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NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗
DOE OSTI2020

Challenges and Solutions in Operation and Lay-Up of the Modular Caustic- Side Solvent Extraction Unit - 20078

The Modular Caustic-Side Solvent Extraction (CSSX) Unit (MCU) was designed and constructed to provide interim salt waste processing so that the Liquid Waste Disposition Program (LWDP) could continue waste removal and tank closure until start-up of the Salt Waste Processing Facility (SWPF). MCU processes Clarified Salt Solution (CSS), or salt solution (SS) that has undergone actinide removal/filtration, to produce two waste streams: the cesium laden Strip Effluent (SE), which is incorporated into glass at the Defense Waste Processing Facility (DWPF), and the cesium depleted Decontaminated SS (DSS), which is incorporated into grout at the Saltstone Production Facility (SPF). MCU completed start-up in 2008 with an initial operating life of 3 years and design life of 5 years. Prior to reaching the end of the design life, multiple critical components were upgraded and/or replaced to mitigate risks associated with any delays in SWPF start-up. During the remainder of operation (until June 2019), MCU was challenged to maximize processing with minimal additional modifications to repair/improve aging infrastructure, while maintaining low levels of risk to workers and the environment. Notable challenges during the extended operating life of the facility involved both mechanical and operational issues. Two of the most significant issues were the increased frequency of Process Vessel Ventilation (PVV) High Efficiency Particulate Air (HEPA) filter change-outs and the biofouling of the SE coalescer (SEC). The major impacts of both issues were that replacement of these components 1) resulted in high exposure to workers, and 2) required substantial downtime, thus hindering achievement of processing goals. PVV HEPA filter replacements are required based on dose rate and differential pressure (dP) limits. After introducing a higher-curie feed to MCU, the operating time between change outs was reduced by approximately half. SEC replacements are required based on dP limits. After restarting from an extended outage, the operating life of the SEC was significantly reduced by more than half due to biofouling. As a result of extensive troubleshooting and process improvements, the facility was able to recover performance and extend the operating life of the PVV HEPA filters and the SEC media. Through troubleshooting both of these issues, the facility was also able to learn from prior experience and adapt in order to minimize down time and maintain throughput. Although the facility took advantage of short periods of downtime to perform nonintrusive troubleshooting and minor corrective/preventative maintenance activities, longer outages were periodically required for corrective maintenance involving process cell entry. During these outages, remote tools and specialized shield plates were employed to minimize dose to workers. Mock-ups of non-routine maintenance activities were critical to early identification of potential issues and improvements, which shortened outage duration. Consistent with the facility's operational strategy, evolutions needed for MCU layup were identified and sequenced to ensure safe conditions within the facility, meet pre-defined criteria for lay-up configuration, and protect workers, while minimizing the impact to SWPF integration. De-inventory and flushing of process areas was prioritized so that the total residual inventory was reduced as efficiently as possible. This paper discusses lessons learned and best practices from troubleshooting unique issues, performing complex maintenance activities, and implementing a layup strategy that best supports the overall system mission. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations.

Sridhar, Banavar↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
DOE OSTI2022

What We Learned From Analyzing 18 Million Rows of Commercial Buildings’ HVAC Fault Data

To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.

Crowe, Eliot↗
NASA NTRSDate not supplied

NASA's Professional DEVELOPment - Evaluating 10 Weeks of Capacity Building

Under NASA’s Applied Sciences, the NASA DEVELOP Program is centered on dual capacity building. Projects bring together partners and participants to learn how Earth observation data can enhance environmental decision-making practices and inform action. During their time with DEVELOP, participants build technical skills around remotely sensed data and professional skills through interactions with team members, science advisors, and partners. To further refine the participant experience, DEVELOP explores which technical and professional skills individuals report improving during the 10-week program. An analysis of self-reported assessments from 2018 to 2021 included projects that were conducted in-person and virtually. Participants consistently indicated a strong improvement in technical and professional skills yet there was clear variation between in-person and virtual opportunities. When comparing between 2019 and 2021, self-reporting showed a higher percentage of participants reporting an increase in networking in the virtual environment and an increase in building professional relationships with project partners virtually. This analysis suggests that professional opportunities available in the virtual environment may in fact bolster early career scientists’ networking abilities. This presentation will identify DEVELOP’s approach to monitoring and evaluation, highlight interesting trends introduced by the pandemic, and lessons learned.

