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DOE OSTI2020

Anharmonic lattice dynamics and superionic transition in AgCrSe 2

Intrinsically low lattice thermal conductivity ( κ l a t ) in superionic conductors is of great interest for energy conversion applications in thermoelectrics. Yet, the complex atomic dynamics leading to superionicity and ultralow thermal conductivity remain poorly understood. Here, we report a comprehensive study of the lattice dynamics and superionic diffusion in A g C r S e 2 from energy- and momentum-resolved neutron and X-ray scattering techniques, combined with first-principles calculations. Our results settle unresolved questions about the lattice dynamics and thermal conduction mechanism in A g C r S e 2 . We find that the heat-carrying long-wavelength transverse acoustic (TA) phonons coexist with the ultrafast diffusion of Ag ions in the superionic phase, while the short-wavelength nondispersive TA phonons break down. Strong scattering of phonon quasiparticles by anharmonicity and Ag disorder are the origin of intrinsically low κ l a t . The breakdown of short-wavelength TA phonons is directly related to the Ag diffusion, with the vibrational spectral weight associated to Ag oscillations evolving into stochastic decaying fluctuations. Furthermore, the origin of fast ionic diffusion is shown to arise from extended flat basins in the energy landscape and collective hopping behavior facilitated by strong repulsion between Ag ions. These results provide fundamental insights into the complex atomic dynamics of superionic conductors.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Cross-scale dynamics and the evolutionary emergence of infectious diseases

When emerging pathogens encounter new host species for which they are poorly adapted, they must evolve to escape extinction. Pathogens experience selection on traits at multiple scales, including replication rates within host individuals and transmissibility between hosts. We analyze a stochastic model linking pathogen growth and competition within individuals to transmission between individuals. Our analysis reveals a new factor, the cross-scale reproductive number of a mutant virion, that quantifies how quickly mutant strains increase in frequency when they initially appear in the infected host population. This cross-scale reproductive number combines with viral mutation rates, single-strain reproductive numbers, and transmission bottleneck width to determine the likelihood of evolutionary emergence, and whether evolution occurs swiftly or gradually within chains of transmission. We find that wider transmission bottlenecks facilitate emergence of pathogens with short-term infections, but hinder emergence of pathogens exhibiting cross-scale selective conflict and long-term infections. Furthermore, our results provide a framework to advance the integration of laboratory, clinical, and field data in the context of evolutionary theory, laying the foundation for a new generation of evidence-based risk assessment of emergence threats.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A low-cost and realistic noisy light system for studying photosynthesis

Unlike the light conditions commonly used to grow photosynthetic organisms in the research laboratory, the light intensity in real environments is dynamic. A simple and low-cost system is described in which a commercial dimmable LED panel is controlled to simulate a sinusoidal function representing daylight hours and overlaid with stochastic shading events. The output closely resembles light intensity measurements on Earth’s surface on partly cloudy days or in lower levels of plant canopies. Here, this tool may be useful to researchers studying photosynthetic acclimation responses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Using Energy Storage-Based Grid Forming Inverters for Operational Reserve in Hybrid Diesel Microgrids

