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NASA NTRS2000

Applying Technology Ranking and Systems Engineering in Advanced Life Support

According to the Advanced Life Support (ALS) Program Plan, the Systems Modeling and Analysis Project (SMAP) has two important tasks: 1) prioritizing investments in ALS Research and Technology Development (R&TD), and 2) guiding the evolution of ALS systems. Investments could be prioritized simply by independently ranking different technologies, but we should also consider a technology's impact on system design. Guiding future ALS systems will require SMAP to consider many aspects of systems engineering. R&TD investments can be prioritized using familiar methods for ranking technology. The first step is gathering data on technology performance, safety, readiness level, and cost. Then the technologies are ranked using metrics or by decision analysis using net present economic value. The R&TD portfolio can be optimized to provide the maximum expected payoff in the face of uncertain future events. But more is needed. The optimum ALS system can not be designed simply by selecting the best technology for each predefined subsystem. Incorporating a new technology, such as food plants, can change the specifications of other subsystems, such as air regeneration. Systems must be designed top-down starting from system objectives, not bottom-up from selected technologies. The familiar top-down systems engineering process includes defining mission objectives, mission design, system specification, technology analysis, preliminary design, and detail design. Technology selection is only one part of systems analysis and engineering, and it is strongly related to the subsystem definitions. ALS systems should be designed using top-down systems engineering. R&TD technology selection should consider how the technology affects ALS system design. Technology ranking is useful but it is only a small part of systems engineering.

Jones, Harry↗

NASA NTRS2003

Risk-based Classification of Incidents

As the penetration of software into safety-critical systems progresses, accidents and incidents involving software will inevitably become more frequent. Identifying lessons from these occurrences and applying them to existing and future systems is essential if recurrences are to be prevented. Unfortunately, investigative agencies do not have the resources to fully investigate every incident under their jurisdictions and domains of expertise and thus must prioritize certain occurrences when allocating investigative resources. In the aviation community, most investigative agencies prioritize occurrences based on the severity of their associated losses, allocating more resources to accidents resulting in injury to passengers or extensive aircraft damage. We argue that this scheme is inappropriate because it undervalues incidents whose recurrence could have a high potential for loss while overvaluing fairly straightforward accidents involving accepted risks. We then suggest a new strategy for prioritizing occurrences based on the risk arising from incident recurrence.

Greenwell, William S.↗

NASA NTRS2003

Research priorities and plans for the International Space Station-results of the 'REMAP' Task Force

Recent events in the International Space Station (ISS) Program have resulted in the necessity to re-examine the research priorities and research plans for future years. Due to both technical and fiscal resource constraints expected on the International Space Station, it is imperative that research priorities be carefully reviewed and clearly articulated. In consultation with OSTP and the Office of Management and budget (OMB), NASA's Office of Biological and Physical Research (OBPR) assembled an ad-hoc external advisory committee, the Biological and Physical Research Maximization and Prioritization (REMAP) Task Force. This paper describes the outcome of the Task Force and how it is being used to define a roadmap for near and long-term Biological and Physical Research objectives that supports NASA's Vision and Mission. Additionally, the paper discusses further prioritizations that were necessitated by budget and ISS resource constraints in order to maximize utilization of the International Space Station. Finally, a process has been developed to integrate the requirements for this prioritized research with other agency requirements to develop an integrated ISS assembly and utilization plan that maximizes scientific output. c2003 American Institute of Aeronautics and Astronautics. Published by Elsevier Science Ltd. All rights reserved.

NASA Center HQS↗

NASA NTRS2004

Identifying, Assessing, and Mitigating Risk of Single-Point Inspections on the Space Shuttle Reusable Solid Rocket Motor

In the production of each Space Shuttle Reusable Solid Rocket Motor (RSRM), over 100,000 inspections are performed. ATK Thiokol Inc. reviewed these inspections to ensure a robust inspection system is maintained. The principal effort within this endeavor was the systematic identification and evaluation of inspections considered to be single-point. Single-point inspections are those accomplished on components, materials, and tooling by only one person, involving no other check. The purpose was to more accurately characterize risk and ultimately address and/or mitigate risk associated with single-point inspections. After the initial review of all inspections and identification/assessment of single-point inspections, review teams applied risk prioritization methodology similar to that used in a Process Failure Modes Effects Analysis to derive a Risk Prioritization Number for each single-point inspection. After the prioritization of risk, all single-point inspection points determined to have significant risk were provided either with risk-mitigating actions or rationale for acceptance. This effort gave confidence to the RSRM program that the correct inspections are being accomplished, that there is appropriate justification for those that remain as single-point inspections, and that risk mitigation was applied to further reduce risk of higher risk single-point inspections. This paper examines the process, results, and lessons learned in identifying, assessing, and mitigating risk associated with single-point inspections accomplished in the production of the Space Shuttle RSRM.

