NASA's Digital Transformation (DT) Initiative will dramatically enhance NASA's mission impact by reinventing mission and mission support processes, products, and capabilities, enabled by an innovation culture, digital-savvy workforce, and advanced digital technologies, building on a foundation of modern data management and IT security. This presentation, as part of a conference panel, provides an overview of NASA's DT Initiative, and describes how the Initiative supports an "antidisciplinary" future, where the greatest mission transformation opportunities will be found at the intersections and mergers of multiple disciplines

Barthelemy, J.-F.

Biegel, Bryan

English

NASA Technical Reports Server

NASA’s Digital Transformation (DT) 
Initiative
Bryan Biegel, Co-Lead for NASA DT Initiative
J.-F. Barthelemy, Lead for NASA DT Initiative
Presented at AGU, Dec. 12, 2019
https://ntrs.nasa.gov/search.jsp?R=20190033978 2020-03-11T16:17:06+00:00Z
DT Essential for NASA Mission Leadership
12/12/19 NASA's Digital Transformation Initiative 2
External DT Drivers for NASA
• Digital convergence opportunities 
• Competition for mission leadership
• Increasing mission complexity
• Increasing research complexity
• Pervasive collaboration required
Observations
• NASA is already doing DT, mostly at the grass roots (non-integrated)
• New digital capabilities can revolutionize how we achieve our mission
• Our partners are pursuing DT; NASA must continue to collaborate
• Big data challenges
• Resource constraints 
• Workforce competition
• Cybersecurity threats
NASA needs a comprehensive approach for evolving effectively and efficiently 
into a digitally-enabled world
Kepler Planet Search and Robovetter
Kepler spent 4 years monitoring  
approximately 200,000 stars for 
periodic changes in brightness –
threshold crossing events or TCEs 
– and determining which were 
due to transiting exoplanets.
Technical Challenges (why DT was needed):
• Searching for all possible transits in the full Kepler dataset 
requires examining 1 trillion possible combinations -- not 
feasible with standard computer clusters.
• Traditional human-in-the-loop approach too laborious, did 
not yield uniform catalog suitable for statistical studies.
Approach (how DT was employed):
• Kepler pipeline ported to supercomputers at NASA ARC 
and optimized.
• Robovetter intelligent system developed to analyze TCEs.
Benefits:
• Full dataset analysis accelerated from months to hours.
• Kepler pipeline identified over 30,000 TCEs.
• Robovetter created uniformly vetted catalog with over 
4,000 planet candidates.
• Kepler transformed understanding of planetary systems
Lessons Learned:
• Deep understanding of data essential in developing suite 
of algorithms necessary to accurately analyze data.
NASA Earth Exchange (NEX)
NEX is virtual collaborative that brings scientists together in a 
knowledge-based social network and provides the necessary tools, 
computing power, and access to massive data to accelerate research, 
innovation and provide transparency.
Technical Challenges (why DT was needed):
* Earth science at NASA is community effort, 100s of 
investigators spend a majority of their time accessing and 
pre-processing data for further analysis.
* Redundant storage and processing facilities result in larger 
overall computing budgets
* Moving data sets that are getting larger each year is 
expensive & time-consuming
* Sharing KNOWLEDGE, ie. codes, algorithms, workflows, 
intermediate results) is difficult
Approach (how DT was employed):
* NASA ARC scientists/engineers built NEX through co-
locating superomputing with cache copies of widely used 
Earth science data from multiple missions and large fast-
access storage to access, manage and process the data
Benefits:
* Enables research at continental/global scales sharing 
codes/results with collaborators
* Immediate use of research results in applications
* Co-located, georeferenced datasets facilitate public access 
by reducing preprocessing labor, cost and knowledge 
barriers.
Lessons learned, best practices:
* Security, a challenge to provide wider access to the 
community
* NEX is well suited for
- supporting mission teams
- generating products for the community
NASA DT Initiative - Overview
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Vision: DT will…
• dramatically enhance NASA’s mission impact
• by reinventing mission and mission support 
processes, products, and capabilities
• enabled by an innovation culture, digital-
savvy workforce, and advanced  digital 
technologies, 
• building on a foundation of modern
data management and access
Mission Impact: Dramatically…
• Accelerate mission development
• Increase operational efficiency
• Reduce cost
• Increase productivity
• Improve innovation
Foundation
Strategic Thrust: Data
Digital Technologies
Strategic Thrusts: Collaboration,                                
Model-based Work, Process Transformation, AI/ML
Organization
Strategic Thrust: Culture & Workforce
Transformation
products, processes, 
capabilities
Mission
Impact
How NASA’s DT Initiative is Antidisciplinary
• DT requires pervasive and fluid partnering across disciplines
• Real challenges are always multi-disciplinary
• Best opportunities for transformation happen when we merge discipline insights
• If we just toss discipline-specific contributions over the wall, solutions will not be optimal.
• Example: Boots on the Moon by 2024 requires contributions from many disciplines… DT will 
involve technologies, and encourage a culture, that support work across and outside of disciplines 
(e.g., innovative solution ideation).
• DT is already making us budding experts at everything
• Google search, Wikipedia, YouTube, and other resources at our fingertips enable us to answer 
almost any question and undertake almost any task as if we had significant training.
