Numerical modeling of fiber orientation in multi-layer, isothermal material-extrusion big area additive manufacturing
Not Available
36 MATERIALS SCIENCE↗
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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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A semantics-driven framework to enable demand flexibility control applications in real buildings
Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A new 181 Ta neutron resolved resonance region evaluation
A new 181 Ta neutron resolved resonance region evaluation has been performed from the thermal energy range up to approximately 2.5 keV. The R-matrix SAMMY code was used with the Reich–Moore approximation to evaluate resonance parameters from several experimental data sets. A Monte Carlo approach was used for resonance spin assignments and generating 59 small fictitious resonance levels which were shown to improve the cumulative level, Porter-Thomas, and Wigner distributions as compared to theoretical predictions. Covariance information was also generated for the entire resolved resonance region. Finally, the positive impact of the new evaluation was validated through benchmark calculations which were sensitive to the 181 Ta cross section and showed improvement in the reactivity bias for several benchmark cases.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Unresolved resonance parameter evaluation and uncertainty quantification of n + 181Ta reactions
Not Available
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Semi-automatic image annotation using 3D LiDAR projections and depth camera data
Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Characterization of SimulCam, a standoff Raman system for scientific support of SuperCam operations on Mars
During the development activities of SuperCam Calibration Target, target intended for one of the two first Raman instruments to be deployed on another planetary body, our group developed a laboratory instrument that could simulate to some extent the Raman capabilities of one of such instruments and could provide data with similar quality. The use of this kind of laboratory instruments has demonstrated its utility in the evaluation of potential calibration targets or anticipating the science outcome that an instrument could provide. The present work describes our laboratory setup to support SuperCam, evaluating similarities between both instruments, despite of differences in the hardware. Evaluation of data gathered by SuperCam on Mars and the availability of one replica of SuperCam’s Calibration Target allowed the comparison on the same set of targets, demonstrating how similar Signal-to-Noise Ratio (SNR) could be achieved from both instruments. The higher energy per pulse on SimulCam is compensated by a greater analytical footprint and the use of smaller collection optics. The results show how spectra obtained at representative distances of SuperCam are comparable. Operational principles are also comparable in terms of time resolution, and close in terms of spectral resolution. This similarity has allowed different science support works using SimulCam data, as well as the support to Mars detections using our setup. We provide examples of this support that will be shared with the community in different papers, as well as examples of possible operations activities that could benefit from experiments performed with SimulCam. We show how this setup can complement the two laboratory replicas in Los Alamos and Toulouse in providing support data to different experiments.
47 OTHER INSTRUMENTATION↗
Activity and selectivity for CO 2 methanation of clusters and nanoplates of ruthenium dispersed on ceria: In-situ studies with XAFS and DRIFTS
The performance of Ru/CeO 2 catalysts under CO 2 hydrogenation conditions was studied using in situ X-ray absorption fine structure (XAFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) to understand the structural evolution and chemical nature of each component under reaction conditions. The catalysts were prepared using a reverse microemulsion method that maximized the dispersion of RuOx particles on ceria. A RuO x → Ru transformation was observed upon exposure of the RuO x /CeO 2 systems to H 2 at 250 °C. For a sample with 5% molar Ru, two-dimensional clusters (2-4 atoms) of Ru formed on top of the ceria support. An increase in the loading of ruthenium to 20% molar led to the formation of nanoplates of the metal 2-3 atomic layers thick. Both Ru structures were highly dispersed on ceria. They were oxidized after exposure to CO 2 at 250 °C. However, under CO 2 /H 2 mixture, they remained in a metallic state as two-dimensional clusters and nanoplates. In situ DRIFTS studies, the 5% and 20% Ru/CeO 2 catalysts showed distinct reaction intermediates when exposed to CO 2 or CO 2 /H 2 (1/4) reaction mixtures. Normalized to the Ru molar percentage, the 5% Ru/CeO 2 system was the most active catalyst exhibiting a selectivity of ~ 60% CH 4 and 40% CO at 250 °C. On the other hand, under the same reaction conditions, the 20% Ru/CeO 2 system was less active but had a CH 4 selectivity close to 80%. In conclusion, these results highlight the importance of the structure of the metallic Ru particles as a factor that determines the catalytic performance.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Shifting the MLCT of d 6 metal complexes to the red and NIR
