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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

The Habitat Demonstration Unit Project: A Modular Instrumentation System for a Deep Space Habitat

NASA is focused on developing human exploration capabilities in low Earth orbit (LEO), expanding to near Earth asteroids (NEA), and finally to Mars. Habitation is a crucial aspect of human exploration, and a current focus of NASA activities. The Habitation Demonstration Unit (HDU) is a project focused on developing an autonomous habitation system that enables human exploration of space by providing engineers and scientists with a test bed to develop, integrate, test, and evaluate habitation systems. A critical feature of the HDU is the instrumentation system, which monitors key subsystems within the habitat. The following paper will discuss the HDU instrumentation system performance and lessons learned during the 2010 Desert Research and Technology Studies (D-RaTS). In addition, this paper will discuss the evolution of the instrumentation system to support the 2011 Deep Space Habitat configuration, the challenges, and the lessons learned of implementing this configuration. In 2010, the HDU was implemented as a pressurized excursion module (PEM) and was tested at NASA s D-RaTS in Arizona [1]. For this initial configuration, the instrumentation system design used features that were successful in previous habitat instrumentation projects, while also considering challenges, and implementing lessons learned [2]. The main feature of the PEM instrumentation system was the use of a standards-based wireless sensor node (WSN), implementing an IEEE 802.15.4 protocol. Many of the instruments were connected to several WSNs, which wirelessly transmitted data to the command and data handling system via a mesh network. The PEM instrumentation system monitored the HDU during field tests at D-RaTS, and the WSN data was later analyzed to understand the performance of this system. In addition, several lessons learned were gained from the field test experience, which fed into the instrumentation design of the next generation of the HDU.

Rojdev, Kristina↗
NASA NTRS1990

Resource Allocation Planning Helper (RALPH): Lessons learned

The current task of Resource Allocation Process includes the planning and apportionment of JPL's Ground Data System composed of the Deep Space Network and Mission Control and Computing Center facilities. The addition of the data driven, rule based planning system, RALPH, has expanded the planning horizon from 8 weeks to 10 years and has resulted in large labor savings. Use of the system has also resulted in important improvements in science return through enhanced resource utilization. In addition, RALPH has been instrumental in supporting rapid turn around for an increased volume of special what if studies. The status of RALPH is briefly reviewed and important lessons learned from the creation of an highly functional design team are focused on through an evolutionary design and implementation period in which an AI shell was selected, prototyped, and ultimately abandoned, and through the fundamental changes to the very process that spawned the tool kit. Principal topics include proper integration of software tools within the planning environment, transition from prototype to delivered to delivered software, changes in the planning methodology as a result of evolving software capabilities and creation of the ability to develop and process generic requirements to allow planning flexibility.

Durham, Ralph↗
NASA NTRSDate not supplied

Are we ready for the first EASA guidance on the use of ML in Aviation?

NASA has been working for the past 12 years on software tools for the assurance of software in Aviation critical systems. For now two years, NASA has focused more on the use of AI-based techniques in Aviation than the traditional software systems used in the past. The primary focus has been on machine learning (ML), and more specifically, on supervised off-line learning ML systems. NSA’s research has been driven by case studies such as a vision-based centerline tracking system (implemented using deep neural networks) and the new generation of collision avoidance systems developed under the FAA guidance, i.e., the family of ACAS-X products. Since EASA has recently released its first usable guidance for Level 1 machine learning applications, it is opportunity to see how the research done at NASA is mapping to this first guidance for ML. In this talk I will use the EASA guidance document as a guide to present the past, present, and future tools and techniques being developed at NASA. The intent is to not only provide an overview of the research effort at NASA but also to see how this effort is addressing the concerns listed in the EASA first usable guidance for ML.

Guillaume Brat↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
NASA NTRS1987

Relaxation System

Environ Corporation's relaxation system is built around a body lounge, a kind of super easy chair that incorporates sensory devices. Computer controlled enclosure provides filtered ionized air to create a feeling of invigoration, enhanced by mood changing aromas. Occupant is also surrounded by multidimensional audio and the lighting is programmed to change colors, patterns, and intensity periodically. These and other sensory stimulators are designed to provide an environment in which the learning process is stimulated, because research has proven that while an individual is in a deep state of relaxation, the mind is more receptive to new information.

