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DOE OSTI2025

Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

09 BIOMASS FUELS↗

DOE OSTI2024

Regulating catalyst and ionomer interactions to promote oxygen transport in fuel cells

The cost of proton exchange membrane fuel cells (PEMFCs) has been a major impediment to their widespread commercialization. Reducing platinum (Pt) usage in PEMFC represents an essential step in lowering the cost. However, decreasing Pt loading in the cathode catalyst layer (CCL) often leads to a significant increase in the local oxygen transport resistance (R Local ), which not only slows the oxygen reduction reaction but also causes a higher polarization overpotential. The poisoning of Pt by perfluorosulfonic acid ionomers also becomes more predominant at low Pt loadings. Therefore, regulating catalyst and ionomer interactions by optimizing their interface in the CCL is crucial to improving PEMFC performance. Here, in this review, the strategies of improving oxygen transport by controlling Pt-ionomer interactions and interfaces through catalyst design, ionomer structure, and incorporation of additives are summarized. Finally, perspectives on future optimization of catalyst and ionomer interactions are proposed.

25 ENERGY STORAGE↗

DOE OSTI2026

An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models

Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.

CPM↗

DOE OSTI2026

Biocatalyst discovery and design for plastics deconstruction: A multi‐scale perspective

Plastic waste accumulation poses significant environmental challenges due to a lack of economical solutions for the molecular deconstruction of diverse synthetic polymers. Biological‐based degradation offers promise but is hindered by the crystallinity, hydrophobicity, and additive complexity of plastics, which restrict biocatalyst access and activity. To address these problems, we propose a multi‐scale framework that combines detailed materials characterization, optimization of plastic‐biomolecular interfacial interactions, and enhancement of biocatalytic kinetics to develop effective plastic‐deconstructing enzymes. This approach leverages principles from reaction kinetics, transport and interfacial phenomena, and enzyme engineering to systematically address barriers across diverse plastic types. Our framework aims to accelerate the discovery and optimization of biocatalysts capable of scalable, selective, and efficient deconstruction of plastic waste. These advances hold potential to enable sustainable biological recycling and upcycling pathways, contributing to global efforts in mitigating plastic pollution and promoting circular material economies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Electrochemical behavior of SnCl 2 and influence of Cu and Ni ions in molten LiCl−KCl−CaCl 2 eutectic

Reliable transport and thermodynamic data for multivalent ions in complex molten salts are scarce, limiting model fidelity for electrorefining and impurity control. Here, we report a comprehensive electrochemical characterization of SnCl₂ in LiCl–KCl–CaCl₂ (50.5–44.2–5.3 mol%) at 685 K, including the effects of Ni 2+ and Cu + impurities. Using cyclic voltammetry (CV), chronoamperometry (CA), and chronopotentiometry (CP), we quantified Sn 2+ and Ni 2+ diffusion with exceptional agreement across methods: Sn 2+ averaged (1.03 ± 0.10) × 10 −5 cm 2 s −1 , and Ni 2+ averaged (0.75 ± 0.19) × 10 −5 cm 2 s −1 . The tight confidence-interval overlap across CV, CA, and CP strengthens confidence in these values and is uncommon in molten chloride studies. Open-circuit-potential measurements provided standard apparent reduction potentials that closely match LiCl–KCl literature, indicating minimal shift with CaCl₂ present. The Sn 2+ /Sn couple behaves as a reversible two-electron soluble–insoluble process at 685 K; the Sn 4+ /Sn 2+ couple transitions to soluble–soluble behavior near 788 K, which may correlate with the decomposition of surface bound chlorostannates, though direct characterization remains to be established. In mixed systems, Cu+/Cu overlaps Sn 2+ /Sn, limiting Cusingle bondSn electroseparation, whereas the larger potential gap between Ni 2+ /Ni and Sn 2+ /Sn supports selective Ni removal. These internally consistent transport and thermodynamic data establish a validated basis for process modeling and optimization of Sn electrorefining and impurity management in LiCl–KCl–CaCl₂.

Berzins-Delahay↗

DOE OSTI2026

Impact of impurities on peeling–ballooning modes and turbulence in tokamak plasmas

This study investigates the impact of various impurity species on peeling–ballooning (PB) modes and microturbulence in tokamak plasmas through the extension of traditional two-fluid and gyro-landau-fluid (GLF) models. By incorporating finite Larmor radius (FLR) effects, the analysis provides a comprehensive understanding of impurity-driven impact and its interaction with plasma turbulence. Depending on charge state and local plasma conditions, heavy impurities may exhibit gyro-radii larger than those of main ions, which are captured in the extended GLF model presented. Following the presentation of modified two-fluid equations incorporating impurity effects, we systematically analyze the distinctions between impurity and main ion dynamics and their resultant feedback mechanisms on plasma behavior. Derivation of the linear dispersion relation enables quantification of impurity-mediated modifications to: plasma vorticity, diamagnetic drift and gyroviscous effects, electron Hall physics, and FLR effects. BOUT++ – based linear simulations corroborate this formalism, demonstrating systematic stabilization of PB modes upon impurity seeding. And then operational implications for practical impurity control strategies in tokamak devices are proposed. The results underscore the necessity of impurity management to maintain stability and optimize plasma confinement, with specific focus on how FLR effects contribute to transport dynamics. This work paves the way for enhanced modeling and simulation efforts, supporting the development of strategies to control impurity-induced turbulence and improve overall reactor performance.