Sarah Payne↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

SCADA↗
DOE OSTI2020

2020 Exascale Computing Project Annual Meeting (Executive Summary Report)

The Exascale Computing Project (ECP) delivers specific applications, software products, and outcomes on DOE computing facilities. Integration across these elements for specific hardware technologies for exascale system instantiations is fundamental to ECP success. The outcome of the ECP is the delivery of a capable exascale computing ecosystem to provide breakthrough solutions addressing our most critical challenges in scientific discovery, energy assurance, economic competitiveness, and national security. This outcome is not a matter of ensuring more powerful computing systems. The ECP is designed to create more valuable and rapid insights from a wide variety of applications (“capable”), which requires a much higher level of inherent efficacy in all methods, software tools, and ECP-enabled computing technologies to be acquired by DOE laboratories (“ecosystem”). The ECP annual meeting provides a unique opportunity for the core technical expertise in the United States focused on achieving this next plateau of computational science and computing performance to engage in direct discussions on project execution. Face-to-face gatherings in technical communities like this are common and needed for the exchange of scientific ideas and technical performance. The ECP annual meeting stands apart from other technical conferences and meetings in the computing community as it is uniquely and solely focused on the execution of the ECP and the integration of technical activities leading to the creation of the exascale computing ecosystem for the future. The direct interaction of key critical technical staff, who are leaders in their respective fields, and the resulting give-and-take between software, applications, and hardware and the technical co-design therein, is unique and essential to the effective execution of the ECP. The first annual meeting was held in Knoxville, Tennessee, January 31 – February 2, 2017 and brought together, for the first time, a diverse collection of researchers from 16 DOE national laboratories as well as university computer and computational science researchers to discuss shared problems and joint solutions for the development of a capable exascale computing ecosystem. These interactions resulted in focused technical plans and an energized community centered on advances for ECP. The second annual meeting was held in Knoxville, Tennessee, February 5–9, 2018. It included 643 individual thought leaders and performers in application development, software research and deployment, and hardware research and integrators, all of whom are part of the multifaceted, billion dollar HPC community. This meeting provided a platform to discuss and disseminate numerous examples where researchers with common goals and synergistic solutions came together for the first time to deliver tangible results. Additionally, at the 2018 meeting, ECP researchers had the opportunity to digest all US HPC vendor R&D product roadmaps pointing to exascale – not only to learn how their research can play a role, but, more importantly, to influence those roadmaps to ensure successful delivery on DOE applications that will contribute to (if not solve) problems of national interest in national security, science, energy, and health, as well as growing security threats. The third annual meeting was held in Houston, Texas, January 14–17, 2019. With a 19% increase in the number of registrations (768 people), and the change in location, the third annual meeting was considered the most impactful of the three at the time. The new website provided a better platform for the dissemination of the content, the new venue as a meeting hotel instead of a conference center facilitated interactions and discussions after event hours, and the addition of an award-winning mobile event conference app (Whova) transformed dramatically the attendee experience at the event. This fourth annual meeting was held in Houston, Texas, February 3-7, 2020. This meeting had an increase in the number of attendees for a total of 824 people registered (782 attendees) and included numerous enhancements based on feedback and lessons learned from previous meetings, some of which are listed here: Improved quality of the sessions, their material and the whole program.; Had more industry participation and addition of external collaborators from overseas.; Published the full agenda earlier to better accommodate attendance and travel plans based on schedule.; Centralized all sessions in one venue.; Provided additional hotels and room blocks for the attendees.; Improved communication with the audience (links, material, directions, notifications, etc.) to go paperless.; Enhanced side meeting scheduling, management and user experience.; Made available additional space and tables for impromptu meetings and side discussions.; Improved IT and A/V solutions for speakers. In addition, our final survey captured the following points as opportunities for improvement in future meetings: consider a different meeting location that is more pedestrian friendly, reduce talks during working meals to allow more collaboration and informal time, adapt the agenda to acknowledge attendees from different timezones, consider recording some of the tutorials and/or sessions to share broadly with the HPC community, have a larger poster room, provide additional power strips, and improve the WiFi.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Project KEWL: Kinect Engineering With Learning