Source record↗
NASA NTRS2008

The Challenges and Opportunities for International Cooperative Radio Science; Experience with Mars Express and Venus Express Missions

Radio Science is an opportunistic discipline in the sense that the communication link between a spacecraft and its supporting ground station can be used to probe the intervening media remotely. Radio science has recently expanded to greater, cooperative use of international assets. Mars Express and Venus Express are two such cooperative missions managed by the European Space Agency with broad international science participation supported by NASA's Deep Space Network (DSN) and ESA's tracking network for deep space missions (ESTRAK). This paper provides an overview of the constraints, opportunities, and lessons learned from international cross support of radio science, and it explores techniques for potentially optimizing the resultant data sets.

MEX↗
NASA NTRS2009

Earned Value Management Software Projects

JPL has been attempting to apply EVM techniques for the last 15 years with mixed success, especially when applied to software tasks. One of the main causes is that software presents particular challenges that were not considered during the genesis of EVM. A small number of tasks have been able to use EVM effectively and derive great benefits. This paper discusses challenges specific to software management and how they can be successfully addressed. Specifically, it details lessons learned from the $28 million dollar Next Generation Navigation Software Project that replaced NASA’s deep space navigation software for operations. The project was able to successfully use EVM techniques during a four year period, producing 12 releases on their promised delivery dates and projects actual costs that were within 0.3% of the planned budget (under), in contrast to performance before the techniques were put into practice.

Hanna, Robert A.↗
NASA NTRS2018

Auto-Gopher-2 – An Autonomous Wireline Rotary Piezo-Percussive Deep Drilling Mechanism

Drilling deep into the subsurface of planetary bodies in the Solar System for samples acquisition enables critical capabilities for future NASA exploration missions in its quest to understand the origins of the Solar System and potentially the search for life. Such planetary bodies as Mars and Europa are key targets for potential missions that would require reaching great depths. Performing drilling while using minimal mass/volume systems and with low energy consumption are the main requirements that are imposed on such technologies. A wireline deep drill, called Auto-Gopher-2, is currently being developed as a joint effort between JPL and Honeybee Robotics Ltd. The Auto-Gopher II is a wireline rotary piezo-percussive deep drilling mechanism that combines formation breaking by rotating and piezoelectric actuator hammering and cuttings removal by rotating a fluted bit. The hammering mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate ball-shape free-mass to transform high frequency vibrations of a piezoelectric transducer horn tip into sonic hammering of the drill bit. The lessons learned from the previous studies are being implemented into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings and sample management and drive electronics. Subsystems of the wireline drill are being developed in parallel at JPL and Honeybee Robotics, Ltd. Issues related to the bit and its ability to retain the cuttings for caching and removal are currently being addressed. This paper presents the development efforts of the piezoelectric actuator, cuttings removal and retention flutes and drive electronics.

Bar-Cohen, Yoseph↗
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↗
NASA NTRS2017

Auto-Gopher-II – An Autonomous Wireline Rotary-Hammer Ultrasonic Drill

Developing technologies that would enable future NASA exploration missions to penetrate deeper into the subsurface of planetary bodies for sample collection is of great importance. Performing these tasks while using minimal mass/volume systems and with low energy consumption is another set of requirements imposed on such technologies. A deep drill, called Auto-Gopher II, is currently being developed as a joint effort between JPL’s NDEAA laboratory and Honeybee Robotics Corp. The Auto-Gopher II is a wireline rotary-hammer drill that combines formation breaking by hammering using an ultrasonic actuator and cuttings removal by rotating a fluted auger bit. The hammering mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate free-flying mass to transform high frequency vibrations of a piezoelectric transducer horn tip into sonic hammering of the drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher and then integrated into a rotary hammer device to develop the Auto-Gopher-I. The lessons learned from these developments are being integrated into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings and sample management and drive electronics. Subsystems of the wireline drill are being developed in parallel at JPL and Honeybee Robotics Ltd. This paper presents the development efforts of the piezoelectric actuator, cuttings removal and retention flutes and drive electronics.

