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At least 433 records · Page 24

Certifying Acoustic Emission for the Crack Inspection of Integrated Pressure Components

To address the limitations of other nondestructive evaluation (NDE) methods on integrated components due to access constraints, a novel Acoustic Emission (AE) pressure vessel health test was developed in accordance with ASME BPVC.V Article 12. This test was then validated using notched Ti-6Al-4V panels taken to a stress level equivalent to 1.1 times the Maximum Allowable Working Pressure (MAWP) of a flight pressure vessel. The assessment involved utilizing JSC small sample Probability of Detection (POD) calculations, which represents the initial step towards obtaining flight certification for an NDE technique. A robust filtering technique that specifically identifies crack growth was formulated with the assistance of industry partners. This detection method exhibited superior detection ability compared to other NDE methods or microscopy. Within industry, the presence of a group of triangulated energetic crack-like indications typically leads to supplemental inspection or the failure of a tank. The experimental results confirm that this approach, commonly applied to more robust tanks, can be viably extended to integrated aerospace pressure components in scenarios where components can be subjected to pressures up to 1.1 times the MAWP. The testing process encountered delays due to sensor delivery and panel fabrication. Thus far, samples with crack initiators have shown energy levels 10 times higher than blunted controls. This suggests a trend where high individual and total cluster energy could be employed as acceptance criteria. Moreover, the cumulative energy of clusters might serve as a foundation for estimating crack sizes; however, this aspect requires further assessment and localized supplemental inspections are preferred.

certifying↗

ASME – Grappling with the Concept of Component Failure

ASME concepts of component failure. Definition for failure, functionality, damage tolerance, including Magnox reactors and AGRs. How ASME is addressing damage tolerance, and Section III division 5 Design, Classification, Reliability Target for SRC-1 components, Basic analysis approach, simple assessment, and full assessments, Reference to reliability and integrity management and strategy for graphite components.

36 MATERIALS SCIENCE↗

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗

Interaction between the emerging components of online shopping and in-person activities: insights from a behavioral survey

The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.

Digital Divide↗

Multimodal Defect Imaging of Pure Tungsten Components Fabricated via Electron Beam Powder Bed Fusion

The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.

36 MATERIALS SCIENCE↗

C3MechLite: An integrated component library of compact kinetic mechanisms for low-carbon, carbon neutral and zero-carbon fuels

Based on our latest detailed chemical reaction mechanism, C3MechV4.0, we have developed two reduced reaction mechanisms—C3MechLite and C3MechCore—targeting C 0 –C 3 chemical species including NH 3 . C3MechLite (61 species), contains a number of species comparable to GRI-Mech (53 species), that can accurately predict the combustion characteristics of hydrogen, carbon monoxide, ammonia, methane, natural gas, nitrogen oxides, and their mixtures for a wide range of conditions. C3MechCore (118 species) targets a more comprehensive range of C 0 –C 3 fuels, including ammonia, methanol, ethanol, and dimethyl ether. Both mechanisms demonstrate predictive accuracy comparable to C3MechV4.0 for the combustion characteristics of the target fuels. C3MechLite is designed with a component library structure, enabling further reduction in mechanism size depending on the fuel(s) of interest for 2D/3D numerical simulations. Various combinations of component libraries were validated, and the average prediction error remains within 1 % compared to C3MechLite. Furthermore, the mechanism was applied to 3D LES simulations of H 2 lifted flames and was confirmed to reproduce flame characteristics with high accuracy. C3MechLite and its component library structure enable high-fidelity and computationally efficient chemical kinetic mechanisms, paving the way for application in more complex combustion simulations.