Project KEWL is a joint project between NASA/JPL and NASA/JSC to stimulate interest of children in Science, Technology, Engineering and Math (STEM) and bring the NASA space exploration experience to the classroom, museum and ultimately the living room. Using the Kinect game controller KEWL allows children to engage in NASA s missions in a fundamentally new way. KEWL allows children to experiment with gravity on Mars and the Moon; navigate through the International Space Station; fix a torn solar array on the ISS; drive a robot on Mars; visit an Asteroid; learn about the differences in gravity on different planets and control Robonaut 2 using their body as the input device. Project KEWL complements NASA s outreach investments in television, mobile platforms and the web by engaging the public through the rapidly expanding medium of console gaming. In 2008, 97% of teenagers played video games and 86% played on a home gaming console. (source: http://pewresearch.org/pubs/953/) As of March 2011, there have been more than 10 million Kinects sold. (source: http://www.itproportal.com/2011/03/10/kinect-record-breaking-sales-figures-top-10-million/) Project KEWL interacts with children on a platform on which they spend much of their time and teaches them information about NASA while they are having fun. Project KEWL progressed from completely custom C++ code written in house to using a commercial game engine. The art work and 3D geometry models come from existing engineering work or are created by the KEWL development team. Six different KEWL applications have been demonstrated at nine different venues including schools, museums, conferences, and NASA outreach events. These demonstrations have allowed the developers the chance to interact with players and observe the gameplay mechanics in action. The lessons learned were then incorporated into the subsequent versions of the applications.

Norris, Jeff↗
DOE OSTI2021

Overview of the Electrification of Transportation in Hawaii

This document is a summary of electric vehicle (EV) experiences in Hawaii. It is meant to be informative but does not present any new technical analysis except for the development of key lessons learned that could be applied in similar contexts. The electrification of transportation is essential for Hawaii's energy goal. An electrification of transportation strategy complements other energy policy goals, increases clean energy impacts, and provides customer value. By the end of 2020, there were over 12,000 EVs registered in Hawaii (about 1 percent of all cars). That number is expected to grow, based on the results from recent surveys and studies in Hawaii. Surveys pointed out the need for more charging stations, especially in places where people do business or park for long periods of the day. Participation in controlled charging programs should have attractive incentives since a majority of EV owners would not be willing to interrupt their EV charging for demand response. Various studies have confirmed the EV potential in Hawaii. For example, the JUMPSmart Maui demonstration project, a public-private partnership with Japan, helped to establish the EV charging station infrastructure in Maui and provided important information about charging behaviors. A critical backbone study commissioned by the utility recommended that 3,600 public chargers be installed by 2030 on the five islands, which confirms the need for infrastructure improvements expressed in earlier surveys. The process that emerged in Hawaii can be an example to other locations, which could heed the lessons from Hawaii's EV experiences: The importance of an overarching energy goal/objective based on a shared vision; planning and pilot projects; a strategic plan (roadmap) leveraging on initial experiences; evaluation of the effectiveness/success of actions; fine-tuning as needed; close regulatory oversight and stakeholder participation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2004

Hubble Space Telescope Fine Guidance Sensor Post-Flight Bearing Inspection

Aerospace mechanism engineering success stories often, if not always, consist of overcoming developmental, test and flight anomalies. Many times it is these anomalies that stimulate technology growth and more reliable future systems. However, one must learn from these to achieve an ultimately successful mission. It is not often that a spacecraft engineer is able to inspect hardware that has flown in orbit for several years. However, in February 1997, the Fine Guidance Sensor-1 (FGS-1) was removed from the Hubble Space Telescope (HST) and returned to NASA Goddard Space Flight Center (GSFC) during the second Servicing Mission (SM2). At the time of removal, FGS-1 had nearly 7 years of service and the bearings in the Star Selector Servos (SSS) had accumulated approximately 25 million Coarse Track (CT) cycles. The main reason for its replacement was due to a bearing torque anomaly leading to stalling of the B Star Selector Servo (SSS-B) when reversing direction during a vehicle offset maneuver, referred to herein as a Reversal Bump (RB). The returned HST FGS SSS bearings were disassembled for post-service condition assessment to better understand the actual cause of the torque spikes, identify potential process/design improvements, and provide information for remedial on-orbit operation modifications. The methods and technology utilized for this inspection are not unique to this system and can be adapted to most investigations at varying stages of the mechanism life from development, through testing, to post flight evaluation. The systematic methods used for the HST Fine Guidance Sensor (FGS) SSS and specific findings are the subjects presented in this paper. The lessons learned include the importance of cleanliness and handling for precision instrument bearings and the potential effects from contamination. The paper describes in detail, the analytical techniques used for the SSS and their importance in this investigation. Inspection analytical data and photographs are included throughout the paper.

