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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Advanced Lightweight Metallic Fuselage Project Manufacturing Trade Study

Recent advances in large-scale flow forming of integrally stiffened cylinders (ISCs) have motivated evaluation of available technologies for rapid manufacturing of metallic fuselages. The current state-of-the-art in flow forming of ISCs produces 10-ft. diameter, 5-ft. long barrels with integral longitudinal blade stiffeners, and these single-piece ISCs are produced in approximately 1.5 hours. While other manufacturing processes are required to incorporate additional structural elements (ASE) such as circumferential ring frames, reinforcements around window and door cut-outs, and floors to the ISCs to complete the fuselage structure, flow forming technology may assist the aerospace industry in meeting manufacturing rate demands. In order to evaluate this technology, a fuselage manufacturing demonstration article (MDA) fabricated from two ISCs is scheduled for fabrication and delivery to NASA Langley Research Center (LaRC) by the end of 2022. In this study, eight manufacturing technologies were assessed to downselect candidate manufacturing processes for adding ASE to complete the MDA. A literature review and evaluation of contractor-produced panels covering a spectrum of welding and additive manufacturing (AM) processes were conducted at NASA LaRC. The analytical hierarchy process (AHP) was used to evaluate figures of merit (FOMs) for selecting manufacturing process(es) to integrate ASE with the ISCs to form a fuselage MDA. The AHP results revealed that scalability, structural performance, and distortion control were the most valued criteria for downselecting the manufacturing process to construct the internal stiffening structures. Based on the FOMs, this study concluded that a welding process is best suited for integrating the majority of the ASE, namely the circumferential ring frames. All of the welding processes received higher scores than AM processes due to higher maturity, higher structural performance, fewer post-processing requirements in machining and heat treating, and faster deposition rates. Among the welding processes, cold metal transfer (CMT) welding was ranked the most favorable process for assembling the MDA, with the other welding processes (laser welding (LW), friction stir welding (FSW), and refill friction stir spot welding (RFSSW)) scoring slightly lower. This was a consequence of the perceived maturity of the CMT welding process and its relatively high structural performance and low distortion resulting from the low heat input. Among the AM processes, CMT AM showed the greatest promise due to benefits derived from its scalability, lower 1st order process complexity, and low distortion. The AM processes may have potential for select applications, such as adding structural reinforcements around window and door cut-outs, but are not considered optimal for integrating the entire MDA.

Manufacturing↗
NASA NTRS1994

A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Rorvig, Mark E.↗
DOE OSTI2022

SplitML

SplitML (Signal Processing Library for Interference rejecTion by Machine Learning) is a code repository for a set of tools for interference rejection in complex time-domain signals. The goal of the tools is to provide machine learning modeling capabilities for rejecting interference. Recent related machine learning algorithms for signal processing have focused mostly on speech enhancement or multi-speaker speech separation; the tools in SplitML will extend these innovations to generic time-domain signals of interest. SplitML includes tools for generating synthetic noisy signals, code for customized machine learning models applicable to interference rejection, and tools for evaluating such algorithms against standard signal processing techniques. These components are written in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning and signal processing.

Klein, Natalie↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1987

Solution of large linear systems of equations on the massively parallel processor

The Massively Parallel Processor (MPP) was designed as a special machine for specific applications in image processing. As a parallel machine, with a large number of processors that can be reconfigured in different combinations it is also applicable to other problems that require a large number of processors. The solution of linear systems of equations on the MPP is investigated. The solution times achieved are compared to those obtained with a serial machine and the performance of the MPP is discussed.

Ida, Nathan↗
DOE OSTI2020

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Holding fixture for variable-contour parts

Array of vacuum cups on spindles holds parts for safe machining and other processings. Variable-contour part resting on fixture is held firmly enough for machining, coating, or other mechanical treatment.

