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DOE OSTI2024

Design of the Electromagnetic Particle Injector (EPI) for Tokamak Deployment

Both predicting and controlling disruptions are critical and urgent issues for ITER as some disruptions with a short warning time may be unavoidable. For these cases, a rapid response disruption mitigation system (DMS) referred to as the electromagnetic particle injector (EPI) is being developed. The primary advantages of the EPI are its fast response time and high velocity, which have been demonstrated in offline experiments (Raman et al., 2021). The EPI is capable of accelerating a metallic sabot electromagnetically using a rail gun to the required velocities ( > 2 km/s) within 2 ms. Two high-field racetrack magnets able to generate fields over 2 T are positioned above and below the rails to permit high velocity at low rail currents, a requirement to minimize electrode erosion. At the end of the acceleration phase, a sabot capture mechanism retains the spent sabot inside the vacuum chamber that houses the EPI. At this point, it releases well-defined microspheres, or a shell pellet, of a radiative payload into the disrupting plasma. A remotely operated sabot loading system positioned behind the injector contains several pre-equipped sabots that can be loaded by an operator from the tokamak control room. The injector is interfaced to the tokamak through a guide tube attached to the front of the EPI vacuum chamber. Finally, the advantages of the EPI system over other DMSs under consideration are described in conjunction with the details of an EPI system designed for near-term test on an existing large tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗

DOE OSTI2021

Guaranteeing a Physically Realizable Battery Dispatch Without Charge-Discharge Complementarity Constraints

The non-convex complementarity constraints present a fundamental computational challenge in energy constrained optimization problems. In this work, we present a new, linear, and robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable. In addition, we bound the worst-case model mismatch and discuss conservativeness. In conclusion, simulation results further illustrate the effectiveness of this approach.

25 ENERGY STORAGE↗

DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Biomanufacturing and Scale-Up: Pathways to Biochemicals, Biofuels, and Biomaterials

Advancing the bioeconomy requires the development of large-scale microbial bioprocesses capable of converting waste carbon streams into biofuels, biochemicals, and biomaterials at industrially relevant scales. While biomanufacturing has been successfully demonstrated at the laboratory scale for a wide range of chemicals, only a few have reached industrial-scale production. This is partly due to the inherent complexity of microbial systems, which rely on living cells with intricate metabolic pathways that are highly sensitive to environmental changes, making large-scale production difficult to optimize and predict. As a result, scaling-up bioprocesses remains a high-stakes challenge that requires deeper exploration. This involves integrating feedstock and microbial selection, upstream and downstream processes, and computational modelling, among other research efforts. Bulk and specialty chemicals derived from biological processes also face competition from fossil-based production routes, which have been refined through decades of technological advancements. While biologically derived molecules may offer more environmentally friendly production pathways than traditional chemical manufacturing, their widespread adoption depends on achieving cost parity-or superiority-relative to fossil-based methods. This emphasizes the importance of holistic research, including techno-economic analyses and life cycle assessments, to ensure both economic viability and environmental sustainability. This editorial and special issue explores state-of-the-art strategies for converting waste carbon sources into valuable products. It discusses how enzymes, single microbes (e.g., extremophiles), and microbiomes (e.g., through division of labor) can be integrated with upstream and downstream process innovations-such as consolidated bioprocessing and in situ product recovery-to improve the efficiency and scalability of biomanufacturing. The editorial further highlights the role of computational modelling in understanding, predicting, and controlling bioprocess performance across scales, and concludes by emphasizing the importance of techno-economic modelling to identify technologies that can move to market.

09 BIOMASS FUELS↗

DOE OSTI2025

Quantifications of growth rate and particle size of magnesium silicate hydrate cement using small‐angle X‐ray scattering

The focus of recent research on cement binders has shifted to alternatives such as magnesium silicate hydrate (M-S-H) to mitigate the environmental impact of conventional concrete production. However, a detailed understanding of the morphology and growth rate of M-S-H, which informs its applicability as a cementing agent, is lacking. These properties are important as they influence cement hydration and consequently the development of macroscopic properties. Here, in this study, the particle size distribution and growth rate of M-S-H synthesized by mixing aqueous stock solutions were analyzed using synchrotron-based small-angle X-ray scattering. The reaction solutions contained dissolved magnesium and silicon with concentrations ranging from 2 to 500 mM, corresponding to saturation indices with respect to M-S-H endmembers, M 0.75 -S-H and M 1.50 -S-H, between 5.8 and 10.3. The precipitation rates of M-S-H align with affinity-based kinetic models and can be described by the following equations: For M 0.75 -S-H: 𝑟 M 0.75 −S−H =10 −9.54±0.70 ⁢(Ω$^{0.59±0.08}_{M_{0.75}⁢\textrm{SH}}$−1), and for M 1.50 -S-H: 𝑟 M 1.50 −S−H =10 −7.56±0.37 ⁢(Ω$^{0.34±0.04}_{M_{1.50}⁢\textrm{SH}}$−1), where 𝑟 is the rate in mol/L/s, and Ω represents the saturation ratio with respect to M-S-H. Moreover, we observed that changing the [Mg]/[Si] ratio affects particle size, a trend that cannot be fully explained by changes in the saturation index alone. These insights are critical for improving the predictability and control of cementation in concrete that utilizes M-S-H-based binders.

