OSAM Tech Transfer Workshop 2020: SpaceCube Technology Overview
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Advanced Avionics, Hybrid Processing, Data Process↗
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Robotic technology evolution and transfer
A report concerning technology transfer in the area of robotics is presented in vugraph form. The following topics are discussed: definition of technology innovation and tech-transfer; concepts relevant for understanding tech-transfer; models advanced to portray tech-transfer process; factors identified as promoting tech-transfer; factors identified as impeding tech-transfer; what important roles do individuals fulfill in tech-transfer; federal infrastructure for promoting tech-transfer; federal infrastructure for promoting tech-transfer; robotic technology evolution; robotic technology transferred; and recommendations for successful robotics tech-transfer.
Marzwell, Neville I.↗
Strategic directions and mechanisms in technology transfer
An outline summarizing the Working Panel discussion related to strategic directions for technology transfer is presented. Specific topics addressed include measuring success, management of technology, innovation and experimentation in the tech transfer process, integration of tech transfer into R&D planning, institutionalization of tech transfer, and policy/legislative resources.
Mackin, Robert↗
Airspace Technology Demonstration 3 (ATD-3): Dynamic Routes for Arrivals in Weather (DRAW) Technology Transfer Document Summary Version 2.0
Airspace Technology Demonstration – 3 (ATD-3) is part of NASA’s Airspace Operations and Safety Program (AOSP) – specifically, its Airspace Technology Demonstrations (ATD) Project. ATD-3 is a multi-year research and development effort which proposes to develop and demonstrate automation technologies and operating concepts that enable air navigation service providers and airspace users to continuously assess weather, winds, traffic, and other information to identify, evaluate, and implement workable opportunities for flight plan route corrections that can result in significant flight time and fuel savings in en route airspace. In order to ensure that the products of this tech-transfer are relevant and useful, NASA has created strong partnerships with the FAA and key industry stakeholders. This summary document and accompanying technology artifacts satisfy the third Research Transition Product (RTP) defined in the Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan, which is Dynamic Routes for Arrivals in Weather (DRAW). This technology transfer consists of artifacts for DRAW Arrival Metering (AM) Operations delivered in June 2018, DRAW AM updates, and DRAW Extended Metering (XM) Operations. Blue highlighting indicates the new or modified deliverables. Some of the artifacts in this technology transfer have distribution restrictions that need to be followed. Distribution information is noted in each section. DRAW is a trajectory-based system that combines the legacy Dynamic Weather Routes (DWR) weather avoidance technology with an arrival-specific rerouting algorithm and arrival scheduler to improve traffic flows on weather-impacted arrival routes into major airports. First, DRAW identifies flights that could be rerouted to more efficient Standard Terminal Arrival Routes (STARs) that may have previously been impacted by weather. Second, when weather is impacting the arrival routing, DRAW proposes simple arrival route corrections that enable aircraft to stay on their flight plan while avoiding weather. The DRAW system proposes reroutes early enough to allow Time Based Flow Management (TBFM) to make the necessary schedule adjustments. As a result, metering operations can be sustained longer and more consistently in the presence of weather because the arrival schedule accounts for the dynamic routing intent of arrival flights to deviate around weather. The first DRAW tech transfer in June 2018 focused on arrival metering operations with the DRAW algorithm implemented in the NASA Center TRACON Automation System (CTAS) automation software. This tech transfer delivery includes updates for DRAW implemented in FAA’s TBFM 4.7 automation software and preliminary research into DRAW for XM operations.
Isaacson, Douglas R.↗
Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/Departure/Surface (IADS) System Demonstration - Technology Transfer Document Summary (ReadMe) (Version 2.5 - Initial Phase 3 Metroplex IADS)
Airspace Technology Demonstration – 2 (ATD-2) is part of NASA’s Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and industry stakeholders. This summary document and accompanying artifacts satisfy the next delivery of research transition products (RTPs) defined in the IADS research transition team (RTT) plan. With the ATD-2 project extended for another year, this Tech Transfer 2.5 constitutes an interim delivery of artifacts from the Phase 3 field demonstration. The final Phase 3 tech transfer delivery is planned for August 2021.
