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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Progress Towards the Design of a Natural Laminar Flow Wing for a Low-Boom Concept Using CDISC

The X-59 is being developed to evaluate the ability to reduce boom loudness to acceptable levels, with the ultimate goal of enabling supersonic flight overland. If successful, one of the future research challenges will be focused on reducing fuel burn. In this paper, Natural Laminar Flow (NLF) is proposed as a technology to decrease fuel burn for a low-boom configuration. This will require an evaluation of the compatibility of NLF wings for a low-boom configuration. As a first step towards this goal, the CATNLF method was employed using the CDISC design module to perform NLF design on an isolated wing from a low-boom configuration. The results show that the NLF wing features laminar flow over 46\% of the wing upper surface, providing a potential 8.3 drag count reduction relative to the design with no laminar flow. Off-design analyses were additionally performed to evaluate the performance of the NLF wing for changes in angle of attack and Mach number. The results showed that the NLF wing offers a performance benefit over the entire range of off-design conditions considered, with the majority of the laminar flow maintained for near cruise conditions. Future work will consider the full configuration, which will enable an assessment of the impact of the NLF wing design on sonic boom loudness. Additionally, attachment line transition will be addressed, which was predicted to result in a loss of laminar flow on the inboard 50\% span for this work. Finally, future research will focus on developing a method for effectively choosing the frequency-beta pairs used for the stability analysis to improve transition prediction and provide a more accurate estimate of the performance benefit.

Supersonic↗
NASA NTRS2024

Perceptual Evaluation of Sound Exposure Level in Annoyance Ratings to Helicopter Noise

A psychoacoustic test was performed to assess the effectiveness of Sound Exposure Level (SEL) for indicating changes in annoyance to helicopter noise. SEL was evaluated for flyover auralizations of optimized rotor designs and for flyover recordings of different helicopters and maneuvers. The test used paired comparisons of flyovers within 10 dB of the maximum A-weighted sound pressure level. For stimuli of equal SEL, annoyance responses showed whether or not SEL is a good indicator of annoyance. While this work does not seek to determine specific attributes contributing to annoyance that are not included in SEL, the magnitude of this offset is of primary interest. Specifically, annoyance responses to relative differences in SEL allowed the calculation of an Equal Annoyance Point. Reductions in SEL lead to reductions in annoyance as expected, but for certain cases, SEL can fail to capture perceptually significant features such as audible differences due to changes in tail rotor design or unsteadiness in the sound of the helicopter.

Sound Exposure Level↗
NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗
DOE OSTI2020

Source Term Reduction for Advanced and Small Modular Boiling Water Reactors

The United States Department of Energy (US DOE) is currently supporting the development of various advanced and small modular reactor (SMR) designs. Several of these designs have commenced license application with the US Nuclear Regulatory Commission (US NRC). These reactors have improved safety features that may significantly reduce radiological source terms in the event of design and beyond-design basis accidents. Specifically, some reactors feature a smaller containment volume relative to the available fission product depositional surface area, which supports increased fission product retention in the containment vessel. Pressurized water reactors (PWR) and boiling water reactors (BWR) with this feature include the integral pressurized water reactor (iPWR) and the BWRX-300 design by General Electric. A prior research program supported by the US DOE quantified the source term reduction associated with light water iPWRs and developed iPWR-specific theoretical models for fission product deposition rates. This program included a sequence of research projects that started with a feasibility study, development of theoretical models that predict higher deposition rates, and finally, development of empirical data for verification and validation of the theoretical models. The current project, which is a feasibility study, is the first step in a similar program to quantify the source term reduction associated with small and advanced light water BWRs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Real-time data reduction at 100 Tbps: Challenge and opportunity for AI-based data reduction for next-generation large-scale nuclear physics collider experiment