M-S-H↗

DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗

DOE OSTI2025

Cooperative On-Ramp Merging with Time-Varying Vehicle-to-Vehicle Communication Delay Compensation via a Model-Free Approach

Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays-an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A co-simulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low CO2 emissions and fuel consumption, under communication delay across different communication frequencies. In conclusion, its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.

Accounting↗

DOE OSTI2019

Size-dependent thermodynamic structural selection in colloidal crystallization

Nucleation and growth of crystalline phases play an important role in a variety of physical phenomena, ranging from freezing of liquids to assembly of colloidal particles. Understanding these processes in the context of colloidal crystallization is of great importance for predicting and controlling the structures produced. In many systems, crystallites that nucleate have structures differing from those expected from bulk equilibrium thermodynamic considerations, and this is often attributed to kinetic effects. In this work, we consider the self-assembly of a binary mixture of colloids in two dimensions, which exhibits a structural transformation from a non–close-packed to a close-packed lattice during crystal growth. We show that this transformation is thermodynamically driven, resulting from size dependence of the relative free energy between the two structures. We demonstrate that structural selection can be entirely thermodynamic, in contrast to previously considered effects involving growth kinetics or interaction with the surrounding fluid phase.

42 ENGINEERING↗

DOE OSTI2024

Selective formation of metastable polymorphs in solid-state synthesis

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO 4 , we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Observation of Dicke cooperativity in magnetic interactions

The interaction of N two-level atoms with a single-mode light field is an extensively studied many-body problem in quantum optics, first analyzed by Dicke in the context of superradiance. A characteristic of such systems is the cooperative enhancement of the coupling strength by a factor of N. In this study, we extended this cooperatively enhanced coupling to a solid-state system, demonstrating that it also occurs in a magnetic solid in the form of matter-matter interaction. Specifically, the exchange interaction of N paramagnetic erbium(III) (Er 3+ ) spins with an iron(III) (Fe 3+ ) magnon field in erbium orthoferrite (ErFeO 3 ) exhibits a vacuum Rabi splitting whose magnitude is proportional to N. Our results provide a route for understanding, controlling, and predicting novel phases of condensed matter using concepts and tools available in quantum optics.

Li, Xinwei (ORCID:0000000305552624)↗

DOE OSTI2023

Transonic dislocation propagation in diamond

Here, the motion of line defects (dislocations) has been studied for more than 60 years, but the maximum speed at which they can move is unresolved. Recent models and atomistic simulations predict the existence of a limiting velocity of dislocation motion between the transonic and subsonic ranges at which the self-energy of dislocation diverges, though they do not deny the possibility of the transonic dislocations. We used femtosecond x-ray radiography to track ultrafast dislocation motion in shock-compressed single-crystal diamond. By visualizing stacking faults extending faster than the slowest sound wave speed of diamond, we show the evidence of partial dislocations at their leading edge moving transonically. Understanding the upper limit of dislocation mobility in crystals is essential to accurately model, predict, and control the mechanical properties of materials under extreme conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Searches for scalar leptoquarks and differential cross-section measurements in dilepton–dijet events in proton–proton collisions at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV with the ATLAS experiment

Searches for scalar leptoquarks pair-produced in proton–proton collisions at $\sqrt{s}$ =13 TeV at the Large Hadron Collider are performed by the ATLAS experiment. A data set corresponding to an integrated luminosity of 36.1 fb -1 is used. Final states containing two electrons or two muons and two or more jets are studied, as are states with one electron or muon, missing transverse momentum and two or more jets. No statistically significant excess above the Standard Model expectation is observed. The observed and expected lower limits on the leptoquark mass at 95% confidence level extend up to 1.29 TeV and 1.23 TeV for first- and second-generation leptoquarks, respectively, as postulated in the minimal Buchmüller–Rückl–Wyler model, assuming a branching ratio into a charged lepton and a quark of 50%. In addition, measurements of particle-level fiducial and differential cross sections are presented for the Z→ ee, Z→ μμ and $ t\bar{t} $ processes in several regions related to the search control regions. Predictions from a range of generators are compared with the measurements, and good agreement is seen for many of the observables. However, the predictions for the Z→ ℓℓ measurements in observables sensitive to jet energies disagree with the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