ATD-2, IADS, Phase 3, tech transfer↗
Process development and scale-up of value added molecules (CRADA Final Report)
In this project, a genetically modified Saccharomyces cerevisiae was used to produce Histamine via fermentation of dextrose. Zymergen provided a tech transfer document based on their 1 L studies, where they observed a titer of 0.9 g/L with this strain. The ABPDU staff performed two bench scale campaigns, with four 2 L reactors in each campaign, to carry out tech transfer and process optimization studies. Optimization was pursued by varying initial glucose concentration, feed initiation conditions, feed strategies, vitamin supplementation, inoculum size, etc. We chose a 2 L process condition, with 4% inoculum volume, continuous feed initiated after complete ethanol depletion, that yielded the most promising titers (2.41 g/L) for scale up to 300 L.
60 APPLIED LIFE SCIENCES↗
Airspace Technology Demonstration 3 (ATD-3) Multi-Flight Common Route (MFCR) Technology Transfer Document Summary Version 2.0
Airspace Technology Demonstration - 3 (ATD-3) is part of NASA's Airspace Operations and Safety Program (AOSP) - specifically, its Airspace Technology Demonstrations (ATD) Project. ATD-3 is a multi-year research and development effort which proposes to develop and demonstrate automation technologies and operating concepts that enable air navigation service providers and airspace users to continuously assess weather, winds, traffic, and other information to identify, evaluate, and implement workable opportunities for flight plan route corrections that can result in significant flight time and fuel savings in en-route airspace. In order to ensure that the products of this tech-transfer are relevant and useful, NASA has created strong partnerships with the FAA and key industry stakeholders. This summary document and accompanying technology artifacts satisfy the second of three Research Transition Products (RTPs) defined in the Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. The original transfer, completed in December 2017, consisted of NASA's Multi-Flight Common Route (MFCR) research for efficient route corrections for en-route weather avoidance. This transfer updates the Concept of Operations document to a publicly-available NASA Technical Memorandum. The MFCR concept builds on the experience of the legacy Dynamic Weather Routes (DWR) and focuses on a better balance of potential savings with ATC acceptability, common route corrections options for multiple flights on similar routings, and better use of existing and/or modern automation for communication and coordination of route change options. All of these capabilities are expected to improve system performance significantly in terms of actual delay-reducing clearances issued to flights compared to that of the DWR tool and operating concept.
Tech transfer↗
Panel Session 108: Technology Transfer and Commercialization: Innovative Solutions for Global Opportunities
This panel focus was to build the connection between innovative solutions and the various industries looking for more efficient methods, products, and processes in Tech Transfer and Commercialization. This panel had interest across the entire conference base, whether they are encountering a challenge, introducing an innovative solution, or are the host that is trying to get from a pure research / Research, Development, Demonstration, Testing, and Evaluation solution to one that is commercially viable and robust enough for long term and/or high risk deployment scenarios and which will support greater chance of success and positive outcomes. Panelists with presentations: Practical, Successful Technology Transfer (Laurie Judd); Technology Needs for Clean-up of Mercury Contamination on the Oak Ridge Reservation (Renee Echols); US DOE Office of Technology Transitions (Kyle Wiley); Technology Transfer in Support of Delivering Clean-up Missions (Kristan Schruder); Mission to Market (J. Chris Ford)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...
aerodynamics↗
Technology Transfer External Metrics, Research, Success Stories, and Participation on Evaluation Team for the Reusable Launch Vehicle (RLV)
This research report is divided into four sections. The first section is related to participation on the team that evaluated the proposals for the X-33 project and the Reusable Launch Vehicle (RLV) during mid-May; prior to beginning the 1996 Summer Faculty Fellowship. The second section discusses the various meetings attended related to the technology evaluation process. The third section is related to various research and evaluation activities engaged in by this researcher. The final section discusses several success stories this researcher aided in preparing. Despite the fact that this researcher is not an engineer or science faculty, invaluable knowledge and experience have been gained at MSFC. Although related to the previous summer's research, the research has been new, varied, and challenging. This researcher was fortunate to have had maximum interaction with NASA colleague, David Cockrell. It would be a privilege and honor to continue a relationship with the Technology Transfer Office. In addition, we will attempt to aid in the establishment of a continuous formalized relationship between MSFC and Jacksonville State University. Dr. David Watts, Vice President for Academic Affairs, J.S.U., is interested in having the Technology Division cooperating with MSFC in sharing information and working tech transfer inquiries. The principal benefits gained by this researcher include the opportunity to conduct research in a non-academic, real world environment. In addition, the opportunity to be involved in aiding with the decision process for the choice of the next generation of space transportation system was a once in a lifetime experience. This researcher has gained enhanced respect and understanding of MSFC/NASA staff and facilities.
