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NASA NTRSDate not supplied

NASA Plans for In Situ Resource Utilization (ISRU) Development, Demonstration, and Implementation

The United States (US) National Aeronautics and Space Administration’s (NASA) Artemis Moon to Mars program has four major goals: (1) Returning Americans to the Moon: 1st Woman & 1st Person of Color, (2) Learning to live and work on the Moon, (3) Translating lessons learned so that the United States has capabilities and operational experience for a mission to Mars, and (4) Inspires the next generation of explorers, researchers, scientists, and engineers worldwide. Overarching all of this, the NASA Artemis program also continues to follow Space Policy Directive One (SPD-1) which directs the US to lead an innovative and sustainable exploration program with commercial and international partners. A major objective to achieve the Artemis program goals and SPD-1 is to understand and characterize the resources that exist at these destinations, and to learn how to utilize these resources for sustained human exploration and the commercialization of space. This ability, commonly known as In Situ Resource Utilization (ISRU), involves any hardware or operation that harnesses and utilizes local resources to create products and services for robotic and human explo-ration. The NASA ISRU program is focused on the production of mission consumables and com-modities to enable sustained human exploration, such as rocket propellants, life support consuma-bles, fuel cell reactants, feedstock for manufacturing and construction, and nutrients for food and plant growth. In particular, propellants make up a significant fraction of the mass launched from Earth, are critical to mission success, and can reduce the cost for reusable transportation. Important for enabling long term surface stays, greater independence from Earth, and growing lunar infra-structure are the abilities to perform construction and manufacturing from in situ-derived metals and materials to create and expand on the infrastructure and reduce the logistical resupply needed for sustained surface and space operations. To achieve these ISRU capabilities, NASA, in partner-ship with industry, academia, and international partners has initiated a multi-faceted program which involves (i) Determining Customer Needs (Type and Quantity of Commodities), (ii) supporting ground Development of Hardware and Systems until Ready for Lunar Flight, (iii) utilize Commer-cial Lunar Payload Services (CLPS) flights to fly resource assessment missions with the Science Mission Directorate (SMD), and public-private partnership (PPP) ISRU demonstrations of critical technologies and processes, and (iv) performing commercial-led end-to-end ‘Pilot’ Plant production of commodities and demonstration of usage at a scale and duration that minimizes or eliminates risk for full implementation of ISRU-derived commodities in mission critical applications. This paper will discuss the technologies, mission studies, and accomplishments achieved to date for the ISRU multi-faceted program, and plans for continued ground development and flight missions to reduce the risk of full ISRU implementation.

In situ resource utilization↗
NASA NTRS2006

Three Years of Hyperspectral Data from AIRS: What Have We Learned

This viewgraph presentation reviews the Atmospheric Infrared Sounder (AIRS) that is on the Aqua satellite. The expectations that were had about AIRS have been exceeded. The lessons learned from AIRS are reviewed. These lessons are used to enhance the next generation hyperspectral sounder, being planned.

accuracy↗
DOE OSTI2020

The ASHRAE Great Energy Predictor III competition: Overview and results

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. Furthermore, the competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
NASA NTRS1992

Anomalous on-orbit behaviour of the NASA Cosmic Background Explorer (COBE) Dewar

A brief summary of the nominal cryogenic performance of the Dewar is presented, with emphasis on several aspects of the helium and spacecraft dynamical behavior. The occurrence of temperature and pressure oscillators in the Dewar porous plug is examined. The impact of the internal instrument malfunctions and of external radiation sources on the performance of the Dewar and of the instruments is reviewed. From measurements of the COBE spacecraft spin rate, the spin coupling of the liquid helium to the walls of the Dewar was monitored. The spin measurements are analyzed and a model for the coupling is presented. A number of 'lessons learned' from the COBE mission are reviewed, and the applicability of these lessons to future missions involving cryogenic payloads is addressed.

