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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SERVIR: Cross-Comparison of Carbon Emission Estimates Based on Variable Land Use Land Cover Changes within SERVIR Focus Regions

Deforestation in the tropics contributes approximately one-fifth of the annual global greenhouse gas (GHG) emissions. In an effort to reduce GHG emissions, SERVIR - a joint USAID and NASA initiative - is currently implementing the SERVIR CArbon Pilot (S-CAP) activity. By developing a comprehensive CO2 tracking system in Google Earth Engine based on both remotely sensed data and in situ observations, S-CAP aims to build capacity within the SERVIR regional hubs to improve decision making and local monitoring efforts surrounding GHG emissions. The S-CAP estimations are being completed throughout 11 pilot countries within the SERVIR regions of Asia, Africa, and the Americas to integrate partner contributions and local input into the development process. These emission estimate calculations use country-level REDD+ FREL reported values, global remotely sensed data (e.g. Global Forest Watch) and regional datasets and are completed using the IPCC Guidelines for National Greenhouse Gas Inventories. From the S-CAP activity thus far, country CO2 estimations had varied differences, ranging from 56% to 197% between their lowest and highest estimates. The results from this study show that CO2 emission estimates can differ greatly depending on the land cover data sets used as well as the biomass values, and how a comprehensive database is essential in understanding the full range of impacts that land cover change has in these regions. Using this ensemble approach, SERVIR hubs can better select the most appropriate datasets available for estimating emissions.

Carbon Emission↗
NASA NTRS2017

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗
NASA NTRSDate not supplied

3D Construction of Biologically Derived Materials

System for the 3D Construction of Biologically Derived Materials, Structures, and Parts NASA has developed a novel approach for macroscale biomaterial production by combining synthetic biology with 3D printing. Cells are biologically engineered to deposit desired materials, such as proteins or metals, derived from locally available resources. The bioengineered cells build different materials in a specified 3D pattern to produce novel microstructures with precise molecular composition, thickness, print pattern, and shape. Scaffolds and reagents can be used for further control over material product. This innovation provides modern design and fabrication techniques for custom-designed organic or organic-inorganic composite biomaterials produced from limited resources. Benefits Conserves resources. Few raw or bulk starting materials needed Enables custom design of diverse materials Fast, portable, macroscale, on-demand manufacturing High-fidelity microstructures Uses commercially available parts Applications Biomaterials, biotechnology Organic-inorganic composite materials On-demand manufacturing In situ resource utilization Space stations Military Infrastructure materials The Technology Once genes for a desired material type, delivery mode, control method and affinity have been chosen, assembling the genetic components and creating the cell lines can be done with well-established synthetic biology techniques. A 3D microdeposition system is used to make a 3D array of these cells in a precise, microstructure pattern and shape. The engineered cells are suspended in a printable 'ink'. The 3D microdeposition system deposits minute droplets of the cells onto a substrates surface in a designed print pattern. Additional printer passes thicken the material. The cell array is fed nutrients and reagents to activate the engineered genes within the cells to create and deposit the desired molecules. These molecules form the designed new material. If desired, the cells may be removed by flushing. The end product is thus a 3D composite microstructure comprising the novel material. This innovation provides a fast, controlled production of natural, synthetic, and novel biomaterials with minimum resource overhead and reduced pre- and post-processing requirements.

3D↗
NASA NTRSDate not supplied

Spacecraft Line-Of-Sight Jitter Mitigation and Management Lessons Learned and Engineering Best Practices

Predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter caused by micro-vibrations due to onboard internal disturbance sources is a formidable multidisciplinary engineering task. It is especially challenging for those missions hosting high-performance, vibration-sensitive optical sensor payloads with stringent pointing stability requirements. Both NASA and ESA are planning technically aggressive spaceflight missions that include ultra-high-performance optical payloads with delicate, highly vibration-sensitive scientific and observational instruments. The GN&C community of practice will need to leverage and build upon their collective experiences and lessons learned to better address future micro-vibration challenges. To identify lessons learned and best engineering practices the NASA Engineering & Safety Center (NESC) sponsored a two-day Spacecraft LoS Jitter Workshop in late 2019. The workshop’s goal was to provide a multidisciplinary forum to elicit deeper understanding of the issues related to solving the spacecraft LoS jitter/micro-vibration problem. A primary objective was to identify and share best practices, rules of thumb, and options for jitter-related activities. Representatives from NASA, JPL, ESA, along with NASA’s industrial partners, independent consultant subject matter experts, and members of academia participated in the workshop. This paper will describe the motivation for the NASA Spacecraft LoS Jitter Workshop and summarize the identified findings, observations, and recommendations.