Haynie, C. C.↗
NASA NTRS2003

Effect of Molding and Machining on Neoflon CTFE M400H Polychlorotrifluoroethylene Rod Stock and Valve Seat Properties

Since 1997 numerous fires have been reported to the Food and Drug Administration involving cylinder valves installed on medical use oxygen cylinders sold and operated within the United States. All of the cylinder valves in question had polychlorotrifluoroethylene (PCTFE) valve seats. Subsequent failure analysis showed that the main seat was the primary source of ignition. A review of the incidents involving cylinder valve fires indicated three possible ignition mechanisms: contaminant promotion, flow friction, and resonance. However, gas purity analysis showed that uncombusted, residual oxygen was within specification. Infrared and energy dispersive spectroscopy further showed that no contaminants or organic compounds were present in the remaining, uncombusted valve seat material or on seat plug surfaces. Therefore, contaminant-promoted ignition did not appear to be responsible for the failures. Observations of extruded material along the outer edge of the coined or loaded seat area produced by cylinder overuse or poppet overload led to concerns that accelerated gas flow across a deformed seat surface could generate enough localized heating to ignite the polymeric seat. Low molecular weight or highly amorphous quick-quenched PCTFE grades might be expected to be especially prone to this type of deformation. Such a failure mechanism has been described as flow friction; however, the corresponding mechanistic parameters are poorly understood. Subsequent revelation of low-temperature dimensional instability by thermomechanical analysis (TMA) in a variety of PCTFE sheet and rod stock samples led to new concerns that PCTFE valve seats could undergo excessive expansion or contraction during service. During expansion, additional extrusion and accompanying flow friction could occur. During contraction, a gap between the seal and adjacent metal surfaces could form. Gas flowing past the gap could, in turn, lead to resonance heating and subsequent ignition as described in ASTM Guide for Evaluation Nonmetallic Materials for Oxygen Service (G 63). Attempts to uncover the origins of the observed dimensional instability were hindered by uncertainties about resin grade, process history, and post-process heat history introduced by machining, annealing, and sample preparation. An approach was therefore taken to monitor property changes before and after processing and machining using a single, well-characterized lot of Neoflon CTFE.1 M400H resin. A task group consisting of the current PCTFE resin supplier, two molders, and four valve seat manufacturers was formed, and phased testing on raw resin, intermediate rod stock, and finished valve seats initiated. The effect of processing and machining on the properties of PCTFE rod stock and oxygen gas cylinder valve seats was then determined. Testing focused on two types of extruded rod stock and one type of compression-molded rod stock. To accommodate valve seat manufacturer preferences for certain rod stock diameters, two representative diameters were used (4.8 mm (0.1875 in.) and 19.1 mm (0.75 in.)). To encompass a variety of possible sealing configurations, seven different valve seat types with unique geometries or machining histories were tested. The properties investigated were dimensional stability as determined by TMA, specific gravity, differential scanning calorimetry (DSC), compressive strength, zero strength time, and intrinsic viscosity. Findings are discussed in the context of polymer structure-process-property relationships whenever possible.

Waller, Jess M.↗
DOE OSTI2023

Achieving clean production with nanostructured coated milling tools dry machining low carbon steel

The advancement of clean production using nanostructured materials in subtractive manufacturing processes has focused on replacing the use of liquid lubrication with solid lubricants coated to conventional cutting tools machining low carbon steel (~ 0.2 wt. % carbon). However, little is known about the wear mechanisms that dominate such tools when coated with functional graded nanostructured coatings. The present study follows an international standard for characterizing such wear (ISO 8688) but uses advanced measurement techniques such as x-ray fluorescence technology to measure the diffusion of chemical species from tool to workpiece and vice versa, that contributes to tool wear with the view to enhance the principles of cleaner production. The results show that when commercially-available tools coated with the appropriate functionally graded industrial coating, diffusion does not take place and creates an effective chemical barrier during machining. The experimental methods used in the study include machining workpieces using a CNC milling machine, tachometer for measuring spindle speed, dynamometer for measuring cutting forces, infra-red camera for measuring cutting temperatures, chemical species diffusion wear using x-ray florescence detector, and an optical microscope for measuring tool wear. Coupled with the physical measurement of wear, it is concluded that mechanical wear dominates the milling of a low carbon steel and that thermal properties of the coatings do not correlate with such wear. It is also concluded that flank wear as a function of volume of workpiece removed and machining duration (machining index) is a sound method for assessing the transition of the stages of wear during clean machining operations. The present study not only has implication for the design of better cutting tools, but also advances dry machining processes that eliminate the use of liquid lubricants making subtractive production processes cleaner. The main conclusions drawn from this study show that coated tools have a longer life under dry conditions compared to uncoated tools and that there is no correlation between coating properties and tool wear. In conclusion, diffusion of chemical species into the cut chips did not occur because of the lack of thermally induced chemical wear of the cutting tool and that wear is caused by the gradual erosion of the flank face due to the chip abrading the cutting tool.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cybersecurity Training Curriculum Analysis through the application of Machine Learning Text Classification and Natural Language Processing