In remote arctic communities, where access to a bulk electrical grid interconnection is not available, the implementation of islanded microgrids has been the most viable way to produce and distribute electricity services to their inhabitants. Historically, these islanded grids have relied primarily on diesel generators or hydropower resources to supply the baseload. However, this practice can result in increased expense due to the high costs associated with fuel transportation and the significant amounts of on-site storage necessary when fuel transportation is unavailable during winter months. In order to mitigate this problem, arctic microgrids have started to transition to a hybrid-source operational mode by incorporating renewable energy sources that are inherently variable in nature, such as wind or solar. Due to their highly stochastic behavior, these hybrid-source islanded microgrids can pose potential issues related to power quality due to introduction of rapid net load fluctuations and inability of diesel generators to respond rapidly. In addition, non-firm stochastic sources can require significant idling diesel generator resources to serve as spinning reserves, which is inefficient and wasteful. This work studies the problems that may arise in the transient dynamics of a real-world hybrid diesel microgrid when subjected to a loss of wind generation. Moreover, this work proposes a transition from a diesel spinning reserve to a battery energy-storage system (BESS) operating reserve scheme. The study of the proposed transition is important in establishing the fundamental implication of transient dynamics and the potential benefits of integrating a BESS as a spinning reserve in terms of stability, frequency nadir, and transient voltage deviation. The methods to investigate and validate the transient dynamics relied on both electromagnetic simulation models of GFMIs and a commercially available GFMI in an experimental power hardware-in-the-loop setup. The simulation results showed that the proposed operating reserve scheme improves the power quality of the system in terms of voltage deviation and frequency nadir when the microgrid is subjected to a loss of wind generation scenario. Depending on the simulation cases, adding a GFMI reduced the frequency nadir between 65.3% and 86.7%. Moreover, the reductions in the voltage deviations improved between 3.6% and 23.0%. From these results it can be concluded that the integration of a GFMI can reduce the frequency nadir in a hybrid diesel microgrid, and in turn, reduce diesel consumption, which improves system reliability while reducing fuel expenses. Furthermore, the novelty of this work relies on the fact that the offline simulation results were validated using a power hardware-in-the-loop platform that incorporated a 100 kVA commercially available GFMI as the device under test.

Hernandez-Alvidrez, Javier↗

DOE OSTI2020

A Novel use of Direct Simulation Monte-Carlo to Model Dynamics of COVID-19 Pandemic Spread

In this report, we evaluate a novel method for modeling the spread of COVID-19 pandemic. In this new approach we leverage methods and algorithms developed for fully-kinetic plasma physics simulations using Particle-In-Cell (PIC) Direct Simulation Monte-Carlo (DSMC) models. This approach then leverages Sandia-unique simulation capabilities, and High-Performance Computer (HPC) resources and expertise in particle-particle interactions using stochastic processes. Our hypothesis is that this approach would provide a more efficient platform with assumptions based on physical data that would then enable the user to assess the impact of mitigation strategies and forecast different phases of infection. This work addresses key scientific questions related to the assumptions this new approach must make to model the interactions of people using algorithms typically used for modeling particle interactions in physics codes (kinetic plasma, gas dynamics). The model developed uses rational/physical inputs while also providing critical insight; the results could serve as inputs to, or alternatives for, existing models. The model work presented was developed over a four-week time frame, thus far showing promising results and many ways in which this model/approach could be improved. This work is aimed at providing a proof-of-concept for this new pandemic modeling approach, which could have an immediate impact on the COVID-19 pandemic modeling, while laying a basis to model future pandemic scenarios in a manner that is timely and efficient. Additionally, this new approach provides new visualization tools to help epidemiologists comprehend and articulate the spread of this and other pandemics as well as a more general tool to determine key parameters needed in order to better predict pandemic modeling in the future. In the report we describe our model for pandemic modeling, apply this model to COVID-19 data for New York City (NYC), assess model sensitivities to different inputs and parameters and , finally, propagate the model forward under different conditions to assess the effects of mitigation and associated timing. Finally, our approach will help understand the role of asymptomatic cases, and could be extended to elucidate the role of recovered individuals in the second round of the infection, which is currently being ignored.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

X-ray tomography of damage dynamics in advanced materials using a laser wakefield accelerator

Additively manufactured (AM) metals offer the potential for customizable, cost-effective components, but qualification and certification are crucial. Key to this process is understanding pore dynamics under stress, typically analyzed using micro-computed tomography. This study introduces laboratory-scale “betatron” x-rays from laser wakefield acceleration as a high-throughput alternative for x-ray tomography of advanced materials, such as AM AlSi10Mg alloys. Coupled with 3D finite element modeling, this method provides detailed insights into stress-porosity interactions. The approach delivers high-resolution scans, revealing that pore shape and local triaxiality significantly influence fracture dynamics, supporting advanced material characterization. This work also demonstrates the potential and versatility of laser-betatron x-ray μCT for generating large datasets to accelerate our understanding of the stochastic, process-specific nature of pore formation in AM alloys.

