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DOE OSTI2021

The advanced tokamak path to a compact net electric fusion pilot plant

Abstract Physics-based simulations project a compact net electric fusion pilot plant with a nuclear testing mission is possible at modest scale based on the advanced tokamak concept, and identify key parameters for its optimization. These utilize a new integrated 1.5D core-edge approach for whole device modeling to predict performance by self-consistently applying transport, pedestal and current drive models to converge fully non-inductive stationary solutions, predicting profiles and energy confinement for a given density. This physics-based approach leads to new insights and understanding of reactor optimization. In particular, the levering role of high plasma density is identified, which raises fusion performance and self-driven ‘bootstrap currents’, to reduce current drive demands and enable high pressure with net electricity at a compact scale. Solutions at 6–7 T, ∼4 m radius and 200 MW net electricity are identified with margins and trade-offs possible between parameters. Current drive comes from neutral beam and ultra-high harmonic (helicon) fast wave, though other advanced approaches are not ruled out. The resulting low recirculating power in a double null configuration leads to a divertor heat flux challenge that is comparable to ITER, though reactor solutions may require more dissipation. Strong H-mode access (x2 margin over L–H transition scalings) and ITER-like heat fluxes are maintained with ∼20%–60% core radiation, though effects on confinement need further analysis. Neutron wall loadings appear tolerable. The approach would benefit from high temperature superconductors, as higher fields would increase performance margins while potential for demountability may facilitate nuclear testing. However, solutions are possible with conventional superconductors. An advanced load sharing and reactive bucking approach in the device centerpost region provides improved mechanical stress handling. The prospect of an affordable test device which could close the loop on net-electric production and conduct essential nuclear materials and breeding research is compelling, motivating research to validate the techniques and models employed here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids

Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell–Stephan (MS) diffusion rates. In this study we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

A large-scale benchmarking of deterministic and stochastic derivative-free optimization algorithms

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

36 MATERIALS SCIENCE↗

DOE OSTI2023

Higher-order SPOP assembly reveals a basis for cancer mutant dysregulation

The speckle-type POZ protein (SPOP) functions in the Cullin3-RING ubiquitin ligase (CRL3) as a receptor for the recognition of substrates involved in cell growth, survival, and signaling. SPOP mutations have been attributed to the development of many types of cancers, including prostate and endometrial cancers. Prostate cancer mutations localize in the substrate-binding site of the substrate recognition (MATH) domain and reduce or prevent binding. However, most endometrial cancer mutations are dispersed in seemingly inconspicuous solvent-exposed regions of SPOP, offering no clear basis for their cancer-causing and peculiar gain-of-function properties. Herein, we present the first structure of SPOP in its oligomeric form, uncovering several new interfaces important for SPOP self-assembly and normal function. Given that many previously unaccounted-for cancer mutations are localized in these newly identified interfaces, we uncover molecular mechanisms underlying dysregulation of SPOP function, with effects ranging from gross structural changes to enhanced self-association, and heightened stability and activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Power-Capping Metric Evaluation for Improving Energy Efficiency in HPC Applications

With high-performance computing systems now running at exascale, optimizing power-scaling management and resource utilization has become more critical than ever. This paper explores runtime power-capping optimizations that leverage integrated CPU-GPU power management on architectures like the NVIDIA GH200 superchip. We evaluate energy-performance metrics that account for simultaneous CPU and GPU power-capping effects by using two complementary approaches: speedup-energy-delay and a Euclidean distance-based multi-objective optimization method. By targeting a mostly compute-bound exascale science application, the Locally Self-Consistent Multiple Scattering (LSMS), we explore challenging scenarios to identify potential opportunities for energy savings in exascale applications, and we recognize that even modest reductions in energy consumption can have significant overall impacts. Our results highlight how GPU task-specific dynamic power-cap adjustments combined with integrated CPU-GPU power steering can improve the energy utilization of certain GPU tasks, thereby laying the groundwork for future adaptive optimization strategies.

Patrou, Maria [ORNL] (ORCID:0000000339754638)↗

DOE OSTI2020

Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules

Battery packs for electric and stationary applications experience varied operating conditions, including abuse—e.g., fast charging, overcharging, thermal, vibration, shock, etc.—throughout their lifetimes. Innovative diagnostic tools and algorithms that go beyond single cells and deal with modules and packs are essential for early detection of off-normal issues. High-resolution tools with known detection limits are key to developing appropriate mitigation strategies. With the advent of rapid impedance spectroscopy that can generate a broadband impedance spectrum in ~10 sec, the case for impedance-based diagnostics that can be readily aligned with other methods, such as incremental capacity or dQ.dV -1 , has become promising. This study used the aforementioned diagnostic methods to identify realistic in-vehicle battery abnormalities (e.g., localized self-discharge and non-uniform aging), in series (up to 10S) and parallel (4P) strings, using 16 Ah graphite/NMC cells. The impedance-based diagnostic is found to be sensitive to the string size and state. Depending on the type of abnormality, detection frequency varied. The dQ.dV -1 method showed the potential to detect long-term aging-related heterogeneity in modules. In general, both the impedance and dQ.dV -1 methods were able to detect series strings’ abnormalities, but struggled to find those issues within parallel modules.