The modern large-scale nuclear physics (NP) experiments in high-energy particle colliders utilize streaming-readout electronics to digitize detector response at O(100) Tbps bandwidth. Prominent examples at Brookhaven National Lab (BNL) include the sPHENIX experiment at Relativistic Heavy Ion Collider (RHIC), which is under construction, and the experiments proposed for the Electron-Ion Collider (EIC), planned for the 2030s . One of the main challenges for these streaming readout systems is to manage the data rate with sufficient data reduction in real time so the end-data fit persistent storage for offline analysis, which is typically at O(1000) times smaller and O(100) Gbps. Such data reduction traditionally is achieved via real-time high level triggers, which select and save a small subset of collisions of interest. Although triggering is applicable to high energy collider experiments such as those at the Large Hardron Collider at CERN, it is insufficient for these nuclear physics experiments which study diverse collision topologies. And traditional triggering approach is inefficient to preserve the max information harvested from the operation of colliders that costs O(100)M per year to DOE. Meanwhile, in recent years, ML-based high-throughput data reduction has emerged as a promising approach to efficiently preserve max information for a given space of persistent storage, e.g. via AI data compression, feature extraction, and noise filtering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2011

Performance Characterization of Loctite (Registered Trademark) 242 and 271 Liquid Locking Compounds (LLCs) as a Secondary Locking Feature for International Space Station (ISS) Fasteners

Several International Space Station (ISS) hardware components use Loctite (and other polymer based liquid locking compounds (LLCs)) as a means of meeting the secondary (redundant) locking feature requirement for fasteners. The primary locking method is the fastener preload, with the application of the Loctite compound which when cured is intended to resist preload reduction. The reliability of these compounds has been questioned due to a number of failures during ground testing. The ISS Program Manager requested the NASA Engineering and Safety Center (NESC) to characterize and quantify sensitivities of Loctite being used as a secondary locking feature. The findings and recommendations provided in this investigation apply to the anaerobic LLCs Loctite 242 and 271. No other anaerobic LLCs were evaluated for this investigation. This document contains the findings and recommendations of the NESC investigation

Dube, Michael J.↗
NASA NTRS2001

Input Decimated Ensembles

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Tumer, Kagan↗
NASA NTRS1985

Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.

Yurkovich, S.↗
DOE OSTI2023

SonicPy: a suite of programs for ultrasound pulse-echo data acquisition and analysis

Sound speed and elastic constants measurements in solids and liquids are commonly performed using the ultrasound pulse-echo technique. Recent advances have expanded the use of this technique at numerous high pressure synchrotron beamlines and offline laboratories. However, the increased experimental throughput has revealed many limitations in existing software for handling the rapid measurement and the subsequent data-reduction. Here, we report the development of a collection of computer programs for sound speed measurements using the ultrasound pulse-echo technique, compatible with stepped multi-frequency, as well as broadband-pulse, couplant-corrected methods. The programs provide a highly interactive graphical interface, enable efficient measurement, exploration and near real-time analysis of the ultrasound data, and contain features useful for working with samples under high pressure and/or high temperature. The included analysis programs can alleviate the time required for data reduction from hours to less than a minute, allowing users to make timely and informed decisions regarding the appropriate experimental parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Stable abnormal N-heterocyclic carbenes and their applications

Although N-heterocyclic carbenes (NHCs) have been known as ligands for organometallic complexes since the 1960s, these carbenes did not attract considerable attention until Arduengo et al. reported the isolation of a metal-free imidazol-2-ylidene in 1991. In 2001 Crabtree et al. reported a few complexes featuring an NHC isomer, namely an imidazol-5-ylidene, also termed abnormal NHC (aNHCs). In 2009, it was shown that providing to protect the C-2 position of an imidazolium salt, the deprotonation occurred at the C-5 position, affording imidazol-5-ylidenes that could be isolated. Over the last ten years, stable aNHCs have been used for designing a range of catalysts employing Pd(II), Cu(I), Ni(II), Fe(0), Zn(II), Ag(I), and Au(I/III) metal based precursors. These catalysts were utilized for different organic transformations such as the Suzuki–Miyaura cross-coupling reaction, C–H bond activation, dehydrogenative coupling, Huisgen 1,3-dipolar cycloaddition (click reaction), hydroheteroarylation, hydrosilylation reaction and migratory insertion of carbenes. Main-group metal complexes were also synthesized, including K(I), Al(III), Zn(II), Sn(II), Ge(II), and Si(II/IV). Among them, K(I), Al(III), and Zn(II) complexes were used for the polymerization of caprolactone and rac-lactide at room temperature. In addition, based on the superior nucleophilicity of aNHCs, relative to that of their nNHCs isomers, they were used for small molecules activation, such as carbon dioxide (CO 2 ), nitrous oxide (N 2 O), tetrahydrofuran (THF), tetrahydrothiophene and 9-borabicyclo[3.3.1]nonane (9BBN). aNHCs have also been shown to be efficient metal-free catalysts for ring opening polymerization of different cyclic esters at room temperature; they are among the most active metal-free catalysts for ε-caprolactone polymerization. Recently, aNHCs successfully accomplished the metal-free catalytic formylation of amides using CO 2 and the catalytic reduction of carbon dioxide, including atmospheric CO 2 , into methanol, under ambient conditions. Although other transition metal complexes featuring aNHCs as ligand have been prepared and used in catalysis, this review article summarize the results obtained with the isolated aNHCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Field Validation of a Grid-Interactive Efficient Building Software Solution