• Envision a future where digital technologies (VR/AR collaboration systems, 
automation, AI assistants, etc.) enable us to perform like experts across many fields
• Fluid collaboration and partnering
• Needed training and data available to quickly expand knowledge
• W bring our intellect, creativity, and viewpoint to each new challenge, which are always multi-
disciplinary.
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More on NASA’s DT Initiative
• https://www.nasa.gov/offices/oct/what-is-digital-transformation-and-
why-is-nasa-doing-it.html
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More DT Success Stories
at NASA
12/12/19 NASA's Digital Transformation Initiative 8
Interactive Exploration of Multi-Petabyte Earth Science Data
Technical Challenges (why DT was needed):
• Manage, organize, and analyze 5 petabytes of complex, 
vector/scalar field output of a global ocean circulation 
simulation of unprecedented resolution.
• Process extensive output of solution primitives to derive 
quantitative properties.
Approach (how DT was employed):
• Leveraged NAS high-end data access and processing 
capabilities (fast SSD filesystems, high-bandwidth 
InfiniBand network, hyperwall).
• Purpose-built software allowed interactive exploration of 
multi-PB data with user-controlled runtime rendering.
Benefits:
• Researchers can interactively explore massive datasets at full 
resolution on selected time scales, pursuing hunches, with 
custom-tuned multivariate views of any part of time series.
• Working as a group during a session at the 128-screen 
hyperwall, the researchers discovered new features in their 
data and found ways to improve their simulation.
Lessons learned, best practices:
• The group dynamic enabled by the large hyperwall is very useful 
for researchers to work efficiently with their data.
• With today’s NAS resources, successfully completing an extensive 
simulation is just the beginning of science; the resulting rich 
dataset requires specialized tools to exploit the data.
The NASA Advanced Supercomputing (NAS) facility’s 
visualization team at NASA ARC developed a new tool that 
enables ocean scientists to interactively explore their 5-
petabyte dataset—the result of the highest-resolution 
simulation ever produced using the MIT General 
Circulation Model (MITgcm).
9
Lunar Atmosphere & Dust Environment Explorer (LADEE)
10
Benefits to date:
• Accelerated and lower cost development of the onboard software 
by automating the code generation.
• Reduced test costs by automating the generation of Processor and 
Hardware in-the-Loop simulators.
• Improved Mission Operations procedure development and 
training through the use of Simulators with necessarily identical 
features to the onboard software.
Lessons learned, best practices:
• Model based software development through automatic code 
generation is highly effective for onboard flight software 
development.
• SIL layer coupled with CFS architecture were key to “push-button” 
code generation and testing. 
Technical Challenges (why DT was needed):
• LADEE was cost-constrained Class D mission, needed to reduce 
cost of developing onboard flight software.
• Traditional approaches for flight software development required 
separate phases of modeling and analysis, followed by coding 
and verification.
Approach (how DT was employed):
• Models of the Spacecraft Systems were developed in Mathworks
Matlab/Simulink. 
• Simulink Interface Layer (SIL) used to automatically generate 
Core Flight Systems (CFS) compatible software from the models.
• Resulting software verified using Processor and Hardware in-
the-Loop simulators, also generated from the models.
The Lunar Atmosphere and Dust Environment Explorer (LADEE) was a 
highly successful mission that launched in 2013 and orbited the moon 
for several months, collecting valuable information about the tenuous 
lunar atmosphere. The spacecraft and hence the onboard flight 
software was developed at NASA ARC. The ARC flight software team 
utilized Model Based Software Development techniques to generate 
the onboard software.
Resource Prospector (RP)
Technical Challenges (why DT was needed):
• Project team at ARC, KSC, and JSC needed to maintain 
consistency of requirements, ConOps, and system architecture
• Difficult to manage complexity with traditional document-based 
systems engineering methods
Approach (how DT was employed):
• Created RP system model in SysML
• Integrated requirements management database (DOORS) with 
system model (MagicDraw) to perform functional analysis
• Used Agency Cloud to publish model to web interface (Cameo 
Collaborator) so non-modelers could access and 
review/comment on models
Benefits to date:
• Improved communication between systems engineers and 
software developers
• Accelerated development of Rover ground software architecture
and integration with Ground Data System (GDS) software
• Mapping ops team functions to GDS architecture
• Ensuring consistent ConOps development and delineation of 
responsibility
Lessons learned, best practices:
• Lead adoption of new methods as an exemplar
• MBSE training essential for the team, even non-modelers
• Continued access to MBSE consultants to get through roadblocks
• Make the models accessible and navigable to non-modelers
Resource Prospector (RP) showed how America could return to lunar 
surface to prospect for volatiles, including water ice, to determine 
resource potential for future exploration architectures. Systems 
engineering team led adoption of Model-Based Systems Engineering 
(MBSE) methods throughout multi-Center project team. RP rapid 
design was completed, and laid groundwork for VIPER (Volatiles 
Investigating Polar Exploration Rover), planned for launch in 2022.
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NASA's Digital Transformation (DT) Initiative

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