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NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Distributed↗

NASA NTRS2019

Liquid Nitrogen Testing of ISRU Liquefaction Methods in Unsteady Applications

To enable NASA’s planned long duration missions, the agency is putting emphasis on reusable cryogenic systems. Such systems will require replenishing of cryogens on-orbit via a cryogenic tanker or refueling depot, and potentially on the lunar or Martian surfaces with the utilization of in-situ resources. Surface replenishing requires the in-situ production of gaseous oxygen (and hydrogen if on the lunar surface), followed by liquefaction and storage. Funded by NASA’s Advanced Exploration Systems, and managed under the Advanced Cis-Lunar Space Capability Project, the Cryogenic Fluid In-Situ Liquefaction for Landers (CryoFILL) multi center team was formed to develop a liquefaction and storage system that is efficient, reliable and scalable. This presentation will demonstrate the liquefaction and storage of “In-Situ like” propellant via a Tube-On-Tank Heat Exchanger integrated with Active Cooling (cryocooler) (verify proof of concept and obtain relevant data for model validation) and gather lessons learned from “brassboard” testing which will be applied to future liquefaction system prototype testing, then eventually to an end-to-end demonstration.

Stephens, J. R.↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

NASA NTRS2013

Unique Challenges Testing SDRs for Space

This paper describes the approach used by the Space Communication and Navigation (SCaN) Testbed team to qualify three Software Defined Radios (SDR) for operation in space and the characterization of the platform to enable upgrades on-orbit. The three SDRs represent a significant portion of the new technologies being studied on board the SCAN Testbed, which is operating on an external truss on the International Space Station (ISS). The SCaN Testbed provides experimenters an opportunity to develop and demonstrate experimental waveforms and applications for communication, networking, and navigation concepts and advance the understanding of developing and operating SDRs in space. Qualifying a Software Defined Radio for the space environment requires additional consideration versus a hardware radio. Tests that incorporate characterization of the platform to provide information necessary for future waveforms, which might exercise extended capabilities of the hardware, are needed. The development life cycle for the radio follows the software development life cycle, where changes can be incorporated at various stages of development and test. It also enables flexibility to be added with minor additional effort. Although this provides tremendous advantages, managing the complexity inherent in a software implementation requires a testing beyond the traditional hardware radio test plan. Due to schedule and resource limitations and parallel development activities, the subsystem testing of the SDRs at the vendor sites was primarily limited to typical fixed transceiver type of testing. NASA s Glenn Research Center (GRC) was responsible for the integration and testing of the SDRs into the SCaN Testbed system and conducting the investigation of the SDR to advance the technology to be accepted by missions. This paper will describe the unique tests that were conducted at both the subsystem and system level, including environmental testing, and present results. For example, test waveforms were developed to measure the gain of the transmit system across the tunable frequency band. These were used during thermal vacuum testing to enable characterization of the integrated system in the wide operational temperature range of space. Receive power indicators were used for Electromagnetic Interference tests (EMI) to understand the platform s susceptibility to external interferers independent of the waveform. Additional approaches and lessons learned during the SCaN Testbed subsystem and system level testing will be discussed that may help future SDR integrators

Chelmins, David↗

NASA NTRS2013

Unique Challenges Testing SDRs for Space

This paper describes the approach used by the Space Communication and Navigation (SCaN) Testbed team to qualify three Software Defined Radios (SDR) for operation in space and the characterization of the platform to enable upgrades on-orbit. The three SDRs represent a significant portion of the new technologies being studied on board the SCAN Testbed, which is operating on an external truss on the International Space Station (ISS). The SCaN Testbed provides experimenters an opportunity to develop and demonstrate experimental waveforms and applications for communication, networking, and navigation concepts and advance the understanding of developing and operating SDRs in space. Qualifying a Software Defined Radio for the space environment requires additional consideration versus a hardware radio. Tests that incorporate characterization of the platform to provide information necessary for future waveforms, which might exercise extended capabilities of the hardware, are needed. The development life cycle for the radio follows the software development life cycle, where changes can be incorporated at various stages of development and test. It also enables flexibility to be added with minor additional effort. Although this provides tremendous advantages, managing the complexity inherent in a software implementation requires a testing beyond the traditional hardware radio test plan. Due to schedule and resource limitations and parallel development activities, the subsystem testing of the SDRs at the vendor sites was primarily limited to typical fixed transceiver type of testing. NASA's Glenn Research Center (GRC) was responsible for the integration and testing of the SDRs into the SCaN Testbed system and conducting the investigation of the SDR to advance the technology to be accepted by missions. This paper will describe the unique tests that were conducted at both the subsystem and system level, including environmental testing, and present results. For example, test waveforms were developed to measure the gain of the transmit system across the tunable frequency band. These were used during thermal vacuum testing to enable characterization of the integrated system in the wide operational temperature range of space. Receive power indicators were used for Electromagnetic Interference tests (EMI) to understand the platform's susceptibility to external interferers independent of the waveform. Additional approaches and lessons learned during the SCaN Testbed subsystem and system level testing will be discussed that may help future SDR integrators.

