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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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Mercury isotope ratios suggest remobilization of recalcitrant legacy mercury from streambed sediment to stream water and biofilm.
Wire arc additive manufacturing (WAAM) is an energy-efficient manufacturing technique used for near-net-shape production of functional industrial components. However, heat accumulation during deposition and the associated mechanical and metallurgical changes result in complex residual stress profiles across the cross section of the fabricated components. These residual stresses are detrimental to the service life of the components. In this study, sequentially coupled thermomechanical analysis of WAAM B91 steel is conducted to quantify the residual stress variation across the component. The thermomechanical analysis includes a transient heat transfer model and a static stress model that incorporates the transformation-induced plasticity due to martensitic phase transformation. Here, the experimentally calibrated heat transfer model mirrors the temperature variation of the system during the deposition. The results from the stress model are validated via x-ray diffraction measurements, and the numerical results are in good agreement with the experimental data.
DEAD-box ATPases play crucial roles in guiding rRNA restructuring events during the biogenesis of large (60S) ribosomal subunits, but their precise molecular functions are currently unknown. In this study, we present cryo-EM reconstructions of nucleolar pre-60S intermediates that reveal an unexpected, alternate secondary structure within the nascent peptidyl-transferase-center (PTC). Our analysis of three sequential nucleolar pre-60S intermediates reveals that the DEAD-box ATPase Dbp10/DDX54 remodels this alternate base pairing and enables the formation of the rRNA junction that anchors the mature form of the universally conserved PTC A-loop. Post-catalysis, Dbp10 captures rRNA helix H61, initiating the concerted exchange of biogenesis factors during late nucleolar 60S maturation. Our findings show that Dbp10 activity is essential for the formation of the ribosome active site and reveal how this function is integrated with subsequent assembly steps to drive the biogenesis of the large ribosomal subunit.
Classical nuclear core fluidic design techniques require improvement to better align with modern technological innovations. The US Department of Energy’s Office of Nuclear Energy (DOE-NE) Transformational Challenge Reactor (TCR) program is deploying additive manufacturing and advanced modeling and simulation to reimagine these designs. With the aid of modern computing power, computerized design optimization can be implemented to remove unwanted pressure drop while simultaneously optimizing flow structures, resulting in new opportunities to enable advanced instrumentation and monitoring capabilities.Previous development of geometric specifications for the TCR pressure vessel’s outlet plenum used design optimization to (1) limit pressure losses below 3.5 kPa (~0.5 psi) and (2) create a fluidic plane in which the temperature variation would not exceed ±5°C. This significant limit of the allowable pressure drop stems from the overarching goal of the TCR program to apply cutting edge techniques and unconventional thinking to demonstrate potential opportunities in additive manufacturing (AM).This paper expands the previous work by optimizing thermowell locations for robust measurements by explicitly modeling them and the resulting flow impacts. Additionally, a single core coolant channel was chosen to represent an event that causes an increased bulk flow temperature increase of 100°C.High fidelity unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of the conjugate heat transfer problem were run in Siemen’s Star-CCM+ for this study. Next, the bulk flow temperature of a single coolant channel was increased by 100°C and was allowed to converge again. Finally, statistical analysis using a sequential probability ratio test (SPRT) was used to determine the elapsed time the thermocouples took to discover the increased bulk flow temperature.
Nuclear thermal propulsion technology is currently being developed by the National Air and Space Administration for future manned Mars missions. There are multiple on-going activities to develop and enhance computational capabilities, single- and multi-physics, to advance the readiness level of this technology. The prediction of short-lived fission products is crucial to understand the reactivity penalty and the decay heat production during the different burnout stages of the vehicle. Generally, the solution entails coupled neutronic-depletion analysis. However, such simulations are somewhat expensive, especially if the duration of each engine burn is to be optimized. Therefore, there is a need to complement such high-fidelity techniques with time-efficient alternatives that do not require the sequential neutronic and depletion analysis. This paper details the methods and capabilities of pyIsoDep, an open-source code used for depletion analysis of nuclear thermal propulsion reactors. The package is general, in principle, and thus can be applied to any fission system, and can potentially be extended for fusion systems. The package was developed to provide a flexible and efficient alternative to pre-existing higher order depletion codes that could integrate well into mission and transient analysis of nuclear thermal propulsion reactors. Finally, this paper presents a validation and verification of pyIsoDep against analytical and Serpent-generated solutions. (authors)
Using covariance analysis methods, we study the fragmentation dynamics of multiply ionized 1- and 2-iodopropane. Signatures of isomer-specific nuclear motion occurring during sequential fragmentation pathways are identified.