Lessons Learned;↗
NASA NTRS2003

Propel: Tools and Methods for Practical Source Code Model Checking

The work reported here is an overview and snapshot of a project to develop practical model checking tools for in-the-loop verification of NASA s mission-critical, multithreaded programs in Java and C++. Our strategy is to develop and evaluate both a design concept that enables the application of model checking technology to C++ and Java, and a model checking toolset for C++ and Java. The design concept and the associated model checking toolset is called Propel. It builds upon the Java PathFinder (JPF) tool, an explicit state model checker for Java applications developed by the Automated Software Engineering group at NASA Ames Research Center. The design concept that we are developing is Design for Verification (D4V). This is an adaption of existing best design practices that has the desired side-effect of enhancing verifiability by improving modularity and decreasing accidental complexity. D4V, we believe, enhances the applicability of a variety of V&V approaches; we are developing the concept in the context of model checking. The model checking toolset, Propel, is based on extending JPF to handle C++. Our principal tasks in developing the toolset are to build a translator from C++ to Java, productize JPF, and evaluate the toolset in the context of D4V. Through all these tasks we are testing Propel capabilities on customer applications.

Mansouri-Samani, Massoud↗
NASA NTRS2016

Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team (A-Team). The A-Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an ATeam study. In these studies, Concept Maturity Levels (CML) are used to group methods. These levels include idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for exploring the science objectives, feasibility, and scope will be described including use of a new technique for understanding the most compelling science, called a Science Return Diagram (SRD). In the process of developing the SRD, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid towards developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions and reveal the mission concept’s most compelling science. This paper will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.In June of 2011 a new collaborative engineering approach forearly concept formulation began in the JPL InnovationFoundry [1], six months later becoming the “A-Team” [2].Responding to a need for exploring mission architecturelevel trades [3], the A-Team precedes Team X [4,5] in asequence of concurrent engineering teams at JPL that can beused to mature a concept from a “cocktail napkin” level ideato a complete mission point design. The A-Team efficientlyexplores the science, implementation, and programmatictrade space in early concept formulation. Small, facilitatedgroups of experts generate innovative ideas, quantitativelyassess feasibility, and discover key sensitivities in the tradespace through collaborative analysis and use of advancedmethods and tools. The A-Team process builds off theexperience within JPL and other recent approaches to earlyconcept formulation [6] including best practices of the JPLInnovation Foundry, Project Systems Engineering &Formulation Section, Team Eureka and the Rapid MissionArchitecture Team[7].The A-Team is a focal point for innovative formulationapproaches and people within JPL. It relies on a largebackground of study resources, creative thinkers and “greybeard” scrutinizers, advanced tools, and subject matterexperts with both breadth and depth in experience andexpertise that are all available at JPL. The A-Team isdesigned to be a rapid and efficient process takingapproximately 6 weeks (the entire process can be as short asjust a few days or as long as up to three months) and costingthe equivalent of a work-month of a full-time employee orless. Studies begin with detailed planning and client reviewfollowed by study sessions, analysis work, and reporting.The staffing on each study is customized to the study goalsand objectives, and it is addressed early in the A-Teamprocess. Sessions are generally half-day or whole-day eventsand conducted over a series of days with focused agendas thatare moderated by a trained facilitator. Preliminary results andknowledge capture are available within hours of each session,and a final report is generally available two weeks later.One of the biggest challenges facing early conceptdevelopment is understanding the gradient in science returnversus various available mission scenarios and payload options. Often times, major areas of scientific inquiry havealready been prioritized by science groups, including throughthe National Research Council’s Decadal Studies inAstronomy, Planetary, and Earth Science. Yet science teamscontinue to struggle, especially in competitive missionsolicitations, to capture the right amount of scope that’sachievable within the cost constraints of the opportunity.Often the desire to completely and comprehensively study ascience area in just one mission (after all, true missionopportunities are rare) drives teams to take on too much,providing requirements that are unachievable within theresources of the opportunity without inducing unacceptableimplementation risk. Alternatively, science teams can seekto reduce risk by using an established instrument, but havenot thought through the traceability and key aspects of thescience question to justify its use. Both scenarios lead to badassumptions at the beginning of the concept development thatcan then ripple through implementation option choices,potentially preventing what would have been a good scienceinvestigation from being selected.The purpose of this paper is first to provide some additionalbackground and summary of the A-Team process, tools,people, and facilities. We then focus on the A-Teammethodology for overcoming the barriers of defining thescience scope well at the early concept development stage.This includes understanding the science story andtraceability, and then examining the gradient in science returnversus key characteristics of observables, developing theright payload and mission requirement specification throughexamining the science and implementation trade space.

