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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Cellular Mechanisms Underlying Bone-Forming Cell Proliferative Response to Hypergravity

Life on Earth has evolved under the continuous influence of gravity (1-g). As humans explore and develop space, however, we must learn to adapt to an environment with little or no gravity. Studies indicate that lack of weightbearing for vertebrates occurring with immobilization, paralysis, or in a microgravity environment may cause muscle and bone atrophy through cellular and subcellular level mechanisms. We hypothesize that gravity is needed for the efficient transduction of cell growth and survival signals from the extra-cellular matrix (ECM) (consisting of molecules such as collagen, fibronectin, and laminin) in mechanosensitive tissues. We test for the presence of gravity-sensitive pathways in bone-forming cells (osteoblasts) using hypergravity applied by a cell culture centrifuge. Stimulation of 50 times gravity (50-g) increased proliferation in primary rat osteoblasts for cells grown on collagen Type I and fibronectin, but not on laminin or uncoated surfaces. Survival was also enhanced during hypergravity stimulation by the presence of ECM. Bromodeoxyuridine incorporation in proliferating cells showed an increase in the number of actively dividing cells from about 60% at 1-g to over 90% at 25-g. Reverse transcription-polymerase chain reaction was used to test for all possible integrins. Our combined results indicate that beta1 and/or beta3 integrin subunits may be involved. These data indicate that gravity mechanostimulation of osteoblast proliferation involves specific matrix-integrin signalling pathways which are sensitive to g-level. Further research to define the mechanisms involved will provide direction so that we may better adapt and counteract bone atrophy caused by the lack of weightbearing.

Vercoutere, W.↗
NASA NTRS2021

Role of Cyber-Physical Testing in Developing Resilient Extraterrestrial Habitats

Extraterrestrial long-term habitat systems (henceforth referred to as habitat systems) require groundbreaking technological advances to overcome the extreme demands introduced by isolation and challenging environments. A habitat system must operate as intended under continuous disruptive conditions. Designing for the demands that challenging environments will place on habitat systems (e.g., wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts, vibrations, and solar particle events) represents one of the greatest challenges in this endeavor. This engineering problem necessitates that we design and manage habitat systems to be resilient. System resilience requires a comprehensive approach that accounts for disruptions through the design process and adapts to them in operation. As the habitat system evolves—growing in physical size, complexity, population, and connectivity—and diversifies in operations, it must continue to be safe and resilient. In this endeavor, we should take advantage of lessons learned in developing civil infrastructure responsive to catastrophic natural hazards, autonomous robotics platforms, smart buildings, cyber-physical testing, complex systems, and diagnostics and prognostics for intelligent health management. This study highlights the importance of system resilience and cyber-physical testing to address the grand challenge of developing habitat systems.

Public health and safety↗
NASA NTRS2024

System for Photogrammetric Imaging, Detection, and Ranging (SPIDR)

The System for Photogrammetric Imaging, Detection, and Ranging (SPIDR) project aims to advance the state-of-the-art (SOA) technology in camera tracking. SPIDR will advance this technology in the following ways: 1) perform autonomous tracking of rocket launches, 2) allow modularity for camera payloads to enable various types of imagery capture (standard video, high-speed, infrared, etc.), 3) enable additional imagery assets for increased scope of imagery analysis, and 4) serve as a viable tracking replacement for the existing Kineto Tracking Mount (KTM) system used by the Exploration Ground Systems (EGS) program. The project initially aimed to build an in-house mechanical design and control system, but realigned to a commercial off-the-shelf (COTS) mechanical design. During the FY23 CIF timeline, the SPIDR team made significant advances in building a machine-learning (ML) based object detection model for various rockets and their plumes. The team will continue advancing the model, and begin work on the pan and tilt unit (PTU) control system and tracking algorithm development.

Alden Param↗
NASA NTRSDate not supplied

Prediction of High-Latitude Ionospheric Electrodynamics Using the Machine Learning Based Auroral Ionospheric Electrodynamics Model

We introduce a new framework for Machine-Learning (ML) based Auroral Ionosphere Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents (FACs) of Kunduri et al., 2020 (https://doi.org/10.1029/2020JA027908), the FAC-derived aurora conductance model of Robinson et al., 2020 (https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen & Brekke (1993). The ML-AIM inputs are 60min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pederson/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity on 14 May 2013 and a geomagnetic storm on 7-8 September 2017. ML-AIM produces reasonable ionospheric potential patterns such as two cell convection patterns and the enhancement of electric potentials during active times. The cross polar cap potential drop from ML-AIM is also comparable to the ones from the Weimer 2005 model, Super Dual Auroral Radar Network (SuperDARN), and Defense Meteorological Satellite Program (DMSP) F17 observations. ML-AIM is unique in a sense that it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while other traditional empirical model like Weimer 2005 is designed to provide static ionospheric conditions under steady solar wind/IMF conditions. In future, ML-AIM will include ML-based models of aurora precipitation and ionospheric conductance, improving its performance during active times.

