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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optical leakage mitigation in ortho-mode transducer detectors for microwave applications

Ortho-mode transducers (OMT) are a commonly used method of coupling opti-cal signals between waveguides and on-chip circuitry and detectors. While the ideal OMT-waveguide coupling requires minimal disturbance to the waveguide, when used for mm-wave applications the waveguide is often slide though and the OMT probes inserted on a thin support membrane. This interface is a potential source of signal leakage and can lead to signal loss and experimental systematics (e.g. in CMB polarization experiments where lost signal can result in temperature to polarization leakage). Here we report on the development of new OMT- waveguide coupling structures with the goal of reducing leakage at the horn-to-detector-wafer interface. The pixel to pixel optical leakage due to the gap be-tween the coupling waveguide and the backshort is reduced by means of a protrusion that passes through the OMT membrane and connects the waveguide sections on each side of the wafer. High frequency electromagnetic simulations indicate that these protrusions can reduce optical leakage in the gap by ~80% percent, with a ~60% filling factor, respect to an ordinary OMT coupling without protrusion, using designs that are compatible with current fabrication processes. We describe the design of the new coupling structure including the impact of varying two key parameters: wafer to optical coupling gap and protrusion fill factor. Prototype devices have been designed that will characterize the performance of the new design using a relative measurement with varying filling factors. We describe the simulation setup and results, and present a chip layout and sample box designed to perform an initial measurement.

Detectors↗
DOE OSTI2022

Epoxy IR Filters for Superconducting Resonators

Infrared radiation leakage through dielectrics in coaxial cabling can create unwanted quasiparticles in superconducting resonators if allowed to reach devices unattenuated. Modern experimental setups commonly incorporate epoxy-based filter blocks on readout and control lines in order to mitigate this effect; however, most of these multi-channel designs require specialized mounting hardware. Here, this investigation details the design and construction of single-channel inline infrared filters, and finds that their installation in the readout system for an aluminum resonator increased its quality factor by an average of 12% across a range of operating temperatures and powers. The option to incorporate general-purpose inline filters instead of designing custom filter blocks and mounts should prove more convenient for many newer experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments

Many scientific applications from rare-event searches to condensed matter system characterization to high-rate nuclear experiments require time-domain triggering on a raw stream of data, where the triggering is generally threshold-based or randomly acquired. When carrying out detector R &D, there is a need for a general data acquisition (DAQ) system to quickly and efficiently process such data. In the SPLENDOR collaboration, we are developing the Python-based SPLENDAQ package for this exact purpose—it offers two main features for offline analysis of continuous data: a threshold triggering algorithm based on the time-domain optimal filter formalism and an algorithm for randomly choosing nonoverlapping segments for noise measurements. Further, combined with the commercially available Moku platform, developed by Liquid Instruments, we have a full pipeline of event building off raw data with minimal setup. Here, we review the underlying principles of this detector-agnostic DAQ package and give concrete examples of its utility in various applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Signatures of Thermal and Electrical Crosstalk in a Microwave Multiplexed Hard X-ray Transition Edge Sensor Array

We investigate the crosstalk between Transition-Edge Sensor (TES) pixels in a 24-pixel hard X-ray spectrometer array fabricated at the Advanced Photon Source, Argonne National Laboratory. Analysis shows thermal cross talk, possibly associated with insufficient thermalization, and rare but larger in magnitude electrical crosstalk between specific perpetrator-victim pixel combinations, potentially due to defects in the bias wiring or microwave multiplexing circuit. We use a method based on group-triggering and averaging to isolate the crosstalk response despite only having access to X-ray photon illumination uniform across the entire array. This allows us to identify thermal and electrical crosstalk between pixel pairs in repeated measurements to the level of 1 part in 1000 or better. In the array under study, the magnitude of observed crosstalk is small but comparable to the resolving power of this pixel design (E/Delta E similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E/\Delta {}E \sim$$\end{document} 1000 at 20 keV) and so potentially responsible for a degradation in energy resolution of the array at high incident photon rates. Having proven the methods to identify and quantify crosstalk in our setup, we can now consider mitigations.