Badescu, Mircea↗
NASA NTRS2018

Auto-Gopher-II – A wireline rotary-hammer ultrasonic drill that operates autonomously

An important challenge of exploring the solar system is the ability to penetrate at great depths the subsurface of planetary bodies for sample collection. The requirements of the drilling system are minimal mass, volume and energy consumption. To address this challenge, a deep drill, called the Auto-Gopher II, is currently being developed as a joint effort between JPL’s NDEAA laboratory and Honeybee Robotics Corp. The Auto-Gopher II is a wireline rotaryhammer drill that combines breaking formations by hammering using a piezoelectric actuator and removing the cuttings by rotating a fluted bit. The hammering is produced by the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed by the JPL team as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate free-flying mass to convert high frequency vibrations of a piezoelectric transducer horn tip into sonic hammering of the drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher and then integrated into a rotary hammer device to develop the Auto-Gopher-I. The lessons learned from these developments are being integrated into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings and sample management and drive electronics. In this paper the latest development will be reviewed including the piezoelectric actuator, cuttings removal and retention flutes and drive electronics.

Paulsen, Gale L.↗
NASA NTRS2015

A Novel Approach for Engaging Academia in Collaborative Projects with NASA through the X-Hab Academic Innovation Challenge

The X-Hab Academic Innovation Challenge, currently in its sixth year of execution, provides university students with the opportunity to be on the forefront of innovation. The X-Hab Challenge, for short, is designed to engage and retain students in Science, Technology, Engineering and Math (STEM). NASA identifies necessary technologies and studies for deep space missions and invites universities from around the country to develop concepts, prototypes, and lessons learned that will help shape future space missions and awards seed funds to design and produce functional products of interest as proposed by university teams according to their interests and expertise. Universities propose on a variety of projects suggested by NASA and are then judged on technical merit, academic integration, leveraged funding, and outreach. The universities assemble a multi-discipline team of students and advisors that invest months working together, developing concepts, and frequently producing working prototypes. Not only are students able to gain quality experience, working real world problems that have the possibility to be implemented, but they work closely with subject matter experts from NASA who guide them through an official engineering development process.

Habitats↗
NASA NTRS2015

A Novel Approach for Engaging Academia in Collaborative Projects with NASA through the X-Hab Academic Innovation Challenge

The X-Hab Academic Innovation Challenge, currently in its sixth year of execution, provides university students with the opportunity to be on the forefront of innovation. The X-Hab Challenge, for short, is designed to engage and retain students in Science, Technology, Engineering and Math (STEM). NASA identifies necessary technologies and studies for deep space missions and invites universities from around the country to develop concepts, prototypes, and lessons learned that will help shape future space missions and awards seed funds to design and produce functional products of interest as proposed by university teams according to their interests and expertise. Universities propose on a variety of projects suggested by NASA and are then judged on technical merit, academic integration, leveraged funding, and outreach. The universities assemble a multi-discipline team of students and advisors that invest months working together, developing concepts, and frequently producing working prototypes. Not only are students able to gain quality experience, working real world problems that have the possibility of be implemented, but they work closely with subject matter experts from NASA who guide them through an official engineering development process.

X-Hab Challenge↗
NASA NTRS1993

Link monitor and control operator assistant: A prototype demonstrating semiautomated monitor and control

This article describes the approach, results, and lessons learned from an applied research project demonstrating how artificial intelligence (AI) technology can be used to improve Deep Space Network operations. Configuring antenna and associated equipment necessary to support a communications link is a time-consuming process. The time spent configuring the equipment is essentially overhead and results in reduced time for actual mission support operations. The NASA Office of Space Communications (Code O) and the NASA Office of Advanced Concepts and Technology (Code C) jointly funded an applied research project to investigate technologies which can be used to reduce configuration time. This resulted in the development and application of AI-based automated operations technology in a prototype system, the Link Monitor and Control Operator Assistant (LMC OA). The LMC OA was tested over the course of three months in a parallel experimental mode on very long baseline interferometry (VLBI) operations at the Goldstone Deep Space Communications Center. The tests demonstrated a 44 percent reduction in pre-calibration time for a VLBI pass on the 70-m antenna. Currently, this technology is being developed further under Research and Technology Operating Plan (RTOP)-72 to demonstrate the applicability of the technology to operations in the entire Deep Space Network.

Lee, L. F.↗
NASA NTRS1996

QFD Application to a Software - Intensive System Development Project

This paper describes the use of Quality Function Deployment (QFD), adapted to requirements engineering for a software-intensive system development project, and sysnthesizes the lessons learned from the application of QFD to the Network Control System (NCS) pre-project of the Deep Space Network.

QFD Quality Function Deployment NCS Network Contro↗