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Study of plasma equilibrium in toroidal fusion devices using mesh-free numerical calculation method
Plasma confinement using external magnetic field is one of the successful ways leading to the controlled nuclear fusion. Development and validation of the solution process for plasma equilibrium in the experimental toroidal fusion devices is the main subject of this work. Solution of the nonlinear 2D stationary problem as posed by the Grad-Shafranov equation gives quantitative information about plasma equilibrium inside the vacuum chamber of hot fusion devices. This study suggests solving plasma equilibrium equation which is essential in toroidal nuclear fusion devices, using a mesh-free method in a condition that the plasma boundary is unknown. The Grad-Shafranov equation has been solved numerically by the point interpolation collocation mesh-free method. Important features of this approach include truly mesh free, simple mathematical relationships between points and acceptable precision in comparison with the parametric results. The calculation process has been done by using the regular and irregular nodal distribution and support domains with different points. The relative error between numerical and analytical solution is discussed for several test examples such as small size Damavand tokamak, ITER-like equilibrium, NSTX-like equilibrium, and typical Spheromak.
A reactant-coordinate-based approach to state-to-state differential cross sections for tetratomic reactions
A new algorithm is proposed to compute quantum mechanically state-to-state differential cross sections for reactions involving four atoms in full dimensionality. This algorithm, which is based on the propagation of an initial state specific wave packet exclusively in reactant coordinates, extracts the S-matrix elements in the product channel by first interpolating the time-dependent wave packet using a collocation method at selected time intervals on the product coordinate grid and then projecting out the contributions of all final product states. Here, this approach is efficient and accurate, particularly for reactions that are dominated by a product well or long-range interactions. Validation of this approach is demonstrated for the H 2 + OH → H + H 2 O reaction.
Propagation of input parameter uncertainties in transport models
Here, the many sources of uncertainty in validation studies of plasma turbulence in magnetically confined fusion devices are well-known. In this paper, we investigate how to efficiently transform uncertainties in experimentally derived transport model inputs into model prediction uncertainties, using the quasilinear trapped-gyro-Landau-fluid (TGLF) turbulent transport model. We use the rapidly converging and computationally inexpensive non-intrusive probabilistic collocation method (PCM) to propagate input parameter uncertainty probability distribution functions (PDFs) through TGLF, yielding PDFs of predicted transport fluxes. We observe in many cases that the flux PDFs exhibit significant non-normal features such as strong skewness, even when the input distributions were normal. To illustrate the utility of the PCM approach, we apply this methodology to transport predictions for a DIII-D ITER baseline plasma in which the mix of neutral beam injection (NBI) and electron cyclotron heating (ECH) was varied. The model predictions show clear changes in the parametric dependencies and sensitivities of the turbulence between the two heating mixes. Specifically, when only NBI heating was used, the transport fluxes responded significantly only to the ion temperature gradient scale length. However, when both NBI and ECH were applied, the electron transport channels demonstrate a strong sensitivity to the electron temperature and density gradients not observed in the NBI-only case. Additional context for the PCM approach is provided by comparing its predictions with those obtained via a local flux-matching approach. A new set of validation metrics based on the Wasserstein distance is proposed for PDF-based comparisons.
Evaluation of Solar Reference Cells on a Two-Axis Tracker Using Spectral Measurements
A simple model calculating the performance of an IMT solar reference cell is proposed and tested under clear sky periods. One-minute data from EKO spectroradiometers collocated with an IMT reference cell are used for the analysis. Data are obtained at the NREL Solar Radiation Research Laboratory in Golden, Colorado and the UO Solar Radiation Monitoring Laboratory in Eugene, Oregon. The model is also applied and evaluated under cloudy conditions. A match of better than 1% is found between calculated clear sky values and IMT measurements for July, 2020. One- minute cloudy sky comparisons show a much larger variation, but when averaged over one hour, a standard deviation of 4% between the calculated and measured values is found.
