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At least 1,225 records · Page 68

Toward Addressing the Challenge to Predict the Heat Capacities of RDX and HMX Energetic Materials

Availability of heat capacity as function of pressure and temperature is an essential prerequisite for development of a computational multiscale strategy capable to address the evolution of microstructure and energy release in advanced high energy density materials. In the case of 1,3,5-trinitro-1,3,5-triazinane (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) systems as two of the most studied energetic materials, there are substantial gaps in experimental data, with available heat capacities values distributed only in a region close to standard ambient conditions. In this study we demonstrate how these major experimental limitations can be addressed in the case of the RDX and HMX systems based on the combined use of classical and quantum mechanical calculations. We show that by considering ideal gas properties evaluated using quantum mechanical methods, and residual properties obtained from molecular simulations using fully flexible atomistic force field models, excellent agreement can be obtained for the predicted heat capacities to the most recent experimental values. An important advantage of the current computational methodology is that it allows evaluation of both constant-volume and constant-pressure heat capacities for a broad interval of temperatures and pressures, which encompasses solid and liquid phases conditions. In the case of the solid α and γ phases of RDX and the β phase of HMX, the predicted results follow closely both the available experimental data at standard ambient conditions and the results obtained using density functional theory calculations at high pressures, a regime where experimental data are not available. A perspective to expand the current methodology is also discussed.

36 MATERIALS SCIENCE↗

Lattice $ \mathcal{N} $ = 4 super Yang-Mills at strong coupling

In this paper we present results from numerical simulations of N = 4 super Yang-Mills for two color gauge theory over a wide range of ’t Hooft coupling 0 < λ ≤ 30 using a supersymmetric lattice action. Numerical study of this lattice theory has been stymied until recently by both sign problems and the occurrence of lattice artifact phases at strong coupling. We have recently developed a new action that appears capable of solving both problems. The resulting action possesses just SU(2) rather than U(2) gauge symmetry. By explicit computations of the fermion Pfaffian we present evidence that the theory possesses no sign problem and exists in a single phase out to arbitrarily strong coupling. Furthermore, preliminary work shows that the logarithm of the supersymmetric Wilson loop varies as the square root of the ’t Hooft coupling λ for large λ in agreement with holographic predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Acceleration of Glass Alteration Rates Induced by Zeolite Seeds at Controlled pH

As the glass-water reaction proceeds, certain glass compositions undergo a delayed acceleration in the alteration rate – a phenomenon that is commonly referred to as Stage III behavior – which generally coincides with zeolite formation. However, studying Stage III in the laboratory is difficult because the time scale required to observe Stage III behavior can vary from months to several years, and the solution conditions that initiate this acceleration remain poorly understood. Consequently, the development of a corrosion test method capable of quantifying Stage III rates in a timely manner would be of great benefit to both the scientific community and policy makers concerned with nuclear waste disposal. In this work, we induced and measured the acceleration in the corrosion rate of the AFCI glass by seeding static corrosion tests with zeolite Na-P2 at fixed pH90°C values ranging from 9.5 to 11.5. The induced Stage III rates increased linearly with increasing pH and were measured to be between the forward and residual rates of AFCI. Tests designed to compare induced Stage III rates obtained for different seeding times showed that the induced Stage III rates were faster in the absence of an alteration layer. In summary, the use of the seeded test method allows for a conservative quantification of Stage III rates in a relatively short, one-month time period.

Neeway, James J.↗

Construction of deep-learning based WWBs parameterization for ENSO prediction

Westerly wind bursts (WWBs) significantly impact the occurrence and development of the El Niño-Southern Oscillation (ENSO). Current dynamical models, however, face significant challenges in representing WWBs. In this study, deep learning techniques were used to develop a new parameterization scheme for WWBs and further compared against two widely used schemes. Further, the results show that the scheme developed in this study has greater capability than previous schemes in reproducing WWBs characteristics, particularly in terms of occurrence probability, location, and duration. This improvement was mainly reflected in El Niño years, especially in strong events when the deep-learning-based scheme much realistically captures the location and strength of WWBs. It is expected that the new parameterization scheme will further improve ENSO prediction in dynamical models.

54 ENVIRONMENTAL SCIENCES↗

Flame Chemistry Workshop: a perspective on challenges and strategic actions in combustion experiments and chemical kinetics modeling

Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.

Chemical kinetics↗

Dual frequency comb spectroscopy of solid fuel pyrolysis and combustion: Quantifying the influence of moisture content in Douglas fir

As the fire research community develops multi-scale models to predict the spread of wildland fires, there is a need for well-controlled, small-scale experiments which provide benchmark data and fundamental understanding of the effects of key parameters on the combustion of wildland fuels. Here, we implement a unique suite of non-intrusive, quantitative diagnostics that simultaneously measure mass loss, surface temperature, in situ gas temperature and water vapor concentration, including the first implementation of dual frequency comb spectroscopy for the study of solid fuels. As a demonstration, we examine the effect of fuel moisture content on the burning of Douglas fir wood samples that are ignited under constant radiative heat flux from a cone calorimeter to provide benchmark data for multi-scale models. Peak flame temperatures 3 mm above the sample surface measured with the dual frequency comb laser spectrometer are in excess of 1200 K. The flame temperature decreases by over 150 K as fuel moisture content increases from 0.7% to 40%. This work demonstrates a platform for simultaneous, quantitative measurements of well-controlled small-scale experiments which provide benchmark data for the development of advanced modeling and simulations capable of capturing the complex physics present in wildland fire systems.

