microgravity in parabolic flight

For if one considers an observer in free fall, e.g. from the roof of a house, there exists for him during his fall no gravitational field

Albert Einstein (1907, “happiest thought of my life”)

mid September 2021 at the Dübendorf Swiss Airforce field, close to Zürich: After almost 2 years of waiting due to the covid-19 pandemic and its effects on aerospace activities, I was very happy to finally stand on the tarmac right next to the Zero-G Airbus A310. The aircraft has modified controls and therefore is released in the “experimental” category. With a total of three pilots who independently operate pitch, roll and throttle during parabolic flight, ballistic parabolas can be flown with high precision. In fact, the plane is routinely used by ESA for training missions, for international research and, capacity permitting, also for chartered flights.

This flight was mentored by ESA senior astronaut Jean-François Clervoy (that lucky guy flew on board the Space Shuttle for 3 times and took part in the EVAs for in-flight repair of the Hubble Space Telescope). We flew a total of 14 zero-g parabolas and 2 additional parabolas with marsian (3.7 m/s2) and lunar (1.6 m/s2) gravity respectively.

J-F. Clervoy and me

The flight track went from Zürich via Grenoble to the Côte d’Azur and to a position abeam the northern tip of Sardinia – and back. The crew kept everyone well informed about the sequence of maneuvers: Countdown to an initial raise in pitch angle (“pull-up”, during which we experience roughly 2g of “heaviness”) … 10° … then climbing steeper 30° … up to (a pilot’s horror) 50° nose-up attitude:

“Injection !”, engine noise is reducing to idle and here we are: Weightless for about 25 seconds – what a beautiful sensation, I could not get enough from it !

Eventually, the voice from the cockpit is counting backwards for “pull-out”, smashing everyone back to that side of the aircraft’s interior designated by the gravitational field of the Earth to be the floor rather than its ceiling. For the first few parabolas, I had to admit to myself that all prior mental or physical resolutions on how to best deal with weightlessness had to be thrown overboard and experience had to be gathered by practice.

inside the Zero-G Airbus A310

Just a few notes:

  • The vestibular system is unable to work reliably without the usual gravitational acceleration from “below”. It’s absence however is masked by all kind of other accelerations you pick up by either pushing yourself against the aircraft interior or absorb momentum from other people bumping their body parts into yours. With only visual reference remaining in a nearly all-white cabin, after a few pirouettes, it’s really hard to evaluate the true orientation of one’s body.
  • Once a parabola comes to it’s end, it is vital to make sure you have your feet where your feet are supposed to be when gravity is “turned back on” again. I remember one incident when I was floating right behind the back of another person, taking a glimpse through his pair of legs, eventually finding my head close to his ankles when I heard the countdown towards pull-out. The initial thought of: “I am right, the other guy must be wrong” quickly turned into despair realizing that the other person was one of the cabin safety staff who was correctly “standing” towards firm ground – while I was set to land head first. Eventually, he gave me a helping hand to hurdle me around before it was too late.
  • While floating in the cabin, it is quite difficult to keep a stable position in order to take pictures. The laws of physics, specifically the conservation of (angular) momentum, are playing constant games with you. If you abruptly stretch your arm in a certain direction, i.e. to reach out for a hold or to do an intuitive balance movement, you are actually propelled away into the opposite direction. As a consequence, for the first few parabolas I was floundering around quite helplessly. Eventually, I learned to smoothen my movements and adopt a more “zen” attitude towards the zero-g environment.

two zero-g scenes filmed by myself:

Knowing the sensation of jumping from a springboard, or from turbulence aboard an aircraft, I was prepared to encounter weightlessness as an overwhelming or perhaps fearful sensation of falling. But it wasn’t at all like this ! The effect sets in very smoothly without the nauseating feeling of falling (Note: We could not look outside. For the initial half of the zero-g phase, the aircraft was actually still climbing on a ballistic trajectory for approx. 850m, post apogee falling for the same distance prior to “pull-out”). What prevails is the sensation of feeling light as a feather while still being conscious of one’s own mass.

Finally, here’s a reference to a nice explanatory video from a different flight and crew:

Insights into the complex flying technique, Video by Tom Scott

BirdyIO: bird nesting IoT

In 2020, while watching small birds nesting in my garden, I decided that I wanted to learn more – from a time series data mining point of view – about their apparent restless activity. After some initial brainstorming I realized I got into something really cool relating to digital electronics, sensors and signal processing & storage. Here’s some implementation detail:

+ Birdhouse fitted with dual-channel, pulsed-IR (38kHz) barriers
+ Atmega 328 µ-Controller acting as pulse source for the IR LEDs
+ Postgres database for long term event storage
+ NodeMCU ESP8266-12E µ-Controller as master:
  • fifo-type binary event buffer for 2 event channels (IR light barriers)
  • regular-expression style evaluation of event pattern + pattern duration (see table below):
  • detection of direction: (out > in) vs. (in > out) and depth of a connected action:
    • show: penetrate, retract from single barrier
    • peek: penetrate, retract through two barriers
    • look: penetrate, retract through two barriers, freeing initial one
    • walk: penetrate, retract through two barriers, passing both ones
  • anti-flicker filtering, suppressing repeated state alternations < 10ms
  • detection loop frequency achieved: ~ 800Hz
  • NTP time sync
  • periodic sensor self-checks on IR barrier function
  • birdhouse connected to wifi home network
  • local buffering of up to 200 qualified events in a transactional log
  • birdhouse webservice (json) endpoint to deliver event logs to a backend
  • Server backend (python) polling BirdyIO endpoint for new events

Event table

how this looks like in practice

full in/out transitions and other events in the 2020 nesting season

sample JSON message delivered by BirdyIO endpoint
directed in>out / out>in transitions (blue) and other activities (red) in the 2020 season

AirStation: environmental monitoring

Wifi-enabled weather stations are available in a great variety, usually covering: temperature, atmosperic pressure and relative humidity. Through my own interest in environmental factors related to covid-19 and the possible impact on the 2020 lockdown on air quality, I decided to use my existing knowledge in IoT/digital electronics to build a comprehensive, multi-sensor data source for long term monitoring and time series analysis

current project state:

After a few month of test operations in late 2021, I experiences frequent dropouts and system reboots, specifically in early morning low temp & moisture conditions. I concluded: NodeMCU needs improved sealing to withstand adverse weather conditions, actual sensors appear tolerant to moisture and temperature variations but MCU needs efficient sealing. Moreover, O3 sensor calibration requires temperature and rH% stability over several days of sample time. This needs to be repeated in a controlled environment.

implementation details:

  • NodeMCU ESP8266-12E µController with sensors:
    • BME280, digital, temperature, pressure and rel. humidity
    • SDS011, digital + laser-based, 2.5µm/10µm particulate matter (PM)
    • Winsen MQ131, analog, low-concentration O3 sensor
  • Custom library myTaskScheduler for asynchronous scheduling of data sampling (this library is also central to the StratoExplorer project).
  • µC acting as WiFi client in local network
  • OTA (over the air) updating of NodeMCU code
  • JSON endpoint to deliver sensor data:
    • average + standard deviation in sample interval
    • min/max values in sample interval
    • NTP based time information
    • sensor health status
  • Normal atmospheric pressure (QNH, hPa) corrected for temperature and humidity according to DWD standards
  • O3 sensor corrected for temperature and humidity
  • O3 sensor (long term) calibration mode
  • Backend process (python, cronjob) polling AirStation endpoint for data
  • Postgres database for long term storage and data analysis
prototype assembly (from left to right) of O3 / laser-PM / T-P-rH sensors and NodeMCU ESP8266-12E