The entry probe of the Galileo mission to Jupiter entered the planet's atmosphere in 1995 in fiery fashion. As the probe descended from Mach 50 to Mach 1 and generated enough heat to cause plasma reactions on its surface, it relayed data about the burning of its heat shield that differed from the effects predicted in fluid dynamics models. New work examines what might have caused such a discrepancy.

Researchers at the Universidade de Lisboa and the University of Illinois at Urbana-Champaign report their findings from new fluid radiative dynamics models using data transmitted from the of Galileo's 30-second entry. The paper, published in Physics of Fluids, from AIP Publishing, employs new computational techniques developed in the nearly 25 years since the mission.

"Early simulations for the probe design were conducted in the 1980s," said Mario Lino da Silva, an author on the paper. "There are some things we can do in 2019, because we have the computational power, new devices, new theories and new data."

Galileo's probe entered Jupiter's gravity traveling 47.4 kilometers per second, making it one of the fastest man-made objects ever. The fireball caused by the descent warmed the carbon phenolic heat shield to temperatures hotter than the sun's surface.

Data from the probe revealed the rim of the heat shield burned significantly more than even today's models would predict, measured by what is called the recession rate.

"The fireball is a kind of soup where a lot of things happen at the same time," he said. "One problem with modeling is that there are many sources of uncertainty and only one observed parameter, the heat shield recession rate."

The group recalculated features of the hydrogen-helium mixture the probe passed through, such as viscosity, thermal conductivity and mass diffusion, and found the oft-cited Wilke/Blottner/Eucken transport model failed to accurately model interactions between hydrogen and helium molecules.

They found the radiative heating properties of hydrogen molecules played a significant role in the additional heating the probe's heat shield experienced.

"The built-in heat shield engineering margins actually saved the spacecraft," Lino da Silva said.

Lino da Silva hopes the work helps improve future spacecraft design, including upcoming projects to explore Neptune that will likely not reach their destinations until after he has retired.

"In a way, it's like building cathedrals or the pyramids," he said. "You don't get to see the work when it's finished."

Lino da Silva next looks to validate some of the simulated findings by reproducing similar conditions in a shock-tube facility tailored for reproducing high-speed flows.

Research Report: "Computational fluid radiative dynamics of the Galileo Jupiter entry"

Related Links
American Institute of Physics
Space Technology News - Applications and Research

Tweet

Thanks for being here; We need your help. The Space Media Network continues to grow but revenues have never been harder to maintain. With the rise of Ad Blockers, and Facebook - our traditional revenue sources via quality network advertising continues to decline. And unlike so many other news sites, we don't have a paywall - with those annoying usernames and passwords. Our news coverage takes time and effort to publish 365 days a year. If you find our news sites informative and useful then please consider becoming a regular supporter or for now make a one off contribution.
SpaceMediaNetwork Contributor $5 Billed Once credit card or paypal		SpaceMediaNetwork Monthly Supporter $5 Billed Monthly paypal only

SwRI, international team use deep learning to create virtual 'super instrument'
San Antonio TX (SPX) Oct 08, 2019
A study co-written by a Southwest Research Institute scientist describes a new algorithm that combines the capabilities of two spacecraft instruments, which could result in lower cost and higher efficiency space missions. The virtual "super instrument," is a computer algorithm that utilizes deep learning to analyze ultraviolet images of the Sun, taken by NASA's Solar Dynamics Observatory, and measure the energy that the Sun emits as ultraviolet light. "Deep learning is an emerging capability that ... read more