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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Closing the Loop

Like all research, the concepts of exactly what a slicer should accomplish, and how, continue to evolve. The development of next-generation slicing is already underway, and this chapter introduces several related topics. This includes a new slicing paradigm driven by network connectivity, called “slice on demand.” This paradigm enables additional capabilities such as closing the loop with sensor feedback, experimental system support, and connectivity to simulation and visualization software, all of which are also discussed. Each of these concepts represents future opportunities for slicing software and supports the growing expectations of additive manufacturing technology.

Borish, Michael↗
DOE OSTI2024

Nuclear Structural Component Relevant Properties of Nickel-Based Alloys Produced via Additive Manufacturing

Idaho National Laboratory initiated examination of nickel-based alloys manufactured via three different additive manufacturing methods for potential applications in nuclear, high temperature structural components. The three methods analyzed included laser powder bed fusion, blown powder laser directed energy deposition, and wire-fed gas metal arc directed energy deposition. With the rapid push towards additive manufacturing, codes do not exist that definitively define what is or is not tolerable for each process and application, such as with conventional, wrought products. This report contains the initial work to understand possible manufacturing methods for high temperature alloys, and specifically, void formation, microstructure evolution, corrosion, and mechanical properties. To generate mechanical test data, specimens were tested irrespective of voids and microstructures were analyzed to better understand how to negate/improve these issues. The preliminary results showed major decreases in mechanical performance for material tested. Test specimens will continue to be produced to further improve each additive manufacturing processes, quantify void acceptance, and better understand the most suitable high temperature alloys receptive to additive manufacturing and high temperature nuclear applications.

36 MATERIALS SCIENCE↗
NASA NTRS2015

A Fully Non-Metallic Gas Turbine Engine Enabled by Additive Manufacturing

In a NASA Aeronautics Research Institute (NARI) sponsored program entitled "A Fully Non-Metallic Gas Turbine Engine Enabled by Additive Manufacturing", evaluation of emerging materials and additive manufacturing technologies was carried out. These technologies may enable fully non-metallic gas turbine engines in the future. This paper highlights the results of engine system trade studies which were carried out to estimate reduction in engine emissions and fuel burn enabled due to advanced materials and manufacturing processes. A number of key engine components were identified in which advanced materials and additive manufacturing processes would provide the most significant benefits to engine operation. In addition, feasibility of using additive manufacturing technologies to fabricate gas turbine engine components from polymer and ceramic matrix composite were demonstrated. A wide variety of prototype components (inlet guide vanes (IGV), acoustic liners, engine access door) were additively manufactured using high temperature polymer materials. Ceramic matrix composite components included first stage nozzle segments and high pressure turbine nozzle segments for a cooled doublet vane. In addition, IGVs and acoustic liners were tested in simulated engine conditions in test rigs. The test results are reported and discussed in detail.

Additive Manufacturing↗
NASA NTRS1997

Ion-Molecule Association in Acrylonitrile

Acrylonitrile (propernenitrile or vinyl cyanide) polymerizes readily via a radical mechanism in solution at room temparature. The propensity to polymerize is sufficiently strong that it is usual to add a radical scavenger to the solution to prevent polymerization when oxygen (an inhibitor) is removed. Polymerization of acrylonitrile is also know to occur via nucleophilic addition of an anion by a michael-type reaction.

Ion-Molecule Acrylonitrile↗
DOE OSTI2025

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange–correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. Furthermore, in order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Chemical bonding↗
NASA NTRS1989

DATAMAP upgrade version 4.0

The changes made on the data analysis and management program DATAMAP (Data from Aeromechanics Test and Analytics - Management and Analysis Package) are detailed. These changes are made to Version 3.07 (released February, 1981) and are called Version 4.0. Version 4.0 improvements were performed by Sterling Software under contract to NASA Ames Research Center. The increased capabilities instituted in this version include the breakout of the source code into modules for ease of modification, addition of a more accurate curve fit routine, ability to handle higher frequency data, additional data analysis features, and improvements in the functionality of existing features. These modification will allow DATAMAP to be used on more data sets and will make future modifications and additions easier to implement.

Watts, Michael E.↗
NASA NTRS1993

Coherent anti-Stokes Raman spectroscopy temperature measurements in a hydrogen-fueled supersonic combustor

Coherent anti-Stokes Raman spectroscopy (CARS) thermometry has been used to obtain static temperature cross sections in a three-dimensional supersonic combustor flowfield. Data were obtained in three spanwise planes downstream of a single normal fuel injector which was located downstream of a rearward-facing step. The freestream flow was nominally Mach 2 and was combustion heated to a total temperature of 1440 K (yielding a static temperature of about 800 K in the freestream) to simulate the inflow to a combustor operating at a flight Mach number of about 5.4. Since a broadband probe laser was used an instantaneous temperature sample was obtained with each laser shot at a repetition rate of 10 Hz. Thus root-mean-square (rms) temperatures and temperature probability density functions (pdf's) were obtained in addition to mean temperatures.

Smith, Michael W.↗
NASA NTRS1996

Methods of Measuring Vapor Pressures of Lubricants With Their Additives Using TGA and/or Microbalances

The life of a space system may be critically dependent on the lubrication of some of its moving parts. The vapor pressure, the quantity of the available lubricant, the temperature and the exhaust venting conductance passage are important considerations in the selection and application of a lubricant. In addition, the oil additives employed to provide certain properties of low friction, surface tension, antioxidant and load bearing characteristics, are also very important and need to be known with regard to their amounts and vapor pressures. This paper reports on the measurements and analyses carried out to obtain those parameters for two often employed lubricants, the Apiezon(TM)-C and the Krytox(TM) AB. The measurements were made employing an electronic microbalance and a thermogravimetric analyzer (TGA) modified to operate in a vacuum. The results have been compared to other data on these oils when available. The identification of the mass fractions of the additives in the oil and their vapor pressures as a function of the temperature were carried out. These may be used to estimate the lubricant life given its quantity and the system vent exhaust conductance. It was found that the Apiezon(TM)-C has three main components with different rates of evaporation while the Krytox(TM) did not indicate any measurable additive.

Scialdone, John J.↗
NASA NTRS2010

Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.

Hinchey, Michael G.↗