In the domain of cybersecurity, there are a wide variety of education and training courses offered by a variety of training providers (commercial vendors, governmental, and academic institutions). Unfortunately, a comprehensive cross-provider mapping of the body of course offerings does not currently exist. Furthermore, although individual providers have made efforts, an accepted cross-provider mapping of education and training courses (or categories) to an accepted framework of work-roles by cybersecurity work-role and competency level has not yet been developed. The inability to compare training courses by topic or cybersecurity work-role and the level of difficulty (competency level) affects all organizations in identifying and selecting potential training opportunities for their personnel performing cybersecurity duties. In this work, we report on our application of Machine Learning (ML) Text Classification and Natural Language Processing (NLP) methodologies to begin to create a process to organize an accurate and thorough catalog of course offerings by work-role and competency level. Through the analysis of text-based course attributes (description, learning objectives, prerequisites, etc.) along with the text-based attributes for identified cybersecurity work-roles, our ML efforts examined methods to determine the work-role "best-fit" for each course as well as the competency level through action-verb text comparison.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Embedding Sensors in 3D Printed Metal Structures

The Transformational Challenge Reactor (TCR) program is leveraging recent advances in modeling and simulation, materials, and additive manufacturing (AM) technologies to design a modern nuclear reactor. Some of the main TCR technologies include in situ monitoring and the integration of sensors during the manufacturing of quality-significant nuclear reactor components. This report describes the general procedure and process optimization for embedding sensors within generic stainless steel 316 (SS316) components using laser powder bed fusion (LPBF). A more detailed, quality-significant test plan and supporting procedures are available upon request (ORNL/TM-2021/2127). LPBF involves the use of a scanning laser to selectively melt regions of a powder bed, additively building a part layer by layer. This report describes the LPBF processing technique and discusses the effects of LPBF processing parameters on the success of the sensor embedding process. Experiments used machined cavities in the form of channels in an SS316 base for the sensors to lay in while material is additively built over the top, thereby embedding them in an SS316 matrix. A preliminary investigation involved using empty SS316 sheaths as surrogates to explore the effects of various LPBF processing parameters and the dimensional requirements of the machined channels. Microstructural investigations showed that a smaller channel width/depth combination closer to the sensor’s diameter was best for the embedding process. After the desired parameters were selected, Type-K thermocouples were embedded and evaluated post-embedding using nondestructive thermal testing, as well as destructive sectioning and microscopy. Post-build characterization showed that the thermocouples were well-bonded to the SS316 matrix and were fully functional after embedding. During thermal testing to temperatures up to 500 °C, the embedded thermocouples read consistently with one another and deviated only slightly from the readings of a nonembedded thermocouple located within the furnace. This slight discrepancy was most likely due to differences in the thermal time constants for a nonembedded thermocouple vs. a thermocouple embedded in a solid SS316 block. The results presented in this report will serve as the foundation for future work that will focus on embedding sensors in relevant TCR reactor components and eventually testing those components under neutron irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Better Living Through Biology: Studying Enzymes to Make Industrial Processes More Efficient

In biology, enzymes are the molecular machinery needed to speed up slow chemical reactions for life to occur. These molecular machines enhance chemical processes to a large degree, allowing for improbable and challenging chemical reactions to efficiently happen in water at room temperature. Many microorganisms have specialized enzymes used to tackle particularly challenging chemistry that they experience in their own environment. Our group is studying a specific enzyme using a reaction called "electron bifurcation" that energetically pairs electrons and is analogous to a trampoline, using one person's jump to propel another person higher than they could by themselves. We have learned that certain metal- and vitamin-containing pathways in this protein funnel electrons in specific directions. Our investigation into this enzyme is the foundation for future industrial applications, ranging from biological production of sustainable aviation fuel from CO2, production of nitrogen-based fertilizers, and biologically remediating environmental contamination.

BASIC BIOLOGICAL SCIENCES↗