Pellicciotti, J.↗
NASA NTRS2004

Hubble Space Telescope Fine Guidance Sensor Post-Flight Bearing Inspection

Aerospace mechanism engineering success stories often, if not always, consist of overcoming developmental, test and flight anomalies. Many times it is these anomalies that stimulate technology growth and more reliable future systems. However, one must learn from these to achieve an ultimately successful mission. It is not often that a spacecraft engineer is able to inspect hardware that has flown in orbit for several years. However, in February 1997, the Fine Guidance Sensor-I (FGS-1) was removed from the Hubble Space Telescope (HST) and returned to NASA Goddard Space Flight Center (GSFC) during the second Servicing Mission (SM2). At the time of removal, FGS-1 had nearly 7 years of service and the bearings in the Star Selector Servos (SSS) had accumulated approximately 25 million Coarse Track (CT) cycles. The main reason for its replacement was due to a bearing torque anomaly leading to stalling of the B Star Selector Servo (SSS-B) when reversing direction during a vehicle offset maneuver, referred to herein as a Reversal Bump (RB). The returned HST FGS SSS bearings were disassembled for post-service condition assessment to better understand the actual cause of the torque spikes, identify potential process/design improvements, and provide information for remedial on-orbit operation modifications. The methods and technology utilized for this inspection are not unique to this system and can be adapted to most investigation ai varying stages of the mechanism life from development, through testing, io post night evaluation. The systematic methods used for the HST Fine Guidance Sensor (FGS) SSS and specific findings are the subjects presented in this paper. The lessons learned include the importance of cleanliness and handling for precision instrument bearings and the potential effects from contamination. The paper describes in detail, the analytical techniques used for the SSS and their importance in this investigation. Inspection analytical data and photographs are included throughout the paper.

Pellicciotti, Joseph↗
NASA NTRS2011

NASA's Space Launch System Progress Report

Exploration beyond Earth will be an enduring legacy for future generations, confirming America's commitment to explore, learn, and progress. NASA's Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is responsible for designing and developing the first exploration-class rocket since the Apollo Program's Saturn V that sent Americans to the Moon. The SLS offers a flexible design that may be configured for the MultiPurpose Crew Vehicle and associated equipment, or may be outfitted with a payload fairing that will accommodate flagship science instruments and a variety of high-priority experiments. Both options support a national capability that will pay dividends for future generations. Building on legacy systems, facilities, and expertise, the SLS will have an initial lift capability of 70 metric tons (mT) and will be evolvable to 130 mT. While commercial launch vehicle providers service the International Space Station market, this capability will surpass all vehicles, past and present, providing the means to do entirely new missions, such as human exploration of asteroids and Mars. With its superior lift capability, the SLS can expand the interplanetary highway to many possible destinations, conducting revolutionary missions that will change the way we view ourselves, our planet and its place in the cosmos. To perform missions such as these, the SLS will be the largest launch vehicle ever built. It is being designed for safety and affordability - to sustain our journey into the space age. Current plans include launching the first flight, without crew, later this decade, with crewed flights beginning early next decade. Development work now in progress is based on heritage space systems and working knowledge, allowing for a relatively quick start and for maturing the SLS rocket as future technologies become available. Together, NASA and the U.S. aerospace industry are partnering to develop this one-of-a-kind asset. Many of NASA's space centers across the country will provide their unique expertise to the Space Launch System endeavor. Unique infrastructure to be used includes the Michoud Assembly Facility for tank manufacturing, Stennis Space Center for engine testing, and Kennedy Space Center for processing and launch. As this panel will discuss, the SLS team is dedicated to doing things differently-from applying lean oversight/insight models to smartly using legacy hardware and existing facilities. Building on the foundation laid by over 50 years of human and scientific space flight--and on the lessons learned from the Apollo, Space Shuttle, and Constellation Programs-the SLS team has delivered both technical trade studies and business case analyses to ensure that the SLS architecture will be safe, affordable, reliable, and sustainable.