25 ENERGY STORAGE↗

DOE OSTI2022

Assessing Physical Relationships Between Atmospheric State, Fluxes, and Boundary Layer Stability at McMurdo Station, Antarctica

Observations at McMurdo Station, Antarctica from 24 November 2015 through 3 January 2017 were used to characterize the physical relationships between boundary layer stability and atmospheric state and fluxes. The basis of this analysis was self-organizing maps (SOMs), a neural network algorithm, used to identify the range of potential temperature profiles present in the twice-daily radiosonde data during the ARM (Atmospheric Radiation Measurement) West Antarctic Radiation Experiment (AWARE) campaign. The SOM identified profiles ranging from strongly stable to weakly stable regimes over the lowest 500 m of the atmosphere. It was found that in the winter (MJJA), moderate and strongly stable regimes occur most frequently (61%), while weakly stable regimes dominate in the summer (DJ, 83.4%). The mechanisms responsible for the dominance of different stability regimes in each season were analyzed to determine why these regimes occur with varying frequency throughout the year. This analysis found that wind speed variations and radiative cooling are responsible for the stability observed in the winter, radiative warming, as well as weaker wind speeds, are responsible for summer weak stability, and stability variations in the transition seasons (FMA, SON) are characterized by a change in sign of net radiation with increasing stability, as wind speed changes little across stability regimes. Low-level jets were observed to occur about 50% of the time below areas of enhanced stability aloft and were observed most frequently in the transition seasons. The boundary layer depth, as determined by the Bulk Richardson number, was found to decrease with increasing stability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A simple model of COVID-19 explains disease severity and the effect of treatments

Abstract Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

AFM-IR and s-SNOM-IR measurements of chemically addressable monolayers on Au nanoparticles

The performance of catalysts depends on their nanoscale properties, and local variations in structure and composition can have a dramatic impact on the catalytic reactivity. Therefore, probing the localized reactivity of catalytic surfaces using high spatial resolution vibrational spectroscopy, such as infrared (IR) nanospectroscopy and tip-enhanced Raman spectroscopy, is essential for mapping their reactivity pattern. Two fundamentally different scanning probe IR nanospectroscopy techniques, namely, scattering-type scanning near-field optical microscopy (sSNOM) and atomic force microscopy-infrared spectroscopy (AFM-IR), provide the capabilities for mapping the reactivity pattern of catalytic surfaces with a spatial resolution of ~20 nm. Herein, we compare these two techniques with regard to their applicability for probing the vibrational signature of reactive molecules on catalytic nanoparticles. For this purpose, we use chemically addressable self-assembled molecules on Au nanoparticles as model systems. We identified significant spectral differences depending on the measurement technique, which originate from the fundamentally different working principles of the applied methods. While AFM-IR spectra provided information from all the molecules that were positioned underneath the tip, the s-SNOM spectra were more orientation-sensitive. Due to its field-enhancement factor, the s-SNOM spectra showed higher vibrational signals for dipoles that were perpendicularly oriented to the surface. The s-SNOM sensitivity to the molecular orientation influenced the amplitude, position, and signal-to-noise ratio of the collected spectra. Ensemble-based IR measurements verified that differences in the localized IR spectra stem from the enhanced sensitivity of s-SNOM measurements to the adsorption geometry of the probed molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Spikey: self-lensing flares from eccentric SMBH binaries

Here, we examine the light curves of two quasars, motivated by recent suggestions that a supermassive black hole binary (SMBHB) can exhibit sharp lensing spikes. We model the variability of each light curve as due to a combination of two relativistic effects: the orbital relativistic Doppler boost and gravitational binary self-lensing. In order to model each system, we extend previous Doppler plus self-lensing models to include eccentricity. The first quasar is identified in optical data as a binary candidate with a 20-yr period (Ark 120), and shows a prominent spike. For this source, we rule out the lensing hypothesis and disfavour the Doppler-boost hypothesis due to discrepancies in the measured versus recovered values of the binary mass and optical spectral slope. The second source, which we nickname Spikey, is the rare case of an active galactic nucleus identified in Kepler’s high-quality, high-cadence photometric data. For this source, we find a model, consisting of a combination of Doppler modulation and a narrow symmetric lensing spike, consistent with an eccentric SMBHB with a mass of $M_{\text{tot}} = 3\times 10^{7} {\, \mathrm{M}_{\odot }}$, rest-frame orbital period T = 418 d, eccentricity e = 0.5, and seen at an inclination of 8° from edge-on. This interpretation can be tested by monitoring Spikey for periodic behaviour and recurring flares in the next few years. In preparation for such monitoring, we present the first X-ray observations of this object taken by the Neil Gehrels Swift Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗

DOE OSTI2023

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

RolyPoly (rp) v0.1.0

The Rolypoly pipeline is designed to process raw RNA-seq data and identify potential RNA viral sequences. It is split into several self contained steps: 1. input data filtering and QC, 2. Genome assembly and refinement, 3. Assembly filtering, 4. Mapping to known RNA viral genomes, 5. Searching for RNA viral marker genes. 6. Genome functional and structural annotation. 6. Report preparation and potential downstream analysis The last module, may include taxonomic assignment, host range estimation, and phenotypic prediction. There are many similar software, but they focus on human related viruses, and lack the downstream applications or differ in their sensitivity. The initial user base are non-computational microbial ecologists who wish to better understand the potential RNA viruses in their own generated samples.