Triamkitsawat, Panthita↗
DOE OSTI2023

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Nonlinear acoustic methods demonstrate high sensitivity for concrete damage evaluation. Among these methods, nonlinear resonance acoustic methods have been widely used in laboratory tests on small scale test samples. In this study, a nonlinear impact-echo (IE) method for concrete damage evaluation was introduced, in which the IE frequency shift was measured at different impact force levels. The nonlinear IE test and the nonlinear impact resonance acoustic spectroscopy (NIRAS) test share similarities in the experimental setup and analysis method. However, the nonlinear IE test excites a local thickness resonance mode of a member instead of the global resonance vibration. Therefore, the analyzed mode is unaffected by member boundary conditions and is applicable to large concrete structural members. To identify the fundamental IE frequencies, multiple impacts were applied along the members. Once the fundamental IE frequencies were determined, the effectiveness of the nonlinear IE method was evaluated by testing seven concrete beam specimens with varying levels of alkali-silica reaction (ASR) damage. In conclusion, the nonlinear IE test demonstrates high sensitivity to concrete damage and allows for quantitative assessment of the damage state of concrete without a baseline measurement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interlaboratory comparison of secondary ion mass spectrometry analysis results from the 7th international technical nuclear forensics working group collaborative material exercise

A review and interlaboratory comparison of secondary ion mass spectrometry (SIMS) data obtained by laboratories participating in the International Technical Nuclear Forensics Working Group (ITWG) 7th Collaborative Material Exercise (CMX-7) has been conducted. The analyzed materials were two uranium compounds in powder form and two pieces of uranium metal. The instruments used in this comparison were a small-geometry (SG) SIMS, CAMECA IMS 7f from the Research Centre Rez, and a large-geometry (LG) SIMS, CAMECA IMS 1280 from Los Alamos National Laboratory. Despite the differences in instruments and analytical procedures, e.g., sample preparation, SIMS setups, and data post-processing, there was good agreement for the 234 U/ 238 U, 235 U/ 238 U, and 236 U/ 238 U ratios in the analyzed materials. The main difference was in the precision, which was, as expected, higher for the LG-SIMS. In addition, a comparison between the laboratories was also made for the image processing algorithms applied to raw data acquired in automated particle measurement (APM) mode. In conclusion, the result of this comparison has led to identification of best practices for setting up parameters of the APM software.

and nuclear chemistry↗
DOE OSTI2025

An Internal Digital Image Correlation Technique for High-Strain Rate Dynamic Experiments

Full-field, quantitative visualization techniques, such as digital image correlation (DIC), have unlocked vast opportunities for experimental mechanics. However, DIC has traditionally been a surface measurement technique, and has not been extended to perform measurements on the interior of specimens for dynamic, full-scale laboratory experiments. This limitation restricts the scope of physics which can be investigated through DIC measurements, especially in the context of heterogeneous materials. The focus of this study is to develop a method for performing internal DIC measurements in dynamic experiments. The aim is to demonstrate its feasibility and accuracy across a range of stresses (up to 650 MPa), strain rates (10 3 - 10 6 s -1 ), and high-strain rate loading conditions (e.g., ramped and shock wave loading). Internal DIC is developed based on the concept of applying a speckle pattern at an inner-plane of a transparent specimen. The high-speed imaging configuration is coupled to the traditional dynamic experimental setups, and is focused on the internal speckle pattern. During the experiment, while the sample deforms dynamically, in-plane, two-dimensional deformations are measured via correlation of the internal speckle pattern. In this study, the viability and accuracy of the internal DIC technique is demonstrated for split-Hopkinson (Kolsky) pressure bar (SHPB) and plate impact experiments. The internal DIC experimental technique is successfully demonstrated in both the SHPB and plate impact experiments. In the SHPB setting, the accuracy of the technique is excellent throughout the deformation regime, with measurement noise of approximately 0.2% strain. In the case of plate impact experiments, the technique performs well, with error and measurement noise of 1% strain. The internal DIC technique has been developed and demonstrated to work well for full-scale dynamic high-strain rate and shock laboratory experiments, and the accuracy is quantified. Here, the technique can aid in investigating the physics and mechanics of the dynamic behavior of materials, including local deformation fields around dynamically loaded material heterogeneities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Iron Oxide Reduction Rate Affects Iron Product Morphology

Magnetite concentrates from different parts of the Mesabi Range, exhibiting varying tendencies to encapsulate, were reduced using thermogravimetric analysis (TGA) and a high-temperature confocal scanning laser microscope (CSLM). Gas-phase mass-transfer conditions were intentionally varied by employing two reactors with different geometries: the TGA produced significantly slower reduction, whereas the CSLM setup yielded reduction rates up to 20 times faster under mass-transfer-controlled conditions. A preliminary mechanism is proposed to explain the transition from a porous iron product to a dense encapsulating layer. Encapsulation is interpreted as the outcome of competition between pore creation, driven by the reduction rate, and pore elimination, driven by surface diffusion. When pore formation is insufficient to counteract pore coarsening, a dense iron layer develops and restricts further reduction.

encapsulation↗
DOE OSTI2021

Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest

In this work, we investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.