Senthilkumaran, Vigneshvar↗

DOE OSTI2023

Stochastic and mixed density functional theory within the projector augmented wave formalism for simulation of warm dense matter

Stochastic density functional theory (DFT) and mixed stochastic-deterministic DFT are burgeoning approaches for the calculation of the equation of state and transport properties in materials under extreme conditions. In the intermediate warm dense matter regime, a state between correlated condensed matter and kinetic plasma, electrons can range from being highly localized around nuclei to delocalized over the whole simulation cell. The plane-wave basis pseudopotential approach is thus the typical tool of choice for modeling such systems at the DFT level. Unfortunately, stochastic DFT methods scale as the square of the maximum plane-wave energy in this basis. To reduce the effect of this scaling and improve the overall description of the electrons within the pseudopotential approximation, we present stochastic and mixed DFT approaches developed and implemented within the projector augmented wave formalism. In conclusion, we compare results between the different DFT approaches for both single-point and molecular dynamics trajectories and present calculations of self-diffusion coefficients of solid density carbon from 1 to 50 eV.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Zonal shear layer collapse and the power scaling of the density limit: old L-H wine in new bottles

Edge shear layer collapse causes edge cooling and aggravates radiative effects. This paper details on the microscopic dynamics of the emergence of power (Q) scaling of density limit (DL) from the shear layer collapse transport bifurcation scenario. The analysis is based on a novel 4-field model, which evolves turbulence energy, zonal flow energy, temperature gradient and density, including the neoclassical screening of zonal flow response. Bifurcation analysis yields power scaling of critical density for shear layer collapse as $n_{crit}\sim Q^{1/3}$. The favorable Q scaling of the DL emerges from the fact that the shear layer strength increases with Q, thus preventing shear layer collapse. This in turn reduces particle transport and improves particle confinement. RMP induced ambient stochastic fields degrade the shear layer by inducing decoherence in the Reynolds stress. As a result the particle transport increases and particle confinement degrades. This leads to the emergence of unfavorable stochastic field intensity ($b_{st}^{2}$) scaling of the critical density as $n_{crit}\sim(1+b_{st}^{2})^{-5/3}$. All fields, including zonal flow shear, exhibit hysteresis when the power (Q) is ramped cyclically across the bifurcation point. The hysteresis is due to dynamical delay in bifurcation on account of critical slowing down. Furthermore, the dynamical hysteresis here is fundamentally different from the hysteresis associated with the existence of bi-stable states.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Emergent quantum mechanics at the boundary of a local classical lattice model

We formulate a model in which quantum mechanics emerges from classical mechanics. Given a local Hamiltonian H acting on n qubits, we define a local classical model with an additional spatial dimension whose boundary dynamics is approximately—but to arbitrary precision—described by Schrödinger's equation and H. The bulk consists of a lattice of classical bits that propagate towards the boundary through a circuit of stochastic matrices. The bits reaching the boundary are governed by a probability distribution whose deviation from the uniform distribution can be interpreted as the quantum-mechanical wave function. Bell nonlocality is achieved because information can move through the bulk much faster than the boundary speed of light. Finally, we analytically estimate how much the model deviates from quantum mechanics, and we validate these estimates using computer simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Collisional simulations of the modulator section in coherent electron cooling

The first section of any coherent electron cooling (CeC) system is the modulator, where the density of the electron beam is modulated by the copropagating ion beam. This density modulation is a result of Coulomb collisions between the individual particles of the two beams. The pairwise, stochastic part of the interactions impacts the overall performance of the CeC process. We present the first simulations of the density modulations of the electron beams from a collisional picture of the dynamics, considering the proof-of-principle CeC experiments at Brookhaven National Laboratory. These simulations were performed using PHAD, which is the first efficient, large-scale collisional numerical method in beam physics that we have previously developed and benchmarked. Realistic beam distributions and external fields have been optimized to provide strong modulation signals necessary for variations of coherent electron cooling systems. Cooling performance limits and potential collisionless simulation pitfalls are pointed out. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Influence of inhomogeneous stochasticity on the falsifiability of mean-field theories and examples from accretion disc modelling

