red horizontal bard indicate my error in assuming gradual cabin pressurization. The dashed line represents the altitude at the time of the accident.

The second assumption we need is the pressure inside the cabin. First, I assume that the target inside pressure is equivalent to about 6,000 feet (1,800 meters), which is at the lower end of the reported range. This type of pressurization balances the passengers’ comfort with the force that the fuselage has to withstand.

Second, I (erroneously) assumed that the pressure is gradually adjusted to reach the target at the cruising altitude of 33,000 feet, the green curve on the above diagram. I later learned that the target pressure is typically reached relatively quickly after takeoff and that the cabin pressure was most likely at its target at the time of the accident. This means that my green curve would drop much more quickly and remain flat for the rest of the flight. The green and red vertical bars at the height of the accident represent this error.

To compute the outward pressure on the fuselage (red), we take the difference between the two curves at 16,300 feet, which gives us 26 kPa, assuming the aircraft was pressurized to about 1,800 meters at the time of the accident. (If we incorrectly assume gradual pressurization, the pressure would be about 36 kPa.)

To make this more interpretable, we can compute how much force (in lb or kg-equivalent units) was applied to the door. We will assume the door is nearly rectangular with 72 x 34 inches (183 x 86 cm) dimensions, taken from a 737 manual. To compute the weight in pounds:

\[
W = P \cdot A \cdot 2.2/g
\]

P is the pressure in kPa, A is the area in square meters, and g is the gravitation constant. This turns out to be about 9,400 lb (4,300 kg), which I rounded to 9,000 lb in the diagram. At the cruising altitude of 10,000 meters, the weight on the door would be approximately 19,000 lb (~ 8,600 kg).

Conclusions

When the aircraft finally landed, the passengers were treated to the following view of the airfield.

The calculations can make us appreciate how much force every 1.6 square meters of fuselage must withstand, so constructing a well-pressurized cabin is an impressive engineering achievement, while forgetting to put a few screws into the door plug during a final assembly is a massive management oversight.

One can imagine a situation where the door was a bit more secure than it was (but still not fully installed) and that the separation would have happened at 33,000 feet instead of at 16,000. What then? I am not sure what the immediate depressurization at that altitude would do to a human body (if your mouth and nose are closed, your lungs may rupture; you may also suffer from decompression sickness; if you don’t put on the oxygen mask, you will suffocate), much less if the pilots could descend fast enough to avoid hypothermia and other pleasantries.

Up or Down

In June 2023, a submersible operated by OceanGate imploded near the remains of the Titanic. The Titanic is located at 12,500 feet (3,800 meters) under the sea, and the pressure at that depth is about 38,000 kPa, 380 times more than at the surface (~ 380 atmospheres). When the implosion was originally detected, the Coast Guard undertook a massive search and rescue operation. The rescue part was a fool’s errand — at this pressure, the passengers were instantly disintegrated.

  1. In 2019 and 2020, the 737 Max was involved in two accidents relating to the angle of attack sensors. ↩︎
  2. Why should the bag inflate inside the plane? That’s a good question, and this should be the clue that the cabin’s pressure is not kept at sea level but adjusted downward as the plane climbs. ↩︎

Brighter days ahead

As the dark and frustrating 2020 is winding down, I feel incredibly optimistic about 2021 and beyond. Part of it is my entrepreneurial nature that requires it and part of it is a number of recent developments that bring me hope and I love drinking hope for breakfast.

A number of recent readouts from SARS-CoV-2 trials, particularly those from Pfizer and Moderna, which use a novel mRNA platform, look efficacious and safe in the short-term. (Long-term safety can not be evaluated in rapid clinical trials but the FDA guidelines provide for long-term, post-licensure, safety monitoring.) As soon as these vaccines are made available to the general public and assuming no major safety issues are surfaced, I am getting vaccinated and resuming my pre-pandemic travel schedule.

In case you missed it, on May 30th we witnessed the first American manned space flight since 2011. I watched it in real-time that Saturday and then many more times with my son Andrei who just loves watching rockets being launched into space. Since then, watching launches on Saturdays became a Novik family tradition. Seeing Andrei’s eyes light up every time we do it, brings me an unreasonable amount of joy.

Two weeks ago I ordered my first Virtual Reality headset — Oculus Quest 2. I don’t love the Facebook login requirement but as a newcomer to the world of VR, I am completely blown away by how easily my senses are fooled and by the near-perfect rendering of 3D worlds. It is clear to me that VR is a major technological trend with ramifications far beyond gaming. I can’t wait to virtually sit in front of my family and friends all over the world and interact with them as if we are in the same room. The technology is not quite there to make the experience realistic but I have little doubt that it’s coming. (As a side note, the game Room in VR is nothing short of amazing.)

During the summer, we made a lot of progress at Generable with fitting large meta-analytic models for oncology drugs, and our abstract was accepted at SITC 2020, an immuno-oncology conference. This is a big deal for us as it represents the first publication outside of statistics journals. Most of the work was done by Jacqueline Buros and Krzysztof Sakrejda and is a culmination of our year-long research collaboration with AstraZeneca.

And if this is not enough, 2021 promises to be a much more sane US Federal Government with adults finally taking over and mitigating the Fifth Risk. No doubt countless problems remain (I don’t want to list them) but I am feeling lucky and optimistic.