42 ENGINEERING↗
DOE OSTI2020

In-situ Observations of Directed Energy Deposition Additive Manufacturing Using High-Speed X-ray Imaging

In laser-based directed energy deposition (DED) additive manufacturing, interactions among the laser beam, particle flow, and melt pool influence the properties of the solidified final part. Two separate DED systems, one with high powder flow rates to represent industrial-scale DED processing and the other with low powder flow rates for individual particle tracking, were synchronized with the high-speed imaging setup at the Advanced Photon Source in Argonne National Laboratory. In-situ x-ray imaging of the DED process using both systems highlighted the influence of powder flow rates. Increased powder flow rates resulted in less laser absorption into the melt pool, leading to a transition from a keyhole mode to a melt pool without a keyhole but with surface fluctuations due to powder flow. Increased velocities of particles during powder flow resulted in a decrease in particle melting times and a greater propensity for porosity formation. Altogether, better understanding of the interactions that occur during various scales of the DED process will enable flexibility, control, and new materials development in DED-based additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

In-Situ X-Ray Imaging High Strain Rate Compression of Laminate Al-Graphene Composite and Mechanical Property Characterization

Owing to the opaque nature of the laminated structures, traditional high-speed optical camera cannot be used to detect the dynamic process of sub-surface deformation. In this article, we report a study of using high speed X-ray imaging to study the high strain rate deformation in laminated Al structures. We used a Kolsky bar apparatus to apply dynamic compression and a high-speed synchrotron X-ray phase contrast imaging (PCI) setup to conduct the in situ X-ray imaging study. The in situ X-ray imaging captures the shock wave propagation in the laminated structures. After shock compression, we characterized the microstructures by using transmission electron microscopy (TEM), which demonstrates an increase of dislocation density. In conclusion, the micro-pillar compression tests show that the yield strength at 0.2% offset of laminated Al-graphene composite has a significant increase of 67%, from 30 to 50 MPa, compared to laminate Al after shock loading.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Carbon flowers as electrocatalysts for the reduction of oxygen to hydrogen peroxide

Small-scale and decentralized production of H 2 O 2 via electrochemical reduction of oxygen is of great benefit, especially for sanitization, air and water purification, as well as for a variety of chemical processes. The development of low-cost and high-performance catalysts for this reaction remains a key challenge. Carbon-based materials have drawn substantial research efforts in recent years due to their advantageous properties, such as high chemical stability and high tunability in active sites and morphology. Deeper understanding of structure–activity relationships can guide the design of improved catalysts. We hypothesize that mass transport to active sites is of great importance, and herein we use carbon materials with unique flower-like superstructures to achieve high activity and selectivity for O 2 reduction to H 2 O 2 . The abundance of nitrogen active sites controlled by pyrolysis temperature resulted in high catalytic activity and selectivity for oxygen reduction reaction (ORR). The flower superstructure showed higher performance than the spherical nanoparticles due to greater accessibility to the active sites. Chemical activation improves the catalysts’ performances further, driving the production of H 2 O 2 to a record-setting rate of 816 mmol·g cat -1 ·h -1 using a bulk electrolysis setup. In conclusion, this work demonstrates the development of a highly active catalyst for the sustainable production of H 2 O 2 through rational design and synthetic control. The understanding from this work provides further insight into the design of future carbon-based electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ruthenium-lead oxide for acidic oxygen evolution reaction in proton exchange membrane water electrolysis

Developing an active and stable anode catalyst for the proton exchange membrane water electrolyzer (PEM-WE) is a critical objective to enhance the economic viability of green hydrogen technology. However, the expensive iridium-based electrocatalyst remains the sole practical material with industrial-level stability for the acidic oxygen evolution reaction (OER) at the anode. Ruthenium-based catalysts have been proposed as more cost-effective alternatives with improved activity, though their stability requires enhancement. The current urgent goal is to reduce costs and noble metal loading of the OER catalyst while maintaining robust activity and stability. Here, in this study, we design a Ru-based OER catalyst incorporating Pb as a supporting element. This electrocatalyst exhibits an OER overpotential of 201 mV at 10 mA.cm -2 , simultaneously reducing Ru noble metal loading by similar to 40%. Normalization of the electrochemically active surface area unveils improved intrinsic activity compared to the pristine RuO 2 catalyst. During a practical stability test in a PEM-WE setup, our developed catalyst sustains stable performance over 300 h without notable degradation, underscoring its potential for future applications as a reliable anodic catalyst.