The U.S. General Services Administration's (GSA's) Green Proving Ground (GPG) program, in partnership with the National Laboratory of the Rockies (NLR), completed a field study of a Grid-Interactive Efficient Buildings (GEB) software solution. The study focused on a single testbed facility to test the GEB functionality of the software solution, along with other features. The testbed facility - a courthouse - is a common building type in GSA's vast building portfolio, offering potentially impactful findings on a scalable level. The study evaluated Prescriptive Data's technology, Nantum OS, a connected building operating system ("GEB Solution") which aggregates multiple sources of previously siloed building data and combines that data with external sources, such as weather information or utility signals, into a single integrated platform. A GEB Solution is a type of Energy Management Information System (EMIS). EMIS is defined as a system of devices, data services, and software applications that communicates with any building system or third-party data source to aggregate and transform data into new capabilities to aid in the optimization of energy use at the building, campus, or agency level. This specific GEB Solution is an EMIS with ASO, automated system optimization, offering supervisory control of certain aspects of the Building Automation System (BAS). Multiple features were evaluated including, but not limited to, Continuous Demand Management to avoid setting new monthly kilowatt (kW) peaks, energy efficiency for reduction of kilowatt hours (kWh) and natural gas consumption, and automated demand response (ADR) for purposes of lowering demand during a utility called Demand Response (DR) event. The testbed facility was the Foley Federal Building and US Courthouse ("Foley Federal Building") located in Las Vegas, NV. This is a 209,496 sq. ft. building constructed in the 1960s with major renovations in 2004. The facility was a good candidate due to the large prevalence of office and courthouse spaces in the GSA portfolio of buildings. It also has many features which allow integration into and control of the building and a strong facilities team to assist with the study. Quantitative and qualitative performance objectives were developed using GSA's GPG GEB project template along with input from the vendor and building facility staff; these are outlined in Table 1. The quantitative performance objectives focused on continuous demand management, energy efficiency, and automated demand response. The qualitative performance objectives focused on the ease of installation and commissioning as well as the operability of the GEB solution. Other performance metrics that are reported on include carbon reduction, cost effectiveness, and occupant acceptance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Adaptive filtering of Echelle spectra of distant Quasars

The study of the Ly alpha - forest of distant (approximately greater than 3) Quasars is an important tool in obtaining a more detailed picture of the distribution of matter along the line of sight and thus of the general distribution of matter in the Universe and is therefore of important cosmological significance. Obviously, this is one of the tasks where spectral resolution plays an important role. The spectra used were obtained with the EFOSC at the ESO 3.6m telescope. Applying for the data reduction the standard Echelle procedure, as it is implemented for instance in the MIDAS-package, one uses stationary filters (e.g. median) for noise and cosmic particle event reduction in the 2-dimensional Echelle image. These filters are useful if the spatial spectrum of the noise reaches essentially higher frequencies then the highest resolution features in the image. Otherwise the resolution in the data will be degraded and the spectral lines smoothed. However, in the Echelle spectra the highest resolution is already in the range of one or a few pixels and therefore stationary filtering means always a loss of resolution. An Echelle reduction procedure on the basis of a space variable filter described which recognizes the local resolution in the presence of noise and adapts to it is developed. It was shown that this technique leads to an improvement in resolution by a factor of 2 with respect to standard procedures.