Ziemer, John K.↗
NASA NTRS1981

International aerospace engineering: NASA shuttle and European Spacelab

NASA negotiations and contractual arrangements involving European space research organizations' participation in manned space operations and efforts in building Spacelab for the U.S. Reusable Space Shuttle are discussed. Some of the diplomatic and technical collaboration involved in the international effort is reviewed.

Bilstein, R. E.↗
NASA NTRS2001

Use of Soft Computing Technologies for a Qualitative and Reliable Engine Control System for Propulsion Systems

The problem to be addressed in this paper is to explore how the use of Soft Computing Technologies (SCT) could be employed to improve overall vehicle system safety, reliability, and rocket engine performance by development of a qualitative and reliable engine control system (QRECS). Specifically, this will be addressed by enhancing rocket engine control using SCT, innovative data mining tools, and sound software engineering practices used in Marshall's Flight Software Group (FSG). The principle goals for addressing the issue of quality are to improve software management, software development time, software maintenance, processor execution, fault tolerance and mitigation, and nonlinear control in power level transitions. The intent is not to discuss any shortcomings of existing engine control methodologies, but to provide alternative design choices for control, implementation, performance, and sustaining engineering, all relative to addressing the issue of reliability. The approaches outlined in this paper will require knowledge in the fields of rocket engine propulsion (system level), software engineering for embedded flight software systems, and soft computing technologies (i.e., neural networks, fuzzy logic, data mining, and Bayesian belief networks); some of which are briefed in this paper. For this effort, the targeted demonstration rocket engine testbed is the MC-1 engine (formerly FASTRAC) which is simulated with hardware and software in the Marshall Avionics & Software Testbed (MAST) laboratory that currently resides at NASA's Marshall Space Flight Center, building 4476, and is managed by the Avionics Department. A brief plan of action for design, development, implementation, and testing a Phase One effort for QRECS is given, along with expected results. Phase One will focus on development of a Smart Start Engine Module and a Mainstage Engine Module for proper engine start and mainstage engine operations. The overall intent is to demonstrate that by employing soft computing technologies, the quality and reliability of the overall scheme to engine controller development is further improved and vehicle safety is further insured. The final product that this paper proposes is an approach to development of an alternative low cost engine controller that would be capable of performing in unique vision spacecraft vehicles requiring low cost advanced avionics architectures for autonomous operations from engine pre-start to engine shutdown.

Trevino, Luis↗
NASA NTRS2024

Long-Term Groundwater Monitoring Report Industrial Area Kennedy Space Center, Florida

