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At least 127 records · Page 7

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DOE OSTI2025

A parallel-kinetic-perpendicular-moment model for magnetised plasmas

We describe a new model for the study of weakly collisional, magnetised plasmas derived from exploiting the separation of the dynamics parallel and perpendicular to the magnetic field. This unique system of equations retains the particle dynamics parallel to the magnetic field while approximating the perpendicular dynamics through a spectral expansion in the perpendicular degrees of freedom, analogous to moment-based fluid approaches. In so doing, a hybrid approach is obtained that is computationally efficient enough to allow for larger-scale modelling of plasma systems while eliminating a source of difficulty in deriving fluid equations applicable to magnetised plasmas. We connect this system of equations to historical asymptotic models and discuss advantages and disadvantages of this approach, including the extension of this parallel-kinetic-perpendicular moment beyond the typical region of validity of these more traditional asymptotic models. This paper forms the first of a multi-part series on this new model, covering the theory and derivation, alongside demonstration benchmarks of this approach that include shocks and magnetic reconnection.

astrophysical plasmas↗

DOE OSTI2026

Role of ion acoustic instability in magnetic reconnection

We report on a first-principles numerical study of magnetic reconnection in plasmas with different initial ion-to-electron temperature ratios. In cases where this ratio is significantly below unity, we observe intense wave activity in the diffusion region, driven by the ion-acoustic instability. Our analysis shows that the dominant macroscopic effect of this instability is to drive substantial ion heating. In contrast to earlier studies reporting significant anomalous resistivity, we find that anomalous contributions due to the ion-acoustic instability are minimal. These results shed light on the dynamical impact of this instability on reconnection processes, offering new insights into the fundamental physics governing collisionless reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Resonant dynamics of one-side multipactor on dielectric

Breakdown of dielectric radio frequency (RF) windows is an important issue for particle accelerators and high-power RF sources. One of the common reasons for RF window failure is the multipactor on a dielectric surface. The multipactor may be responsible for excessive heating of the dielectric and discharge of charges that accumulated in the ceramic due to secondary emission. In this study, comprehensive self-consistent particle-in-cell simulations with space charge effect were performed. This was to better understand the dynamic of one-side multipactor development and floating potential on the dielectric induced by the emission. The important correlations between the multipactor parameters at saturation, the secondary emission properties of dielectric, and the applied RF field parameters have been found, which led to the conclusion that the dynamics of one-side multipactor on dielectric is a resonant phenomenon.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

First fluctuation measurements using an Imaging Neutral Particle Analyzer on DIII-D

A recent upgrade to the Imaging Neutral Particle Analyzer (INPA) on DIII-D has allowed for the first fluctuation measurements using an INPA to be taken during a neoclassical tearing mode (NTM). The INPA signal tracked the mode over 150 ms as the mode frequency dropped to zero, capturing both the NTM with poloidal and toroidal mode numbers $m/n=2/1$ and a $3/2$ mode at double the frequency. Analysis shows that the signals originate from charge exchange events near the edge of the plasma, and relative fluctuation amplitudes are greater than 25% for the duration of the NTM. Filtered signals show frequency beating patterns that are phase-space dependent. Simulated signal is dominated by prompt transport from the neutral beams to the INPA sightline, while the contribution from the slowing down distribution is significantly lower. Simulated measurements in the range of pitches that the diagnostic is sensitive to (0.5≤|v∥/v|≤ 0.75) show the signal is dominated by trapped orbits that pass through magnetic islands near the edge of the plasma. Calculations of expected fluctuation levels show that only direct interaction with the NTM can provide the strong relative fluctuation levels seen experimentally. The prompt nature of the orbits and thin radial layer found to contribute to synthetic signals suggest INPA passive data may be used to measure the perturbations of confined orbits on a single pass through a plasma instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Formation of Complex Spin Textures in Thermally Demagnetized La 0.7 Sr 0.3 Mn O 3 Artificial-Spin-Ice Structures

