Hybrid analog-digital techniques and random-process studies, 1964-1965
Hybrid computer techniques for evaluating statistics from quantized data
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Hybrid computer techniques for evaluating statistics from quantized data
Here, using cationic ligands containing both aromatic and aliphatic coordination sites, we have synthesized and structurally characterized five new CuX-based hybrid materials consisting of anionic inorganic motifs that also form coordinate bonds with the cationic organic ligands. As a result of the unique bonding nature at the inorganic/organic interfaces, these compounds demonstrate strong resistance toward heat and can be readily processed in solution. They emit light in the visible region ranging from cyan to yellow color, with the highest photoluminescence quantum yield (PLQY) reaching 71%. The influence of the different coordination modes of the ligands on their emission behavior was investigated employing both experimental and theoretical methods, which have provided insight in understanding structure–property relationships in these materials and guidelines for tuning and enhancing their chemical and physical properties.
Explore the source record for details and available documents.
Progress has been made in a continuing effort to develop a spaceborne computer system for processing readout data from a Fourier-transform infrared (FTIR) spectrometer to reduce the volume of data transmitted to Earth. The approach followed in this effort, oriented toward reducing design time and reducing the size and weight of the spectrometer electronics, has been to exploit the versatility of recently developed hybrid field-programmable gate arrays (FPGAs) to run diverse software on embedded processors while also taking advantage of the reconfigurable hardware resources of the FPGAs.
Hybrid manufacturing combines additive and subtractive (machining) processes in a single platform to reduce the total time to fabricate a component in its final form. Here we show that if sufficient plastic deformation can be imparted to the material during machining, recrystallization can be triggered during post-fabrication heat treatment. This can enable highly localized microstructure control. Additionally, we show that stainless steel 316L, when machined without coolant accumulates significant plastic strain compared to when machined with coolant that results in faster recrystallization kinetics during heat treatment. These effects are limited to the surface while the bulk microstructure remains unaffected.
On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and record flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds and by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts.
Planetary boundary layer height (PBLH) is often used to characterize the structure of the lower atmosphere. Aerosol lidar, a ground-based remote sensing method, provides the vertical distribution of aerosol at a high temporal resolution observation data, from which, the PBL structure and the position of the PBL top can be comprehensively studied. PBLH determination with lidar data depends primarily on the characteristic turbulent motions in the atmosphere and the geophysical location. However, lidar determination of PBLH over densely populated subtropical locations has rarely been discussed; thus, developing retrieval techniques suitable to these areas is necessary. In this study, four PBLH determination methods (Gradient, δ–threshold, Haar wavelet transform, and hybrid image processing) are applied to estimate the PBLH from lidar observations over an urban area in East Asia, and one—the Gradient method—relied on potential temperature measurements from an unmanned aerial vehicle (UAV) flights to validate our results. Our results indicate that a combination of the gradient method and δ-threshold method can provide better results, in terms of diurnal pattern, than using either method individually. Furthermore, the Haar wavelet and the Hybrid image processing can detect the PBL development comparably well, but both methods are dependent on their initial conditions and optimized algorithm settings. In addition, the accompanying UAV observations are conclusively shown to have a high degree of efficacy for validating the lidar data. This research highlights that a combination of PBLH determination methods can better describe the PBLH evolution throughout a day in some cases, while in others less common determination methods are proving useful, and a suite of retrieval methods should still be explored for precisely mapping the PBL in densely populated subtropical areas.
A previously developed computer program was used to model two transient hybrid combustion processes involving tubes of solid Plexiglas. In the first study, representing combustion of a hybrid rocket, the oxidizing gas was oxygen, and calculations were continued sufficiently long to obtain steady-state values. Systematic variations were made in reaction rate constant, mass flow rate, and pressure, alternatively using constant and temperature dependent regression rate models for the fuel surface. Consistent results were obtained, as is evidenced by the values for the mass function of the reaction product and the flame temperature, for which plots are supplied. In the second study, fire initiation in a duct was studied, with an air mixture as the oxidizing gas. It was demonstrated that a satisfactory flame spread mechanism could be reproduced on the computer. In both of the above applications, the general, transient, two-dimensional conservation equations were represented, together with chemical reactions, solid-fuel interface conditions, and heat conduction in the solid fuel.
Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.
Not Available
The hydrotreatment of bio-oil derived from the pyrolysis and biocrude from hydrothermal liquefaction of lignocellulosic materials to produce hydrocarbons faces significant technological challenges, mainly due to the high reactivity and poor thermal stability of bio-oil, resulting in the formation of large quantities of coke. This problem has been addressed by existing PNNL patents with a two-step hydrotreatment technology in which the bio-oil is first stabilized with a noble hydrogenation metal (often Pt or Ru). Then, in the second step, the bio-oil is deoxygenated with a Ni-Mo or Co-Mo sulfide catalyst. The main problem with this approach is that the Pt/Ru catalysts deactivate easily in the presence of S or other impurities, which are commonly present in pyrolysis oils. In this project, we explored technological solutions to mitigate coke formation, avoiding the use of Pt/Ru catalysts. Our strategy is based on three actions: (1) Bio-oil stabilization in the presence of alcohols. In this project, we studied the stabilization with butanol. (2) the use of a cosolvent to solubilize the bio-oil. Because coke formation reactions are second-order reactions a reduction in the concentration of reactive bio-oil molecules. In this case, we used yellow greases as a co-solvent. (3) Separation of bio-oil reactive fractions. In this project, we studied the removal of water-soluble fractions. Our batch co-hydrotreatment studies confirmed that the addition of butanol and methanol and the blend with lipids effectively contributed to mitigating coke formation (reducing coke yield to about 1 wt.% %). The removal of sugars did not have a noticeable effect on the overall coke yield, suggesting that coke precursors are present in all bio-oil fractions.
