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DOE OSTI2025

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Dynamic Ride-Matching for Large-Scale Transportation Systems

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Photon detector response function methodology using MCNP and shift hybrid radiation transport code for wide-area contamination assay applications

Here, radiation transport modeling using the Monte Carlo N-Particle (MCNP) radiation transport code and Monte Carlo code, Shift, were employed to model detector responses for a variety of wide-area photon contamination scenarios. In this study, 2" × 2" and 3" × 3" cylindrical NaI(Tl) scintillation detector configurations at source detector-distances of 0.5 cm, 1 cm, 2.54 cm, 10 cm, and 30 cm were modeled. Media of soil, concrete, and steel were evaluated for contamination depths ranging from surface to a depth of an infinite thickness in each medium for photon energies ranging from 20 keV to 3 MeV, which correspond to the energies that current detectors can discern. Monoenergetic photon surface contamination detector responses for each of the media, source–detector distances, and detectors were estimated using MCNP v6.2. Shift was harnessed for improved variance reduction of particle transport in highly attenuating media to obtain average cell fluxes in the two MCNP NaI(Tl) scintillation detector configurations. Average cell flux values in Shift were coupled with detector responses from MCNP to convert average cell flux in a void to energy distribution of pulses in the NaI(Tl) scintillation detector crystal of interest. An optimized detector response function methodology was developed by coupling the MCNP radiation transport method with the Consistent Adjoint Driven Importance Sampling (CADIS) hybrid radiation transport method built into Shift to significantly decrease the runtime of thousands of MCNP pulse height simulations. The methodology may be utilized to quickly and accurately facilitate the assessment of a broad range of wide-area environmental contamination assay and decommissioning cleanup applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Towards Resilient Near Real-Time Analysis Workflows in Fusion Energy Science

Nuclear fusion holds the promise of an endless source of energy. Several research experiments across the world and joint modeling and simulation efforts between the nuclear physics and high performance computing communities are actively preparing the operation of the International Thermonuclear Experimental Reactor (ITER). Both experimental reactors and their simulated counterparts generate data that must be analyzed quickly and in a resilient way to support decision making for the configuration of subsequent runs or prevent a catastrophic failure. However, the cost if the traditional techniques used to improve the resilience of analysis workflows, i.e., replicating datasets and computational tasks, becomes prohibitive with explosion of the volume of data produced by modern instruments and simulations. Therefore, we advocate in this paper for an alternate approach based on data reduction and data streaming. The rationale is that by allowing for a reasonable, controlled, and guaranteed loss of accuracy it becomes possible to transfer smaller amounts of data, shorten the execution time of analysis workflows, and lower the cost of replication to increase resilience. We develop our research and development roadmap towards resilient near real-time analysis workflows in fusion energy science and present early results showing that data streaming and data reduction is a promising way to speed up the execution and improve the resilience of analysis workflows.

Suter, Fred↗

DOE OSTI2022

Reversible Solid Oxide Fuel Cell Systems for Energy Storage and Hydrogen Production

FuelCell Energy (FCE) studied the performance, design, and economics of a reversible solid-oxide fuel cell/electrolyzer integrated with a natural gas-based electricity generation plant and used as an energy storage system. Increased market penetration of renewable energy is challenging for operators of fossilbased plants which are not suited for quick turndown and ramp-up. Process simulations studies show that with utilization of waste heat from the power plant, a high roundtrip efficiency (RTE) around 70% is possible, providing excellent market potential for this energy storage technology, potentially providing improved economics and lifetime for such power plant. The market potential and economics as a function of production volume are analyzed in detail. FCE is continuing the development and commercialization of its solid oxide technology.

08 HYDROGEN↗

DOE OSTI2023

Enhanced dynamic contingency analysis for power systems

The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.