Trivoli, George W.↗
Airspace Technology Demonstration 3 (ATD-3): Dynamic Routes for Arrivals in Weather (DRAW) Technology Transfer Document Summary Version 1.0
Airspace Technology Demonstration – 3 (ATD-3) is part of NASA’s Airspace Operations and Safety Program (AOSP) – specifically, its Airspace Technology Demonstrations (ATD) Project. ATD-3 is a multi-year research and development effort which proposes to develop and demonstrate automation technologies and operating concepts that enable air navigation service providers and airspace users to continuously assess weather, winds, traffic, and other information to identify, evaluate, and implement workable opportunities for flight plan route corrections that can result in significant flight time and fuel savings in en route airspace. In order to ensure that the products of this tech-transfer are relevant and useful, NASA has created strong partnerships with the FAA and key industry stakeholders. This summary document and accompanying technology artifacts satisfy the third of three Research Transition Products (RTPs) defined in the Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. This transfer consists of NASA’s Dynamic Routes for Arrivals in Weather (DRAW) –Arrival Metering Operations. This research enables continued use of arrival metering operations while efficiently rerouting traffic in weather
DRAW↗
Airspace Technology Demonstration 3 (ATD-3): Dynamic Weather Routes (DWR) Technology Transfer Document Summary Version 1.0
Airspace Technology Demonstration #3 (ATD-3) is part of NASA's Airspace Operations and Safety Program (AOSP) - specifically, its Airspace Technology Demonstrations (ATD) Project. ATD-3 is a multiyear research and development effort which proposes to develop and demonstrate automation technologies and operating concepts that enable air navigation service providers and airspace users to continuously assess weather, winds, traffic, and other information to identify, evaluate, and implement workable opportunities for flight plan route corrections that can result in significant flight time and fuel savings in en route airspace. In order to ensure that the products of this tech-transfer are relevant and useful, NASA has created strong partnerships with the FAA and key industry stakeholders. This summary document and accompanying technology artifacts satisfy the first of three Research Transition Products (RTPs) defined in the Applied Traffic Flow Management (ATFM) Research Transition Team (RTT) Plan. This transfer consists of NASA's legacy Dynamic Weather Routes (DWR) work for efficient routing for en-route weather avoidance. DWR is a ground-based trajectory automation system that continuously and automatically analyzes active airborne aircraft in en route airspace to identify opportunities for simple corrections to flight plan routes that can save significant flying time, at least five minutes wind-corrected, while avoiding weather and considering traffic conflicts, airspace sector congestion, special use airspace, and FAA routing restrictions. The key benefit of the DWR concept is to let automation continuously and automatically analyze active flights to find those where simple route corrections can save significant time and fuel. Operators are busy during weather events. It is more effective to let automation find the opportunities for high-value route corrections.
ATD-3↗
Overview of Research Transition Products
Demonstrate increased, more consistent use of Performance‐ Based Navigation (PBN). Accelerate transfer of NASA scheduling and spacing technologies for inclusion in late mid‐term NAS. During high‐fidelity human‐in‐the‐loop simulations of Terminal Sequencing and Spacing, air traffic controllers have significantly improved their use of PBN procedures during busy traffic periods without increased workload. Executed an aggressive, short timeframe development schedule. Developed TSS prototype based upon FAA operational systems. Conducted multiple joint FAA/NASA human‐in‐the‐loop simulations. Performed repeated incremental deliveries of tech transfer material to non‐traditional RTT stakeholders. Will continue to participate in later phases of FAA acquisition process. ATD‐1 transferred Terminal Sequencing and Spacing (TSS) technologies to the FAA. TSS enables routine use of underutilized advanced avionics and PBN procedures. Potential benefits to airlines operating at initial TSS sites estimated to be $300‐400M/year. FAA is planning for an initial capability in the NAS in 2018.