ABSTRACT Despite spatial and temporal fluctuations in turbulent astrophysical systems, mean-field theories can be used to describe their secular evolution. However, observations taken over time scales much shorter than dynamical time scales capture a system in a single state of its turbulence ensemble. Comparing with mean-field theory can falsify the latter only if the theory is additionally supplied with a quantified precision. The central limit theorem provides appropriate estimates to the precision only when fluctuations contribute linearly to an observable and with constant coherent scales. Here, we introduce an error propagation formula that relaxes both limitations, allowing for non-linear functional forms of observables and inhomogeneous coherent scales and amplitudes of fluctuations. The method is exemplified in the context of accretion disc theories, where inhomogeneous fluctuations in the surface temperature are propagated to the disc emission spectrum – the latter being a non-linear and non-local function of the former. The derived precision depends non-monotonically on emission frequency. Using the same method, we investigate how binned spectral fluctuations in telescope data change with the spectral resolving power. We discuss the broader implications for falsifiability of a mean-field theory.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Riverine bacterioplankton and phytoplankton assembly along an environmental gradient induced by urbanization

In the Anthropocene, human activities are intensifying stresses on biological communities across ecosystems. Knowledge on how environmental changes influence microbial community dynamics is crucial but currently limited. Here we unraveled ecological processes generating distribution patterns of bacterioplankton and phytoplankton communities in a river draining an urbanizing watershed in southern China. In this ecosystem, longitudinal environmental heterogeneity and directional flow potentially have counteracting effects on planktonic community assembly. Using simultaneously multiple approaches, we showed that the observed compositional patterns result from an interplay of stochastic and deterministic processes, indicating that dispersal via fluvial connectivity could not override local selective pressures. Successional shifts in the relative importance of the above processes were associated with trends in climatic factors. High diversity was observed at the intermediate levels of ecological processes alongside intense inter-species interactions maintained by a few keystone taxa. Altogether, interactions between climatic conditions, landscape and hydrology control plankton community dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Runaway electron transport in stochastic toroidal magnetic fields

Here, we study the transport and confinement properties of runaway electrons (RE) in the presence of magnetic fields with perturbations producing different levels of stochasticity. We use Kinetic Orbit Runaway Electron Code (KORC) [Carbajal et al. , Phys. Plasmas 24, 042512 (2017) and del-Castillo-Negrete et al. , Phys. Plasmas 25, 056104 (2018)] for simulating the full-orbit (FO) and guiding-center (GC) dynamics of RE in perturbed magnetic fields that exhibit magnetic islands. We extend previous works on this problem [Wingen et al. , Nucl. Fusion 46, 941 (2006); Izzo et al. , Nucl. Fusion 51, 063032 (2011); Papp et al. , Nucl. Fusion 51, 043004 (2011); V. Izzo and P. Parks, Phys. Plasmas 24, 060705 (2017); and Sommariva et al. , Nucl. Fusion 58, 016043 (2018)] by studying in detail full-orbit effects on the RE dynamics. We quantify FO effects on RE transport by performing one-to-one comparisons between FO and GC simulations. It is found that, for the magnetic field configurations considered, GC simulations predict twice the RE losses of FO simulations for 1 MeV and four times the RE losses of FO simulations for 25 MeV. Similarly, we show how different GC and FO dynamics of RE moving around magnetic islands can be, especially in the scenario where the RE Larmor radius is on the order of the size of the magnetic island. We also study the role of rotation of the magnetic islands on RE confinement, and we find that low-frequency toroidal rotation has no observable effect on RE transport in the cases considered. These results shed some light into the potential of avoidance or mitigation mechanisms based on magnetic perturbations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

PowderJet: Spherical metal powder production via multi-orifice droplet-on-demand metal jetting