Samaan, Nader A.↗

DOE OSTI2020

Parallel algorithms for hyperdynamics and local hyperdynamics

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In [Kim, Perez, Voter, J Chem Phys, 139:144110, 2013)1, a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. In this paper, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost 3-5x that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of Pt(100) surface for 80 and 160 μs.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Evaluation of the Effect of Burnup Acceleration on UO 2 Microstructure Evolution

Accelerated fuel qualification (AFQ) is a methodology by which new nuclear fuels are developed in an accelerated time frame compared with historical fuel qualification approaches. AFQ generally relies on high-fidelity physics-based modeling and simulation tools to adequately describe fuel performance as well as on revolutionary methods to accelerate burnup accumulation and collect relevant data more quickly. This report summarizes the use of advanced fuel modeling and simulation tools to evaluate microstructures from commercially irradiated fuel and microstructures from proposed MiniFuel irradiations, in which burnup accumulation is accelerated while prototypic temperature conditions are maintained. In this milestone, we used the mesoscale fuel performance code MARMOT to model the evolution of irradiated UO 2 microstructures and their potential restructuring at high burnup. The simulation conditions were informed by BISON models of both commercially irradiated fuel and MiniFuel. A first set of simulations investigated the recrystallization behavior of fully dense microstructures and showed full recrystallization at burnups as low as 52 MWd/kgU at 950°C. However, these simulations did not account for the presence of fission gas bubbles (predicted by BISON). Therefore, a second set of simulations including fission gas bubbles was performed and indicated that at the lowest temperature considered (650°C), the porous UO 2 microstructures have the highest total Gibbs free energies and are likely to recrystallize earlier than higher temperature cases (800 and 950°C), which agrees with high-burnup fuel characterization data. The results also showed that at lower temperature (650°C), the total free energies of the PWR fuel and MiniFuel microstructures are not significantly different. However, at the highest temperature (950°C), MiniFuel microstructures have a lower free energy than that of the PWR fuel microstructure. The competing effects between the temperature-dependent grain nucleation rate and the reduction of the free energy of the microstructure at higher temperature as a result of diffusion indicated that restructuring may occur at even higher temperatures than those considered in this study. In addition to the microstructure evolution modeling efforts, the burnup gradient across a single fuel specimen was also considered. This evaluation was for the VXF-15 position of the High Flux Isotope Reactor (HFIR) using the code suite HFIRCON, which was developed to automate the workflow for evaluating targets and fuel as they are irradiated in HFIR. The burnup gradient evaluation showed a dependence on both the axial and radial locations within the specimen, with a maximum difference of 1.7 between the inner and outermost radial layers. This relationship was further supported by considering the fission product speciation with respect to location within the specimen, which showed a higher concentration of 239 Pu, 240 Pu, and 241 Pu on the outer radial locations of the specimen than the center. The findings of the burnup and speciation evaluation show that some amount of self-shielding is occurring in the specimen when irradiated in the high-flux environment of HFIR; however, this impact is more pronounced for natural uranium when compared to 6% enrichment due to the higher ratio of 238 U in the specimen. Further analyses are required to understand the sensitivity of this gradient to spatial mesh and enrichment of the specimen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Multiscale Modeling of Radiation Damage in UO 2 under Accelerated Burnup Conditions

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Reinforcement learning for bluff body active flow control in experiments and simulations

Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.

42 ENGINEERING↗

DOE OSTI2023

Seeking Self-regulating Simulations of Idealized Milky Way–like Galaxies

Abstract Precipitation is potentially a mechanism through which the circumgalactic medium (CGM) can regulate a galaxy’s star formation. Here, we present idealized simulations of isolated Milky Way–like galaxies intended to examine the ability of galaxies to self-regulate their star formation, in particular via precipitation. We also examine the impact of rotation in the CGM. Using six simulations, we explore variations in the initial CGM t cool / t ff ratio and rotation profile. Those variations affect the amount of gas accretion and star formation within the galactic disk. To encourage this accretion and better study its dependence on CGM structure, we gradually increase the efficiency of stellar feedback during the first half of our simulations. Yet despite this gradual increase, the resulting outflows quickly evacuate large, hot cavities within the CGM and even beyond r 200 . Some of the CGM gas avoids interacting with the cavities and is able to feed the disk along its midplane, but the cooling of feedback-heated gas far from the midplane is too slow to supply the disk with additional gas. Our simulations illustrate the importance of physical mechanisms in the outer CGM and IGM for star formation regulation in Milky Way–scale halos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. In this work, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors ( 2 χ n , topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