transmitters receivers↗

NASA NTRS2017

Integrated Systems Health Management for Sustainable Habitats (Using Sustainability Base as a Testbed)

Habitation systems provide a safe place for astronauts to live and work in space and on planetary surfaces. They enable crews to live and work safely in deep space, and include integrated life support systems, radiation protection, fire safety, and systems to reduce logistics and the need for resupply missions. Innovative health management technologies are needed in order to increase the safety and mission-effectiveness for future space habitats on other planets, asteroids, or lunar surfaces. For example, off-nominal or failure conditions occurring in safety-critical life support systems may need to be addressed quickly by the habitat crew without extensive technical support from Earth due to communication delays. If the crew in the habitat must manage, plan and operate much of the mission themselves, operations support must be migrated from Earth to the habitat. Enabling monitoring, tracking, and management capabilities on-board the habitat and related EVA platforms for a small crew to use will require significant automation and decision support software.Traditional caution and warning systems are typically triggered by out-of-bounds sensor values, but can be enhanced by including machine learning and data mining techniques. These methods aim to reveal latent, unknown conditions while still retaining and improving the ability to provide highly accurate alerts for known issues. A few of these techniques will briefly described, along with performance targets for known faults and failures. Specific system health management capabilities required for habitat system elements (environmental control and life support systems, etc.) may include relevant subsystems such as water recycling systems, photovoltaic systems, electrical power systems, and environmental monitoring systems. Sustainability Base, the agency's flagship LEED-platinum certified green building acts as a living laboratory for testing advanced information and sustainable technologies that provides an opportunity to test novel machine learning and controls capabilities. In this talk, key features of Sustainability Base that make it relevant to deep space habitat technology and its use of these kinds of subsystems previously listed will be presented. The fact that all such systems require less power to support human occupancy can be used as a focal point to serve as a testbed for deep space habitats that will need to operate within finite energy budgets.

Systems Health Management↗

DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2006

Logistics Lessons Learned in NASA Space Flight

The Vision for Space Exploration sets out a number of goals, involving both strategic and tactical objectives. These include returning the Space Shuttle to flight, completing the International Space Station, and conducting human expeditions to the Moon by 2020. Each of these goals has profound logistics implications. In the consideration of these objectives,a need for a study on NASA logistics lessons learned was recognized. The study endeavors to identify both needs for space exploration and challenges in the development of past logistics architectures, as well as in the design of space systems. This study may also be appropriately applied as guidance in the development of an integrated logistics architecture for future human missions to the Moon and Mars. This report first summarizes current logistics practices for the Space Shuttle Program (SSP) and the International Space Station (ISS) and examines the practices of manifesting, stowage, inventory tracking, waste disposal, and return logistics. The key findings of this examination are that while the current practices do have many positive aspects, there are also several shortcomings. These shortcomings include a high-level of excess complexity, redundancy of information/lack of a common database, and a large human-in-the-loop component. Later sections of this report describe the methodology and results of our work to systematically gather logistics lessons learned from past and current human spaceflight programs as well as validating these lessons through a survey of the opinions of current space logisticians. To consider the perspectives on logistics lessons, we searched several sources within NASA, including organizations with direct and indirect connections with the system flow in mission planning. We utilized crew debriefs, the John Commonsense lessons repository for the JSC Mission Operations Directorate, and the Skylab Lessons Learned. Additionally, we searched the public version of the Lessons Learned Information System (LLIS) and verified that we received the same result using the internal version of LLIS for our logistics lesson searches. In conducting the research, information from multiple databases was consolidated into a single spreadsheet of 300 lessons learned. Keywords were applied for the purpose of sorting and evaluation. Once the lessons had been compiled, an analysis of the resulting data was performed, first sorting it by keyword, then finding duplication and root cause, and finally sorting by root cause. The data was then distilled into the top 7 lessons learned across programs, centers, and activities.