Virtual tower measurements during the American WAKE ExperimeNt (AWAKEN)
Dual-Doppler lidar measurements were made during the American WAKE ExperimeNt to provide height-resolved measurements of wind speed and direction at multiple locations immediately south of the leading row turbines in the King Plains wind farm in Oklahoma. These so-called virtual tower measurements were performed to characterize the inflow into the wind farm and to assess possible upwind blockage effects due to the collective action of the wind farm. The campaign was conducted from 12 November 2022 to 17 October 2023, during which time 14 unique virtual tower locations were sampled with heights ranging from 240 to 490 m AGL. The wind retrieval algorithm provided estimates of the horizontal winds and their uncertainties with a vertical resolution of about 10 m, while also accounting for the tilt of the lidar platform. The virtual tower results are compared to collocated lidar wind profiling data at the A1 site, which was located roughly 2.4 rotor diameters south of the nearest turbine. The wind speed difference between the wind profiler and the virtual tower was found to be quite sensitive to atmospheric stability and wind direction below 250 m AGL. The largest differences were observed for inflow under stable conditions, where the profiler wind speeds were observed to be about 22% lower than the virtual tower near hub height. These results suggest that there are persistent horizontal gradients in the flow upwind of the wind farm which result in biased estimates using standard ground-based lidar wind profiling methods.
Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems
Using collocated measurements from geostationary and polar-orbital satellites over tropical continents, in this paper we provide a large-scale statistical assessment of the relative influence of aerosols and meteorological conditions on the lifetime of mesoscale convective systems (MCSs). Our results show that MCSs’ lifetime increases by 3–24 h when vertical wind shear (VWS) and convective available potential energy (CAPE) are moderate to high and ambient aerosol optical depth (AOD) increases by 1 SD (1σ). However, this influence is not as strong as that of CAPE, relative humidity, and VWS, which increase MCSs’ lifetime by 3–30 h, 3–27 h, and 3–30 h per 1σ of these variables and explain up to 36%, 45%, and 34%, respectively, of the variance of the MCSs’ lifetime. AOD explains up to 24% of the total variance of MCSs’ lifetime during the decay phase. This result is physically consistent with that of the variation of the MCSs’ ice water content (IWC) with aerosols, which accounts for 35% and 27% of the total variance of the IWC in convective cores and anvil, respectively, during the decay phase. The effect of aerosols on MCSs’ lifetime varies between different continents. AOD appears to explain up to 20–22% of the total variance of MCSs’ lifetime over equatorial South America compared with 8% over equatorial Africa. Aerosols over the Indian Ocean can explain 20% of total variance of MCSs’ lifetime over South Asia because such MCSs form and develop over the ocean. Finally, these regional differences of aerosol impacts may be linked to different meteorological conditions.
Nonnuclear Experimental Capabilities to Support Design, Development, and Demonstration of Microreactors
This work provides a summary of selected experimental capabilities being developed to support nonnuclear testing and demonstration of technology in support of microreactors under the U.S. Department of Energy’s (DOE’s) Microreactor Program. Major capabilities include the Single Primary Heat Extraction and Removal Emulator (SPHERE) and the Microreactor Agile Non-nuclear Experimental Test Bed (MAGNET). The SPHERE facility allows for controlled testing of the steady-state and transient heat rejection capabilities of a single heat pipe using electrical heaters that simulate nuclear heating. The facility is capable of monitoring axial temperature profiles along the heat pipe and surrounding test articles during startup, steady-state operation, and transients. Instrumentation includes noncontact infrared thermal imaging, surface thermocouples, spatially distributed fiber optic temperature and strain sensors, electrical power meters, and a water-cooled, gas-gap calorimeter for quantifying heat rejection from the heat pipe. The facility can be operated under both vacuum and inert-gas conditions. The MAGNET facility is a large-scale, 250-kW electrically heated microreactor test bed to enable nonnuclear experimental evaluation of a variety of microreactor concepts. It can be supplied to electrically heat a scaled section of a microreactor and further test the capabilities of heat rejection systems. The initial MAGNET experiments will support technology maturation and reduce uncertainty and risk associated with the design, operation, and deployment of monolithic heat pipe–based reactors. However, this test bed can broadly be applied to multiple microreactor concepts to evaluate a wide range of thermal-hydraulic and structural phenomena such as interface coupling with power conversion units and other collocated systems. MAGNET can evaluate integral thermomechanical effects during electrical heating of an array of heat pipes in a larger test article. Examples of initial testing will include thermal stresses in the monolith and the impact of debonding of a heat pipe from the core block and how that failure could impact surrounding heat pipes, i.e., understanding the potential for cascading failure. This work also discusses some modeling capabilities that can support experiment design, analysis, and interpretation, including the heat pipe code Sockeye and a comparison of thermal-structural simulations performed using ABAQUS and STAR-CCM+.