The Complexation Properties of Self-Defensive Microgel-Modified Antimicrobial Surfaces

The complexation of cationic antimicrobials with polyanionic microgels on a biomaterial surface can render that surface self-defensive against bacteria by killing those bacteria which physically contact the antimicrobial-loaded microgels. This killing has been attributed to the contact-driven transfer of antimicrobial from a microgel to a challenging bacterium, though much remains unknown about this process. Here, in this study, we use a combination of experiments and computational modeling to identify key aspects of the complexation phenomena which influence the self-defensive properties. We synthesize poly(acrylic acid) (PAA) microgels (∼2–5 μm diameter) via membrane emulsification and electrostatically deposit them onto polycaprolactone (PCL) coupons or onto glass to form a discontinuous submonolayer. Subsequent microgel loading with colistin or with Sub5 antimicrobial peptide (AMP) causes microgel deswelling. Under physiological conditions Sub5 remains stably sequestered whereas colistin is quickly released. Coarse-grained molecular dynamics (CGMD) simulations confirm stronger Sub5/PAA complexation. CGMD calculations also indicate that Sub5 forms dimers and higher-order structures, a prediction confirmed experimentally by Small-Angle X-ray Scattering (SAXS). Supramolecular structure entropically enhances the complexation strength because of enhanced counterion release per complexation event, and this finding can help identify other antimicrobials well suited for such a nonelutive yet self-defensive strategy. CGMD simulations also show that Sub5 has a higher complexation strength with the Staphylococcus aureus membrane than it does with PAA, confirming that there is a thermodynamic driving force for antimicrobial transfer. Such self-defensive surfaces significantly reduce S. aureus colonization (over 90% reduction relative to unmodified controls) in an in vitro hematogenous contamination model and remain cyto-compatible as evidenced by mesenchymal stem cell spreading and proliferation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Climate Drives Modeled Forest Carbon Cycling Resistance and Resilience in the Upper Great Lakes Region, USA

Forests dominate the global terrestrial carbon budget, but their ability to continue doing so in the face of a changing climate is uncertain. A key uncertainty is how forests will respond (resistance) and recover from (resilience) to rising levels of disturbance of varying intensities. This knowledge gap can optimally be addressed by integrating manipulative field experiments with ecophysiological modeling. We used the ED-2.2 model to project carbon fluxes for a northern temperate deciduous forest subjected to a real-world disturbance severity manipulation experiment. ED-2.2 was run for 150 years, starting from near bare ground in 1900, and subjected to three disturbance treatments under an ensemble of climate conditions. Both disturbance severity and climate strongly affected carbon fluxes, and interacted with one another. We then calculated metrics of functional ecosystem stability. Modeled gross primary production exhibited a mean resistance of -0.13, -0.23, and -0.42 (unitless) in response to increasing disturbance severity of 45%, 65%, and 85% mortality, respectively; and resilience of 0.002, 0.004, 0.008 (yr-1) respectively. This pattern held true for other carbon fluxes, meaning that in general simulated GPP declined more initially but recovered more quickly from higher mortality. Notably, however, heterotrophic respiration responded more slowly to disturbance, and its highly variable response was affected by different drivers. This work provides insight into how future conditions might affect the functional stability of mature forests in this region under ongoing climate change and changing disturbance regimes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Finite temperature auxiliary field quantum Monte Carlo in the canonical ensemble