Singer, Joan A.↗
NASA NTRS2009

Evaluating Failures and Near Misses in Human Spaceflight History for Lessons for Future Human Spaceflight

There have been a number of studies done in the past drawn on lessons learned with regard to human loss-of-life events. Generally, the systemic causes and proximate causes for fatal events have both been examined in considerable detail. However, an examination of near-fatal accidents and failures that narrowly missed being fatal could be equally useful, not only in detecting causes, both proximate and systemic, but also for determining what factors averted disaster, what design decisions and/or operator actions prevented catastrophe. Additionally, review of risk factors for upcoming or future programs will often look at trending statistics, generally focusing on failure/success statistics. Unfortunately, doing so can give a skewed or misleading view of past reliability or a reliability that cannot be presumed to apply to a new program. One reason for this might be that failure/success criteria aren't the same across programs, but also that apparent success can hide systemic faults that, under other circumstances, can be fatal to a program with different parameters. A program with a number of near misses can look more reliable than a consistently healthy program with a single out-of-family failure and provide very misleading data if it is not examined in detail. This is particularly true for a manned space program where failure/success includes more than making a particular orbit. Augmenting reliability evaluations with this near miss data can provide insight and expand on the limitations of a strictly pass/fail evaluation. Even more importantly, a thorough understanding of these near miss events can identify conditions that prevented fatalities. Those conditions may be key to a programs reliability, but, without insight to the repercussions if such conditions were not in place, their importance may not be readily clear. As programs mature and political and fiscal responsibilities come to the fore, often there is considerable incentive to eliminate unnecessary conservatism, design margin, redundancy, operational support, testing, training, or safety oversight. An evaluation that demonstrates how these features and capabilities averted disaster can ensure processes that saved lives or missions are not discarded without appropriate review and understanding. Close examination of accidents that almost were can also highlight differences in design from one program to another, either justifying reliability comparisons or negating them. It can also provide considerable insight into how those saving factors were developed and implemented so that similar methods can be used to ensure appropriate life-saving and mission saving factors can be developed, even for a dissimilar space program. The lessons are appropriate for seasoned manned space programs and agencies, but crucial for untried agencies and organizations that are interested in sending man into space. The large body of publicly available near miss and accident data available provide invaluable insight into programmatic, technical, and even political issues that can be addressed before they impact safety. In this paper, we examine a number of these near misses and accidents and steps a program, agency, or potential spacefaring company might take to improve their chances of success and avoid mission or safety disasters using this data.

Barr, Stephanie↗
NASA NTRSDate not supplied

2024 Nasa Lunabotics University Competition: Site Preparation With Bulk Regolith

Introduction: Lunabotics provides accredited institutions of higher learning students (vocational-technical, college, university) an opportunity to apply the NASA systems engineering process to design and build a prototype robot. This robot would be capable of performing the proposed operations on the Lunar surface in support of future Artemis mission goals. Lunabotics features a systems engineering design challenge to engage students in the next phase of hu-man space exploration supporting the Artemis missions. This two-semester event encourages students to design and build an autonomous or telerobotic robot designed to traverse the simulated Lunar surface and complete the assigned construction tasks. The number of teams accepted into this challenge is not predetermined but is based on the scores and overall quality of the Project Management Plans received and other factors. The culmination of the Lunabotics virtual challenge will be the design, build and operation of a functional prototype Lunar robot. Teams are required to submit the following: (1) Project Management Plan, (2) Systems Engineering Paper, a (3) STEM Engage-ment Report, and a (4) Proof of Life Video. This is an optional item, but to qualify for the grand prize a team must also submit a: (5) Presentation and Demonstration. Background: The NASA Lunabotics University Competition was first held in 2010 as a follow on to the NASA Regolith Excavation Challenge [1]. The high level of interest and participation from over 50 universities each year has led to a sustained annual competi-ion cadence: it has been held every May for the past 14 years [2,3]. Over 6,000 students have participated and been inspired to pursue Science, Technology, En-gineering and Mathematics careers (STEM). 2024 Competition: The necessary lunar surface tasks are evolving to meet the NASA Artemis Mission requirements. In the past Lunabotics challenges we gathered data to support Lunar mining for consuma-bles in the Lunar regolith. Now, in 2024, the task is to gather data on Lunar site preparation and construction by designing and building a robot that will traverse the chaotic Lunar terrain and construct a regolith-based berm. The goal is to build a berm structure which would be useful to the Artemis Mission for blast and ejecta protection during lunar landings and launches, shading cryogenic propellant tank farms, providing radiation protection around a nuclear power plant and other mission critical uses. Lunabotics will consist of three separate events this year. The first event is NASA’s Lunabotics Project Development Challenge, where teams submit various deliverables to be scored by judges. The second event will be the University of Central Florida (UCF) Lunabotics Qualification challenge, in the Exolith laboratory, where teams will put their de-signs to the test. The top ten scoring teams from the Qualification challenge are then invited to the third and final event, NASA’s Lunabotics On-Site Challenge at Kennedy Space Center in Florida. This presentation will summarize the results and lessons learned from the NASA Lunabotics University Competition held in May 2024.

