SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This is the data archive for manuscript PPCF-104238 at Plasma Physics and Controlled Fusion. Authors: Ligia D. Amorim, Carlo Benedetti, Stepan S. Bulanov, Davide Terzani, Axel Huebl, Carl B. Schroeder, Jean-Luc Vay, Eric Esarey
We report on the analyses of x-ray-imaging spectroscopy data from experiments to study interspecies ion separation in direct-drive inertial-confinement-fusion experiments on the Omega laser facility. This is a continuation of recent, related research [S. C. Hsu et al., Euro Phys. Lett. 115, 65001 (2016); T. R. Joshi et al., Phys. Plasmas 24, 056305 (2017)]. The targets were argon (Ar)-doped, deuterium (D2)-filled spherical plastic shells of varying D2-Ar relative and total gas pressures. We used a time- and space-integrated spectrometer, streaked crystal spectrometer, and up to three time-gated multi-monochromatic x-ray imagers (MMIs) fielded along different lines of sight to record x-ray spectral features obtained from the implosions. The MMI data were recorded between first-shock convergence and slightly before the neutron bang time. We confirm the presence of interspecies ion separation as reported in our recent work. Extensions to the previous work include (a) the inclusion of shell mix in the data analysis, which slightly changes the amount of inferred species separation, (b) observation of species separation closer to the neutron bang time, and (c) fielding of the particle x-ray temporal diagnostic (PXTD) [H. Sio et al., Rev. Sci. Instrum. 87, 11D701 (2016)] to infer the relative timing between the neutron bang time and peak x-ray emission. Experimentally inferred species separation is compared with radiation-hydrodynamic simulations that include a multi-ion-species transport model.
DUF89 family proteins occur widely in pro- and eukaryotes but their functions are unknown. Here we define three DUF89 subfamilies (I, II, and III), subfamily II being split into standalone proteins and proteins fused to pantothenate kinase (PanK). We demonstrated that DUF89 proteins have metaldependent phosphatase activity against reactive phosphoesters or their damaged forms, notably sugar phosphates (subfamilies II and III), phosphopantetheine and its S-sulfonate or sulfonate (subfamily II-PanK fusions), and nucleotides (subfamily I). Genetic and comparative genomic data strongly associated DUF89 genes with phosphoester metabolism. The crystal structure of the yeast (Saccharomyces cerevisiae) subfamily III protein YMR027W revealed a novel phosphatase active site with fructose 6-phosphate and Mg 2+ bound near conserved signature residues Asp254 and Asn255 that are critical for activity. These findings indicate that DUF89 proteins are previously unrecognized hydrolases whose characteristic in vivo function is to limit potentially harmful buildups of normal or damaged phosphometabolites.
Double shell targets are an alternative ignition platform for inertial confinement fusion. One design consideration for double shell targets is the choice of inner shell material to help trap radiation emitted by the hot fuel to aid ignition. Materials such as molybdenum and tungsten are of interest for the inner shell layer of the targets. While molybdenum has a lower density that could inhibit instability growth and allow for radiography and code benchmarking, tungsten has a higher density that could provide more compression and confinement. These tradeoffs have been explored using optimized designs for each material. Our previous work [Vazirani et al., “Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 28, 122709 (2021); Vazirani et al., “Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 30, 062704 (2023); and Vazirani et al., “Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design,” Stat. Anal. Data Min. 17, e11698 (2024)] resulted in a multi-fidelity Bayesian optimization framework to find yield-optimized double shell target geometries. By leveraging simulations of varying fidelities (one-dimensional and two-dimensional) to inform one another, the multi-fidelity optimization was able to optimize a design in the highest fidelity with significantly fewer simulations than would be used in a systematic parameter scan. In this work, we apply the multi-fidelity Bayesian optimization to explore the optimized designs of double shell targets with molybdenum and tungsten inner shells as well as the physics producing the high performing implosions. A physics exploration of all the simulations used in this study shows trends in designs that contribute to high yields, ion temperatures, and fuel areal densities. Comparison of molybdenum and tungsten simulations shows that they can produce similar implosion conditions with different geometries, which would be important to study in experiments. Graded density layers produce varying performances with the two materials but continue to be of interest for future studies along with studies of doped inner shell materials and applied surface roughness.