Evans, William A.↗

NASA NTRS2023

Technology Infusion in U.S. Spacesuits - A Comparative System Analysis

The National Aeronautics and Space Administration (NASA) has evolved multiple spacesuit systems for performing extravehicular activity (EVA) or space walks. These spacesuit systems include the Apollo Extravehicular Mobility Unit (EMU), Space Shuttle and International Space Station (ISS) EMU, and Exploration EMU (xEMU). Each spacesuit system is like the other for functionality. However, each spacesuit system is different in configuration based on the technology infused into the system associated with the purpose of the mission. Each spacesuit system is made up of many components and the integrate environment targeted for operations leads to an integrated system that is complex. Since Apollo, NASA has invested in multiple technologies that make up these spacesuit systems in different iterations. The Apollo EMU was designed in the 1960’s with a focus to facilitate the first human to walk on the moon. The Space Shuttle EMU was designed in the 1970’s for reusable microgravity operations that began in the early 1980’s. The Space Shuttle EMU was enhanced to facilitate extended operation on the ISS. Over the last 15 years, NASA has been designing, developing, and testing a new spacesuit system, the xEMU which is considered a design, verification, and test unit. NASA is planning to land the first woman and first person of color on the Moon. NASA recently engaged industry through a new contractual arrangement to provide EVA services needed to return to the Moon and to continue operations on the ISS. Spacesuit systems are complex. Understanding the requirements, operational environment, the necessary technologies, and the integrated spacesuit system are paramount. In addition, understanding the technology infusion process to meet the mission objectives is critical. This paper will review the spacesuit systems for EVA and several component functions within the spacesuits, along with a system comparison of those technologies from Apollo to xEMU.

Extravehicular Activity (EVA)↗

DOE OSTI2022

Distribution Function Instead of Steady-State Assumption in Time-Series Simulation

The quasi-steady-state assumption in time-series simulation is inadequate to model phenomena of interest to energy system integration such as inverter clipping, sell-back of excess electricity to a utility and utility hosting capacity, and battery throughput. Researchers are working on stochastic modeling, higher-resolution time series data, and machine learning approaches to address this need. This poster describes a distribution function that allows a maximum value, minimum value, and shape to the curve that can vary within each time-step based only on data inputs at the resolution of that time step (eg. Hourly). The distribution function is not used to synthesize high-resolution data, rather scalar integrals are used to calculate the quantities of interest within each time step. The form of the distribution function shows significant reduction in error when compared to 1 minute data and commercial software employing the distribution function (HOMER Pro version 14.3 and higher) shows a much improved estimate of inverter clipping in an example.

battery↗

DOE OSTI2021

Tribal Revegetation Project Final Project Report: 92-Acre Area, Area 5 Radioactive Waste Management Complex, Nevada National Security Site, Nevada