H. K. Connor↗
NASA NTRS2011

ISS Regenerative Life Support: Challenges and Success in the Quest for Long-Term Habitability in Space

This presentation will discuss the International Space Station s (ISS) Regenerative Environmental Control and Life Support System (ECLSS) operations with discussion of the on-orbit lessons learned, specifically regarding the challenges that have been faced as the system has expanded with a growing ISS crew. Over the 10 year history of the ISS, there have been numerous challenges, failures, and triumphs in the quest to keep the crew alive and comfortable. Successful operation of the ECLSS not only requires maintenance of the hardware, but also management of the station resources in case of hardware failure or missed re-supply. This involves effective communication between the primary International Partners (NASA and Roskosmos) and the secondary partners (JAXA and ESA) in order to keep a reserve of the contingency consumables and allow for re-supply of failed hardware. The ISS ECLSS utilizes consumables storage for contingency usage as well as longer-term regenerative systems, which allow for conservation of the expensive resources brought up by re-supply vehicles. This long-term hardware, and the interactions with software, was a challenge for Systems Engineers when they were designed and require multiple operational workarounds in order to function continuously. On a day-to-day basis, the ECLSS provides big challenges to the on console controllers. Main challenges involve the utilization of the resources that have been brought up by the visiting vehicles prior to undocking, balance of contributions between the International Partners for both systems and resources, and maintaining balance between the many interdependent systems, which includes providing the resources they need when they need it. The current biggest challenge for ECLSS is the Regenerative ECLSS system, which continuously recycles urine and condensate water into drinking water and oxygen. These systems were brought to full functionality on STS-126 (ULF-2) mission. Through system failures and recovery, the ECLSS console has learned how to balance the water within the systems, store and use water for contingencies, and continue to work with the International Partners for short-term failures. Through these challenges and the system failures, the most important lesson learned has been the importance of redundancy and operational workarounds. It is only because of the flexibility of the hardware and the software that flight controllers have the opportunity to continue operating the system as a whole for mission success.

Bazley, Jesse A.↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
DOE OSTI2026

3D reconstruction and neural rendering for adversarial machine learning

While evasion attacks on computer vision systems have been widely studied, creating attacks that remain effective under significant changes in viewpoint continues to be challenging. Traditional approaches often rely on affine transformations of images, but these approaches degrade at larger perspective shifts and often produce unrealistic or ineffective perturbations. Recent methods use differentiable renderers to improve viewpoint robustness, but they typically depend on manually constructed 3D models. We introduce a semi-automated pipeline that generates physically printable and perspective-invariant adversarial patches using only a small set of 2D images. Our method integrates 3D reconstruction, neural rendering, adversarial patch optimization, and an object detection victim model into a unified workflow. We use 2D Gaussian Splatting for high fidelity mesh reconstruction and FlexPara for surface parameterization that produces texture maps suitable for patch editing. Together, these components form a fully differentiable pipeline in PyTorch3D that links texture modification to model outputs, enabling efficient optimization of patches that remain effective across many viewpoints. The complete process, from image capture to patch printing and physical evaluation, can be completed within a few hours. We demonstrate the effectiveness of the resulting patches through attacks on the YOLOv8 object detection model and discuss remaining challenges and opportunities for improving robustness and scalability.

Singhvi, Vivaan [ORNL] (ORCID:0009000586288221)↗
NASA NTRSDate not supplied

NASA’s Continued Partnerships with High School Students during a Global Pandemic

NASA HUNCH’s mission is to empower students through Project-Based Learning where they learn skills and can launch their careers through participation in the design and fabrication of real-world valued products. Our goal is to mentor the next generation of students to solve NASA’s greatest challenges even during a global pandemic

HUNCH↗
DOE OSTI2025

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗
NASA NTRS2024

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗
DOE OSTI2024

Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction accuracy from 0.69% to 0.87%. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.

SSTDR, FDR, Reflectometry, Machine Learning, ARENA↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We discuss the current technical status, review recent scientific highlights, indicate a few future directions, and present what we feel is the most important lesson learned from our experience of building and operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS1994

Neural network applications in telecommunications

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Alspector, Joshua↗
NASA NTRS2002

Research on Intelligent Synthesis Environments

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Noor, Ahmed K.↗
NASA NTRS2004

Space Exploration Technologies Developed through Existing and New Research Partnerships Initiatives

The Space Partnership Development Program of NASA has been highly successful in leveraging commercial research investments to the strategic mission and applied research goals of the Agency through industry academic partnerships. This program is currently undergoing an outward-looking transformation towards Agency wide research and discovery goals that leverage partnership contributions to the strategic research needed to demonstrate enabling space exploration technologies encompassing both robotic spacecraft missions and human space flight. New Space Partnership Initiatives with incremental goals and milestones will allow a continuing series of accomplishments to be achieved throughout the duration of each initiative, permit the "lessons learned" and capabilities acquired from previous implementation steps to be incorporated into subsequent phases of the initiatives, and allow adjustments to be made to the implementation of the initiatives as new opportunities or challenges arise. An Agency technological risk reduction roadmap for any required technologies not currently available will identify the initiative focus areas for the development, demonstration and utilization of space resources supporting the production of power, air, and water, structures and shielding materials. This paper examines the successes to date, lessons learned, and programmatic outlook of enabling sustainable exploration and discovery through governmental, industrial, academic, and international partnerships. Previous government and industry technology development programs have demonstrated that a focused research program that appropriately shares the developmental risk can rapidly mature low Technology Readiness Level (TRL) technologies to the demonstration level. This cost effective and timely, reduced time to discovery, partnership approach to the development of needed technological capabilities addresses the dual use requirements by the investing partners. In addition, these partnerships help to ensure the attainment of complimenting human and robotic exploration goals for NASA while providing additional capabilities for sustainable scientific research benefiting life and security on Earth.