Greenhalgh, Phillip O.↗

NASA NTRS2009

A Mixed Integer Linear Program for Airport Departure Scheduling

Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced delays as compared to FCFS, and the magnitude of the savings depends on the queue and departure fix structure. The MILP assumes deterministic aircraft arrival times at the runway queues. However, due to taxi time uncertainty, aircraft might arrive either earlier or later than these deterministic times. Thus, to incorporate this uncertainty, we present a method for using the MILP with "overlap discounted rolling planning horizon". The approach is based on valuing near-term decision results more than future ones. We develop a model of taxitime uncertainty based on real-world data, and then compare the baseline FCFS delays with delays using the above MILP in a simple rolling-horizon method and in the overlap discounted scheme.

Gupta, Gautam↗

NASA NTRS2011

An Interim Report on NASA's Draft Space Technology Roadmaps

NASA has developed a set of 14 draft roadmaps to guide the development of space technologies under the leadership of the NASA Office of the Chief Technologist (OCT). Each of these roadmaps focuses on a particular technology area (TA). The roadmaps are intended to foster the development of advanced technologies and concepts that address NASA's needs and contribute to other aerospace and national needs. OCT requested that the National Research Council conduct a study to review the draft roadmaps, gather and assess relevant community input, and make recommendations and suggest priorities to inform NASA's decisions as it finalizes its roadmaps. The statement of task states that "based on the results of the community input and its own deliberations, the steering committee will prepare a brief interim report that addresses high-level issues associated with the roadmaps, such as the advisability of modifying the number or technical focus of the draft NASA roadmaps." This interim report, which does not include formal recommendations, addresses that one element of the study charge. NASA requested this interim report so that it would have the opportunity to make an early start in modifying the draft roadmaps based on feedback from the panels and steering committee. The final report will address all other tasks in the statement of task. In particular, the final report will include a prioritization of technologies, will describe in detail the prioritization process and criteria, and will include specific recommendations on a variety of topics, including many of the topics mentioned in this interim report. In developing both this interim report and the final report to come, the steering committee draws on the work of six study panels organized by technical area, loosely following the organization of the 14 roadmaps, as follows: A Panel 1: Propulsion and Power TA01 Launch Propulsion Systems TA02 In-Space Propulsion Technologies TA03 Space Power and Energy Storage Systems TA13 Ground and Launch Systems Processing B Panel 2: Robotics, Communications, and Navigation TA04 Robotics, TeleRobotics, and Autonomous Systems TA05 Communication and Navigation Systems C Panel 3: Instruments and Computing TA08 Science Instruments, Observatories, and Sensor Systems TA11 Modeling, Simulation, Information Technology, and Data Processing D Panel 4: Human Health and Surface Exploration TA06 Human Health, Life Support, and Habitation Systems TA07 Human Exploration Destination Systems E Panel 5: Materials Panel TA10 Nanotechnology TA12 Materials, Structures, Mechanical Systems, and Manufacturing TA14 Thermal Management Systems F Panel 6: Entry, Descent, and Landing Panel TA09 Entry, Descent, and Landing Systems In addition to drawing on the expertise represented on the steering committee and panels, the committee obtained input from each of 14 public workshops held on each of the 14 roadmaps. At these 1-day workshops, invited speakers, guests, and members of the public engaged in discussions on the different technology areas and their value to NASA. Broad community input was also solicited from a public website, where more than 240 public comments were received on the draft roadmaps in response to application of criteria (such as benefit, risk and reasonableness, and alignment with NASA and national goals) that the steering committee established. This interim report reflects the results of deliberations by the steering committee in light of these public inputs as well as additional inputs from the six panels. The steering committee's final report will be completed early in 2012. That report will prioritize the technologies that span the entire scope of the 14 roadmaps and provide additional guidance on crosscutting themes and other relevant topics.