Electrocatalysis↗
DOE OSTI2021

AFRL Additive Manufacturing Modeling Series: Challenge 1, Characterization of Residual Strain Distribution in Additively-Manufactured Metal Parts Using Energy-Dispersive Diffraction

A machine component fabricated by additive manufacturing (AM) of metal powders often possesses steep residual stress gradients with significant magnitudes due to large temperature gradients that transpire within a localized area during fabrication. These processing-induced residual stresses can cause distortion of the part, and if they are of significant magnitude, they could induce cracking of the component, degrade printability, and/or diminish subsequent mechanical performance. The ability to predict these residual stresses imparted by AM is an important step in permeating AM technology for advanced manufacturing. Calibration and validation of AM process models used for prediction are, therefore, a critical step in understanding the origin and mitigating the challenges associated with residual stresses inherent to the AM process. In the present work, the residual strain distributions in components with simple geometries fabricated by a laser powder bed fusion (LBPF) process were characterized non-destructively using energy-dispersive X-ray diffraction in support of the US Air Force Research Laboratory Additive Manufacturing Modeling Challenge Series Groeber et al. (JOM 70:441-448, 2018). The measurement setup and approach are described in detail so that the data can be used as a benchmark to calibrate and validate models for the prediction of macro-scale residual stresses due to the LPBF process.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Comparisons of Image Quality for Flash X-Ray Detectors

Due to X-rays’ ability to penetrate materials, flash X-ray radiography can be used for high-speed measurements where direct optical access is not possible. Choice of detector has a pronounced impact on resulting image quality. Four different detector systems were evaluated with a 450kVp flash source to quantitatively compare image quality metrics. The scintillating digital detector had less image noise than the three different storage phosphor computed radiography detectors across all transmission levels, but lacked the spatial resolution of the computed radiography detectors. For the screens tested here, the HPX-DR digital system had the highest signal to noise ratio of 68.24 and contrast to noise ratio of 35.53, but had the lowest spatial resolution, resolving 2.5 line pairs per millimeter at 1.78% contrast. At a value of 37.59, the Flex GP imaging plate had a signal to noise value above its storage phosphor counterparts under a 450kVp flash source. For radiographic setups typically used for dynamic experiments, the Flex XL Blue and Flex HR detectors had signal to noise ratios of 18.44 and 26.56 respectively. The highest resolved spatial frequencies of the Flex GP, Flex XL Blue, and Flex HR with the flash source are 3.85, 5.00, and 3.85 line pairs per millimeter, respectively. In conclusion, the Flex GP detector had the best combination of signal to noise ratio, contrast to noise ratio, and spatial resolution under a flash source.

computed radiography↗
DOE OSTI2021

Microstructure and Mechanical Properties of the AA7075 Tube Fabricated using Shear Assisted Processing and Extrusion (ShAPE)

Shear Assisted Processing and Extrusion (ShAPE) experimental setup and tooling were adopted for extruding thin-walled AA7075 aluminum tube from as-cast non-homogenized billet material in a single run. The mechanical and microstructural characterizations were performed on the extrudate in the as-extruded condition without subsequent heat treating. It was shown that the ShAPE process developed a significantly refined microstructure with uniform and almost equiaxed grain structure on both hoop and axial cross-sections of the extrudate as well as through the thickness of the material. The pole figures and inverse pole figures of the EBSD data showed an strong shear texture development, and it was found out that axial shear is the dominant deformation mechanism in the regions near the inner surface of the tube, while combined axial and torsional shears are the two dominant modes of deformation near the outer surface of the extrudate. As for the mechanical properties, there was an increase of 150% and 73% in the yield and ultimate strengths of the tube produced using ShAPE process, respectively, and a 18% decrease in maximum uniform plastic elongation compared to the conventionally extruded AA7075-O tube.

Shear Assisted Processing and Extrusion, Friction ↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