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SBIR/STTR Programs
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:22:21.000Z<p>The NASA SBIR and STTR programs fund the research, development, and demonstration of innovative technologies that fulfill NASA needs as described in the annual Solicitations and have significant potential for successful commercialization. If you are a small business concern (SBC) with 500 or fewer employees or a non-profit RI such as a university or a research laboratory with ties to an SBC, then NASA encourages you to learn more about the SBIR and STTR programs as a potential source of seed funding for the development of your innovations.</p><p><strong>The SBIR and STTR programs have 3 phases</strong>:</p><ul><li><strong>Phase I</strong> is the opportunity to establish the scientific, technical, and commercial feasibility of the proposed innovation in fulfillment of NASA needs.</li><li><strong>Phase II</strong> is focused on the development, demonstration and delivery of the proposed innovation.</li></ul><p>The SBIR and STTR Phase I contracts last for 6 months with a maximum funding of $125,000, and Phase II contracts last for 24 months with a maximum funding of $750,000 - $1.5 million.</p><ul><li><strong>Phase III</strong> is the commercialization of innovative technologies, products, and services resulting from either a Phase I or Phase II contract. Phase III contracts are funded from sources other than the SBIR and STTR programs and may be awarded without further competition.</li></ul><p><strong>Opportunity for Continued Technology Development Post-Phase II</strong>:</p><p>The NASA SBIR/STTR Program currently has in place two initiatives for supporting its small business partners past the basic Phase I and Phase II elements of the program that emphasize opportunities for commercialization. Specifically, the NASA SBIR/STTR Program has the Phase II Enhancement (Phase II-E) and Phase II eXpanded (Phase II-X) contract options.&nbsp;</p><p><strong>Please review the links below to obtain more information on the SBIR/STTR programs.</strong></p><ul><li><strong><a target="_blank" href="http://sbir.gsfc.nasa.gov/sites/default/files/ParticipationGuide.pdf">Participation Guide</a></strong></li></ul><p>Provides an overview of the SBIR and STTR programs as implemented by NASA</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/solicitations">Program Solicitations</a></strong></li></ul><p>Provides access to the annual SBIR/STTR Solicitations containing detailed information on the program eligibility requirements, proposal instructions and research topics and subtopics</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/prg_sched_anncmnt">Schedule and Awards</a></strong></li></ul><p>Schedule and links for the SBIR/STTR solicitations and selection announcements</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/content/additional-sources-assistance">Sources of Assistance</a></strong></li></ul><p>Federal and non-Federal sources of assistance for small business</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/abstract_archives">Awarded Abstracts</a></strong></li></ul><p>Search our complete archive of awarded project abstracts to learn about what NASA has funded</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/content/frequently-asked-questions">Frequently Asked Questions</a></strong></li></ul><p>&nbsp;Still have questions? Visit the program FAQs</p>
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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Polymer Derived Rare Earth Silicate Nanocomposite Protective Coatings for Nuclear Thermal Propulsion Systems Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:33:39.000ZLeveraging a rapidly evolving state-of-the-art technical base empowered by Phase I NASA SBIR funding, NanoSonic's polymer derived rare earth silicate EBCs will provide a paradigm breaking advancement for NTPs by extending the operational utility of NTP rocket thrust chambers and nozzles. Unlike competing deposition technologies severely limited by substrate size and dimensions, NanoSonic's rare earth silicate coatings may be spray deposited under ambient conditions onto large area complex substrates and converted to mechanically robust, thermally insulative EBCs on a production basis. In fact, legacy spray equipment employed for hardcoat deposition within the marine, automotive and aerospace industries has been used for successful EBC deposition. Simulated NTP testing completed by the University of Washington on coated Inconel 625 substrates indicate five candidate EBCs have exceptional environmental, dimensional, and adhesive durability within flow conditions representative of NTP rocket engines. In fact, zero spallation, erosion, or any other form of coating degradation was observed at the thermal limit of testing of 1,950 C. All candidate resins may be transitioned to 200-gallon batch production quantities within an established manufacturing infrastructure.