Artificial spin ices (ASIs) have traditionally been designed such that each nanomagnet possesses a single-domain magnetic configuration that is assumed to be minimally perturbed by interisland dipolar interactions. Using x-ray photoemission electron microscopy to perform magnetic domain imaging, we study thermally demagnetized La 0.7 Sr 0.3 MnO 3 -based brickwork ASI arrays and showed that complex spin textures (CSTs) can be stabilized through an appropriate selection of nanoisland width and interisland spacing. While the width dependence can be explained through the dominance of shape anisotropy in isolated nanoislands, the ASIs we investigate demonstrate a complex dependence on both the nanoisland width and interisland spacing. Micromagnetic simulations reveal that interisland dipolar interactions play a role in the formation of CSTs, which are composed of single- and double-vortex states. Furthermore, energy analysis of the simulations provides an understanding of the system energetics that arises from a delicate balance between intraisland effects (i.e., shape anisotropy and exchange energy) and interisland effects (i.e., dipolar interactions between nearest-neighbor nanoislands).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Secure Wireless Communication Using Distributed Coherent Transmission and Spatial Signal Decomposition

We present a new approach to secure wireless communications using coherent distributed transmission of signals that are spatially decomposed between a two-element distributed antenna array. High-accuracy distributed coordination of microwave wireless systems supports the ability to transmit different parts of a signal from separate transmitters such that they combine coherently at a designated destination. In this article, we explore this concept using a two-element coherent distributed phased array where each of the two transmitters sends a separate component of a communication signal, where each symbol is decomposed into a sum of two pseudorandom signal vectors, the coherent summation of which yields the intended symbol. By directing the transmission to an intended receiver using distributed beamforming, the summation of the two vector components is largely confined to a spatial region at the destination receiver. We implement the technique in a $50−λ$ array operating at 3 GHz. We evaluate the symbol error rate (SER) in 2-D space through simulation and measurement, showing that the approach yields a spatially confined secure region where the information is recoverable (i.e., the received signal has low SER), and outside of which the information is unrecoverable (high SER). The proposed system is also compared against a traditional beamforming system where each node sends the same data. We validate experimentally that our approach achieves a low SER of 0.0082 at broadside and an SER above 0.25 at all other locations compared to a traditional beamforming approach that achieves a SER of 0 at all locations measured.

Engineering - Electronic and electrical engineerin↗

DOE OSTI2022

Descriptor Aided Bayesian Optimization for Many-Level Qualitative Variables With Materials Design Applications

Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those required to evaluate optimization objectives in materials design applications, constitute the main portion of the cost of the design process and underline the need for efficient search strategies—Bayesian optimization (BO) being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High-dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors, which embody the physics of the underlying physical phenomena, to infer the effect of unobserved levels that have not been sampled yet. We show that physical descriptors can be intuitively embedded into the latent variable Gaussian process approach—a mixed-variable GP modeling technique—and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. This physics-informed approach is particularly useful when one or more qualitative variables are high dimensional (many-level) and the modeling dataset is small, containing observations for only a subset of levels. Through a combination of mathematical test functions and materials design applications, our method is shown to be robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.

Engineering↗

DOE OSTI2022

Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control

Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.

Kurte, Kuldeep↗

DOE OSTI2024

RetroTide v1.0

RetroTide is a retrobiosynthesis software tool, designed to guide the design of biological pathways to make valuable small molecules. It takes as input a desired small molecule chemical structure, and outputs a design for a polyketide synthase enzyme (PKS) that has the closest possible chemical structure, as accessible by PKS chemistry. It is developed as a Python software library, and internally performs a search of the PKS design space, while simulating the chemicals produced by each design.

Backman, Tyler↗

DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks

Confidently designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear applications. This type of analysis relies on matching new nuclear applications with existing benchmark experiments. The Whisper-1.1 software package included in MCNP6.2 ®1 contains more than 1,100 International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks. These benchmarks however rarely match new nuclear applications. The number of benchmarks available to match a given set of materials or geometric configurations varies significantly. Furthermore, recently performed benchmark experiments may not have had enough time to be properly documented and published. Benchmarks are vital for determining the accuracy of nuclear data and can assist nuclear physics and evaluators in improving nuclear data libraries. Exhaustively exploring the parameter space using simulations with software such as MCNP is too computationally expensive. In this work, Gaussian process optimization was implemented to reduce the number of simulations needed for optimization over multiple parameters. This optimization scheme was designed to selectively generate new benchmarks with high sensitivity-based similarity metrics to user-defined nuclear applications. Two benchmark models of spherically nested shells containing plutonium, uranium, tantalum, and water were used in the optimization to match an application containing plutonium plates, stacked in a tantalum reflector, surrounded by water.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Full-Cycle Simulations of the Fermilab Booster

