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Alex Pelkey

Reliable Thermal Protection Systems for Planetary Missions

Human Space Exploration
Examples of methods used to characterize the microscale architecture of thermal protection system materials and quantifying manufacturing variabilities through stochastic modeling.
Examples of methods used to characterize the microscale architecture of thermal protection system materials and quantifying manufacturing variabilities through stochastic modeling: a multitude of primary structures obtained using XRCT to obtain the primary dataset (left), extraction of volumes from the primary structure to obtain the probability distribution functions (middle), and a surface mesh generated on the extracted volumes (right). Luis Chacon and Savio Poovathingal, University of Kentucky

To protect a spacecraft, crew, and payload during atmospheric entry, vehicles are equipped with a thermal protection system (TPS), which includes the heat shield, back shell, and supporting structure. Since TPS is essential to many of NASA’s highest priority space exploration missions, the research team at the University of Kentucky is aiming to improve the reliability of computational modeling of these systems during the design phase of a vehicle. A significant limitation with current capabilities is that the uncertainties—which stem from material variability caused during manufacturing, availability of raw materials, and other mitigating circumstances—are so large that it is not always possible to provide accurate analyses in time to meet mission milestones. The Kentucky team has developed new TPS material modeling methods using a multi-scale approach, where the smallest scale directly informs the largest ones, allowing a holistic evaluation of the TPS. In this approach, detailed atomic-scale information is first fed to the micro-scale, then the scatter at the micro-scale is captured through probability distribution functions that are fed to the macro-scale, where a stochastic modeling approach is used to predict TPS behavior. The stochastic approach—where a random probability distribution or pattern is used—provides nominal behavior and helps capture material response caused by variabilities and other unexpected scenarios, allowing researchers across NASA a pathway for robust quantification of TPS reliability.

The computing environment at the NASA Advanced Supercomputing facility is amazing. Once I set my automated workflow, the computations are done at breakneck speed. I love it.

Luis Chacon,
Graduate student researcher at the University of Kentucky

Quick Facts

  • The new model developed to predict material behavior using real material microstructures is one of the most complete picture in TPS design. To predict TPS behavior, the team runs thousands of simulations over material microstructures to compute material properties and performs aero-thermal-chemical-structural simulations on the microstructure to quantify TPS reliability. Most of the simulations are run on the Pleiades supercomputer at the NASA Advanced Supercomputing (NAS) facility at NASA’s Ames Research Center, using 1.2 million CPU core-hours, with a current run rate of 200,000 CPU hours every month.
  • The team quantified the potential variabilities in TPS materials arising from inconsistencies in manufacturing. Even if material billets—solid bars or rods—are manufactured through different processes or contain defects, the models can accurately predict TPS performance during atmospheric entry.
  • The ability to accurately model TPS performance regardless of manufacturing processes will significantly reduce lead time in qualifying TPS materials for planetary entry missions.
  • The researchers are working to develop an optimized workflow to select and qualify an optimal TPS material architecture for a given mission. Therefore, instead of choosing a mission’s vehicle design based on existing TPS technology, they want to be able design a TPS to meet the unique needs of a planetary mission.

Researchers

  • Savio Poovathingal, University of Kentucky
  • Alexandre Martin, University of Kentucky

More Information

All projects

Optimizing Aircraft Performance for Enhanced Efficiency and Reduced Environmental Impact
Brandon M. Lowe Scale-Resolving Methods for Accurately Predicting the Aerodynamics of In-Flight Icing
David A. Craig Penner Enabling Sky-High Commutes: Simulating NASA’s Tilt-Wing Air Taxi Concept
David Garcia Perez Scale-Resolving Simulations for Jet Noise Prediction
Olivia Martin Supercomputing in Supersonics: Analyzing Noise Predictions from High-Speed Aircraft
Marian Nemec Parallel Geometry-Based Mesh Adaptation for Supersonic Jet Acoustic Analysis
Chase Ashby
Future-Proof Performance Portability for Weather and Climate Models
Florian Deconinck Machine Learning Enhancements to NASA Weather and Climate Modeling
Katherine H. Breen AI Foundation Models for Science
Manil Maskey High-Resolution Land Hydrology Modeling at NASA
Jim Geiger Forecasting Extreme Weather at Local Scales with the Global NASA GEOS Model
Gary Partyka Prithvi WxC: Foundation Model for Weather and Climate
Sujit Roy Foundation Models for Generalist Geospatial Artificial Intelligence
Amy Lin
NASA-GPT: Searching the Entire NASA Technical Reports Server Using AI
Derek Dalle NASA’s OCSDO: Creating a Culture of Innovation and Collaboration
Andrew Mitchell Data-Driven Movies from the NASA Scientific Visualization Studio
AJ Christensen NASA's Newest Hybrid-Chip Systems
Reid Ormseth, Brian Roth Inspire – Next-Generation HPC at NASA Goddard
Robert Budden, Jonathan Mills HEC: Meeting Today’s Most Challenging Problems and Preparing for the Future
William Thigpen
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Ronald Caplan To Infinity and Beyond! NASA Plunges into a Black Hole
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Spyros Kasapis Unveiling Neutron Stars: Simulations and AI Insights into Cosmic Emissions
Constantinos Kalapotharakos, Greg Olmschenk The Sun in Action! 3D Realistic Modeling from Tiny to Large Scales
Irina Kitiashvili
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Laura Carriere How NASA Uses Filmmaking Tools for Scientific Data Visualization
AJ Christensen Introduction to NASA and Early Career Pathways
Matt Bruzek Computational Methods for Evaluating Transonic Aircraft Buffet
Daniel Maldonado Supercomputing in Supersonics: Quiet Supersonic Flight
Marian Nemec Becoming a NASA Intern and Exploring the World of CFD Simulations
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Dave Chau Managing and Patching Multiple Architectures at NASA
Don Story Designing Ansible for Large Scale Environments
Alex Pelkey