54 ENVIRONMENTAL SCIENCES↗

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗

Measurements of the $^{5}D^{°}_{4}$–$^{5}P_{3}$ transition of singly ionized atomic iodine using intermodulated laser induced fluorescence

Iodine has been an element of recent interest for commercial use as fuel in electrostatic propulsion systems. A lingering problem when investigating ionized iodine using non-perturbative, laser-based techniques is determining the spectral width, i.e., the species temperature, of iodine. To this end, the hyperfine structure must be well understood to develop a spatially resolved diagnostic technique capable of ion flow and temperature measurements. Previous work investigated the lineshape of the transition between the $^{5}D^{°}_{4}$ and $^{5}P_{3}$ states of singly-ionized atomic iodine (I II) with laser induced fluorescence (LIF), but the hyperfine structure of the transition was unresolved in those measurements. In this work, an intermodulated LIF technique is used to measure an enhanced lineshape of the same I II transition. Here, a linear least squares fitting algorithm is used to fit the transition lineshape, where hyperfine transition locations and theorized relative amplitudes are constrained by theory. A lineshape model that incorporates hyperfine transition amplitude enhancement introduced from an intermodulated laser technique is implemented into the fitting function, as well as a nonlinear laser saturation effect. We report converged hyperfine coupling coefficients for these I II states.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effect of supplementary cementitious materials on the degradation of cement-based barriers in radioactive waste repository: A case study in Korea

This study focuses on investigating the chemical degradation characteristics of cementitious barriers used in low- and intermediate-level radioactive waste repository by reactive transport modeling. The impact of the blending with supplementary cementitious materials (SCMs) in the barriers on the chemical degradation was evaluated to find the optimum barrier design. A number of different barrier designs were examined by replacing ordinary Portland cement (OPC) by SCMs (i.e., fly ash, silica fume, and blast-furnace slag). The simulation results showed that silica fume blended barrier has better durability against chemical degradation by rainwater compared to fly ash or blast-furnace slag blended barriers. In addition, the chemical durability of silica fume-based barrier increased with increasing replacement level up to about 20 %. It seems that the amount of formed calcium silicate hydrate (CSH) in the initial cement-based barrier highly affects the overall chemical durability. The newly developed reactive transport model demonstrated its capability for understanding the barrier performance and investigating the optimal design of the barrier system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Integral LOCA fragmentation test on high-burnup fuel

Increasing the fuel burnup limit in light water reactors is sought to enhance fuel cycle economics and requires establishing a technical basis. Experimental observations of severe fuel fragmentation under loss-of-coolant-accident conditions at Halden and Studsvik had raised the need for additional considerations during the development of this technical basis. These test data suggested that the burnup threshold for high burnup fuel fragmentation may be influenced by pre-transient power. Additional loss-of-coolant-accident test data is therefore valuable to complement these tests and enhance the current state of understating. Oak Ridge National Lab has developed the Severe Accident Test Station capable of examining the oxidation kinetics and accident response of irradiated fuel and cladding materials for design basis accident and beyond design basis accident scenarios. Severe Accident Test Station provides various temperature profiles, steam, and the thermal shock conditions necessary for integral loss of coolant accident testing, defueled oxidation quench testing, and high-temperature beyond design basis accident testing. Severe Accident Test Station has been successfully installed and demonstrated in the Irradiated Fuels Examination Laboratory at Oak Ridge National Lab. Furthermore, descriptions of the in-cell re-fabrication capabilities and assembly of the loss-of-coolant-accident test train are provided. Installation of the Severe Accident Test Station system and in-cell re-fabrication restores United States capability to examine postulated and extended loss-of-coolant-accident conditions on spent fuel and cladding and provides a platform for evaluating advanced fuel and accident-tolerant fuel cladding concepts. Lastly, three in-cell integral loss-of-coolant-accident test were performed in the Severe Accident Test Station and compared to the Nuclear Regulatory Commission sponsored loss-of-coolant-accident test as well as the original loss-of-coolant-accident test performed at Argonne National Lab. Finally, the results of these tests as well as all publicly available integral loss-of-coolant-accident test were used to validate a threshold for high burnup fuel fragmentation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Ultra-Fast Electron Microscopic Imaging of Single Molecules With a Direct Electron Detection Camera and Noise Reduction

Time-resolved imaging of molecules and materials made of light elements is an emerging field of transmission electron microscopy (TEM), and the recent development of direct electron detection cameras, capable of taking as many as 1,600 fps, has potentially broadened the scope of the time-resolved TEM imaging in chemistry and nanotechnology. However, such a high frame rate reduces electron dose per frame, lowers the signal-to-noise ratio (SNR), and renders the molecular images practically invisible. Here, we examined image noise reduction to take the best advantage of fast cameras and concluded that the Chambolle total variation denoising algorithm is the method of choice, as illustrated for imaging of a molecule in the 1D hollow space of a carbon nanotube with ~1 ms time resolution. Through the systematic comparison of the performance of multiple denoising algorithms, we found that the Chambolle algorithm improves the SNR by more than an order of magnitude when applied to TEM images taken at a low electron dose as required for imaging at around 1,000 fps. Open-source code and a standalone application to apply Chambolle denoising to TEM images and video frames are available for download.