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A method and apparatus for evaluating the chemical composition of airborne particles by sequentially collecting and analyzing airborne particles in-situ. The method includes: collecting particles; enlarging the particles through water condensation; accelerating the enlarged particles onto a surface to collect enlarged particles; and analyzing the enlarged particles by: isolating the surface; passing a carrier gas over the surface; heating the surface to thermally desorb collected particles into the carrier gas; transporting this evolved vapor into detectors; and assaying the evolved vapor as a function of a desorption temperature. The apparatus includes: a sample flow inlet; a condensational growth tube; a collection and thermal desorption (CTD) cell; a carrier gas source; a heater coupled to the CTD; one or more gas detectors; and a controller configured to operate valves, the heater, the growth tube, and the CTD cell in at least an in-situ sequential collection mode and analysis mode.
We report state estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings.
The Message Passing Interface (MPI) standard plays a crucial role in enabling scientific applications for parallel computing and is an essential component in high-performance computing (HPC). However, implementing MPI code manually—especially applying a proper domain decomposition and communication pattern—is a challenging and error-prone task. We present ChatMPI, an AI assistant for MPI parallelization of sequential C codes. In our analysis, we focus on testing six essential HPC workloads, which are based on Basic Linear Algebra Subprograms levels 1, 2, and 3 as well as sparse, stencil, and iterative operations. We analyze the process of creating ChatMPI by using the ChatHPC library. This lightweight large language model (LLM)–based infrastructure enables HPC experts to efficiently create and supervise trustworthy AI capabilities for critical HPC software tasks. We study the data required for training (fine-tuning) ChatMPI to generate parallel codes that not only use MPI syntax correctly but also apply HPC techniques to reduce memory communication and maximize performance by using proper work decomposition. With a relatively small training dataset composed of a few dozen prompts and fewer than 15 minutes of fine-tuning on one node equipped with two NVIDIA H100 GPUs, ChatMPI elevates trustworthiness for MPI code generation of current LLMs (e.g., Code Llama, ChatGPT-4o and ChatGPT 5). Additionally, we evaluate the performance of the MPI codes generated by ChatMPI in comparison with the ones generated by ChatGPT-4o and ChatGPT-5. The codes generated by ChatMPI provide up to a 4 × boost in performance by using better problem decomposition, communication patterns, and HPC techniques (e.g., communication avoiding).
Efficient analysis of computer models is essential for the validation and uncertainty quantification of those models. Surrogate models, and Gaussian processes in particular, are a common and powerful approach to analyzing computer models that treat computer models as a black-box function. Gaussian processes form a Bayesian model over a space of functions that gives a measure of uncertainty about the computer model output at unobserved locations and a framework for sequential sampling. This tutorial will show how to fit Gaussian processes on a series of test functions and apply sequential design techniques to estimate extrema, level sets, and reliabilities of those test functions.
We generalize the relation between discontinuities of scattering amplitudes and cut diagrams to cover sequential discontinuities (discontinuities of discontinuities) in arbitrary momentum channels. The new relations are derived using time-ordered perturbation theory, and hold at phase-space points where all cut momentum channels are simultaneously accessible. As part of this analysis, we explain how to compute sequential discontinuities as monodromies and explore the use of the monodromy group in characterizing the analytic properties of Feynman integrals. We carry out a number of cross-checks of our new formulas in polylogarithmic examples, in some cases to all loop orders.
How materials evolve at thermal equilibrium and under external excitations at small length and time scales is crucial to the understanding and control of material properties. X-ray photon correlation spectroscopy (XPCS) at X-ray free electron laser (XFEL) facilities can in principle capture dynamics of materials that are substantially faster than a millisecond. However, the analysis and interpretation of XPCS data is hindered by the strongly fluctuating X-ray intensity from XFELs. Here we examine the impact of pulse-to-pulse intensity fluctuations on sequential XPCS analysis. We show that the conventional XPCS analysis can still faithfully capture the characteristic time scales, but with substantial decrease in the signal-to-noise ratio of the g2 function and increase in the uncertainties of the extracted time constants. We also demonstrate protocols for improving the signal-to-noise ratio and reducing the uncertainties.