Finite temperature auxiliary field-based quantum Monte Carlo methods, including determinant quantum Monte Carlo and Auxiliary Field Quantum Monte Carlo (AFQMC), have historically assumed pivotal roles in the investigation of the finite temperature phase diagrams of a wide variety of multidimensional lattice models and materials. Despite their utility, however, these techniques are typically formulated in the grand canonical ensemble, which makes them difficult to apply to condensates such as superfluids and difficult to benchmark against alternative methods that are formulated in the canonical ensemble. Working in the grand canonical ensemble is furthermore accompanied by the increased overhead associated with having to determine the chemical potentials that produce desired fillings. Given this backdrop, in this work, we present a new recursive approach for performing AFQMC simulations in the canonical ensemble that does not require knowledge of chemical potentials. To derive this approach, we exploit the convenient fact that AFQMC solves the many-body problem by decoupling many-body propagators into integrals over one-body problems to which non-interacting theories can be applied. We benchmark the accuracy of our technique on illustrative Bose and Fermi–Hubbard models and demonstrate that it can converge more quickly to the ground state than grand canonical AFQMC simulations. We believe that our novel use of HS-transformed operators to implement algorithms originally derived for non-interacting systems will motivate the development of a variety of other methods and anticipate that our technique will enable direct performance comparisons against other many-body approaches formulated in the canonical ensemble.

Shen, Tong (ORCID:0000000206292468)↗

DOE OSTI2022

liionpack: A Python package for simulating packs of batteries with PyBaMM

Electrification of transport and other energy intensive activities is of growing importance as it provides an underpinning method to reduce carbon emissions. With an increase in reliance on renewable sources of energy and a reduction in the use of more predictable fossil fuels in both stationary and mobile applications, energy storage will play a pivotal role and batteries are currently the most widely adopted and versatile form. Therefore, understanding how batteries work, how they degrade, and how to optimize and manage their operation at large scales is critical to achieving emission reduction targets. The electric vehicle (EV) industry requires a considerable number of batteries even for a single vehicle, sometimes numbering in the thousands if smaller cells are used, and the dynamics and degradation of these systems, as well as large stationary power systems, is not that well understood. As increases in the efficiency of a single battery become diminishing for standard commercially available chemistries, gains made at the system level become more important and can potentially be realised more quickly compared with developing new chemistries. Mathematical models and simulations provide a way to address these challenging questions and can aid the engineer and designers of batteries and battery management systems to provide longer lasting and more efficient energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2020

An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

This paper presents an enhanced vertical ground heat exchanger (GHE) model for whole-building energy simulation (WBES). WBES programs generally have computational constraints that affect the development and implementation of component simulation sub-models. WBES programs require models that execute quickly and efficiently due to how the programs are utilized by design engineers. WBES programs also require models to be formulated so their performance can be determined from boundary conditions set by upstream components and environmental conditions. The GHE model developed during this work utilizes an existing response factor model and extends its capabilities to accurately and robustly simulate at timesteps that are shorter than the GHE transit time. This was accomplished by developing a simplified dynamic borehole model and then exercising that model to generate exiting fluid temperature response factors. This approach blends numerical and analytical modeling methods. The existing response factor models are then extended to incorporate the exiting fluid temperature response factor to provide a better estimate of the GHE exiting fluid temperature at short simulation timesteps.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine-Height Winds in the MYNN-EDMF Parameterization

The Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization applied in the Weather Research and Forecasting (WRF) model has been augmented to include the Eddy-Diffusion Mass-Flux (EDMF) approach to better represent transport by boundary-layer eddies. This change includes the addition of new parameters associated with convective updrafts and boundary-layer clouds. These additions lead to new parametric sensitivities in the hub-height wind speed compared to simulations using the standard MYNN parameterization that were explored previously. This work builds on efforts focused on WRF’s MYNN parameterization by examining the sensitivity of hub-height wind speed to parameters in the MYNN-EDMF parameterization as a function of simulation lead time. Summer and winter periods were selected from the second Wind Forecast Improvement Project (WFIP2). Five sets of simulations were completed for each season, with lead times ranging from 2 to 6 days. The results show that the sensitivity to the new parameters associated with the EDMF scheme is generally small compared to other parameters, with the largest impact in convective boundary layers. The spread in the perturbed parameter ensembles was found to grow quickly over the first 8 to 19 hours in the summer simulations and 17 to 24 hours in the winter simulations with little change after that, regardless of the simulation length. A strong diurnal cycle in the parameter sensitivity was also found associated with changes in the atmospheric stability, as well as an increase in the sensitivity to the entrainment parameter used in the EDMF parameterization that is associated with increasing fractional areas covered by plumes.

54 ENVIRONMENTAL SCIENCES↗