Light-active d 6 -coordination compounds hold great promise for light energy conversion, sensors and therapeutic applications. However, the activity in the red-to-NIR spectral region is highly desirable to convert solar light more efficiently, use low-cost red-light sources and activate these chromophores in biological tissue environment. Due to their versatility, tunability and broad intense absorption, d 6 -coordination compounds with metalto-ligand charge transfer (MLCT) transitions are especially interesting. This review article offers a comprehensive collection of strategies to tune MLCT excited states in d 6 metal complexes and gives insights on group 6 to group 9 transition metals and their respective state-of-the-art MLCT engineering towards red-shifted absorption and emission properties with long-lived excited states. Strategies comprise lowering the π* level of the ligands, destabilizing and mixing of the metal-based HOMO, switching within a group of transition metals, matrix effects and insights into dealing with excited state deactivation in the context of the energy gap law.
Metal to ligand charge transfer (MLCT)↗
Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies
Not provided.
Biochemistry & Molecular Biology↗
Understanding and mitigating degradation in amine-based sorbents for CO 2 direct air capture
The success of direct air capture (DAC) of CO 2 depends on sorbents that combine high capacity, low energy requirements, and long-term durability. Amine-based sorbents-including solid-supported aminopolymers, grafted amines, and amine-functionalized resins-remain the leading candidates, but their limited lifetimes drive up costs and constrain deployment. In this review, we outline the current understanding of amine-based sorbent degradation with an emphasis on clearly identifying what is known about structure-property-performance relationships, as well as important knowledge gaps. More specifically, we discuss how polymer chemistry, sorbent design variables, and environmental and process conditions contribute to performance loss. In parallel, we outline how advances in spectroscopy, modeling, and accelerated testing are beginning to illuminate chemical and physical degradation mechanisms. Looking forward, we identify future research directions that will be critical for gaining a deeper understanding of degradation, as well as opportunities for developing innovative mitigation strategies for improving the lifetime of amine-based sorbents.
organic↗
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.
42 ENGINEERING↗
Experimental and modeling study of the autoignition behavior of a saturated heterocycle: Pyrrolidine
Experiments are conducted in both rapid compression machine (RCM) and shock tube (ST) to better quantify autoignition behavior (e.g., ignition delay, heat release) and understand heteroatomic effects in heterocyclic compounds, which are important reference components for the combustion of biomass-derived liquid fuels. These tests focus on the nitrogen-containing, five-membered saturated ring, pyrrolidine, at diluted conditions covering pressures of 20 and 50 bar, temperatures of 720–1450 K and a range of stoichiometries (ϕ = 0.5–2). A chemical kinetic model is developed and coupled to an existing combustion kinetics framework describing key nitrogen containing intermediates (e.g. pyrrole, ammonia and NOx). H-abstraction reactions by OH, H, CH 3 and HO 2 , are determined using ab-initio transition state theory methods, while analogies to cyclopentane are adopted for many other reactions, such as ring-opening. Here, the autoignition measurements reveal the lack of negative temperature coefficient (NTC) behavior and low-temperature chemistry for pyrrolidine, as opposed to its saturated hydrocarbon analogue, cyclopentane. Interestingly, at the lowest temperatures (T < 750 K), the reactivity of cyclopentane is greater than pyrrolidine, while at higher temperatures, pyrrolidine becomes more reactive. Agreement between the experimental measurements and the model is good, and it is found that H-abstraction reactions by HO 2 and ensuing chemistry play key roles in controlling the reactivity of this cyclic amine. Most of the fluxes, i.e., >70 %, are predicted to move through 1- or 2-pyrroline (C 4 H 7 N) and then the cyclic C 4 H 6 N radical, at both lower and higher temperatures, to form either CH 2 CHCHCHNH via ring-opening or pyrrole via β-scission. It appears that the ring opens more easily at lower temperature whereas the C–H β-scission dominates at higher temperature and lower pressure, such that the reaction of the fuel radical intermediate carrying an unpaired electron on the nitrogen atom with HO 2 is the next most notable in promoting oxidation. When comparing pyrrolidine and cyclopentane, which exhibits distinct pathways in different temperature regimes, the pyrrolidine pathways and sensitivity analysis align more closely to the high temperature case of cyclopentane where the important role of HO 2 radicals is seen to provide chain branching through HO 2 reaction with the fuel, accompanied by H 2 O 2 formation and decomposition to OH. The formation of 5-membered diene rings and ring opening reactions are also found to be highly relevant. Of particular note, it is found that there is little influence of small molecule nitrogen-chemistry, e.g., NH 2 , HCN, NO/NO 2 on the reactivity of the pyrrolidine mixtures investigated here where no recirculated combustion gases are included.