The hydrotreatment of bio-oil derived from the pyrolysis and biocrude from hydrothermal liquefaction of lignocellulosic materials to produce hydrocarbons faces significant technological challenges, mainly due to the high reactivity and poor thermal stability of bio-oil, resulting in the formation of large quantities of coke. This problem has been addressed by existing PNNL patents with a two-step hydrotreatment technology in which the bio-oil is first stabilized with a noble hydrogenation metal (often Pt or Ru). Then, in the second step, the bio-oil is deoxygenated with a Ni-Mo or Co-Mo sulfide catalyst. The main problem with this approach is that the Pt/Ru catalysts deactivate easily in the presence of S or other impurities, which are commonly present in pyrolysis oils. In this project, we explored technological solutions to mitigate coke formation, avoiding the use of Pt/Ru catalysts. Our strategy is based on three actions: (1) Bio-oil stabilization in the presence of alcohols. In this project, we studied the stabilization with butanol. (2) the use of a cosolvent to solubilize the bio-oil. Because coke formation reactions are second-order reactions a reduction in the concentration of reactive bio-oil molecules. In this case, we used yellow greases as a co-solvent. (3) Separation of bio-oil reactive fractions. In this project, we studied the removal of water-soluble fractions. Our batch co-hydrotreatment studies confirmed that the addition of butanol and methanol and the blend with lipids effectively contributed to mitigating coke formation (reducing coke yield to about 1 wt.% %). The removal of sugars did not have a noticeable effect on the overall coke yield, suggesting that coke precursors are present in all bio-oil fractions. Our analytical work suggests that they may be concentrated in the water-insoluble/CH 2 Cl 2 insoluble fractions of pyrolysis oils. Although the technological strategies tested resulted in significant coke reductions, the levels achieved were not sufficiently low to ensure a reliable operation in continuous, fixed-bed trickle-bed reactors. Long runs of more than 100 hours (maximum: 255 h) of co-processing time on stream were achieved in a continuous 40 mL reactor. When the same test was conducted in a larger 400 mL reactor, pressure drop increases associated with coke formation were observed. This increase in coke formation could be due to larger temperature gradients in the bed. Hydrodeoxygenation tests in moving bed reactors and using more active hydrogenation catalysts (for example Ni) could lead to more reliable operations. Unfortunately, our team did not have access to such experimental setups. The technoeconomic analysis suggests that although alcohol use is an effective means to reduce coke formation, the use of alcohol increases production cost. Thus, its use needs to be minimized. A delicate balance needs to be found between the use of technological solutions that allow the reliable operation of the system (stabilization with Ni catalysts, use of small quantities of solvents, processing in moving bed reactors) with a tolerable level of coke formation for the hydrodeoxygenation reactor used and that result in minimum production costs.
There are no author-identified significant results in this report.
This paper will provide background on the National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD) ARMD Test Data Portal (ATDP) effort along with the rationale that motivated the ATDP team to consider implementing an agile systems engineering (SE) process. The primary motivation for using this new SE process was based on the desire to mitigate a major risk to project success. Since the agile SE process had never been used before at the NASA Armstrong Flight Research Center (AFRC), utilizing this new SE process required a trailblazing effort to introduce it to NASA AFRC leadership. As a result, the ATDP team chose to adhere to the agile SE process in addition to the trusted classical waterfall SE process on ATDP. Through successful execution of this hybrid SE process during Phase I, along with demonstrating the value and rigor of the agile SE process, NASA AFRC leadership ultimately permitted the ATDP project to solely use the agile SE process for developments beyond Phase I. While it is the opinion of the authors that the classical SE process is more effective for most projects at NASA AFRC, some projects may deem that the agile SE process is more advantageous. Although this paper is focused on introducing the agile SE process to NASA AFRC, lessons learned will be valuable to other organizations as well. Details of how the ATDP team implemented the agile SE process during Phase I will be summarized, along with how the team met all required classical SE milestones. Lessons learned throughout Phase I of this effort will also be highlighted.
Explore the source record for details and available documents.
Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.
Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.
Hybrid plasmonic nanostructures are built on plasmonic metalnanostructures surrounded by catalytic metals or metal oxides. We report that recent studies have shown that hybrid plasmonic nanocatalysts can concurrently utilize thermal energy and photon stimuli and exhibit high catalytic activity, selectivity, and stability that are not attainable in conventional purely thermally activated catalytic processes. The hybrid plasmonic photocatalytic approach has recently emerged as an attractive concept for the conversion of solar energy into chemical energy, the distributed synthesis of valuable chemicals such as ammonia with little to no requirement of external heating, and the development of coke-resistant and selective catalytic processes. The field of hybrid plasmonic photocatalysis has grown tremendously in the last decade. In this review article, the advantages of visible-light-augmented hybrid plasmonic photocatalysis over conventional pure thermally activated heterogeneous catalysis are discussed. Fundamental insights are provided into photocatalytic mechanisms by which the photoexcited charge carriers (electrons and holes) are formed and transferred to adsorbates triggering chemical transformations on the surface of hybrid plasmonic nanocatalysts. Computational modeling used for predicting and understanding the photocatalytic activity and selectivity on hybrid plasmonic nanostructures is also reviewed. The review closes with a discussion of the current challenges, new opportunities, and future outlook for hybrid plasmonic photocatalysis.