Volz, S. M.↗
NASA NTRS2011

Legacy of Environmental Research During the Space Shuttle Program

The Space Shuttle Program provided many opportunities to study the role of spaceflight on human life for over the last 30 years and represents the longest and largest U.S. human spaceflight program. Risks to crewmembers were included in the research areas of nutrition, microbiology, toxicology, radiation, and sleep quality. To better understand the Shuttle environment, Crew Health Care System was developed. As part of this system, the Environmental Health Subsystem was developed to monitor the atmosphere for gaseous contaminants and microbial contamination levels and to monitor water quality and radiation. This program expended a great deal of effort in studying and mitigating risks related to contaminations due to food, water, air, surfaces, crewmembers, and payloads including those with animals. As the Shuttle had limited stowage space and food selection, the development of nutritional requirements for crewmembers was imperative. As the Shuttle was a reusable vehicle, microbial contamination was of great concern. The development of monitoring instruments that could withstand the space environment took several years and many variations to come up with a suitable instrument. Research with space radiation provided an improved understanding of the various sources of ionizing radiation and the development of monitoring instrumentation for space weather and the human exposure within the orbiter's cabin. Space toxicology matured to include the management of offgassing products that could pollute the crewmembers air quality. The Shuttle Program implemented a 5-level toxicity rating system and developed new monitoring instrumentation to detect toxic compounds. The environment of space caused circadian desynchrony, sleep deficiency, and fatigue leading to much research and major emphasis on countermeasures. Outcomes of the research in these areas were countermeasures, operational protocols, and hardware. Learning Objectives: This symposium will provide an overview of the major environmental lessons learned and the development of countermeasures, monitoring hardware, and procedures.

Lane, Helen W.↗
NASA NTRS2017

Monitoring of the Atmosphere on the International Space Station with the Air Quality Monitor

During the early years of human spaceflight, short duration missions allowed for monitoring of the spacecraft environment to be performed via archival sampling, in which samples were returned to Earth for analysis. With the construction of the International Space Station (ISS) and the accompanying extended mission durations, the need for enhanced, real-time monitors became apparent. The Volatile Organic Analyzer (VOA) operated on ISS for 7 years, where it assessed trace volatile organic compounds in the cabin air. The large and fixed-position VOA was eventually replaced with the smaller Air Quality Monitor (AQM). Since March 2013, the atmosphere of the U.S. Operating Segment (USOS) has been monitored in near real-time by a pair of AQMs. These devices consist of a gas chromatograph (GC) coupled with a differential mobility spectrometer (DMS) and currently target detection list of 22 compounds. These targets are of importance to both crew health and the Environmental Control and Life Support Systems (ECLSS) on ISS. Data is collected autonomously every 73 hours, though the units can be controlled remotely from mission control to collect data more frequently during contingency or troubleshooting operations. Due to a nominal three-year lifetime on-orbit, the initial units were replaced in February 2016. This paper will focus on the preparation and use of the AQMs over the past several years. A description of the technical aspects of the AQM will be followed by lessons learned from the deployment and operation of the first set of AQMs. These lessons were used to improve the already-excellent performance of the instruments prior to deployment of the replacement units. Data trending over the past several years of operation on ISS will also be discussed, including data obtained during a survey of the USOS modules. Finally, a description of AQM use for contingency and investigative studies will be presented.

Wallace William T.↗
DOE OSTI2021

The CanBikeCO Mini Pilot: Procedure and Preliminary Results

In fall 2020, the Colorado Energy Office, as part of the State of Colorado's "Can Do Colorado" initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, start in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The non-motorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO2. Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2016

Human Flight to Lunar and Beyond - Re-Learning Operations Paradigms

For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond LEO. The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM): EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related tacking/navigation needs. This paper will focus on the lessons learned by the team designing the architecture and operations for the missions. Many of these Beyond Earth Orbit lessons had to be re-learned, as the HSF program has operated for many years in Earth orbit. Unlike the Apollo missions that were largely tracked by a dedicated ground network, the HSF planned missions will be tracked (at distances beyond GEO) by the DSN, a network that mostly serves robotic missions. There have been surprising challenges to the DSN as unique modern human spaceflight needs stretch the experience base beyond that of tracking robotic missions in deep space. Close interaction between the DSN and the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a Concept of Operations (ConOps) that leverages both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique challenges the team faced in establishing the communications and tracking capabilities for HSF missions beyond Earth Orbit, including: Navigation. At LEO, HSF missions can rely on GPS devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually possibly on-board navigation will be used. At the same time, HSF presents a challenge to navigators, beyond those presented by robotic missions - navigating a dynamic/"noisy" spacecraft. Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec or more delay inserted (for lunar distances) or 20 minutes delay (for Mars distances). Balanced communications link. For robotic missions, there has been a heavy emphasis on higher downlink data rates, e.g. bringing back science data. Higher uplink data rates were of secondary importance, as uplink was used only to send commands (and occasionally small files) to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, a continuous forward link is established and rates for uplink and downlink are more similar.

