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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mutually magnetically insulated two-species Brillouin flow

In this work, we use the Brillouin flow analytic framework to examine the physics of Magnetically Insulated Transmission Lines (MITL). Here, we derive a model applicable to any particle species, including both positive and negative ions, in planar and cylindrical configurations. We then show how to self-consistently solve for two-species simultaneously, using magnetically insulated electrons and positive ions as an example. We require both layers to be spatially separated and magnetically insulated (mutually magnetically insulated); for a 7.5 cm gap with a 2 MV bias voltage, this condition requires magnetic fields in excess of 2.73 T. We see a close match between mutually insulated MITL performance and “superinsulated” (high degree of magnetic insulation) electron-only theory, as may be expected for these high magnetic fields. However, the presence of ions leads to several novel effects: (1) Opposite to electron-only theory, total electron currents increase rather than decrease as the degree of magnetic insulation becomes stronger. The common assumption of neglecting electrons for superinsulated MITL operation must be revisited when ions are present—we calculate up to 20× current enhancement. (2) The electron flow layer thickness increases up to double, due to ion space-charge enhancement. (3) The contributions from both ions and electrons to the MITL flow impedance are calculated. The flow impedance drops by over 50% when ions fill the gap, which can cause significant reflections at the load if not anticipated and degrade performance. Additional effects and results from the inclusion of the ion layer are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Resonance cones in cold plasma: Origin, singularities, and power flow

In magnetized tenuous plasma, typical at the plasma edge of fusion devices, a nearly electrostatic wave mode with relatively enhanced electric field can propagate along a specific angle with the magnetic field. For this characteristic, it is known as a “resonance cone.” For instance, these waves can be excited by radio frequency antennas in the ion-cyclotron and lower-hybrid range of frequencies. We consider the resonance cones emitted by idealized spatially extended sources. In 2D, we use a novel geometric construction which generalizes the d'Alembert solution to curved boundaries/moving sources, and show, for the first time, that singular electric fields arise under these conditions, thereby bringing the resonance cones in line with the other resonances of the cold plasma theory. Still in 2D, we give an expression for the amount of power radiated by resonance cones in terms of surface quantities on the source, which is finite despite the singular electric field. In conclusion, we generalize the conclusions regarding the presence and location of singular electric fields to the 3D electromagnetic case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Learning physics-based reduced-order models from data using nonlinear manifolds

Here we present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred from the data using operator inference. Numerical experiments on a number of nonlinear problems demonstrate the generalizability of the methodology and the increase in accuracy that can be obtained over reduced-order modeling methods that employ a linear subspace approximation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Asymptotic vacuum solution at tokamak X-point tip

In the H-mode regime of diverted tokamaks, the presence of strong pressure gradients in the pedestal gives rise to a sizable bootstrap current, together with the Ohmic and Pfirsch–Schlueter currents, close to the separatrix. For such equilibria, the presence of finite current density close to the separatrix requires the reexamination of equilibrium properties. It is almost universally assumed that the two branches of the separatrix (the stable and unstable manifolds) are straight as they cross at the X-point. However, the opposite angles of the plasma-filled segment and vacuum one cannot be equal if the current density does not vanish at the separatrix on the plasma side. We solve this difficulty by chipping off a thin layer of plasma edge so that the sharp corner of the plasma-filled segment becomes a hyperbola. Using the conformal transformation, we found that in the assumption of a hyperbolic boundary, the X point moves beyond the plasma boundary to fall in the vacuum region. An acute angle of the plasma-filled segment leads to an obtuse opposite angle of vacuum segment and vice versa. In the case of an acute angle of the plasma-filled segment, the new X point shifts inside the X point formed by the asymptotes of a hyperbolic boundary; in the case of an obtuse angle of the plasma-filled segment, the new X point shifts outside the X point formed by the asymptotes of a hyperbolic plasma boundary. Furthermore, the results are important for understanding the X point features, which affect the tokamak edge stability and transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Theory and simulations of cross-beam energy transfer between speckled laser beams

In this paper, we introduce a realistic modeling of laser beams smoothed by random phase plates to investigate cross-beam energy transfer (CBET). We compute the total field of the smoothed laser beams, which allows us to perform accurate simulations and to build a model accounting for the realistic structure of the speckles. This lets us confirm the results already discussed in a previous work given by Oudin et al. [Phys. Plasmas 29, 112112 (2022)]. When CBET is induced by a plasma flow, plane wave models can predict the exchange between smoothed laser beams. By contrast, when the exchange is caused by a wavelength shift between the laser fields of each beam, plane wave models overestimate the energy transfer, even for strongly Landau-damped ion acoustic waves. We also show that the laser angular spread, which results from its focusing, affects the resonance between the ponderomotive force induced by the laser fields and the ion acoustic wave. It increases the resonance width, which weakly depends on the Landau damping rate when there is a wavelength shift and no flow. In addition, the power transfer obtained with or without a phase plate is essentially the same in the perturbative regime and for small crossing angles.