Nuwu (Southern Paiute), Newe (Western Shoshone), and Nuumu (Owens Valley Paiute) are linguistically related, Numic-speaking peoples who are part of the broader Uto-Aztecan language group. Numic peoples view the land as a holistic, living, sentient being with feelings and purpose. The land is personified with human characteristics and it needs to be experienced to be understood through “learning by doing.” Numic peoples do not support ground disturbing activities within their ancestral lands, including activities tied to the storage of low-level radioactive waste or classified materials on the NNSS, which they view as culturally inappropriate. These deep-rooted ancestral connections are the impetus for reinforcing Numic responsibility for healing disturbed areas by integrating respect and patience with consistent Tribal interaction. Tribal Ecological Knowledge (TEK) is the science of describing Tribal approaches for understanding natural resources. Numic TEK is embedded in traditional teachings learned incrementally over time though experience and it evolves through lessons learned and responses to environmental changes over millennia. Therefore, TEK can broaden and enhance Western scientific knowledge associated with revegetation, especially in highly disturbed areas. The project blended TEK with Western scientific ecological methods to create a vegetative cover within test plots on the 92-Acre Area located at the Radioactive Waste Management Complex (RWMC) located in Area 5 on the Nevada National Security Site (NNSS). The vegetated test plots were systematically created for the Department of Energy (DOE) in tandem with the existing Federal Facilities Agreement and Consent Order (FFACO) with the Nevada Department of Environmental Protection (NDEP). Three previous contractor-lead attempts at revegetation, one targeting full cover revegetation and two targeting test plot revegetation, did not achieve the anticipated results at this location. When presented to the 16 American Indian Tribal nations and affiliated groups with cultural and historical ties to the NNSS, the group appointed a Tribal Revegetation Committee (TRC) that included six expert Tribal knowledge holders to collaborate with an ethnoecologist/cultural anthropologist and two biologists. Project design, planning, seed and outplant selection, spiritual land preparation, and methodology were guided by the TRC and an ethnoecologist/cultural anthropologist from Portland State University (PSU) and biologists from Desert Research Institute (DRI). Using TEK, the TRC recommended a specific seed mixture that contained nine native plant species and three species of outplants. The revegetation effort included preparing and planting thirty 10 m × 10 m (32.8 ft × 32.8 ft) seeded plots, twelve of which also included outplants; and eight 10 m × 100 m (32.8 ft × 328 ft) plots that only received outplants, all atop a waste cell cover. The TRC and the project team creatively adapted TEK with Western scientific methods so that the planned revegetation efforts could occur within the safety and security parameters of the RWMC. Test plots were subjected to one of five soil treatments with varying combinations of straw or mulch applications, soil amendment, and/or outplant planting and one of two watering treatments (watered or unwatered). Planting was divided into two events: one in the fall season and another during the subsequent spring season based on TEK and a corresponding recommendation from the TRC. Monitoring and spiritual management was conducted by the TRC to evaluate plant progress on a monthly basis (in conjunction with the biologist and anthropologist) during each respective growing season for a period of three years after planting. This approach allowed Tribal members the opportunity to conduct traditional blessings and other culturally appropriate activities to restore balance to the land in accordance with Tribal protocols. Following TEK-guided methods, successful plant establishment from seed stock and outplants was observed in many plots. Overall, plots planted in the spring, as recommended by the TRC, showed higher rates of outplant survival and native seedling emergence than those planted in the fall. This finding is significant because it is contrary to the original guidance and previous approaches provided for planting in this region. The TRC believes the frequent co-occurrence of native seedlings near surviving outplants indicates an important symbiotic relationship understood by Tribal communities and overlooked by others. Watered outplants displayed much higher survivability than unwatered plants, even after watering was reduced after the plants were established. Soil amendments and mulch created higher densities of native plants from seed. Many native seedlings showed significant delays in germination, which is considered a normal adaptation to desert climates. Some native plants did not germinate until the third growing year, whereas others germinated during the first growing year, which demonstrates the complexity of the desert environment. Evidence of native insects, reptiles, mammals, and birds, as well as native plants that were not part of the planted species, were noted and considered culturally significant. Despite the presence of non-native plants, native outplants continued to thrive and the incidence of native plant germination from seed increased over time. These successful revegetation results where previous efforts were unsuccessful reinforce the importance of integrating regionally appropriate, TEK-guided methodology with Western science to achieve positive results and the necessity of integrating Tribal involvement in all stages of the revegetation effort. Expanded approaches coupled with Tribal knowledge and tools from Western science addressed a complex problem tied to revegetating atop a low-level radioactive waste cell. The level of Tribal participation serves as a progressive model for building collaborative relationships and addressing ecological challenges on the NNSS.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Understanding and Improving Energy Efficiency of Regional Mobility Systems Leveraging System-Level Data