The Proton Improvement Plan phase II (PIP-II) project currently under construction at FNAL will replace the existing 400 MeV normal conducting linac with a new 800 MeV superconducting linac. The beam power in the downstream rapid-cycling Booster synchrotron will be doubled by raising the machine cycle frequency from 15 to 20 Hz and by increasing the injected beam intensity by a factor 1.5. This has to be accomplished without raising uncontrolled losses beyond the administrative limit of 500 W. In addition, slip-stacking efficiency in the Recycler, the next machine in the accelerator chain, sets an upper limit on the longitudinal emittance of the beam delivered by the Booster. As part of an effort to better understand potential losses and emittance blow-up in the Booster, we have been conducting full cycle 6D simulations using the code PyORBIT. The simulations include space charge, wall impedance effects and transition crossing. In this paper, we discuss our experience with the code and present representative results for possible operational scenarios.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

PIC Simulations of One-side Multipactor on Dielectric

Breakdown of dielectric RF windows is an important issue for particle accelerators and high-power RF sources. One of the generally considered reasons for the RF windows failure is the multipactor on dielectric surface. The multipactor may be responsible for excessive heating of dielectric and discharge of charges that accumulated in ceramic due to secondary emission. In this study the comprehensive self-consistent PIC simulations with space charge effect were performed in order to better understand the dynamic of one-side multipactor development and floating potential on dielectric induced by the emission. The important correlations between the multipactor parameters at saturation and the secondary emission properties of dielectric and the applied RF field parameters were found and are reported in the paper.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Full Cycle Simulations of The Fermilab Booster

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

On the Life Expectancy Ot High-power CW Magnetrons for Superconducting Accelrators

Modern CW or pulse Superconducting RF (SRF) accelerators require efficient RF sources controllable in phase and power with a reduced cost. Therefore, utilization of the high-power CW magnetrons as RF sources in SRF accelerator projects was proposed in a number of works, e.g., [1, 2]. But typically, the CW magnetrons are designed as RF sources for industrial heating, and the lifetime of the tubes is not the first priority as it is required for high-energy accelerators. The high-power industrial CW magnetrons use the cathodes made of pure tungsten. The emission properties of the tungsten cathodes are not deteriorated much by electron and ion bombardments, but the latter causes sputtering of the cathode in the magnetron crossed fields. The sputtered cathode material covers the magnetron interior. This leads to sparks and discharges that limit the life of the magnetrons. We considered an analysis of magnetron failure modes vs. output power [3]. We developed a model of ionization of the residual gas in the magnetrons interaction space and simulated the spattering of the cathode in 100 kW CW magnetrons to estimate the life expectancy. Basing on results we proposed ways to increase the CW magnetrons longevity for SRF accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Solenoidal Fields from Permanent Magnets for a Polarized Electron Preinjector

This technical note describes the design of a compact focusing solenoid constructed from permanent bar magnets, intended for the low-energy section of a spin-polarized electron injector. Typically, quadrupole triplets provide appropriate focusing for the low-energy spin rotator section; however, their large footprint precludes the insertion of sufficient beam diagnostics. The primary motivation of this study is to provide beam focusing while preserving the vertical spin orientation of the electron beam. We propose a solenoidal field composed of multiple permanent magnets arranged to naturally yield a zero longitudinal magnetic field integral ($\int B_zdz = 0$). This design serves as a compact, passive, and non-coupling alternative to traditional quadrupole triplets. We present the magnet arrangement, field calculation methodology, spin tracking simulations using space charge codes, and calculations demonstrating that the required field strength is achievable.

43 PARTICLE ACCELERATORS↗