Abstract Motivation Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. Results We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is “task agnostic”, in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer’s disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. Availability and implementation miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.
In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.
The successful development of large volume data storage systems will depend not only on the ability of the designers to store data, but on the ability to manage such data once it is in the system. The hypothesis is that mass storage data management can only be implemented successfully based on highly intelligent meta data management services. There now exists a proposed mass store system standard proposed by the IEEE that addresses many of the issues related to the storage of large volumes of data, however, the model does not consider a major technical issue, namely the high level management of stored data. However, if the model were expanded to include the semantics and pragmatics of the data domain using a Semantic Data Model (SDM) concept, the result would be data that is expressive of the Intelligent Information Fusion (IIF) concept and also organized and classified in context to its use and purpose. The results are presented of a demonstration prototype SDM implemented using the expert system development tool NEXPERT OBJECT. In the prototype, a simple instance of a SDM was created to support a hypothetical application for the Earth Observing System, Data Information System (EOSDIS). The massive amounts of data that EOSDIS will manage requires the definition and design of a powerful information management system in order to support even the most basic needs of the project. The application domain is characterized by a semantic like network that represents the data content and the relationships between the data based on user views and the more generalized domain architectural view of the information world. The data in the domain are represented by objects that define classes, types and instances of the data. In addition, data properties are selectively inherited between parent and daughter relationships in the domain. Based on the SDM a simple information system design is developed from the low level data storage media, through record management and meta data management to the user interface.
Experimental near-barrier fusion cross sections for 17 F + 12 C are compared to the fusion excitation functions for 16,17,18 O, 19 F, and 20 Ne ions on a carbon target. Normalized or reduced fusion cross sections are utilized in order to compare the different systems and account for the differing static size of the incident ions as well as changes in fusion barrier. Fusion excitation functions for the case of the mirror nuclei 17 F and 17 O with a single sd nucleon are compared. The 17 F data are also juxtaposed with nuclei involving multiple sd nucleons. Trends of the fusion cross section above the barrier beyond the expected systematic behavior are observed. These trends are interpreted as the interplay of the sd protons and neutrons. Furthermore, the experimental data are also compared to a widely used analytic model of near-barrier fusion, a time-dependent Hartree-Fock model, and coupled channels calculations.
Under the auspices of the IAEA Atomic and Molecular Data Center and the Korean Atomic Energy Research Institute, our assembled group of authors has reviewed the current state of dielectronic recombination (DR) rate coefficients for various ion stages of tungsten (W). Subsequent recommendations were based upon available experimental data, first-principle calculations carried out in support of this paper and from available recombination data within existing atomic databases. If a recommendation was possible, data were compiled, evaluated and fitted to a functional form with associated uncertainty information retained, where available. This paper also considers the variation of the W fractional abundance due to the underlying atomic data when employing different data sets.
A new resistive bolometer system has been developed for MAST-Upgrade. It will measure radiated power in the new Super-X divertor, with millisecond time resolution, along 16 vertical and 16 horizontal lines of sight. The system uses a Xilinx Zynq-7000 series Field-Programmable Gate Array (FPGA) in the D-TACQ ACQ2106 carrier to perform real time data acquisition and signal processing. The FPGA enables AC-synchronous detection using high performance digital filtering to achieve a high signal-to-noise ratio and will be able to output processed data in real time with millisecond latency. The system has been installed on 8 previously unused channels of the JET vertical bolometer system. Initial results suggest good agreement with data from existing vertical channels but with higher bandwidth and signal-to-noise ratio.