Laser plasma interactions↗
DOE OSTI2020

2D microspatial distribution uniformity of photon detection efficiency and crosstalk probability of multi-pixel photon counters

Two-dimensional (2D) microspatial distribution uniformity of photon detection efficiency (PDE) and optical crosstalk probability P{sub ct} of multi-pixel photon counters (MPPCs) is studied. The experimental results show that the 2D spatial distribution of P{sub ct} is obviously uneven, i.e. P{sub ct} is larger at the corners and edges of a single pixel in MPPCs, which suggest a higher electrical field in the depletion region of the pixel at the corners and edges. The nonuniformity of the 2D spatial distribution of PDE also become evident when the size of the pixels of MPPCs is small, which signifies higher nonuniformity of the electric field distribution in MPPCs with small pixel size. A method is proposed for characterization of the 2D electrical field spatial distribution uniformity in a single pixel of MPPCs, which can be used for guiding the optimisation of the fabrication process of MPPCs and their properties. This promising method can naturally be extended to any Geiger avalanche photodiodes (G-APDs) and their arrays. (laser applications and other topics in quantum electronics)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Autocrine and paracrine interferon signalling as ‘ring vaccination’ and ‘contact tracing’ strategies to suppress virus infection in a host

The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as ‘contact tracing’ or ‘ring vaccination’. Thus, our results here may have implications for the outbreak control at the population scale more broadly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Probabilistic measures for biological adaptation and resilience

This paper introduces an approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from nonequilibrium statistical mechanics, we propose a measure termed “ecological resilience through adaptation,” specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. Importantly, the resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Electromagnetic Transient Simulation Algorithms for Evaluation of Large-Scale Extreme Fast Charging Systems (Distribution Grid Models)

The distribution and transmission grids are observing an increased penetration of power electronics in loads and generations. For example, there is increasing interest in integrating in extreme fast charging (XFC) systems for fast charging of electrical vehicles. As these systems are integrated, developing high-fidelity electromagnetic transient model of XFC systems in distribution grids and evaluating their interactions with the power grid would be of significant interest. This model will be utilized for design of XFC systems, to identify upgrades in distribution and/or transmission grids, for planning purposes by transmission planners or operators or owners, among others. It can also be utilized in operations for improved reliable performance of the grid and/or XFC station. The challenge with simulating these models is the high computational complexity introduced by the large number of states present in the system and the time-step needed to simulate the system. In this paper, advanced simulations algorithms are applied to reduce the computational complexity of simulating large-scale XFC systems. The algorithms include numerical stiffness-based segregation, time constant-based segregation, clustering and aggregation on differential algebraic equations (DAEs), and multi-order integration approaches. While the first three algorithms split the matrix that needs to be inverted from a large matrix to much smaller matrices, the final algorithm reduces the computational burden of applying higher-order integration approaches in the complete system. The comparison made in the previous sentence is with respect to use of homogeneous integration approaches used in conventional electromagnetic transient simulators like power systems computer aided design (PSCAD). The approaches mentioned here have resulted in speed-up of 36x in the simulation of a single distribution system with 15 XFCs.

Debnath, Suman↗
DOE OSTI2021

Electromagnetic Transient (EMT) Simulation Algorithm for Evaluation of Photovoltaic (PV) Generation Systems

Use of inverter-based resources facilitating renewable energy resources such as photovoltaic (PV) generation is increasing rapidly with decreasing costs and reduced emissions associated. To accommodate such rapid growth of inverter-based resources like PV systems, electromagnetic transient (EMT) simulation models of both PV systems and grids are required to analyze the interaction of PVs in the grid (like the post-event analysis). In addition, the EMT simulation would help with the planning of future power grid with a large number of PVs as well as other inverter-based distributed generation systems. In this paper, the EMT simulation models of PV systems and grids are developed based on the differential algebraic equations (DAEs) representing their EMT dynamics. Furthermore, advanced simulation algorithms including numerical stiffness-based hybrid discretization, DAE clustering and aggregation, multi-order integration, and matrix splitting approaches are applied to accelerate the EMT simulation. The proposed algorithm was applied to 125 PV inverters within 52-bus medium-voltage (MV) distribution grid.

Choi, Jongchan↗
DOE OSTI2024

Linear Solvers for Collector Systems of Generalized Large-scale Inverter-Based Resources

Collector systems for inverter-based resources (IBRs) are typically represented by equivalent circuits for electromagnetic transient (EMT) simulations. Recent studies have revealed that modeling a detailed collector system is essential to accurately represent the behavior of IBRs, especially when dealing with partial tripping during external disturbances. However, there are several challenges in simulating a detailed EMT model of a collector system due to the time required to simulate such systems. Thus, this paper investigates the modeling of a detailed collector system, taking into account its configuration and components as defined in IEEE standard 2800. The configurations include the collector systems of generalized large-scale IBR plants. The components include the main IBR transformer, collector bus, and feeders with lines and/or cables. The EMT model of the collector system is represented by differential algebraic equations (DAEs) that are discretized to form linear equations that are solved using linear solvers. In this paper, linear solvers are proposed based on the Schur complement method, which are utilized for simulation of the EMT model of collector systems of generalized large-scale IBRs to accelerate simulation speed while maintaining the accuracy of the results. The proposed solvers are verified by comparing the performance to that of linear solvers provided in MATLAB.

Choi, Jongchan↗
DOE OSTI2024

Reinforcement Learning-Based Approach for EMT Automation of Large-Scale PV Plants

In the pursuit of efficient and precise modeling of large-scale power systems, particularly utility-scale photovoltaic (PV) plants, Electromagnetic Transient (EMT) simulations play a crucial role. As utility-scale PV plants increase in size and complexity, traditional computational methods become inadequate, necessitating more advanced techniques. This paper highlights the progressive efforts made to accelerate EMT simulations. A novel continuous reinforcement learning (RL) strategy is explored to automate the differentiation and categorization of stiff and non-stiff differential algebraic equations (DAEs). The use of stiff and non-stiff integration methods applied to relevant parts of the DAEs assists with the speed-up of the simulations. The paper details the data acquisition, development and offline training of the RL model, leading to its validation that demonstrates a high precision in optimizing simulation methods. The proposed RL promises to significantly enhance the efficacy of EMT simulations, offering a robust framework for the future of power system analysis.

Xia, Qianxue↗