SPIKANs: separable physics-informed Kolmogorov–Arnold networks
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov–Arnold Network (KAN), which has demonstrated benefits over traditional MLPs, including faster neural scaling and better interpretability. The application of KANs to physics-informed learning has led to the development of Physics-Informed KANs (PIKANs), enabling the use of KANs to solve PDEs. However, despite their advantages, KANs often suffer from slower training speeds, particularly in higher-dimensional problems where the number of collocation points grows exponentially with the dimensionality of the system. To address this challenge, we introduce Separable Physics-Informed Kolmogorov–Arnold Networks (SPIKANs). This novel architecture applies the principle of separation of variables to PIKANs, decomposing the problem such that each dimension is handled by an individual KAN. This approach drastically reduces the computational complexity of training without sacrificing accuracy, facilitating their application to higher-dimensional PDEs. Through a series of benchmark problems, we demonstrate the effectiveness of SPIKANs, showcasing their superior scalability and performance compared to PIKANs and highlighting their potential for solving complex, high-dimensional PDEs in scientific computing.
On the Lebesgue constant of weighted Leja points for Lagrange interpolation on unbounded domains
Here, this work focuses on weighted Lagrange interpolation on an unbounded domain and analyzes the Lebesgue constant for a sequence of weighted Leja points. The standard Leja points are a nested sequence of points defined on a compact subset of the real line and can be extended to unbounded domains with the introduction of a weight function w:R → [0,1]. Due to a simple recursive formulation in one dimension, such abscissas provide a foundation for high-dimensional approximation methods such as sparse grid collocation, deterministic least squares and compressed sensing. Just as in the unweighted case of interpolation on a compact domain, we use results from potential theory to prove that the Lebesgue constant for the Leja points grows subexponentially with the number of interpolation nodes.
IMG-ABC v.5.0: an update to the IMG/Atlas of Biosynthetic Gene Clusters Knowledgebase
Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). Here, high-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.
Computation of high-frequency magnetoelastic waves in layered materials
Here, the direct calculation of magnetoelastic wave dispersion in layered media is presented using an efficient, accurate computational technique. The governing, coupled equations for elasticity and magnetism, the Navier and Landau-Lifshitz equations, respectively, are linearized to form a quadratic eigenvalue problem that determines a complex web of wave-number–frequency dispersion branches and their corresponding mode profiles. Numerical discretization of the eigenvalue problem via a spectral collocation method (SCM) is employed to determine the complete dispersion maps for both a single, finite-thickness magnetic layer and a finite magnetic-nonmagnetic double-layer. The SCM, previously used to study elastic waves in nonmagnetic media, is fast, accurate, and adaptable to a variety of sample configurations and geometries. Emphasis is placed on the extremely high-frequency regimes being accessed in ultrafast magnetism experiments. The dispersion maps and modes provide insight into how energy propagates through the coupled system, including how energy can be transferred between elastic- and magnetic-dominated waves as well as between different layers. The numerical computations for a single layer are further understood by a simplified analytical calculation in the high-frequency, exchange-dominated regime where the resonance condition required for energy exchange (an anticrossing) between quasi-elastic and quasi-magnetic dispersion branches is determined. Nonresonant interactions are shown to be well approximated by the dispersion of uncoupled elastic and magnetic waves. The methods and results provide fundamental theoretical tools to model and understand current and future magnetic devices powering spintronic innovation.
Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers
Neuromorphic computers offer the opportunity for low-power, efficient computation. Though they have been primarily applied to neural network tasks, there is also the opportunity to leverage the inherent characteristics of neuromorphic computers (low power, massive parallelism, collocated processing and memory) to perform non-neural network tasks. Here, we demonstrate how an approach for performing sparse binary matrix-vector multiplication on neuromorphic computers. We describe the approach, which relies on the connection between binary matrix-vector multiplication and breadth first search, and we introduce the algorithm for performing this calculation in a neuromorphic way. We validate the approach in simulation. Finally, we provide a discussion of the runtime of this algorithm and discuss where neuromorphic computers in the future may have a computational advantage when performing this computation.
Solubility and Diffusivity: Important Metrics in the Search for the Root Cause of Light- and Elevated Temperature-Induced Degradation
Light- and elevated temperature-induced degradation (LeTID) is a detrimental effect observed under operating conditions in p-type multicrystalline silicon (mc-Si) solar cells. In this paper, we employ synchrotron-based techniques to study the dissolution of precipitates due to different firing processes at grain boundaries in LeTID-affected mc-Si. The synchrotron measurements show clear dissolution of collocated metal precipitates during firing. We compare our observations with degradation behavior in the same wafers. The experimental results are complemented with process simulations to provide insight into the change in bulk point defect concentration due to firing. Several studies have proposed that LeTID is caused by metal-rich precipitate dissolution during contact firing, and we find that the solubility and diffusivity are promising screening metrics to identify metals that are compatible with this hypothesis. While slower and less soluble elements (e.g., Fe and Cr) are not compatible according to our simulations, the point defect concentrations of faster and more soluble elements (e.g., Cu and Ni) increase after a high-temperature firing process, primarily due to emitter segregation rather than precipitate dissolution. Finally, these results are a useful complement to lifetime spectroscopy techniques, and can be used to evaluate additional candidates in the search for the root cause of LeTID.
Preface IEEE LDAV 2023
Join us for the 13th IEEE Symposium on Large Data Analysis and Visualization (IEEE LDAV) on Monday, October 23rd 2023 collocated with IEEE VIS 2023 in Melbourne, Victoria, Australia.
IEEE LDAV 2024 Preface
Join us here for the 14th IEEE Symposium on Large Data Analysis and Visualization (IEEE LDAV) on Sunday, October 13th 2024 collocated with IEEE VIS 2024 in St. Pete Beach, Florida, USA.
An Energy Efficient Ethernet Strategy Based on Traffic Prediction and Shaping
Recently, different communities in computer science, telecommunication, and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy, which exploits the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard (known as energy efficient Ethernet) is based on the possibility of predicting the future traffic from the analysis of the current data flow. In agreement with the results of such a dynamic prediction, Ethernet links can be forced into a low power consumption state for variable intervals. Theoretical bounds are derived to detail how the performance figures depend on the parameters of the designed strategy and scale with respect to traffic load. Furthermore, simulation results carried out with both real and synthetic traffic traces are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.
Joint Optimization for Transport and Bucket Loading Phases of Automated Wheel Loaders
This article investigates optimization of fuel-efficiency and productivity for automated wheel loaders. A control-oriented model for both the transport phase and bucket loading phase is proposed. Here, the vehicle model includes an automatic gear shift schedule that can be incorporated into the optimization problem. Based on the model, the multistage optimization problem is formulated to simultaneously consider all phases of a short cycle with physical constraints. Cycle time and fuel efficiency are used as the weighted performance indexes in a multiobjective cost function. Bucket fill factor is included as a constraint during the bucket loading phase. A nonlinear programming problem is created with collocation using MATLAB and CasADi. The optimization solver IPOPT solves the problem to obtain the optimal state and control trajectories, which can be used as a reference for automated wheel loaders or even as a driver advisory for human-driven wheel loaders.