This report presents the 2023 groundwater sampling results from the National Aeronautics and Space Administration (NASA) Industrial Area (IA) Long-Term Monitoring (LTM) activities. Due to the project contract ending in 2023, this report presents one year of LTM activities instead of the two-year timeframe presented in previous reports. The NASA IA LTM Program includes the following 12 sites: - Ransom Road Landfill (RRLF) – Solid Waste Management Unit (SWMU) 003 - Building M7-0505 Treatment Tank Area (M505) – SWMU 039 - Operations and Checkout Building (O&C) – SWMU 076 - Vertical Processing Facility (VPF) – SWMU 077 - Environmental Health Facility (EHF) – SWMU 079 - Kennedy Athletic, Recreation, and Social Park 1 (KARS Park 1) – SWMU 084 - Engineering Development Laboratory (EDL) – SWMU 085 - Launch Equipment Test Facility (LETF) – SWMU 091 - Mobil Service Station (MOBIL) – SWMU 093 - General Services Administration Seized Property (GSSP) – SWMU 095 - Space Station Processing Facility (SSPF) – SWMU 098 - Fuel Storage Area #1 Underground Storage Tank [Building 1044] (FSA1) – Potential Release Location (PRL) 157 Sampling events for the IA LTM Program are conducted seasonally during the dry and wet seasons, which occur in May and November, respectively. The sites in the NASA IA LTM Program are sampled on annual, biennial, or 5-year sampling frequencies based on historical trends. The current sampling frequencies for the NASA IA LTM sites are as follows: - FSA1 is sampled annually, alternating between wet and dry seasons. - GSSP is sampled annually during the wet season. - Seven sites are sampled biennially, alternating between the wet and dry seasons (RRLF, M505, O&C, VPF, LETF, MOBIL, and SSPF). - EHF and EDL are sampled biennially during the wet season. - KARS Park 1 is sampled once every 5 years.

remediation↗
NASA NTRS1990

A3I visibility modeling project

The Army-NASA Aircrew Aircraft Integration program is supporting a joint project to build a visibility computer-aided design (CAD) tool. CAD has become an essential tool in modern engineering applications. CAD tools are used to create engineering drawings and to evaluate potential designs before they are physically realized. The visibility CAD tool will provide the design engineer with a tool to aid in the location and specification of windows, displays, and control in crewstations. In an aircraft cockpit the location of instruments and the emissive and reflective characteristics of the surfaces must be determined to assure adequate aircrew performance. The visibility CAD tool will allow the designer to ask and answer many of these questions in the context of a three-dimensional graphical representation of the cockpit. The graphic representation of the cockpit is a geometrically valid model of the cockpit design. A graphic model of a pilot, called the pilot manikin, can be placed naturalistically in the cockpit model. The visibility tool has the capability of mapping the cockpit surfaces and other objects modeled in this graphic design space onto the simulated pilot's retinas for a given visual fixation.

Larimer, James↗
NASA NTRS1997

Future Directions for Astronomical Image Display

In our "Future Directions for Astronomical Image Display" project, the Smithsonian Astrophysical Observatory (SAO) and the National Optical Astronomy Observatories (NOAO) will evolve our existing image display software into a fully extensible, cross-platform image display server that can run stand-alone or be integrated seamlessly into astronomical analysis systems. We will build a Plug-in Image Extension (PIE) server for astronomy, consisting of a modular image display engine that can be customized using "plug-in" technology. We will create plug-ins that reproduce all the current functionality of SAOtng. We also will devise a messaging system and a set of distributed, shared data objects to support integrating the PIE server into astronomical analysis systems. Finally, we will migrate our PIE server, plug-ins, and messaging software from Unix and the X Window System to a platform-independent architecture that utilizes cross-platform technology such as Tcl/Tk or Java.

Mandel, Eric↗
NASA NTRS2020

Aircraft Engine Run-To-Failure Data Set Under Real Flight Conditions

The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modelling strategy from previous work [1] and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet [2]. Secondly, it extends the degradation modelling by relating the degradation process to the operation history. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model [3]. More details about the generation process can be found in [4].

CMAPSS↗
NASA NTRS2023

2021-2022 Long-Term Groundwater Monitoring Report Industrial Area Kennedy Space Center, Florida