Kenny, Edward (Ted)↗
NASA NTRS2016

Human Flight to Lunar and Beyond - Re-Learning Operations Paradigms

For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond Low-Earth Orbit (LEO). The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM) to Lunar orbit: EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related navigation, needs. This paper will focus on the lessons learned in the team, planning for the missions' parts that are beyond Earth orbit. Many of these lessons had to be re-learned, as the HSF program after operated for many years in Earth orbit. Fortunately, the experience base from tracking robotic missions in deep space by the Deep Space Network (DSN) and close interaction with the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a ConOps that leverages of both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique operational needs for HSF missions beyond Earth Orbit, including: - Navigation. At LEO, HSF missions can rely on Global Positioning System (GPS) devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually on-board navigation will be used. - Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec delay inserted (for Lunar distances) or 20 minutes delay (for Mars distances). - Balanced communications link. For robotic missions, there has been a heavy emphasis on the downlink data rates, bringing back science data from the instruments on-board the spacecraft. Uplink data rates were of secondary importance, used to send commands to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, rates for uplink and downlink, at least for high-quality video, need to be similar.

Kenny, Ted↗
NASA NTRS2010

Mars Science Laboratory CHIMRA: A Device for Processing Powdered Martian Samples

The CHIMRA is an extraterrestrial sample acquisition and processing device for the Mars Science Laboratory that emphasizes robustness and adaptability through design configuration. This work reviews the guidelines utilized to invent the initial CHIMRA and the strategy employed in advancing the design; these principles will be discussed in relation to both the final CHIMRA design and similar future devices. The computational synthesis necessary to mature a boxed-in impact-generating mechanism will be presented alongside a detailed mechanism description. Results from the development testing required to advance the design for a highly-loaded, long-life and high-speed bearing application will be presented. Lessons learned during the assembly and testing of this subsystem as well as results and lessons from the sample-handling development test program will be reviewed.

Sunshine, Daniel↗
NASA NTRS2020

COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

Interplanetary exploration occurs at vast distancesthat severely limit communication bandwidth to spacecraft exploringother planets. It is possible to collect much morescientific data than can ever be downlinked given current communicationcapabilities. Therefore, we are developing a systemcalled COSMIC (Content-based Onboard Summarization toMonitor Infrequent Change) that will opportunistically analyzedata onboard a Mars orbiter to alert scientists when meaningfulchanges have occurred. COSMIC will allow future spacecraftto continuously collect data to search for rare, transient phenomenasuch as fresh impacts or seasonally changing polarlandforms under a constrained downlink budget. In this paper,we describe the overall goals and architecture of COSMIC,plans to enable specific scientific studies, label acquisition toenable supervised approaches to surface landform classification,a new machine learning evaluation framework for analyzingthe trade-offs between classifier accuracy and computationalrequirements, and lessons learned about constraints that COSMICwill face operating onboard a spacecraft. In particular, wediscuss design considerations surrounding computational andstorage constraints, change detection strategies, and localizingdetected landforms of interest within a global coordinate frame.Finally, we describe challenges and open research questions thatmust be addressed prior to deploying COSMIC.

Trockman, Asher↗
NASA NTRS2009

Learning from Experience Case Studies of the Hyper-X Project

The Hyper-X project (X-43A) provides a number of "lessons learned" which can be applied to other aerospace project. The specific areas examined were the selection of the goals of the Hyper-X. How the technical unknowns and assumptions were handled. The final lesson was the ambiguous nature of risk assessment, and how trying to remove a technical unknown can have unintended consequences.