Source record↗

NASA NTRS2012

Developing a Strategic Plan for NASA JSC's Technology Investments

Human space exploration has always been heavily influenced by goals to achieve a specific mission on a specific schedule. This approach drove rapid technology development, the rapidity of which adds risks as well as provides a major driver for costs. The National Aeronautics and Space Administration (NASA) is now approaching the extension of human presence throughout the solar system by balancing a proactive yet less schedule-driven development of technology with opportunistic scheduling of missions as the needed technologies are realized. This approach should provide cost effective, low risk technology development that will enable efficient and effective manned spaceflight missions. As a first step, the NASA Human Spaceflight Architecture Team (HAT) has identified a suite of critical technologies needed to support future manned missions across a range of destinations, including in cislunar space, near earth asteroid visits, lunar exploration, Mars space, and Mars exploration. The challenge now is to develop a strategy and plan for technology development that efficiently enables these missions over a reasonable time period, without increasing technology development costs unnecessarily due to schedule pressure, and subsequently mitigating development and mission risks. NASA fs Johnson Space Center (JSC), as the nation's primary center for human exploration, is addressing this challenge through an innovative approach allocating Internal Research and Development funding to projects that have been prioritized using four focus criteria, with appropriate importance weighting. These four focus criteria are the Human Space Flight Technology Needs, JSC Core Technology Competencies, Commercialization Potential, and Partnership Potential. The inherent coupling in these focus criteria have been captured in a database and have provided an initial prioritization for allocation of technology development research funding. This paper will describe this process and this database, and the preliminary technology development prioritization results.

Stecklein, Jonette M.↗

NASA NTRS2016

Decision Analysis Methods Used to Make Appropriate Investments in Human Exploration Capabilities and Technologies

NASA is transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities in low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond Earth for extended periods of time. However, pioneering space involves daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. Prudent investments in capability and technology developments, based on mission need, are critical for enabling a campaign of human exploration missions. There are a wide variety of capabilities and technologies that could enable these missions, so it is a major challenge for NASA's Human Exploration and Operations Mission Directorate (HEOMD) to make knowledgeable portfolio decisions. It is critical for this pioneering initiative that these investment decisions are informed with a prioritization process that is robust and defensible. It is NASA's role to invest in targeted technologies and capabilities that would enable exploration missions even though specific requirements have not been identified. To inform these investments decisions, NASA's HEOMD has supported a variety of analysis activities that prioritize capabilities and technologies. These activities are often based on input from subject matter experts within the NASA community who understand the technical challenges of enabling human exploration missions. This paper will review a variety of processes and methods that NASA has used to prioritize and rank capabilities and technologies applicable to human space exploration. The paper will show the similarities in the various processes and showcase instances were customer specified priorities force modifications to the process. Specifically, this paper will describe the processes that the NASA Langley Research Center (LaRC) Technology Assessment and Integration Team (TAIT) has used for several years and how those processes have been customized to meet customer needs while staying robust and defensible. This paper will show how HEOMD uses these analyses results to assist with making informed portfolio investment decisions. The paper will also highlight which human exploration capabilities and technologies typically rank high regardless of the specific design reference mission. The paper will conclude by describing future capability and technology ranking activities that will continue o leverage subject matter experts (SME) input while also incorporating more model-based analysis.

Williams-Byrd, Julie↗

NASA NTRS2015

Asteroid Characterization Priorities for Planetary Defense

We propose a prioritized list of asteroid characterization needs for planetary defense. In particular, we consider the properties of asteroids that are of greatest interest for assessment of planetary defense options, including gravity tractors, kinetic impactors, and nuclear explosives. In addition, much of our discussion is relevant for impact assessments and subsequent emergency-response planning. Rather than intending this as a definitive answer, however, our purpose is to stimulate and focus disscussion regarding characterization needs for planetary defense, with a specific list as a starting point. A key theme is understanding the sensitivity of the outcome of an asteroid deflection or disruption effort to the asteroids physical properties.There is a range of previous work relevant to our topic, including some explicit discussions as well as many more that are implicitly relevant. We incorporate elements from such reports while extending them using our own experience and perspectives. After introducing our prioritized list, we provide further discussion on each element, with details on the relevance of each characteristic for modeling purposes, our rationale for the assigned priority, and examples of analyses that require improved characterization information. Our goal is to establish a framework that can be modified and adapted by the community for a variety of purposes, such as mission design and optimization, development of new measurement techniques, or prioritization of research efforts. The objective is to increase understanding and reduce uncertainties in the specific aspects of characterization that most benefit accurate assessments of practical techniques for planetary defense.