Ammonia↗

Process-level cost analysis of hybrid manufacturing pathways for aerospace structural components

Hybrid manufacturing is a promising route for producing complex aerospace components, yet systematic cost benchmarking across multiple additive-subtractive pathways remains limited. This study presents a comprehensive process-based cost analysis of seven hybrid manufacturing routes, including laser powder bed fusion (L-PBF), powder- and wire-directed energy deposition (DED), wire arc additive manufacturing (WAAM), additive friction stir deposition (AFSD), metal binder jetting (MBJ), and agility forging, followed by scanning and finish machining. Parametric cost models incorporating direct material, labor, and energy costs were developed. L-PBF results are discussed in detail for a pickle fork component and directly compared with commercial pricing. Across all hybrid routes, labor emerged as the dominant cost driver, contributing more than 70% of total manufacturing cost in some cases. AFSD exhibited the lowest cost for aluminum components, with MBJ being its 316 L stainless steel counterpart, after accounting for geometric scaling. Benchmarking against industrial quotes suggests that hybrid manufacturing can achieve cost levels comparable to those of commercial services, although labor-intensive processes exhibit greater deviation. The analysis highlights automation of material handling, setup, and supervision as key opportunities for improving economic competitiveness. Overall, the proposed framework provides a quantitative basis for evaluating and optimizing hybrid manufacturing pathways for aerospace applications.

Baruah, Sweta [ORNL] (ORCID:0009000174256207)↗

A simple three-component mixing problem for the evaluation of a new reaction rate model

A simple computational mixing problem is presented which can be utilized to assess the behavior of Reynolds-averaged reaction rate models in a problem with temporally varying mixedness. In this problem, three mixing components are homogeneously distributed but initially separated in a triply periodic domain. Further, these components are initialized within a Taylor–Green-like velocity field, which creates a mixing history evolving from the so-called “no-mix limit” to a well-mixed state. Large-eddy simulation results from this problem in configurations involving both premixed and nonpremixed reactants are then compared with zero-dimensional Reynolds-averaged Navier–Stokes results utilizing a new model for multicomponent reacting mixtures. The new model is shown to appropriately respect the no-mix limit and outperforms an earlier model (Morgan, 2022), particularly at early times when components are near the no-mix limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Component innovations for lower cost mechanical vapor compression

Despite significant capital and operating costs, mechanical vapor compression (MVC) remains the preferred technology for challenging brine concentration applications. Here, this work seeks to assess the dependence of MVC costs on feedwater salinity and desired water recovery and to quantify the value of improved component performance or reduced component costs for reducing the levelized cost of water (LCOW) of MVC. We built a cost optimization model coupling thermophysical, heat and mass transfer, and technoeconomic models to optimize and identify low cost MVC system designs as a function of feedwater salinity and water recovery. The LCOW ranges over 3.6 to 6.1 $\$$/m 3 for seawater feed salinities of 25–150 g/kg and water recoveries of 40–80 %. We then perform sensitivity analysis on parameter inputs to isolate irreducible costs and determine high value component innovation targets. The LCOW was most sensitive to evaporator material costs and performance, including the overall heat transfer coefficient in the evaporator. Process and material innovations such as polymer-composite evaporator tubes that reduce evaporator costs by 25 % without reducing heat transfer performance by more than 10 % would result in MVC cost reductions of 8 %.

42 ENGINEERING↗

Two-component exciton condensates in an electron–hole bilayer

Macroscopic quantum coherence emerges when bosons condense into a Bose–Einstein condensate (BEC). Excitons are a long-sought solid-state route to high-temperature BECs with strong interactions, electrical tunability and potentially multicomponent spinor order, but conclusive evidence for equilibrium condensation has remained elusive. Here we report evidence for two-component exciton BECs in MoSe 2 /hBN/WSe 2 electron–hole bilayers by probing the spin–valley susceptibility of constituent electrons and holes. This heterostructure hosts equilibrium exciton fluids with four spin–valley flavours. Magneto-optical spectroscopy in a dilution refrigerator reveals three exciton condensate phases with distinct flavour polarizations. At zero magnetic field, the many-body ground state is a coherent superposition of two condensed intravalley exciton flavours. Under a magnetic field, the intravalley exciton condensate first switches to a two-component intervalley condensate through a first-order quantum phase transition at a weak critical field and then turns into a fully polarized single-component condensate at high fields. The condensate signatures form a dome in density–temperature space, persisting up to approximately 1.8 K. Our results establish van der Waals electron–hole bilayers as a versatile platform for strongly interacting, multicomponent exciton BECs.