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Nano Dust Analyzer Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:42:34.000Z<p> We propose to develop a new highly sensitive instrument to confirm the existence of the so-called nano-dust particles, characterize their impact parameters, and measure their chemical composition. Simultaneous theoretical studies will be used to derive the expected&nbsp; mass and velocity ranges of these putative particles to formulate science and measurement requirements for the future deployment of&nbsp; the proposed Nano-Dust Analyzer (NDA)&nbsp;</p> <p> Early dust instruments onboard Pioneer 8 and 9 and Helios spacecraft detected a flow of submicron sized dust particles coming from the direction of the Sun. These particles originate in the inner solar system from mutual collisions among meteoroids and move on&nbsp; hyperbolic orbits that leave the Solar System under the prevailing radiation pressure force. Later dust instruments with higher&nbsp; sensitivity had to avoid looking toward the Sun because of interference from the solar wind and UV radiation and thus contributed&nbsp; little to the characterization of the dust stream. The one exception is the Ulysses dust detector that observed escaping dust particles&nbsp; high above the solar poles, which confirm the suspicion that charged nanometer sized dust grains are carried to high heliographic&nbsp; latitudes by electromagnetic interactions with the Interplanetary Magnetic Field (IMF). Recently, the STEREO WAVES instruments&nbsp; recorded a large number of intense electric field signals, which were interpreted as impacts from nanometer sized particles striking the&nbsp; spacecraft with velocities of about the solar wind speed. This high flux and strong spatial and/or temporal variations of nanometer&nbsp; sized dust grains at low latitude appears to be uncorrelated with the solar wind properties. This is a mystery as it would require that&nbsp; the total collisional meteoroid debris inside 1 AU is cast in nanometer sized fragments. The observed fluxes of inner-source pickup ions&nbsp; also point to the existence of a much enhanced dust population in the nanometer size range.&nbsp;</p> <p> This new heliospherical phenomenon of nano-dust streams may have consequences throughout the planetary system, but as of yet no dust instrument exists that could be used to shed light on their properties. &nbsp;We propose to develop a dust analyzer capable to detect and&nbsp; analyze these mysterious dust particles coming from the solar direction and to embark upon complementary theoretical studies to&nbsp; understand their characteristics. The instrument is based on the Cassini Dust Analyzer (CDA) that has analyzed the composition of&nbsp; nanometer sized dust particles emanating from the Jovian and Saturnian systems but could not be pointed towards the Sun. By&nbsp; applying technologies implemented in solar wind instruments and coronagraphs a highly sensitive dust analyzer will be developed and&nbsp; tested in the laboratory. The dust analyzer shall be able to characterize impact properties (impact charge and energy distribution of&nbsp; ions from which mass and speed of the impacting grains may be derived) and chemical composition of individual nanometer sized&nbsp; particles while exposed to solar wind and UV radiation. The measurements will enable us to identify the source of the dust by&nbsp; comparing their elemental composition with that of larger micrometeoroid particles of cometary and asteroid origin and will reveal&nbsp; interaction of nano-dust with the interplanetary medium by investigating the relation of the dust flux with solar wind and IMF&nbsp; properties.&nbsp;</p> <p> Complementary theoretically studies will be performed to understand the characteristics of nano-dust particles at 1 AU to answer the&nbsp; following questions:&nbsp; - What is the speed range at which nanometer sized particles impact
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CODE STEM - Moon, Mars, and Beyond; DLESE-Powered On-Line Classroom Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:28:30.000Z"CODE (COrps DEvelopment) STEM (Science, Technology, Engineering, and Math) ? Moon Mars and Beyond; DLESE-Powered On-Line Classroom" shares the excitement of President Bush's vision of space exploration with 8th grade students. Program fulfills standards-based 8th grade curriculum and will be implemented in two diverse Escondido schools (Grant, Rincon). San Diego County Office of Education will ensure relevance and integration into formal curriculum. Through thematic approach to learning and multi-disciplinary exhibit-centered final project, Program strives to engage broad cross-section of students and teachers. Focus is on developing a theme-specific DLESE (Digital Library for Earth System Education) collection and portal. On-line learning communities and inquiry based learning protocols (San Diego State University) will enhance DLESE effectiveness. Implementation culminates with multi-disciplinary student exhibit at participating schools and San Diego Aerospace Museum, sharing excitement of space exploration with general public. In Phase II DLESE collection will be expanded to support additional grade levels (3rd, 5th, 8th, high school), there will be broad implementation in San Diego and Nashville (through Fisk University), and teacher training.