Technology Transfer↗
Partnership and Pipeline Office Overview [Slides]
Partnerships ensure a strong Laboratory and support our missions. Partnerships are critical to our success as a Laboratory: community relations, colleges and universities and technical communities, regional and national economic development through industrial partners, DOE Laboratories, and other federal agencies. The Laboratory has many tools to build and enhance partnerships. The Community Programs Office in our Northern New Mexico Region has K-12 educational programs, supports teacher professional development and retention in regional elementary, middle and high schools, and provides community college engagement. The Partnerships and Pipeline Office works with the National Security Education Center (NSEC) which has broad university engagement including New Mexico universities. The Feynman Center for Innovation is involved in 'tech transfer' to innovation asset stewardship. The External Assignments Office shares LANL's technical expertise. The Student and Postdoc Program Offices build our workforce of the future.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Optimizing liquid free ionomer binders for high-temperature polymer electrolyte membrane fuel cells for heavy duty vehicles
This report documents tech transfer from Penn State to Ionomer Solutions LLC for high-temperature polymer electrolyte membrane fuel cells. High-temperature polymer electrolyte membrane (HT-PEM) fuel cells are enticing power plants for heavy-duty vehicle (HDV) transportation because their elevated temperature operation fosters greater heat rejection through larger temperature differentials. In 2021, over 6 million battery electric light duty vehicles (LDVs) were sold. Conversely, only 19,000 LDVs using fuel cell power trains were sold in 2021. Given the meteoric rise of battery-based powertrains for LDVs and its projected growth, stakeholders for fuel cell technology for transportation have shifted their focus towards HDVs rather than LDVs given fuel cells specific and volumetric energy density advantage over batteries when the vehicle is large, heavy, and/or needs to move large distances before refueling.
30 DIRECT ENERGY CONVERSION↗
Engineered Microorganisms for Enhanced Rare Earth Element Bio-mining and Separations (Final Technical Report)
Rare earth elements (REE) are critical ingredients of sustainable energy technologies, but their extraction from ore and separation from one another pose formidable challenges. To solve the challenge of REE supply, we used advanced genomics, high-throughput screening with synthetic REE minerals, and synthetic biology to engineer two sets of exotic microbes to (1) extract REE from ores, spent cracking catalysts, coal ash and electronic waste with high efficiency and selectivity, and (2) to purify REE into single element batches, all under benign conditions without the need of harsh solvents and high temperatures. This work integrated our expertise in systems and synthetic biology (Buz Barstow); rare-earth geochemistry (Esteban Gazel) and mineral synthesis (Megan Holycross); and microsystems engineering (Mingming Wu) by first elucidating the set of rules that predict an organism’s phenotype and then applying them to solve this critical problem in sustainable energy. These new technologies could help to revitalize the US rare earth industry and provide a new source of these critical elements for future energy technologies. We have already had some big success in tech transfer. Two of our team members (postdoctoral fellow Alexa Schmitz and graduate student Sean Medin) were able to study the supply chain for REE in the United States, and identify an opportunity to commercialize our REE mineral-dissolution technology. Alexa and Sean recently founded REEgen, Inc., an REE biomining company. Dr. Schmitz was recently awarded a fellowship from the Activate Foundation to support the first two years of REEgen. Cornell showed its support for this technology and company, and Dr. Schmitz was awarded the Rising Women Innovator’s award. These two awards unlocked support from Cornell’s Praxis Incubator.
58 GEOSCIENCES↗
NASA Armstrong Flight Research Center (AFRC) Fiber Optic Sensing System (FOSS) Technology
Attached is a power point presentation created to assist the Tech Transfer Office and the FOSS project team members in responding to inquiries from the public about the capabilities of the Fiber Optic Sensing System.
strain sensor↗