Peebles, Curtis↗
DOE OSTI2024

Solar Energy Innovation Network 2017-2024: Abbreviated Final Technical Report

This material is based upon work supported by the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office under the Agreement/Award Number 32954 for Solar Energy Innovation Network (SEIN) Project, 2017-2024. SEIN is a dynamic program that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. In conjunction with its partner organizations, NREL implemented the program by providing research, analysis, and technical expertise directly to project teams and groups of teams (cohorts), by facilitating networked learning through cohorts and peer exchange, and by facilitating dissemination and replication of solutions and lessons learned among stakeholders across the U.S. with similar challenges.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

BioSentinel: Forging the path for Deep Space CubeSat Missions

The BioSentinel mission was launched in 2022 aboard the SLS launch vehicle as part of the Artemis-I campaign and continues mission operations into 2024. The 6U CubeSat has been characterizing deep space radiation at large distances from Earth. This presentation gives a status of the mission performance to date, as well as some of the lessons learned from project. BioSentinel has achieved unprecedented performance as an SLS secondary payload due to preparation, planning, and a robust design. Pre-launch antenna and interface testing with both DSN and ESA confirmed command and data pathways and allowed for operational flexibility in the critical early hours post-deployment. Mission Operations simulations prior to launch identified potential risks and trained operators to respond in flight, preparing the team to react quickly and successfully to detumble the spacecraft and enter a power-positive state. The spacecraft would not have survived without the inclusion of the trailblazing 3D-printed composite cold gas propulsion system. The non-standard tank geometry enabled efficient use of the limited space available in the CubeSat form factor as well as the capability to detumble the spacecraft and manage momentum for extended mission durations, while providing sufficient margin to execute potential delta-V maneuvers. Following the conclusion of the primary science mission, the Linear Energy Transfer (LET) Spectrometer has continued to collect solar and galactic radiation data from its unique location in heliocentric orbit. The free space dataset offered by the BioSentinel LET is a valuable source of data for both model validation and future mission planning. As the spacecraft travels farther from Earth it is poised to provide longitudinally distributed measurements of solar particle events during solar maximum. NASA Ames led development of the BioSentinel spacecraft to operate for long durations in deep space. The novel subsystems and COTS components that comprise the BioSentinel bus can serve as a template for future deep space missions, while the lessons the team has learned from well over a year of continuous operations will enable improved performance in the generation of deep space CubeSat missions.

BioSentinel↗
NASA NTRS2010

The Emergence of Agent-Based Technology as an Architectural Component of Serious Games

The evolution of games as an alternative to traditional simulations in the military context has been gathering momentum over the past five years, even though the exploration of their use in the serious sense has been ongoing since the mid-nineties. Much of the focus has been on the aesthetics of the visuals provided by the core game engine as well as the artistry provided by talented development teams to produce not only breathtaking artwork, but highly immersive game play. Consideration of game technology is now so much a part of the modeling and simulation landscape that it is becoming difficult to distinguish traditional simulation solutions from game-based approaches. But games have yet to provide the much needed interactive free play that has been the domain of semi-autonomous forces (SAF). The component-based middleware architecture that game engines provide promises a great deal in terms of options for the integration of agent solutions to support the development of non-player characters that engage the human player without the deterministic nature of scripted behaviors. However, there are a number of hard-learned lessons on the modeling and simulation side of the equation that game developers have yet to learn, such as: correlation of heterogeneous systems, scalability of both terrain and numbers of non-player entities, and the bi-directional nature of simulation to game interaction provided by Distributed Interactive Simulation (DIS) and High Level Architecture (HLA).

Phillips, Mark↗
DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
NASA NTRS2014

Cutting More than Metal: Breaking the Development Cycle

New technology is changing the way we do business at NASA. The ability to use these new tools is made possible by a learning culture able to embrace innovation, flexibility, and prudent risk tolerance, while retaining the hard-won lessons learned of other successes and failures. Technologies such as 3-D manufacturing and structured light scanning are re-shaping the entire product life cycle, from design and analysis, through production, verification, logistics and operations. New fabrication techniques, verification techniques, integrated analysis, and models that follow the hardware from initial concept through operation are reducing the cost and time of building space hardware. Using these technologies to be more efficient, reliable and affordable requires we bring them to a level safe for NASA systems, maintain appropriate rigor in testing and acceptance, and transition new technology. Maximizing these technologies also requires cultural acceptance and understanding and balancing rules with creativity. Evolved systems engineering processes at NASA are increasingly more flexible than they have been in the past, enabling the implementation of new techniques and approaches. This paper provides an overview of NASA Marshall Space Flight Center's new approach to development, as well as examples of how that approach has been incorporated into NASA's Space Launch System (SLS) Program, which counts among its key tenants - safety, affordability, and sustainability. One of the 3D technologies that will be discussed in this paper is the design and testing of various rocket engine components.

Singer, Chris↗