Autoignition↗
In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations
Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.
97 MATHEMATICS AND COMPUTING↗
Unraveling the role of temperature on the onset of ejecta formation at atomic scales
Shock wave interactions with perturbations on a free surface can lead to the inversion and growth of the perturbation and eventual ejection of a jet of material, referred to as an ejecta microjet, from shocked surfaces. Here, this study carries out large-scale molecular dynamics (MD) simulations using single-crystal Cu and Sn systems with a pre-existing groove to characterize the localized gradients in temperatures and pressures generated that render the microjet formation. MD simulations are carried out for three loading orientations (along the [001], [011], and [111] directions) and shock pressures ranging from 16 GPa to 100 GPa to understand the role of temperatures generated at the groove vertex on the formation of ejecta. The simulations suggest that the interaction of the shock wave with the groove results in a localized increase in temperatures, leading to localized softening at the groove vertex and the generation of an ejecta microjet. For Cu systems, the simulations suggest that the orientation effects on shock wave structures, velocities, and localized softening affect the ejecta formation at low pressures. Jetting is only observed when temperatures at the groove vertex are high enough to induce localized softening (close to melting temperature), and the jet velocity increases with shock pressure. In contrast, the loading orientation is rendered inconsequential for Sn systems due to the melting of the material during shock compression at the pressures chosen. The jet velocities are similar, regardless of crystal orientation in Sn systems.
36 MATERIALS SCIENCE↗
Fiber orientation and porosity in large-format extrusion process: The role of processing parameters
Controlling fiber orientation and porosity in short-fiber thermoplastic composites is important for enhancing mechanical, electrical and thermal properties in large-format additive manufacturing. This study employs a factorial design of experiments (DoE) to assess the effects of nozzle diameter (5.08 mm–10.16 mm), temperature (230–250 °C), and extruder screw speed (150–280 rpm) on flow rate, shear rate, porosity, fiber orientation, fiber length and tensile strength in 20 % carbon fiber-filled acrylonitrile butadiene styrene. ANOVA results show that screw speed significantly impacts flow rate, while nozzle diameter and temperature have lesser effects. Shear rate increases with smaller nozzles and higher speeds. Porosity decreases from 5.58 % with a 10.16 mm nozzle to 3.11 % with a 5.08 mm nozzle at 150 rpm due to increased shear rates, which induce shear thinning, reducing viscosity and facilitating gas escape. Larger nozzles (10.16 mm) produce larger, more heterogeneous pores, while smaller nozzles (5.08 mm) yield smaller, uniform pores. Beads produced with the 5.08 mm nozzle exhibit longer fiber lengths due to reduced residence time, lower shear stress, and better alignment. Fiber orientation improves with smaller nozzles due to higher shear rates but decreases with higher screw speeds (280 rpm) due to shorter residence times. The highest fiber alignment (A xx ∼ 0.65) and low porosity (∼3%) were achieved with a 5.08 mm nozzle at 150 rpm, while equivalent additive manufacturing-compression molding samples exhibited better tensile strength (∼93 MPa) under these conditions. In conclusion, these findings emphasize the importance of optimizing processing parameters to enhance fiber alignment and reduce porosity for improved mechanical performance.