Lunabotics↗
NASA NTRS2016

NASA's Earth Science Flight Program Meets the Challenges of Today and Tomorrow

NASA's Earth science flight program is a dynamic undertaking that consists of a large fleet of operating satellites, an array of satellite and instrument projects in various stages of development, a robust airborne science program, and a massive data archiving and distribution system. Each element of the flight program is complex and present unique challenges. NASA builds upon its successes and learns from its setbacks to manage this evolving portfolio to meet NASA's Earth science objectives. NASA fleet of 16 operating missions provide a wide range of scientific measurements made from dedicated Earth science satellites and from instruments mounted to the International Space Station. For operational missions, the program must address issues such as an aging satellites operating well beyond their prime mission, constellation flying, and collision avoidance with other spacecraft and orbital debris. Projects in development are divided into two broad categories: systematic missions and pathfinders. The Earth Systematic Missions (ESM) include a broad range of multi-disciplinary Earth-observing research satellite missions aimed at understanding the Earth system and its response to natural and human-induced forces and changes. Understanding these forces will help determine how to predict future changes, and how to mitigate or adapt to these changes. The Earth System Science Pathfinder (ESSP) program provides frequent, regular, competitively selected Earth science research opportunities that accommodate new and emerging scientific priorities and measurement capabilities. This results in a series of relatively low-cost, small-sized investigations and missions. Principal investigators whose scientific objectives support a variety of studies lead these missions, including studies of the atmosphere, oceans, land surface, polar ice regions, or solid Earth. This portfolio of missions and investigations provides opportunity for investment in innovative Earth science that enhances NASA's capability for better understanding the current state of the Earth system. ESM and ESSP projects often involve partnerships with other US agencies and/or international organizations. This adds to the complexity of mission development, but allows for a greater scientific return on NASA's investments. The Earth Science Airborne Science Program provides manned and unmanned aircraft systems that further science and advance the use of satellite data. NASA uses these assets worldwide in campaigns to investigate extreme weather events, observe Earth system processes, obtain data for Earth science modeling activities, and calibrate instruments flying aboard Earth science spacecraft. The Airborne Science Program has six dedicated aircraft and access to many other platforms. The Earth Science Multi-Mission Operations program acquires, preserves, and distributes observational data from operating spacecraft to support Earth Science research focus areas. The Earth Observing System Data and Information System (EOSDIS), which has been in operations since 1994, primarily accomplishes this. EOSDIS acquires, processes, archives, and distributes Earth Science data and information products. The archiving of NASA Earth Science information happens at eight Distributed Active Archive Centers (DAACs) and four disciplinary data centers located across the United States. The DAACs specialize by topic area, and make their data available to researchers around the world. The DAACs currently house over 9 petabytes of data, growing at a rate of 6.4 terabytes per day. NASA's current Earth Science portfolio is responsive to the National Research Council (NRC) 2007 Earth Science Decadal Survey and well as the 2010 NASA Response to President Obama's Climate Plan. As the program evolves into the future it will leverage the lessons learned from the current missions in operations and development, and plan for adjustments to future objectives in response to the anticipated 2017 NRC Decadal Survey.

Flight Program↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