Priebe, A.↗
DOE OSTI2024

Adaptive Scalpel Scanning Probe Microscopy for Enhanced Volumetric Sensing in Tomographic Analysis

Controlling nanoscale tip‐induced material removal is crucial for achieving atomic‐level precision in tomographic sensing with atomic force microscopy (AFM). While advances have enabled volumetric probing of conductive features with nanometer accuracy in solid‐state devices, materials, and photovoltaics, limitations in spatial resolution and volumetric sensitivity persist. This work identifies and addresses in‐plane and vertical tip‐sample junction leakage as sources of parasitic contrast in tomographic AFM, hindering real‐space 3D reconstructions. Novel strategies are proposed to overcome these limitations. First, the contrast mechanisms analyzing nanosized conductive features are explored when confining current collection purely to in‐plane transport, thus allowing reconstruction with a reduction in the overestimation of the lateral dimensions. Furthermore, an adaptive tip‐sample biasing scheme is demonstrated for the mitigation of a class of artefacts induced by the high electric field inside the thin oxide when volumetrically reduced. This significantly enhances vertical sensitivity by approaching the intrinsic limits set by quantum tunneling processes, allowing detailed depth analysis in thin dielectrics. The effectiveness of these methods is showcased in tomographic reconstructions of conductive filaments in valence change memory, highlighting the potential for application in nanoelectronics devices and bulk materials and unlocking new limits for tomographic AFM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Lewis Acid Supported Nickel Nitrenoids

Abstract Metalation of the polynucleating ligand F,tbs LH 6 (1,3,5‐C 6 H 9 (NC 6 H 3 −4‐F−2‐NSiMe 2 t Bu) 3 ) with two equivalents of Zn(N(SiMe 3 ) 2 ) 2 affords the dinuclear product ( F,tbs LH 2 )Zn 2 ( 1 ), which can be further deprotonated to yield ( F,tbs L)Zn 2 Li 2 (OEt 2 ) 4 ( 2 ). Transmetalation of 2 with NiCl 2 (py) 2 yields the heterometallic, trinuclear cluster ( F,tbs L)Zn 2 Ni(py) ( 3 ). Reduction of 3 with KC 8 affords [KC 222 ][( F,tbs L)Zn 2 Ni] ( 4 ) which features a monovalent Ni centre. Addition of 1‐adamantyl azide to 4 generates the bridging μ 3 ‐nitrenoid adduct [K(THF) 3 ][( F,tbs L)Zn 2 Ni(μ 3 ‐NAd)] ( 5 ). EPR spectroscopy reveals that the anionic cluster possesses a doublet ground state ( S = ). Cyclic voltammetry of 5 reveals two fully reversible redox events. The dianionic nitrenoid [K 2 (THF) 9 ][( F,tbs L)Zn 2 Ni(μ 3 ‐NAd)] ( 6 ) was isolated and characterized while the neutral redox isomer was observed to undergo both intra‐ and intermolecular H‐atom abstraction processes. Ni K‐edge XAS studies suggest a divalent oxidation state for the Ni centres in both the monoanionic and dianionic [Zn 2 Ni] nitrenoid complexes. However, DFT analysis suggests Ni‐borne oxidation for 5 .

Juda, Cristin E.↗
DOE OSTI2023

Lewis Acid Supported Nickel Nitrenoids

Metalation of the polynucleating ligand F,tbs LH 6 (1,3,5-C 6 H 9 (NC 6 H 3 −4-F−2-NSiMe 2 t Bu) 3 ) with two equivalents of Zn(N(SiMe 3 ) 2 ) 2 affords the dinuclear product ( F,tbs LH 2 )Zn 2 (1), which can be further deprotonated to yield ( F,tbs L)Zn 2 Li 2 (OEt 2 ) 4 (2). Transmetalation of 2 with NiCl 2 (py) 2 yields the heterometallic, trinuclear cluster ( F,tbs L)Zn 2 Ni(py) (3). Reduction of 3 with KC 8 affords [KC 222 ][( F,tbs L)Zn 2 Ni] (4) which features a monovalent Ni centre. Addition of 1-adamantyl azide to 4 generates the bridging μ 3 -nitrenoid adduct [K(THF) 3 ][( F,tbs L)Zn 2 Ni(μ 3 -NAd)] (5). EPR spectroscopy reveals that the anionic cluster possesses a doublet ground state (S = 1/2). Cyclic voltammetry of 5 reveals two fully reversible redox events. The dianionic nitrenoid [K 2 (THF) 9 ][( F,tbs L)Zn 2 Ni(μ 3 -NAd)] (6) was isolated and characterized while the neutral redox isomer was observed to undergo both intra- and intermolecular H-atom abstraction processes. Here, Ni K-edge XAS studies suggest a divalent oxidation state for the Ni centres in both the monoanionic and dianionic [Zn 2 Ni] nitrenoid complexes. However, DFT analysis suggests Ni-borne oxidation for 5.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2025