Laser ablation-inductively coupled plasma-time-of-flight-mass spectrometry (LA-ICP-TOF-MS) was employed to rapidly analyze environmental particle samples collected using aerosol contaminate extractors (ACE). The ACE particle collectors were placed at various distances (0.5, 1.3, and 4.5 km) from a source that released Ru-bearing particles. Samples for measurement were then generated (as sub-samples) from the ACE collection plates via particle “lift off” with gunshot residue (GSR) tabs. The LA-ICP-TOF-MS method was employed such that 10+ samples could be analyzed in a single unattended analytical session. A 3 × 1 mm area of individual GSR tab samples were analyzed in less than 30 minutes. This provided spatially resolved elemental and isotopic measurements of the particulate content and confirmed the presence of Ru-bearing particles within the complex background environmental particle loading. As anticipated, measurements showed collectors closest to the source had the highest concentration of the released Ru-bearing particles, while all collectors, regardless of distance, contained similar levels of background particles (e.g., Fe and Sr). Sequential scanning electron microscopy – automated particle analysis (SEM-APA) and LA-ICP-TOF-MS analysis was employed for method validation and a demonstration of the multi-modal approach. The same 2-dimensional region was analyzed by both methods and the particles identified via SEM-APA were also detected using LA-ICP-TOF-MS, with 100% accuracy. Overall, LA-ICP-TOF-MS demonstrated its utility for rapid elemental and isotopic particle analysis from environmental air samples.
We present a novel methodology for optimizing nuclear fuel cycle transitions that incorporates a game-theoretic approach and captures interactions among multiple decision makers. The methodology is demonstrated using a two-person sequential game with uncertainty, where the two players represent a policy maker and an electric utility company, though the method generalizes to any number and type of individual decision making entities. Coupled with a sophisticated nuclear fuel cycle simulator, rich transition scenarios may be analyzed to identify robust transition strategies. These strategies explicitly treat uncertainties using a stochastic programming approach, devising optimal near-term hedging strategies that simultaneously consider all possible states of the world, maintaining flexibility to allow for intelligent recourse decisions once uncertainties are resolved. In the demonstration game, reactor technology and fuel cycle scheme adopted by the electric utility are shown to depend on both the policy maker’s decisions and the distributions over uncertain technological and economic outcomes.
Flubendazole (FBZ) is a poorly water-soluble drug, and different methodologies have been proposed to improve its oral bioavailability. Obtaining the amorphous drug phase is an alternative to improve its water solubility. Several techniques for drug amorphization, such as spray drying, lyophilization, melt quenching, solvent-evaporation, and ball milling, can yield various types of structural disorder and possibly render variations in physicochemical properties. Herein, we focus on evaluating the influence of the ball-milling process on the amorphization of FBZ. The characterization of the average global and local structures before, during, and after the milling process is described by sequential Rietveld refinements, pair distribution function analysis, and the Reverse Monte Carlo method. In conclusion, we show that preserving the local structure (nearest molecules) can be responsible for avoiding the fast structure recrystallization commonly observed when using the solvent-evaporation process for the studied drug.
Controlling the monomer sequence of synthetic polymers is a grand challenge in polymer science. Conventional sequence control has been achieved in dispersed polymers by exploiting the kinetic tendencies of monomers and their order of addition. While the sequence of blocks in multiblock copolymers can be readily tuned using sequential addition of monomers (SAM), control over the sequence distribution is eroded as the targeted block size approaches a single monomer unit (i.e., X n ~ 1) due to the stochastic nature of chain-growth reactions. Thus, unique monomers are needed to ensure precise single additions. Herein, we investigate common classes of cyclic olefin monomers for ring-opening metathesis polymerization (ROMP) to identify monomers for single unit addition during sequential monomer addition synthesis. Through careful analysis of polymerization kinetics, we find that easily synthesized oxanorbornene imide monomers are suitable for single-addition reactions. With the identified monomers, we demonstrate the synthesis of multiblock copolymers containing up to three precise functionalization sites and singly cross-linked four-armed star copolymers. In conclusion, we envision that expanded kinetic analyses of monomer reactivities in ROMP reactions will enable novel polymer synthesis capabilities such as the autonomous synthesis of sequence-defined polymers or one-shot multiblock copolymer syntheses.