Highlights: •Multiple functions of Rab5 GTPase in fission yeast were found. •Roles of Rab5 in fission yeast were discussed. •Relation between Rab5 and actin cytoskeleton were discussed. -- Abstract: Inner-membrane transport is critical to cell function. Rab family GTPases play an important role in vesicle transport. In mammalian cells, Rab5 is reported to be involved in the regulation of endosome formation, phagocytosis and chromosome alignment. Here, we examined the role of the fission yeast Rab5 homologue Ypt5 using a point mutant allele. Mutant cells displayed abnormal cell morphology, mating, sporulation, endocytosis, vacuole fusion and responses to ion stress. Our data strongly suggest that fission yeast Rab5 is involved in the regulation of various types of cellular functions.
The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.
Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ{sup 2} method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ{sup 2} method. The best results were obtained for 10{sup 3} training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ{sup 2} method and performs the calculation twenty times faster.
In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ~1.1 μm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ~50 × 100 μm area. In conclusion, we also discuss the utility of these measurements for model validation and process improvement.
Large-amplitude, intermittent fluctuations are ubiquitous in the boundary region of magnetically confined plasmas and lead to detrimental plasma-wall interactions in next-generation, high duty cycle fusion power experiments. Using gas puff imaging data time series from the scrape-off layer in the Alcator C-Mod device, it is demonstrated in this work that the large-amplitude fluctuations can be described as a super-position of pulses with a fixed shape and a constant duration. By applying a new deconvolution algorithm on the data time series with a two-sided exponential pulse function, the arrival times and amplitudes of the pulses can be estimated, and the measurement time series can be reconstructed with high accuracy. The pulse amplitudes are shown to follow an exponential distribution. The waiting times between pulses are uncorrelated, their distribution has an exponential tail, and the number of arrivals is a linear function of time. This demonstrates that pulse arrivals follow a homogeneous Poisson process. Identical statistical properties apply to both ohmic and high confinement mode plasmas, clearly demonstrating the universality of the fluctuation statistics in the boundary region of Alcator C-Mod.
To study the direct effects of a low gravity environment on skeletal and cardiac muscle cells, experiments were initiated to determine whether skeletal and/or cardiac muscule cells would grow within the lumen of XM-80 hollow fibers (i.d. = 0.5 mm). Cells were prepared from skeletal or cardiac muscle tissue of 12 day embryos and were cultured for up to 7 days in the hollow fiber environment. Light microscopy revealed that cells proliferated to confluency over this period of time and fusion was apparent in the skeletal muscle cells. Once it was verified that cells would grow to confluency, additional XM-80 fibers containing cells were placed in a Clinostat in the horizontal position at 100 rpm. Fibers were stretched by a built-in spring mechanism to hold the fiber tightly at the center of rotation. Under these conditions, the gravity vector approaches zero and the cells are in an environment that simulates microgravity. Examination of skeletal muscle cells by electron microscopy revealed that myoblast fusion and myofibril accumulation were extensive. Although data obtained thus far are preliminary, they suggest that myofibril organization in chicken skeletal muscle cultures is somewhat more poorly defined in Clinostat rotated cultures than in controls that were not subjected to Clinostat conditions.
In the mid-1980s, Kinetic Systems and Langley Research Center determined that high speed CAMAC (Computer Automated Measurement and Control) data acquisition systems could significantly improve Langley's ARTS (Advanced Real Time Simulation) system. The ARTS system supports flight simulation R&D, and the CAMAC equipment allowed 32 high performance simulators to be controlled by centrally located host computers. This technology broadened Kinetic Systems' capabilities and led to several commercial applications. One of them is General Atomics' fusion research program. Kinetic Systems equipment allows tokamak data to be acquired four to 15 times more rapidly. Ford Motor company uses the same technology to control and monitor transmission testing facilities.
The simulation of plasma-wall interactions of fusion plasmas is extremely costly in computer power and time - the running time for a single parameter setting is easily in the order of weeks or months. We propose to exploit the already gathered results in order to predict the outcome for parametric studies within the high dimensional parameter space. For this we utilize Gaussian processes within the Bayesian framework and perform validation with one and two dimensional test cases from which we learn how to assess the outcome. Finally, the newly implemented method is applied to simulated data from the scrape-off layer of a fusion plasma. Uncertainties of the predictions are provided which point the way to parameter settings of further (expensive) simulations.