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GPM, DPR, GMI Level 3 Combined Monthly Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:55.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product. ABSTRACTThe purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1) – Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) – Conditional mean rate for liquid precipitation. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) – Conditional mean water content for all precipitation phases. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) – Conditional mean liquid water content. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) – Conditional mass-weighted mean particle diameter. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) – Conditional mean total surface precipitation rate indexed by local time. • count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. • mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of ...
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GPM, DPR, GMI Level 3 Combined Monthly Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:56.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product. ABSTRACTThe purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1) – Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) – Conditional mean rate for liquid precipitation. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) – Conditional mean water content for all precipitation phases. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) – Conditional mean liquid water content. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) – Conditional mass-weighted mean particle diameter. • count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. • mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. • stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. • hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) – Conditional mean total surface precipitation rate indexed by local time. • count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. • mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. • stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of ...
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INVESTIGATION OF NON ERODING NOZZLE MATERIALS FOR OPTIMIZED COATED HYBRID LEADING EDGE DESIGNS FOR REUSABLE LAUNCH VEHICALS WITH LEADING EDGE RADII OF 0.03? TO 1? AND TEMPERATURES NEAR 4000?F Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:32:08.000ZEffort explores using innovative hybrid reinforced carbon-carbon, refractory ceramics, super alloys and composite materials as thermal protection system specifically in the 4000?F range with leading edge radii of between 0.03? and 1.0?. The RLV leading edge is the primary TPS that space vehicles use re-entering the atmosphere traveling at hypersonic speeds. Depending on the Mach number spacecraft surface temperatures are as high 4000?F. The shape of the RLV leading edge, primarily the radius affects the functionality of the spacecraft including RLV drag, lift and leading edge aero-thermal heating. Sharper leading edges create lift and re-entry cross range capabilities. The downside of sharp leading edges is that aero-thermal heating is increased, resulting in steep thermal gradients. These thermal gradients create high thermal stresses. Blunt leading edges leading edges have less thermal gradient and therefore thermal stresses are lower. However, the cross range capabilities of the vehicle are reduced. Tasks include parametric definition of hybrid composite material architectures RLV leading edge for maximum lift, cross range and durability at temperatures of 4000?F for radii in the 0.03 to 1? range. The goal is finding the optimal hybrid composite material combinations/coatings and architectures for given leading edge radii. RLV. Analyses for hybrid leading edge designs include: Micro-mechanical material computations for hybrid material property, calculation of leading edge aerothermal heating heat transfer coefficient, heat rate and pressure load as a function of leading edge radius. Transient heat transfer analyses for calculation of leading edge thermal gradients. Thermal stress analyses using temperature gradients. Evaluation of leading edge response, as a function of hybrid material architecture via material failure ratios. The result of these analyses will provide the best hybrid material candidates and RLV leading edge designs.
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Heated Thermoplastic Fiber Placement Head for NASA Langley Research Center Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:29:25.000ZReduced mass composite materials are crucial to the success of aerospace systems, but are inhibited by expensive autoclave consolidation, especially for large parts. To remedy this, NASA-LaRC has been developing cost-effective high-performance thermoplastic composite materials for years. NASA materials could dramatically reduce the cost of large aerospace structures, because those materials avoid the autoclave. However, NASA lacks a robust, cost-effective fabrication process to tow-place these emerging materials into laminates, and thus can?t evaluate their usefulness to industry. This program develops for NASA-LaRC the processing equipment that allows material evaluation and allows out-of-autoclave fiber placement. In particular, this program will deliver a heated in situ deposition head to fit on NASA-LaRCs placement machine. Heads can also be sold to industrial companies for existing placement machines so that aerospace composites can be fabricated out of the autoclave. In phase I, the deposition head will be designed and reviewed with NASA. The process window requirements for the placement head for NASA materials will be verified. In phase II, we will complete the design, fabricate, install, and prove-out the head equipment. We then start up the deposition head at NASA so that the emerging NASA-LaRC materials can be proven in laminates.