Design of experiments↗
Bridging semantics, control specifications and assessment: A library for scalable demand flexibility controls
There is growing recognition that Demand Flexibility (DF) can play a major role in enhancing grid reliability, with building control applications emerging as key enablers for DF. However, the traditional approach to deploying new control applications in buildings, including those for DF, remains largely manual and tailored to individual buildings, making it difficult to scale. While research efforts have explored semantics-driven portability, DF controls specification, and assessment approaches, these initiatives are fragmented and limited in scope. This paper proposes a novel methodology, grounded in design science research, to integrate these elements and create a comprehensive DF controls library for both industry and academia. This approach is applied to develop the Demand FLEXibility controls LIBrary using Semantics (DFLEXLIBS), an extensible open-source library that provides DF controls for HVAC systems in Python. DFLEXLIBS enables portable, easy-to-deploy controls that abstract building-specific data points, facilitating assessment across diverse buildings. DFLEXLIBS features nine different control applications, and it is successfully implemented and tested across four virtual and two real buildings, bridging the gap between semantics-driven portability, DF controls specification, and rigorous performance assessment. Its benefits are measured by a reusability ratio greater than 90% and a functional overlap ratio of around 70% for the most common functions used in the library, significantly reducing time for deploying new controls.
Controls library↗
Residual resistance ratio measurement system for Nb 3 Sn wires extracted from Rutherford cables
Residual resistance ratio (RRR) of superconducting strands is an important parameter for magnet electrical stability. RRR serves as a measure of the low-temperature electrical conductivity of the copper within a conductor that has a copper stabilization matrix. For Nb 3 Sn, due to the need of a reaction heat treatment, the technical requirements for high quality measurements of strands extracted from Rutherford cables are particularly demanding. Quality of wire, cabling deformation, heat treatment temperature, heat treatment atmosphere, sample handling, and measurement methods can all affect the RRR. Therefore, as an integral part of the electrical quality control (QC) of Nb 3 Sn Rutherford cables manufactured at the Lawrence Berkeley National Laboratory, it was prudent that we established a RRR measurement system that can isolate the assessment of cable-fabrication-related impacts from sample preparation and measurement factors. Here we describe a bespoke cryocooler-based measurement system, capable of measuring RRR of over 80 samples in a single cooldown. The samples are mounted on custom-designed printed circuit boards that accommodate the shape of strands extracted from a Rutherford cable without added deformation, which we will show is critical in ensuring that the measurements accurately represent the RRR values of the conductor within the cable. Using this sample mounting solution, we routinely measure the overall RRR of the strand as well as individual intra-strand sections corresponding to both cable edges and cable broad faces with high reproducibility. Such measurements provide valuable information on the variation of RRR along the length of the strands as well as across strand productions and cable runs over time.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Nano zero valent iron electrodeposition at boron doped diamond electrodes
Here, in this study, we present an ecofriendly and simple electrochemical method for synthesizing nano zero-valent iron particles (nZVIs) directly on boron-doped diamond (BDD) electrode surfaces. A BDD electrode served as the substrate for electrodeposition using a 5 mM FeCl 3 /0.1 M KCl solution and chronoamperometry at an applied potential of −1.3 V versus Ag/AgCl (1 M KCl), as determined by cyclic voltammetry (CV) and supported by Pourbaix diagram analysis. The electrochemical behavior and surface modification were characterized using CV, electrochemical impedance spectroscopy (EIS), and surface analysis and microscopy techniques. The results confirm that BDD electrodes can serve as effective platforms for a controlled deposition of 56 nm nZVIs, offering a promising strategy for the development of advanced materials for environmental remediation, catalysis, and sensing applications.
36 MATERIALS SCIENCE↗