Neri, Uri↗

DOE OSTI2023

Real-Time In Situ Observation of CsPbBr 3 Perovskite Nanoplatelets Transforming into Nanosheets

The manipulation of nano-objects through heating is an effective strategy for inducing structural modifications and therefore changing the optoelectronic properties of semiconducting materials. Despite its potential, the underlying mechanism of the structural transformations remains elusive, largely due to the challenges associated with their in situ observations. To address these issues, we synthesize temperature-sensitive CsPbBr 3 perovskite nanoplatelets and investigate their structural evolution at the nanoscale using in situ heating transmission electron microscopy. We observe the morphological changes that start from the self-assembly of the nanoplatelets into ribbons on a substrate. We identify several paths of merging nanoplates within ribbons that ultimately lead to the formation of nanosheets dispersed randomly on the substrate. These observations are supported by molecular dynamics simulations. We correlate the various paths for merging to the random orientation of the initial ribbons along with the ligand mobility (especially from the edges of the nanoplatelets). This leads to the preferential growth of individual nanosheets and the merging of neighboring ones. These processes enable the creation of structures with tunable emission, ranging from blue to green, all from a single material. Our real-time observations of the transformation of perovskite 2D nanocrystals reveal a route to achieve large-area nanosheets by controlling the initial orientation of the self-assembled objects with potential for large-scale applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Observation of two different cool flame regimes of diethyl ether in a counterflow burner

This short communication reports, for the first time, the existence of two different self-sustaining cool flame regimes of diethyl ether (DEE) in a diffusion counterflow burner: a weaker autoignition-assisted cool flame near the fuel burner and a normal diffusion cool flame near the stagnation plane. Here, the results show that the normal diffusion cool flame extinction limit increases monotonically with the fuel mole fraction, while the autoignition-assisted cool flame approaches a plateau and can exist at a fuel mole fraction below the normal diffusion cool flame. It is shown that both flame regimes are governed by the same low-temperature chain-branching reaction pathway of DEE. By using in situ laser diagnostics, entrainment of unburned fuel stream to oxygen stream at the outer edge of the fuel burner is identified as the governing physical mechanism causing a partially premixed self-sustained hollow cool flame structure. The results reveal that when a fuel with high low-temperature reactivity, two different cool flame regimes can be observed in a counterflow flame experiment. Future studies with high-reactivity fuels in a counterflow burner must ensure to distinguish between the two self-sustaining cool flame regimes. Moreover, the existence of these different cool flame regimes needs to be examined so that they would not trigger an uncontrolled combustion phasing in advanced engines fed with high low-temperature reactivity fuels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Start-to-end self-consistent simulation of the UEM beamline

Addressing the preliminary design review report of the ultrafast electron microscope (UEM), start-to-end self-consistent simulations were carried out to explore fundamental aspects affecting the resolution (space-charge effects, energy spread, shot-to-shot energy jitter, contrast formation, etc.). The electron beam energy spread in the single-shot mode and the effective energy spread in the accumulation mode, equivalent to the convolution of the single-shot energy spread and shot-to-shot energy jitter, have been identified as the ultimate limits of the instrument resolution. A full self-consistent simulation from the photocathode to the detector is being performed using the GPT code to find out what resolution can be achieved. Preliminary results of the start-to-end simulations are reported in this note and the plan of further simulations is presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Reconfigurable Grating Diffraction Structural Color in Self-Assembled Colloidal Crystals

Self-assembled colloidal crystals display structural colors due to light diffracted from their microscale, ordered structure. This color arises due to Bragg reflection (BR) or grating diffraction (GD); the latter mechanism is much less explored than the former. Here the design space for generating GD structural color is identified and its relative advantages are demonstrated. Electrophoretic deposition is used to self-assemble crystals with fine crystal grains from colloids of diameter 1.0 µm. The structural color in transmission is tunable across the full visible spectrum. The optimum optical response—represented by both color intensity and saturation—is observed at low layer number (≤5 layers). The spectral response is well predicted by Mie scattering of the crystals. Taken together, the experimental and theoretical results demonstrate that vivid grating colors with high color saturation can be produced from thin layers of micron-sized colloids. These colloidal crystals extend the potential of artificial structural color materials.

42 ENGINEERING↗