Leading metal additive manufacturing techniques, such as laser powder bed fusion and directed energy deposition, rely on high-quality spherical metal powders. However, traditional powder production methods like gas atomization face limitations, including low in-spec yield, asphericity, and internal porosity. We introduce PowderJet, a powder production platform that uses electromagnetic pulses to eject liquid metal droplets from a multi-orifice nozzle. Unlike stochastic methods, PowderJet tightly controls powder size, distribution, and purity through a droplet-on-demand approach. We detail the system’s design, operation, and performance using a combined experimental and computational fluid dynamics (CFD) framework. Initial results with Al4008 and Cu110 alloys demonstrate successful production, yielding unsieved aluminum powder batches with a mean diameter of 200 µm and a narrow size distribution (15 µm standard deviation). The produced powders are highly spherical, achieving a roundness > 0.95. PowderJet operates with a small melt volume (3 mL) and supports continuous refilling, enabling production rates between 30 and 140 cm³/hr depending on jetting frequency, number of orifices and particle size. CFD simulations show that future systems could achieve rates exceeding 1000 cm³/hr for particle sizes as small as 40 µm. PowderJet’s high yield of in-spec powder makes it ideal for producing precious or hazardous materials that are inefficient to manufacture using conventional methods. This platform offers a scalable, precise, and efficient solution for producing high-quality powders tailored for advanced manufacturing applications.

Atomization↗

DOE OSTI2021

A unifying modeling abstraction for infinite-dimensional optimization

Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional domains. Examples include continuous-time dynamic optimization (time is an infinite domain and components are functions of time), PDE optimization problems (space and time are infinite domains and components are functions of space-time), as well as stochastic and semi-infinite optimization (random space is an infinite domain and components are a function of such random space). InfiniteOpt problems also arise from combinations of these problem classes (e.g., stochastic PDE optimization). Given the infinite-dimensional nature of objectives and constraints, one often needs to define appropriate quantities (measures) to properly pose the problem. Moreover, InfiniteOpt problems often need to be transformed into a finite dimensional representation so that they can be solved numerically. Here in this work, we present a unifying abstraction that facilitates the modeling, analysis, and solution of InfiniteOpt problems. The proposed abstraction enables a general treatment of infinite-dimensional domains and provides a measure-centric paradigm to handle associated variables, objectives, and constraints. This abstraction allows us to transfer techniques across disciplines and with this identify new, interesting, and useful modeling paradigms (e.g., event constraints and risk measures defined over time domains). Our abstraction serves as the backbone of an intuitive Julia-based modeling package that we call InfiniteOpt.jl. We demonstrate the developments using diverse case studies arising in engineering.

42 ENGINEERING↗

DOE OSTI2020

TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo

TurboRVB is a computational package for ab initio Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC) and diffusion Monte Carlo in its robust and efficient lattice regularized variant. A key feature of the code is the possibility of using strongly correlated many-body wave functions (WFs), capable of describing several materials with very high accuracy, even when standard mean-field approaches [e.g., density functional theory (DFT)] fail. The electronic WF is obtained by applying a Jastrow factor, which takes into account dynamical correlations, to the most general mean-field ground state, written either as an antisymmetrized geminal power with spin-singlet pairing or as a Pfaffian, including both singlet and triplet correlations. This WF can be viewed as an efficient implementation of the so-called resonating valence bond (RVB) Ansatz, first proposed by Pauling and Anderson in quantum chemistry [L. Pauling, The Nature of the Chemical Bond (Cornell University Press, 1960)] and condensed matter physics [P.W. Anderson, Mat. Res. Bull 8, 153 (1973)], respectively. The RVB Ansatz implemented in TurboRVB has a large variational freedom, including the Jastrow correlated Slater determinant as its simplest, but nontrivial case. Moreover, it has the remarkable advantage of remaining with an affordable computational cost, proportional to the one spent for the evaluation of a single Slater determinant. Therefore, its application to large systems is computationally feasible. The WF is expanded in a localized basis set. Several basis set functions are implemented, such as Gaussian, Slater, and mixed types, with no restriction on the choice of their contraction. The code implements the adjoint algorithmic differentiation that enables a very efficient evaluation of energy derivatives, comprising the ionic forces. Thus, one can perform structural optimizations and molecular dynamics in the canonical NVT ensemble at the VMC level. For the electronic part, a full WF optimization (Jastrow and antisymmetric parts together) is made possible, thanks to state-of-the-art stochastic algorithms for energy minimization. In the optimization procedure, the first guess can be obtained at the mean-field level by a built-in DFT driver. The code was efficiently parallelized by using a hybrid MPI-OpenMP protocol, which is also an ideal environment for exploiting the computational power of modern Graphics Processing Unit accelerators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