Increased congestion required urban Americans to travel 6.8 billion hours more and purchase 3.1 billion gallons of fuel for a congestion cost of $\$$153 billion, according to the 2019 Urban Mobility Report. How to effectively manage the regional mobility system and improve the energy efficiency presents a big challenge to public agencies. Recent years have witnessed massive multi-jurisdictional multi-modal system-level data from various sources, which provides an unprecedented opportunity to improve the mobility system and its energy efficiency. However, implications of system-level data for mobility and energy efficiency are unclear. Those system-level data sets are siloed, spatially and temporally sparse, biased, not unified, and lacking of insights for system management. Consequently, there is a real need to acquire, fuse, mine and learn from multi-source system-level data to prepare public agencies to deal more effectively with large-scale energy efficiency modeling, management and planning. This project proposes to intensively review inexpensive, replicable and openly-accessible data from multi-modal systems, develop a data-driven system-level modeling framework enabled and validated by data, identify the energy inefficiencies of mobility systems from infrastructure, vehicles, passenger systems, and quantify the benefits of system-level strategies to improve mobility/energy efficiency. In addition, this research develops models to effectively estimate energy consumption and emissions from various types of vehicles on the roadway networks, with high granularity and high fidelity. Traditional models often heavily rely on aggregated infrastructure or vehicle/passenger data, for example, the census survey, land-use, and traffic counts of one or several classes, which may lead to research gaps considering the emerging vehicle technologies. Those models do not contain individual vehicular information. We propose an integrated data-driven method that combines multiple network modeling components, featuring the utilization of state-wide vehicle registration data. The additional vehicle registration data improve the model performance, and produce high-resolution vehicle-specific estimates of emissions and network performance metrics. Two case studies on the Pittsburgh and Philadelphia regional network show that the proposed method can efficiently and effectively estimate the emissions of a large-scale network, and provide valuable information for evaluating common management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

NASA’s Rotating Detonation Rocket Engine Development

The Rotating Detonation Rocket Engine has maintained steady development at NASA with many staggering performance advantages demonstrated to date over the state-of-the-art (SOA). The implementation of additive manufacturing and specialized NASA developed alloys have enabled rapid maturation of the technology. Several hot fire test projects have been successfully conducted at Marshall Space Flight Center under an early career initiative project funded by NASA Space Technology Mission Directorate. In addition, a new start Technology Demonstration Mission (TDM) project has been funded to investigate challenges relating to integration of turbomachinery with an RDRE thrust chamber assembly. This engine system demonstration will leverage a methane/oxygen single shaft turbopump with fuel rich gas generator and a 10,000 lbf thrust chamber assembly. The configuration was down selected based on feedback from both US industry collaborators and power balance trades in combination with technical feasibility. To date, industry has identified several use cases for RDRE ranging from thruster to primary launch vehicle propulsion. A wide range of fuel and oxidizers were also identified including but not limited to Methane, Kerosene and other liquid hydrocarbon (LH) fuels, and hydrogen. Recent work at NASA and in partnership with NASA has investigated these major fuels of interest with oxygen, air, and hydrogen peroxide (HTP) for various applications. NASA Marshall has already investigated the use of hydrogen/oxygen, methane/oxygen, kerosene/oxygen, and has plans in partnership with industry and academia to investigate LH/air and LH/HTP. In addition to propellants, hardware geometry has been investigated with some critical lessons learned toward greater theoretical performance over the SOA. To this end, several experimental and computational activities are ongoing to further advance the RDRE towards flight missions. Given the rate of advancement, it is highly likely the technology will be flown in space mission in the coming decade. This work documents and overviews many of these investigations and overviews NASA’s future plans for the technology maturation.

Thomas Teasley↗

DOE OSTI2021

A Machine Learning-based Reliability Evaluation Model for Integrated Power-Gas Systems

This article proposes a hybrid machine learning method for the reliability evaluation of integrated power-gas systems (IPGS) under the uncertain component failure probability distributions. The Random Forest (RF) method is designed to select important features to solve the insufficient quantity of data and the curse of dimensionality problems. The Extreme Gradient Boosting (XGBoost) regression algorithm is developed to quantify the relationship between the uncertain parameters and reliability metrics. Moreover, a ten-fold cross-validation method is employed to further improve the accuracy of the regression model. Simulation results on three test systems show that the proposed method can achieve high accuracy for the reliability evaluation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