This report presents the groundwater sampling results from the National Aeronautics and Space Administration (NASA) Industrial Area (IA) 2021-2022 Long-Term Monitoring (LTM) activities and results of the 2023 DPT activities at three of the sites. The NASA IA LTM Program includes the following 14 sites: - Ransom Road Landfill (RRLF) – Solid Waste Management Unit (SWMU) 003 - Orsino Storage Yard (ORSY) – SWMU 004 - Building M7-0505 Treatment Tank Area (M505) – SWMU 039 - Hypergol Maintenance Facility Hazardous Waste South Staging Area (HMF South) – SWMU 070 - Operations and Checkout Building (O&C) – SWMU 076 - Vertical Processing Facility (VPF) – SWMU 077 - Environmental Health Facility (EHF) – SWMU 079 - Kennedy Athletic, Recreation, and Social Park 1 (KARS Park 1) – SWMU 084 - Engineering Development Laboratory (EDL) – SWMU 085 - Launch Equipment Test Facility (LETF) – SWMU 091 - Mobil Service Station (MOBIL) – SWMU 093 - General Services Administration Seized Property (GSSP) – SWMU 095 - Space Station Processing Facility (SSPF) – SWMU 098 - Fuel Storage Area #1 Underground Storage Tank (UST) [Building 1044] (FSA1) – Potential Release Location (PRL) 157 Groundwater levels were gauged at each site during the 2021 and 2022 LTM field activities. Sampling events for the IA LTM Program are conducted seasonally during the dry and wet seasons, which occur in May and November, respectively. The sites in the NASA IA LTM Program are sampled on annual, biennial, or 5-year sampling frequencies based on historical trends. Due to contract transitions and monitoring well installations, 2021 dry season activities were conducted during September 2021, while dry season activities for 2022 resumed in May 2022.

groundwater↗
NASA NTRSDate not supplied

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗
NASA NTRSDate not supplied

Implications of the Golden Age of Remote Sensing on Earth Observation Applications in Central America

Appraising the period spanning the launch of the first Landsat satellite in 1972 to the present, the common consensus within the Earth observation (EO) community is that we are currently living in a “Golden Age of Remote Sensing,” with access to datasets, tools, and capacity building opportunities hitherto unavailable (e.g. data from Landsat, the Copernicus program, Planet / NICFI, platforms such as Google Earth Engine and SEPAL, and trainings via NASA’s ARSET and Europe's EO College). With the inter-governmental Group on Earth Observations (GEO) focusing its efforts on helping regions across the world to develop their EO capacities, Central America serves as a useful case study on the development of such capacity, with important implications for other emerging regions. A significant chapter of the region’s development of its EO capacity can be traced to 1998 when a regional body - the Central American Commission for the Environment and Development (CCAD, in Spanish) - signed a Memorandum of Understanding with NASA, which eventually led to the development of the SERVIR-Mesoamerica program, also with the support of the U.S. Agency for International Development, in 2004. The establishment of the Reducing Emissions from Deforestation and forest Degradation (REDD+) initiative in 2007 in the context of the United Nations Framework Convention on Climate Change provided additional impetus for strengthening the region’s environmental monitoring capacities. REDD+ paved the way, over the past decade, for the region’s countries to benefit from cooperation with the Germany development agency, GIZ, and the United States’ SilvaCarbon initiative. By 2021, the CCAD’s parent body - the Central American Integration System (SICA, in Spanish) - had also entered into collaborations with not only NASA, but other entities, including the Copernicus program, the Japanese Aerospace Exploration Agency, and the United Nations Office for Outer Space Affairs, and the region is also engaging with GEO’s AmeriGEO regional initiative. Nevertheless, the aforementioned collaborative efforts only convey part of the case study regarding Central America’s EO capacity. This presentation will also examine applications aspects, as well as implications for the development of EO capacity in other regions, including via regional GEO initiatives.

Earth observation↗
NASA NTRS2023

Development of Directed Energy Deposited NASA HR-1 to Optimize Properties for Liquid Rocket Engine Applications

Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with fabrication of complex propulsion component designs. Laser powder directed energy deposition (LP-DED) is a metal AM technology that has effectively been used to produce a variety of parts for liquid rocket engine applications. The LP-DED technology has enabled new designs and materials for these applications. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique engine applications such as hydrogen environments. One such metal alloy being developed for these applications is NASA HR-1. A high-strength Fe-Ni based superalloy, NASA HR-1 was designed to resist high pressure hydrogen environment embrittlement, oxidation, and corrosion. Enabled by LP-DED, NASA HR-1 has completed build and heat treatment optimization, characterization, mechanical and thermophysical testing, and hot-fire testing to demonstrate its capability as an alloy used in liquid rocket engine applications. The evolution of the alloy through material characterization analysis and mechanical testing results from development and optimization efforts will be presented.

additive manufacturing↗
NASA NTRS1990

Machine learning research 1989-90

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

Porter, Bruce W.↗