Miller, Paul↗

NASA NTRSDate not supplied

Decision Framework for Classifying the State of Knowledge for Pharmaceutical Stability in Spaceflight

The currently approved medication formulary for exploration-class spaceflight missions presently exceeds 200pharmaceuticals. However, these medications' physical and chemical stability during long-term exposure to the spaceflight environment remains uncharacterized. A multi-disciplinary team of pharmacy and spaceflight subject matter experts (SMEs) collaborated to develop a decision framework that will prioritize medications for future stability research. This framework prioritizes the physical stability, drug quality, and safety of the active pharmaceutical ingredient of finished drug products selected for specific design reference mission (DRM) drug formularies. The United States Pharmacopeia (USP) defines drug stability as "the extent to which a drug product retains, within specified limits, and throughout its period of storage and use, the same properties and characteristics that it possessed at the time of its manufacture.1"The candidate medication is assessed by applying a sequence of drug agnostic questions to evaluate the limits of stability information pertinent to spaceflight. The answers to these questions ultimately lead to one of three characterizations: green (not prioritized for further research for this DRM), yellow (requires further literature review/additional studies), or red (not suitable for this DRM based on current knowledge). The decision framework will rely on several information resources to inform the ultimate decision, including the ExMC Pharmacy Information Database, published literature, FDA/drug manufacturer monographs, and USP. The results of this framework will be used to classify the state of knowledge for pharmaceutical stability for any specified DRM and guide future research efforts accordingly. This presentation will provide an overview of how this decision framework was developed, detail the challenges and limitations of the pathways, and discuss how researchers can apply the lessons learned from this project to future work

S Kurian↗

NASA NTRS2022

Development of an Accepted Medical Condition List for Exploration Medical Capability Scoping

Future NASA human spaceflight programs are on the verge of moving beyond Low Earth Orbit (LEO) to implement missions in lunar space and ultimately Mars. The mission constraints for these types of missions are expected to be progressively challenging for integration of Human Systems requirements into the vehicle and mission architectures. Mass and volume allocations are expected to become increasingly restrictive at the same time that mission realities will drive an increasing need for crew self-sufficiency in the maintenance and repair of both vehicle systems and human systems. To meet these challenges, a systematic, traceable, and repeatable approach to identifying, defining, and prioritizing medical capabilities is required. To provide a systematic and repeatable approach to defining and prioritizing clinical capabilities for spaceflight medicine, a clear process is required for delivering a list of prioritized medical capabilities to the Systems Engineering process that will delineate the mass, power, volume, and similar needs and the trade space analysis for a given space vehicle and mission architecture.

Rebecca Blue↗

NASA NTRSDate not supplied

Selection of Next Priority IMPACT Medical Conditions Based on Available Terrestrial and Spaceflight Data

BACKGROUND: As the era of exploration class missions begins, identification of medical conditions that may occur and require management becomes essential for the modeling of medical risk. To this end, NASA has developed IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces), a suite of tools to assist in assessment of medical risk analysis. It has incorporated an initial list of the 120 conditions of highest concern, labeled the IMPACT condition list 1.0 (ICL 1.0). This abstract describes a method for prioritizing the 92 conditions included on the Proposed Future Conditions List (PFCL) for inclusion in future iterations of the ICL. OVERVIEW: To construct the Prioritized Proposed Future Conditions List (P-PFCL), each condition on the PFCL was scored as “low,” “medium,” or “high” on each of four variables: incidence, likelihood of significant task impairment, diagnostic and treatment complexity, and treatment futility. Qualitative assessment using clinical judgement was utilized to score complexity, futility, and likelihood of impairment. Incidence was assessed quantitatively using spaceflight data and/or analog populations where available then assigned a score using established cutoffs. Logarithmic numerical values were assigned to each category label. A Prioritization Score was generated for each condition by taking the product of incidence and likelihood of task impairment (risk) divided by the product of complexity and futility (difficulty of care), with higher values corresponding to higher priority for future inclusion in the ICL. DISCUSSION: The described methods allow for the generation of a ranked P-PFCL to act as a decision support tool for selection of the next generation of modeled medical conditions. Some of the conditions ranked highly on the P-PFCL include EVA-related upper and lower extremity sprain/strain, iron deficiency, delirium, and hypertension, among others. While this effort does not attempt to quantify the absolute risk associated with each condition, it does attempt to semi-quantitatively estimate the risk of each condition relative to the other possible conditions. This tool in concert with subject matter expert opinion could optimize the future use of limited resources thereby producing a more accurate medical risk model, which will be essential to the upcoming exploration class missions.