Nall, Mark↗
NASA NTRS2018

Thermal Protection Systems Technology Transfer from Apollo and Space Shuttle to the Orion Program

This paper describes how the Orion program is utilizing the Thermal Protection System (TPS) experience from the Apollo and Space Shuttle programs to reduce program risk and improve affordability to meet NASA's future manned exploration missions. The Orion program successfully completed the Exploration Flight Test (EFT-1) mission in 2014 and is currently assembling, integrating, and testing the next spacecraft for the Exploration Mission (EM-1) to meet the flight test objectives of an unmanned orbital mission to the moon and return to earth in 2019. The Orion spacecraft production operations are located in the Neil Armstrong Operations and Checkout (O&C) facility at the Kennedy Space Center (KSC) providing an affordable and seamless delivery approach of vehicles directly to the launch site eliminating spacecraft transportation and additional checkout testing. Innovative vehicle design, manufacturing and test operations approaches are maturing and evolving with each Orion vehicle build to support the challenging NASA exploration mission requirements beyond Low Earth Orbit (LEO) while reducing program cost and schedule impacts. An example of Orion's evolution is the incorporation of an improved heat shield design, assembly and testing approach to meet the higher re-entry velocities for a lunar return for the EM-1 mission. The EFT- 1 heat shield was based on the Apollo heat shield manufacturing processes and was assembled at a supplier location and then transported to KSC for final integration. The EM-1 heat shield is now manufactured, assembled, tested, and installed into the spacecraft at the O&C facility reducing program cost and production schedules. The transition of the Space Shuttle TPS capabilities has enabled Orion to provide a human rated capsule design using proven materials and processes established over years of orbiter re-entry missions. The Orion Crew Module (CM) TPS configuration is derived from the Apollo CM approach utilizing improved materials and processes developed from the Space Shuttle program. The Orion EFT-1 heat shield utilized the Avcoat ablative material from Apollo which was injected into a honeycomb substrate and has been updated for EM-1 incorporating a block configuration bonded to a composite shell structure. This approach utilizes the proven Avcoat material for the heat shield ablator and is utilizing derived bonding and inspection methods and techniques from the Space Shuttle tile experience. The Orion back shell TPS configuration is based on Space Shuttle tile designs using proven tile materials and coatings. The Orion forward bay cover utilizes the high temperature tiles similar to the back shell tiles and low temperature blankets derived from the Space Shuttle program reducing weight impacts. Space Shuttle Multi-Layer Insulation (MLI) is installed in the Orion capsule to control the interior temperature environment providing a light weight design. These TPS design approaches have performed successfully on the Orion first flight test on EFT-1 and are incorporated in the configuration for the next flight test for EM-1. Completion of these two flight tests will certify the TPS for the Orion program for human rated exploration missions and has reduced the development cost to the Orion program. In addition to transitioning the Space Shuttle TPS design configurations to Orion, the supporting manufacturing infrastructure, manufacturing processes, and inspection methods are also incorporated into the Orion assembly operations at KSC. This has avoided significant startup schedule and costs impacts of new capabilities and development of support operations necessary to fabricate, install, inspect, and validate the TPS installations for the Orion spacecraft. The Thermal Protection System Facility (TPSF) which supported the Space Shuttle program provides the heat shield Avcoat blocks and the back shell AETB-8 tiles for Orion program. Extending the existing Space Shuttle TPS operations at KSC provides onsite support to the Orion spacecraft assembly operations. A significant benefit to the Orion program is the transition of the Space Shuttle technician work force at KSC. This highly skilled workforce was able to transition to the Orion program and immediately support the TPS installation operations. They brought with them the human rated manufacturing and assembly operations culture that was critical to the Space Shuttle success avoiding the retraining cost and schedule impacts of a new workforce to Orion. The technician skills certification program from the Space Shuttle was found to be compatible with the Lockheed Martin technician certification programs enabling a seamless training process utilizing process similarity avoiding the cost of retraining the work force to Orion standards. The existing workforce is certificated for multiple processes enabling reassignment of technicians to other tasks in the Orion AI&P operations reducing the overall touch labor manpower requirements. Additional benefits of using the Space Shuttle workforce is the incorporation of the lessons learned from Space Shuttle processes to improve the Orion TPS processes. This has resulted in a continuous evolution of TPS processes to improve the producibility and reduce the program cost for the TPS for the Orion program. Transitioning the Apollo and Space Shuttle TPS designs, processes, and technician workforce has been instrumental in enabling Orion to successfully meet the program challenges for NASA's exploration missions of the future.

Stewart, Michael↗