Payload Accommodations in NASA's Space Launch System, Block 1 and Beyond

As part of NASA's new deep space exploration system, the Space Launch System (SLS) will provide the United States with guaranteed access to deep space and an unparalleled capability for launching primary and co-manifested payloads beyond Earth's orbit. Planned missions for the new SLS family of vehicles include launching the Orion spacecraft and elements of the new Gateway astronaut-tended outpost to lunar orbit and sending robotic probes deep into the solar system, such as to Jupiter's moon Europa. If mission parameters allow, secondary payloads in 6U, 12U or larger sizes will also have rideshare opportunities, providing CubeSats with access to deep space. The SLS vehicle will evolve into progressively more powerful variants with fairings in several sizes available to meet an array of mission needs. Superior mass, volume and characteristic energy (C3) enable sending larger, heavier payloads to a variety of destinations. Several elements of the Block 1 vehicle for the first mission, Exploration Mission-1 (EM-1) are complete and have been delivered to the Exploration Ground Systems (EGS) Program at Kennedy Space Center (KSC), which has responsibility for integrating and launching the vehicle. Contractors are already at work manufacturing the second Block 1 vehicle and incorporating numerous lessons learned in manufacturing America's first super heavy-lift deep space rocket since the Apollo Program's Saturn V enabled humankind to take a giant leap forward.

Schorr, Andrew A.↗

NASA NTRS2006

Achieving Operability via the Mission System Paradigm

In the past, flight and ground systems have been developed largely-independently, with the flight system taking the lead, and dominating the development process. Operability issues have been addressed poorly in planning, requirements, design, I&T, and system-contracting activities. In many cases, as documented in lessons-learned, this has resulted in significant avoidable increases in cost and risk. With complex missions and systems, operability is being recognized as an important end-to-end design issue. Never-the-less, lessons-learned and operability concepts remain, in many cases, poorly understood and sporadically applied. A key to effective application of operability concepts is adopting a 'mission system' paradigm. In this paradigm, flight and ground systems are treated, from an engineering and management perspective, as inter-related elements of a larger mission system. The mission system consists of flight hardware, flight software, telecom services, ground data system, testbeds, flight teams, science teams, flight operations processes, procedures, and facilities. The system is designed in functional layers, which span flight and ground. It is designed in response to project-level requirements, mission design and an operations concept, and is developed incrementally, with early and frequent integration of flight and ground components.

ground systems↗

NASA NTRS2016

Enabling Science and Deep Space Exploration through Space Launch System (LSL) Secondary Payload Opportunities

For the first time in almost 40 years, a NASA human-rated launch vehicle has completed its Critical Design Review (CDR). By reaching this milestone, NASA's Space Launch System (SLS) and Orion spacecraft are on the path to launch a new era of deep space exploration. NASA is making investments to expand science and exploration capability of the SLS by developing the capability to deploy small satellites during the trans-lunar phase of the mission trajectory. Exploration Mission 1 (EM-1), currently planned for launch no earlier than July 2018, will be the first mission to carry such payloads on the SLS. The EM-1 launch will include thirteen 6U Cubesat small satellites that will be deployed beyond low earth orbit. By providing an earth-escape trajectory, opportunities are created for advancement of small satellite subsystems, including deep space communications and in-space propulsion. This SLS capability also creates low-cost options for addressing existing Agency strategic knowledge gaps and affordable science missions. A new approach to payload integration and mission assurance is needed to ensure safety of the vehicle, while also maintaining reasonable costs for the small payload developer teams. SLS EM-1 will provide the framework and serve as a test flight, not only for vehicle systems, but also payload accommodations, ground processing, and on-orbit operations. Through developing the requirements and integration processes for EM-1, NASA is outlining the framework for the evolved configuration of secondary payloads on SLS Block upgrades. The lessons learned from the EM-1 mission will be applied to processes and products developed for future block upgrades. In the heavy-lift configuration of SLS, payload accommodations will increase for secondary opportunities including small satellites larger than the traditional Cubesat class payload. The payload mission concept of operations, proposed payload capacity of SLS, and the payload requirements for launch and deployment will be described to provide potential payload users an understanding of this unique exploration capability.

Singer, Jody↗