Laser Powder Directed Energy Deposition of Steels for Nuclear Applications

This comprehensive investigation examines the structure–property relationships in two nuclear alloy systems—Alloy 709 (A709) austenitic stainless steel and Grade 92 (G-92) ferritic/martensitic (F/M) steel—manufactured via directed energy deposition (DED) for sodium-cooled fast reactor applications. This study establishes the fundamental mechanisms for controlling microstructures for optimizing the mechanical performance of additively manufactured nuclear materials through systematic heat treatment optimization and multiscale characterization. As-deposited A709 steel develops a complex multiscale strengthening architecture consisting of a fine cellular solidification structure with diameter of 2-3 µm within10–50 µm grains, elevated dislocation densities from rapid thermal cycling, and grain boundary precipitates that activate concurrent Hall–Petch, dislocation, and precipitation hardening mechanisms to achieve exceptional properties [yield strength (YS): 603 MPa, ultimate tensile strength (UTS): 844 MPa, Vickers hardness: 220 HV] that achieve a 44% superior strength compared to that of the wrought material. Heat treatments produce different results. Solution annealing (SA) dissolves the cellular structure and reduces the hardness to 190 HV. Precipitation treatment (PT) keeps the cellular structure but adds carbides, allowing the hardness to reach 205 HV. The best approach combines both treatments (SA+PT) and creates uniform precipitate distributions with M 23 C 6 carbides at the grain boundaries and MX carbonitrides in the matrix, achieving a hardness of 195 HV. However, directional differences persist, with a 12%–15% strength variation between orientations due to the inherited layered microstructural architecture that survives aggressive heat treatment. While tensile testing at 550°C demonstrates 40%–50% thermal softening with dynamic strain aging, DED A709 steel still maintains a 71% higher YS than that of the wrought material. Ion irradiation studies (100–400 dpa) of DED A709 steel reveal progressive radiation damage with increasing void density and radiation-induced segregation causing nickel enrichment and chromium depletion, which will ultimately compromise mechanical properties. As-deposited G-92 exhibits exceptional strength (UTS: 1650–1700 MPa, 430 HV) through a complex microstructure containing both ferrite and martensite phases, a high geometrically necessary dislocation (GND) density (17.04×10 14 /m 2 ), and fine carbides. Heat treatments create distinct changes. Normalizing produces fresh martensite with the highest hardness (460 HV) and an increased GND density (20.23×10 14 /m 2 ). Tempering develops dual precipitation systems and reduces the hardness to 290 HV. The optimal approach uses sequential normalizing plus tempering, achieving balanced properties with the lowest hardness (250 HV) and a reduced GND density (11.01×10 14 /m 2 ). A processing-dependent anisotropy is observed: horizontal specimens achieve superior ductile behavior, while vertical specimens exhibit brittle failure. A tempering heat treatment successfully mitigates this anisotropic behavior by transforming the hard martensitic as-deposited structure into tempered martensite enabling both horizontal and vertical specimens to exhibit similar stress–strain characteristics with visible necking behavior. Remarkably, testing at 550°C reveals a reversal in the anisotropy, where as-deposited specimens achieve near isotropy with superior thermal stability (a 15%–20% strength reduction), while tempered specimens develop an orientation dependence with a 25%–30% strength reduction. Both alloy systems demonstrate that DED processing creates specimens with a superior strength through refined microstructural features, though with distinct strengthening mechanisms—austenitic through cellular structures and precipitates versus F/M through phase transformations and precipitates. Heat treatment optimization requires alloy-specific approaches, with A709 benefiting from controlled precipitation while G-92 requires careful phase transformation control. The results show that DED manufacturing can produce nuclear materials with exceptional performance, but directional effects and temperature-dependent behavior must be carefully considered for reactor component design and qualification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1969

Magnetron tuner has locking feature

Magnetron tuning arrangement features a means of moving a tuning ring axially within an anode cavity by a system of reduction gears engaging a threaded tuning shaft of lead screw. The shaft positions the tuning ring for the desired magnetron output frequency, and a washer prevents backlash.

Martucci, V. J.↗