Michael Pohlen↗

NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗

NASA NTRSDate not supplied

Autonomous Science: Simulated Solar System Mission to Enceladus, Icy Ocean Moon of Saturn

Future NASA missions to icy ocean worlds such as Europa, Titan, or Enceladus will collect mass spectrometry (MS) data from exospheres, atmospheres, and plume volatiles to assess their geochemistry and potential for microbial life1. These remote missions face challenges related to limited bandwidth, communication, and power, highlighting the need for science autonomy2 to prioritize data for timely downlink. We extend our previous work on machine learning (ML) biosignature detection from isotope ratio mass spectrometry (IRMS) data to distributed systems missions (DSM) by incorporating the ML and autonomous data quality control and prioritization code into an onboard decision-making platform. We use simulated orbital telemetry for eight orbiters around Enceladus with a mothership to illustrate an automated biosignature and novel seawater chemistry detection with data prioritization from MS analyses of volatile CO2.

Conor Williams↗

NASA NTRSDate not supplied

Update on NASA’s ISRU Development and Mission Plans for the Artemis Program

In 2017, NASA initiated the Artemis program to send astronauts back to the lunar surface, create a sustainable human lunar exploration program, and lead the first human exploration mission to the Mars surface. While much of NASA’s plans for the Artemis program currently focus on the Human Lunar Return and the ability for astronauts to explore the lunar surface for limited durations each year, the longer-term vision for the Artemis program is to enable sustained human exploration and commercial operations in cis-lunar space and the lunar surface. An important aspect of achieving this long-term vision, is to better understand and characterize the resources on the Moon and Mars and learn how to extract and use these resources. Known as In Situ Resource Utilization (ISRU), the identification, mapping, extraction, and processing of space resources has the potential to greatly reduce the cost and risk of human exploration. These are achieved by reducing what needs to be delivered from Earth and the dependency on these supplies, lowering costs through commercial operations, and expanding infrastructure for safer and more capable exploration and surface operations. To guide development of ISRU technologies and systems on the ground and demonstrate these capabilities on the Moon and Mars, the NASA Space Technology Mission Directorate (STMD) created and released the ISRU Envisioned Future Priorities (EFP) strategic plan in 2022 and updated it in 2023. While lunar ISRU technology development had already started, these publicly released strategic plans have been used to guide and prioritize technology development, and assess the progress in achieving the vision. Since the release of the ISRU EFP, there have been several significant activities/events that have occurred with respect to human lunar exploration, surface infrastructure, and ISRU. One was the release of the Artemis Architecture Definition Document Revision 1 that included ISRU as a sub-architecture. The second was the release of several STMD solicitations including the Announcement of Collaborative Opportunities (ACO) and Tipping Point (TP). The third was the release of a Request for Information for the Lunar Infrastructure Foundational Technology-1 (LIFT-1) mission with the primary objective of extracting oxygen from lunar regolith. The fourth was the release of STMD technology and capability Shortfalls, and the review, ranking by numerous stakeholders and individuals, and subsequent prioritization of the Shortfalls that will be utilized in future solicitation and development plans. This paper will provide an overview and status of on-going technology and system development activities, an update of ISRU into the Artemis campaign, an update on ISRU-related mission, and the impacts of the Shortfall prioritization.

NASA↗

DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗

DOE OSTI2025

IoT-based retrofit information diffusion in future smart communities

Community-scale building retrofits are not merely scaled-up versions of single-building retrofits. They involve complex challenges, such as reconciling individual interests with collective goals and managing the dynamic interplay between buildings through mechanisms like power grids and social connections. Internet of Things (IoT) connectivity holds the potential to leverage these interplays to balance individual and collective interests effectively in smart communities. One critical aspect of this interplay is information diffusion, which shapes how retrofit decisions spread among neighbors, influencing individual choices and ultimately impacting community-level retrofit outcomes. In other words, IoT-based smart devices automatically push tailored retrofit notifications to homeowners, which completely changes the format of information diffusion in the future. To investigate this influence by such information diffusion, the study used CityBES to simulate energy performance for different retrofits and applied an information diffusion model to analyze how decisions spread in a networked community of 192 buildings. The diffusion process was modeled on a weighted, directed network, capturing the dynamics of information flow and decision-making across 16 scenarios. Individual retrofit benefits were evaluated through payback years, while community-level retrofit outcomes were assessed using greenhouse gas (GHG) emission reductions. The results demonstrate that easier information diffusion among neighbors encourages households to prioritize retrofit measures that align with the majority’s optimal choices, even at the expense of individual financial benefits. In this case, such collective prioritization enhanced community-level retrofit performance, increasing GHG emission reductions by up to 29.4 %. However, this improvement came with trade-offs, as the average payback period for households extended by approximately 1.74 years. These findings highlight the potential of IoT-based information diffusion in future smart communities to coordinate individual interests with collective goals, ultimately accelerating community-level building retrofits.

Shu, Lei↗

DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