California Wave Power Technologies Open Water Demonstration - Budget Period 1 reports

The objective of the project is to advance the Technology Readiness Level (TRL) of the Wave Energy Converter (WEC) developed by CalWave Wave Power Technologies Inc (CalWave) through advanced numerical simulations, dynamic hardware tests, and ultimately a scaled open water demonstration deployment while continuing to exceed DOE's target ACE threshold of 3m/M$. The outcomes of Budget Period 1 will be a detailed design of the scaled demonstration unit and bench testing of the critical hardware components.

16 TIDAL AND WAVE POWER↗

DOE OSTI2019

Early Career: In-situ Monitoring of Dynamic Phenomena during Solidification

Solidification is critical to processes like casting and additive manufacturing and the manufacture of metallic alloy components we use in our everyday lives. State-of-the-art characterization techniques, now available at U.S. DOE User Facilities and in the laboratory, are not only enabling fundamental studies of metallic alloy solidification dynamics, but also in-operando, in-situ deformation, and manufacturing studies. Here we use x-ray, proton, and electron imaging to study solidification dynamics from the micro-scale to the macro-scale, at times ranging from microseconds to minutes. Our experimental results are used to inform, develop, and validate computational models at the same length and time-scales. Integrating in-situ characterization and modeling will yield the prediction and control of metallic alloy solidification dynamics and the creation of microstructures and properties by design with advanced manufacturing. Major research accomplishments during this period include: 1. Exploring dynamical microstructure selection by means of real-time synchrotron x-ray imaging of controlled directional solidification of aluminum alloys and quantitative phase field modeling. 2. In-situ and ex-situ electron microscopy of rapid solidification after laser melting of metallic alloys. 3. Exploring the impact of microstructural heterogeneities that develop during solidification on solid-state phase transitions, with an emphasis on phase growth, (meta)stability, and transition pathways in aluminum alloys. 4. Demonstrations of four-dimensional (three-dimensions plus time) x-ray microscopy during controlled directional solidification and the first quasi-static/dynamic transmission high energy electron microscopy of metallic alloy solidification dynamics. 5. Experiments and modeling of solidification structure evolution and forward modeling and testing of micromechanical behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2019

HEDP Workshop Summary: High-Intensity Laser Plasma (HILP) group.

Advances in HEDP research and limitations in currently established theories and models challenge us to better understand how high-intensity (>10 14 W/cm 2 ) laser light couples to plasmas. Considering both energy transfer processes and the resulting particle-beam and radiation generation, we find one overarching question: How do we understand, predict, and control laser-plasma interactions and laser-driven particle beams?

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Opportunities for Research and Development of Hybrid Power Plants

Hybrid power plants show promise to provide significant value to the electric grid system especially as shares of renewable energy in systems increase from 10% to 20% or more. Many manufacturers and developers are proactively developing hybrid power plant strategies to ensure plant profitability in markets where it’s important to provide energy that is more predictable and controllable, with higher capacity values, as well as various ancillary services to the grid system. However, there still are many questions around if and when hybrid power plants make sense versus leaving it to the larger grid system to ensure low cost and reliable supply by engaging directly with all individual assets. This report summarizes literature on the state-of-the-art in research concerning hybrid power plants from multiple perspectives including: 1) resource and market opportunities, 2) technology selection and sizing, 3) physical design, and 4) operation and control. The emphasis was placed on wind and solar hybrid power plants (with or without storage) but additional technologies were briefly considered and merit further investigation.

17 WIND ENERGY↗

DOE OSTI2026

Inkjet Printing to Scale Perovskite and Silicon Tandem Solar Cell: Cooperative Research and Development Final Report, CRADA Number CRD-17-00690

The proposed project seeks to develop a method for inkjet printing of the active layer in perovskite solar cells for application in stand-alone products or added to conventional silicon solar cells in a mechanically-stacked tandem configuration. This technology presents significant advanced manufacturing challenges and opportunities in getting to scale, including development of novel uses for printing technologies and development of predictive quality control techniques.

14 SOLAR ENERGY↗