2019 Predictions

Prediction is very difficult, especially if it’s about the future.

— Niels Bohr

2018 had turned the page and we are already completed approximately 0.27% of 2019. I don’t know about you but I feel like I am behind. So to procrastinate some more, here are my (silly) predictions for 2019.

  • Trump will remain president with P = 0.60. 2019 will no doubt be a tough year for Trump as the Mueller report will likely become public, but I am betting that Republicans will continue to support him and even though the impeachment in the house is quite likely, the removal from office is not so certain.
  • The market (SP500) will continue to be volatile with the VIX staying well above its historic average (~11) for most of the year with P = 0.70. If we are to believe the model, there is about 90% chance that SPX will be between 3,200 and 2,000 by the end of April or about 45% chance that it will be below its current level and above 2,000. I am more pessimistic and I will give it P = 0.60 that it will be below the current level of 2,500 by April.
SP500 Model Based Price Distribution
  • The UK will not exit the EU (no Brexit) with P = 0.60. This is purely based on my conversation with someone who lives in the EU and spends a lot of time analyzing European economies.
  • I recently bought some cryptocurrency (a tiny amount of BTC and ETH) so I can keep myself informed and also because everyone was aggressively selling. I am pretty bullish on crypto longer term, but less certain about the current crop of offerings, although BTC proved to be very resilient. My prediction for 2019 is that BTC will not recover and will stay under its highs with P = 0.90.
  • We will not find a cure for any cancers with P = 0.80, which is a reversal from my last year’s prediction, and the one I am hoping to lose. I like where the cancer therapies are going, but our understanding of the mechanism is still quite weak, the methods we use to evaluate their effectiveness are quite poor (but getting better), and I am not holding my breath for data mining technologies (also known as AI) making any breakthroughs in this space.
  • I selfishly hope that 2019 will be the year of Bayes. I would like to see more universities offering Bayesian courses at undergraduate and graduate levels (this one from Aki @ Aalto looks amazing, for example), more companies getting started using sound probabilistic approaches, and FDA and EMA moving closer to embracing the Bayesian paradigm (we are rooting for you, Frank). I have no idea how to measure this, so no specific predictions here.

How did I do on my 2018 predictions

On 1 Jan 2018, I made the following entry into my journal

  • Will Trump still be president? Yes. (P = 80%)
  • Will Mueller team link Russia to Trump: a) To Trump campaign yes (P = 60%); b) to Trump No (P = 70%)
  • Will Crypto continue to rise? Yes. (P = 60%)
  • Will the stock market end its rise? No. (P = 55%)
  • Will Republicans lose control of the house in November? Yes. (P = 75%)
  • Will there be a war with North Korea? No. (P = 95%)
  • Will the New York Times go out of business? No. (P = 85%)
  • Will we cure one specific type of cancer? Yes. (P = 60%)
  • Will there be at least one Bayesian-based company that will raise Series B? (P = 70%)

I also said that I would compute my gain/loss using a hypothetical payoff function: \(100*\text{log}(2p) \) if I am right and \(100*\text{log}(2 * (1-p)) \) if I am wrong, where p is the probability I assign to the event occurring. We could use any base for a log but base 2 is natural as it compensates at the notional value ($100) if the bet is made with probability 1. I will describe why this particular payoff function makes sense in another post. (The tacit assumption here is that I would have been able to find a counterparty for each one of these bets, which is debatable.)

  • Trump is still president: \(100*\text{log2}(2*0.80) = 68\)
  • Mueller linked Trump campaign to Russia. The word link was not defined. I think it is reasonable to assume that the link had been established, but I could see how if my counterparty was a strong Trump supported, they could dispute this claim. Anyway: \(100*\text{log2}(2*0.60) = 26\)
  • Mueller linked Trump to Russia. Same as above in terms of the likelihood of it being contested, but think I lost this bet: \(100*\text{log2}(2*0.30) = -74\)
  • Crypto did not continue to rise: \(100*\text{log2}(2*0.40) = -32\)
  • Stock market ended its rise: \(100*\text{log2}(2*0.45) = -15\)
  • Republicans lost control of the house in November: \(100*\text{log2}(2*0.75) = 58\)
  • Thankfully, there is no war with North Korea: \(100*\text{log2}(2*0.95) = 93\)
  • New York Times is still in business: \(100*\text{log2}(2*0.85) = 76\)
  • I am not sure what made me so optimimistic regarding the cure for one type of cancer. Currently, the most promising cancer therapied are PD-1/PD-L1 immune checkpoint inhibitors and there have been documented cases for people who become cancer-free after being treated with one of these drugs, but I think it would be too generous to say that we have cured one type of cancer. Perhaps more impressively, Luxturna will cure your blindness with one shot to each eye if a) you have a rare form of blindness that this drug targets and b) you have $850,000 to spend. \(100*\text{log2}(2*0.40) = -32\)
  • There were a few startups based on the Bayesian paradigm and Gamalon came close with a $20M Series A round, but none raised Series B to my knowledge: \(100*\text{log2}(2*0.30) = -74\)

To summarize, I am up $94. Is this good or bad? It depends. A good forecaster is well-calibrated and we do not enough here to compute my calibration. The second condition is that for the same level of calibration we prefer a forecaster that predicts with higher certainty, a concept known as sharpness. Check out this paper if you are curious.