Qi, Ruishi [University of California, Berkeley, CA↗

Lab-Scale Cable-Driven Parallel Robot Prototype for Automated Prefabricated Component Manipulation

This paper presents the design and evaluation of a lab-scale cable-driven parallel robot (CDPR) developed as a flexible platform for automated installation of prefabricated components onto exterior building envelopes. Traditional manual installation methods for prefabricated components, which depend on scaffolding, cranes, cherry pickers, and verbal coordination, are not only labor-intensive and error-prone but also face significant limitations in dense urban environments due to site access constraints. To address these challenges, we developed a lab-scale CDPR platform capable of autonomously transporting building envelope components from a designated pickup zone to their target installation location, minimizing the need for human intervention. This study describes the system’s mechanical design, actuation architecture, real-time feedback system, and control strategy of the CDPR, and evaluates its performance in a laboratory environment. The robot’s actuation system uses torque control for end-effector manipulation. The robot’s real-time pose feedback comes from a construction-grade total station and a wireless inertial measurement unit (IMU), which together support precise end-effector control. Experimental results demonstrate the successful integration of the hardware, sensing, state estimation, and control subsystems. Preliminary tests showed that our lab-scale prototype can position the end effector with an error of less than 3 mm, which is a level of precision not previously achieved by existing CDPRs in construction applications. The key findings are twofold: (1) torque-only control is necessary but not sufficient for minimizing final pose error, and (2) incorporating real-time pose feedback can achieve the desired placement accuracy.

Liu, Yifang [Oak Ridge National Laboratory (ORNL),↗

Initial Assessment of 3D-Printed Molybdenum Lattices as a Substrate for Liquid Metal Plasma-Facing Components

Many liquid metal plasma-facing component concepts for fusion reactors require a capillary porous material to wick the liquid metal to the surface of the component. In this work, samples of 3D-printed molybdenum lattices designed to have pore sizes ranging from 100 to 1000 µm were fabricated by two different suppliers. The dimensions of the lattice structures were characterized using an optical microscope, revealing that the lattice walls were almost systematically thicker than they were designed to be and in turn the pores are all smaller than they were designed to be. In many cases the pores were completely clogged. Several spectroscopic techniques were used to characterize the chemical composition of the materials, collectively indicating the presence of a thick layer of oxygen-rich material extending more than 1 µm below the surface. Compression testing revealed significant asymmetry in the mechanical behavior depending on which direction the force was applied relative to the print direction. Lastly, lithium wetting tests of several lattice samples were performed, and a sample with ~250-µm pores was found to soak up lithium quickly after initial wetting of the surface occurred. The implications of these results for future liquid metal plasma-facing component design are discussed.

Parsons, Matthew S. [Princeton Plasma Physics Labo↗

Characterization of UV optical components for photon detector calibration in liquid argon TPCs

Large liquid argon time projection chambers (LArTPCs)require stable and well-characterized delivery of ultraviolet (UV)light for in situ calibration of photosensors at cryogenictemperatures. This article reports bench-top and cryogenicmeasurements of the optical components used in a UV lightcalibration system, including multi-mode fused-silica fibers,SMA-to-SMA connectors, optical fiber feedthroughs, andlight-diffuser assemblies. Light loss in several fiber types and SMAconnectors was measured across wavelengths from275–970 mm. In addition,light-loss measurements of the tested fibers after severalliquid-nitrogen thermal cycles showed no statistically significantdegradation relative to baseline measurements, and high-rate pulsedexposure (30–90 million pulses from a275 nm LED) likewise showed nomeasurable aging in jacketed fibers. A compact, palm-sized,3D-printed PEEK diffuser housing with stacked UV-grade fused-silicadiffusers yields Lambertian emission and the most uniform angulardistribution. Optical components exhibiting improved UV transmissionwere deployed successfully in multiple DUNE small- and large-scaleprototypes, demonstrating reliable operation of UV light calibrationsystem. These findings inform component selection and calibrationprocedures for achieving reliable, uniform UV light delivery inlarge-scale cryogenic detectors such as DUNE.