47 OTHER INSTRUMENTATION↗

Metabolic engineering strategies to produce medium-chain oleochemicals via acyl-ACP:CoA transacylase activity

Abstract Microbial lipid metabolism is an attractive route for producing oleochemicals. The predominant strategy centers on heterologous thioesterases to synthesize desired chain-length fatty acids. To convert acids to oleochemicals (e.g., fatty alcohols, ketones), the narrowed fatty acid pool needs to be reactivated as coenzyme A thioesters at cost of one ATP per reactivation - an expense that could be saved if the acyl-chain was directly transferred from ACP- to CoA-thioester. Here, we demonstrate such an alternative acyl-transferase strategy by heterologous expression of PhaG, an enzyme first identified in Pseudomonads , that transfers 3-hydroxy acyl-chains between acyl-carrier protein and coenzyme A thioester forms for creating polyhydroxyalkanoate monomers. We use it to create a pool of acyl-CoA’s that can be redirected to oleochemical products. Through bioprospecting, mutagenesis, and metabolic engineering, we develop three strains of Escherichia coli capable of producing over 1 g/L of medium-chain free fatty acids, fatty alcohols, and methyl ketones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Photosynthetic responses to light levels in drought-tolerant novel peanut (Arachis hypogaea L) genotypes

Abstract Drought is a significant abiotic stressor that reduces peanut production because it alters photosynthetic activity and impacts crop growth. Therefore, developing drought-tolerant peanut genotypes capable of maintaining higher photosynthetic rates (A) under stress is crucial. This study assessed changes in photosynthetic and chlorophyll fluorescence responses to light (photosynthetic photon flux density, PPFD) in newly bred drought-tolerant peanut genotypes. Ten genotypes [NM-3, NM-5, NM-6, NM-23, NM-69, NM-70, NM-74, NM-77, V-C, and C-76–16] were evaluated under full irrigation (FC 100 ) and deficit irrigation (FC 50 ) in a split-plot design with four replications in a greenhouse. Under high PPFD levels, genotype NM-5 with deficit irrigation exhibited significantly higherA, stomatal conductance (gs), quantum efficiency of photosystem II (ΦPSII), and electron transport rate (ETR) by 40–59%, 135–525%, 31–212%, and 31–102%, respectively, than check varieties (V-C and C-76–16) and other genotypes. The NM-74 and NM-77 genotypes also performed well under deficit irrigations but with slightly lowerA,gs, ΦPSII, and ETR. Genotypes NM-5, NM-23, NM-74, and NM-77 exhibited significantly higher quantum efficiency of photosystem II (Fv’/Fm’) and photochemical quenching (qP) with higher light intensities in the daily cycle under deficit irrigation. The decline in ETR at the same PPFD levels in NM-3, NM 69, NM-70, and C-76–16 indicated photoinhibition or saturation of the photosynthetic apparatus compared to other genotypes. Concurrently, FC 100 irrigation level minimizes photoinhibition, enhancingA,gs, ΦPSII, and ETR in the genotypes than FC 50 . Therefore, we conclude that NM-5, NM-74, and NM-77 genotypes can perform better under water deficit environments. As such, chlorophyll fluorescence parameters Fv’/Fm’ and qP can be considered for selective breeding to enhance photosynthetic efficiencies.

Science & Technology - Other Topics↗

Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network

Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗

Recent developments in the PySCF program package

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. Here, we explain the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Upgrade of the gated laser entrance hole imager G-LEH-2 on the National Ignition Facility

A major upgrade has been implemented for the ns-gated laser entrance hole imager on the National Ignition Facility (NIF) to obtain high-quality data for Hohlraum physics study. In this upgrade, the single “Furi” hCMOS sensor (1024 × 448 pixel arrays with two-frame capability) is replaced with dual “Icarus” sensors (1024 × 512 pixel arrays with four-frame capability). Both types of sensors were developed by Sandia National Laboratories for high energy density physics experiments. With the new Icarus sensors, the new diagnostic provides twice the detection area with improved uniformity, wider temporal coverage, flexible timing setup, and greater sensitivity to soft x rays (<2 keV). These features, together with the fact that the diagnostic is radiation hardened and can be operated on the NIF for high neutron yield deuterium–triterium experiments, enable significantly greater return of data per experiment.

47 OTHER INSTRUMENTATION↗