Behera, B. [South Dakota Sch. Mines Tech.] (ORCID:↗

The ability of Salmonella enterica subsp. enterica strains to form biofilms on abiotic surfaces and their susceptibility to selected essential oil components

Abstract The ability of Salmonella enterica subsp. enterica to persist and form biofilms on different surfaces can constitute a source of food contamination, being an issue of global concern. The objective of this study was to understand the biofilm formation profile of 14 S. enterica strains among different serovars and sources and to evaluate the ability of essential oil (EO) components (carveol, citronellol, and citronellal) to disinfect the biofilms formed on stainless steel and polypropylene surfaces. All the strains were able to form biofilms with counts between 5.34 to 6.78 log CFU cm−2. Then, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of EO components were evaluated on two selected strains. All compounds inhibited the growth of Salmonella Typhimurium (strain 1; MIC = 800–1000 µg ml−1) and Salmonella Enteritidis (strain 5; MIC = 400–1000 µg ml−1) and only carveol showed bactericidal activity against strains 1 and 5 (MBC = 1200 µg ml−1). Biofilms were exposed to the EO components at 10 × MIC for 30 min and polypropylene surfaces were more difficult to disinfect showing reductions between 0.9 and <1.2 log CFU cm−2. In general, the S. enterica biofilms demonstrated a significant tolerance to disinfection, demonstrating their high degree of recalcitrance on food processing surfaces.

Pérez-Lavalle, Liliana (ORCID:0000000344825031)↗

Negative components in the representation $\frac{𝑑^{2}⁡(𝜎⁢𝐸)}{𝑑⁡𝐸^2}$ of heavy-ion fusion cross sections

Negative components are observed in the representation $\frac{𝑑^{2}⁡(𝜎⁢𝐸)}{𝑑⁡𝐸^2}$ in heavy-ion fusions related to recent studies of noticeable oscillations in the high-energy region of the fusion excitation functions. A modified multiGaussian model has been developed, which includes contributions for both positive and negative components in the $\frac{𝑑^{2}⁡(𝜎⁢𝐸)}{𝑑⁡𝐸^2}$ representation. The model reproduces the heavy-ion fusion excitation functions very well, including oscillations. The property of these negative components in the $\frac{𝑑^{2}⁡(𝜎⁢𝐸)}{𝑑⁡𝐸^2}$ spectrum and the reaction mechanism of fusion at energies above the Coulomb barrier are investigated. Finally, the model also reproduces well light heavy-ion fusion reactions between 12 C and 16 O at high energy; results imply that the compound nucleus channel effect may have an important contribution at the high energy region for all fusion systems.

low & intermediate energy heavy-ion reactions↗

Microbead Encapsulation for Protection of Electronic Components

Here, this study investigates the application of microbeads as an innovative encapsulation technique to protect electronic components from harsh mechanical strain. Traditional encapsulation methods using hard epoxy provide substantial mechanical support but create thermal expansion mismatch issues, potentially leading to electronic component failure. We explore the use of finely powdered microbeads to achieve protective structures combining stiffness and energy absorption. The research focuses on key variables, including microbead size, microbead roughness, compaction of microbeads, and circuit board mounting in the encapsulation, all of which influence the encapsulation’s effectiveness. Experimental setups and testing protocols were developed to assess the performance of various microbead materials under different impact conditions. Results demonstrate that microbead encapsulation significantly reduces strain on circuit boards, minimizing the risk of damage during mechanical shocks. However, challenges remain, such as optimizing microbead characteristics and modeling their behavior within large-scale circuit board assemblies. Despite these challenges, the findings suggest that microbead encapsulation offers a promising alternative to conventional methods, enhancing the durability and reliability of electronic components in high-stress environments.

42 ENGINEERING↗

IMPACT: Design of Integrated Multiphysics Producible Additive Components for Turbomachinery

The overall objective of the IMPACT program was to enable a dramatic reduction in design maturation time for an additive hot-section turbomachinery component through the following: • A fast crack-risk producibility surrogate model generated from machine learning applied to additive process simulation data generated via exascale computing, • Linking this surrogate model to multi-physics topology optimization (TO) to enable the creation of producible, near-optimal structural/thermal designs for additive hot-section components, • Maturing this toolset to reduce hot-section component design-for-manufacturing iterations by a large fraction, and eventually, • Using these tools to develop more efficient gas turbines in much shorter design cycle times.

42 ENGINEERING↗

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