BOUT++ simulation↗

DOE OSTI2025

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Development and Evaluation of Embrittlement Resistant Alloys for Advanced LWR Cladding

The primary challenge preventing the deployment of reactor designs that leverage wrought FeCrAl as an advanced light-water reactor (LWR) cladding is irradiation hardening and embrittlement. Wrought FeCrAl alloys experience a loss of post-irradiation ductility and fracture toughness under low-temperature neutron irradiation (< 350°C) resulting from the combined effects of dislocation loop formation and the irradiation-enhanced precipitation of Cr-rich alpha-prime precipitates throughout the microstructure. Over the past decade, significant improvements in our understanding regarding the effect of Cr and Al content have been enabled through neutron irradiations, but even optimized wrought alloys such as C26M remain vulnerable to brittle failure during storage, transportation, and handling following irradiation. This report summarizes multiple irradiation campaigns initiated over the past 7 years and provides recent insights into the effect of (1) minor alloying composition, (2) alloy processing strategy, (3) crystal structure, and (4) the use of advanced oxide dispersion strengthened (ODS) alloys. The first irradiation campaign irradiated body-centered cubic FeCrAl alloy C26M with and without elements such as Mo and Y. It also included face-centered cubic alumina-forming austenitic (AFA) alloys that have a steeper neutronic penalty but that have far superior high-temperature strength than wrought FeCrAl. Finally, this campaign compared conventional wrought C26M with the same material produced using modern powder metallurgical hotisostatic pressing (PM-HIP). The second irradiation campaign, initiated in 2018, irradiated wrought and FeCrAl-ODS materials up to 50 dpa to assess their irradiation resistance to high-doses. The results of this work indicate that wrought C26M suffers significant degradation following neutron irradiation at LWR-relevant temperatures regardless of Mo and Y content. The PM-HIP variant appears to be more resilient to irradiation-induced ductility loss in comparison with wrought variants but still loses almost all ductility by 8 dpa. Notably, the AFAs retained at least 5% total elongation after irradiation at the same dose and temperature condition, although additional deterioration may be expected at higher fluence levels. FeCrAl-ODS materials show the greatest promise as a transformative longer-term accident-tolerant fuel (LT-ATF) cladding material. The extruded 106ZY10C alloy retained 10% total elongation after 16 dpa irradiation and retained 8% total elongation after 50 dpa. Additional challenges with FeCrAl-ODS alloys also remain, including optimization of end cap joining methodologies, enhancement of fracture toughness, scaling production to prove economic viability, and optimizing post-pilger heat treatments to maximize ductility and irradiation resistance. This series of irradiations demonstrates the efficacy of the LWR testbed available within the United States for the rapid irradiation and down-selection of LT-ATF candidates. As the country rapidly accelerates its timeline for the deployment of advanced reactor concepts, the effective utilization of the High Flux Isotope Reactor for separate-effects style irradiations should continue to be prioritized to answer the final questions pertaining to LT-ATF candidates necessary for the deployment of advanced boiling water reactors and small modular reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Hydrogen defects in LaBi 2 O 4 X (X = Cl, Br, and I) Sillén oxyhalide phases and their impacts on ionic transport

Sillén oxyhalides have recently emerged as promising materials for both photocatalytic and ionic transport applications, yet the role of likely-ubiquitous hydrogen-related defects in these layered compounds remains largely unexplored. Here, we employ first-principles defect calculations to investigate incorporation energetics for hydrogen- and oxygen-related defects, as well as their migration barriers in LaBi 2 O 4 X (X = Cl, Br, I) phases. We find that hydrogen interstitials, particularly protonic species (H i + ), are readily accommodated within the open Bi–O layers. Protons compete with oxygen vacancy donors (V O 2+ ) and charge-compensate with oxygen interstitial acceptors (O i 2− ). By linking hydrogen defect formation to water- and oxygen-related redox equilibria, we reveal that V O 2+ facilitates H i + incorporation, while O i 2− promotes interstitial hydroxide formation, establishing a direct connection between proton and oxide-ion transport. Calculated migration barriers indicate that ionic diffusion is confined to Bi–O layers with low barriers of 0.20–0.25 eV for H i + and 0.14–0.25 eV for V O 2+ , suggesting that the materials contain intrinsic pathways for mixed ionic conduction. These results provide a microscopic picture of hydrogen behavior in Sillén oxyhalides and point to design strategies for integrating protonic and oxide-ion transport in layered oxyhalide electrolytes. Band-edge alignment analysis shows that LaBi 2 O 4 I provides the optimal combination of hydrogen solubility, oxygen defect stability, and mixed ionic conductivity, highlighting its potential for low-temperature electrochemical and energy-conversion applications. Overall, this work establishes the defect-driven origin of hydrogen transport in Sillén oxyhalides and expands their applicability beyond photocatalysis to mixed ionic conduction and hydrogen electrochemistry.

Energy - Conversion↗

DOE OSTI2025

First-Principles Studies on Sc 2 RuZ (Z = Si, Ge, Sn) Inverse Heusler Alloys: Structural, Electronic, and Transport Properties

The continuous demand for efficient, nontoxic, and thermally stable materials for room-temperature energy conversion motivates the exploration of novel thermoelectric systems beyond the traditional magnetic Heusler alloys. While full and half-Heusler compounds, especially Co-, Ni-, and Mn-based systems, have demonstrated promising thermoelectric properties, their typically high operating temperatures and magnetic complexities limit their applicability in ambient thermal management. In this context, we investigate whether Sc-based inverse Heusler alloys can offer a viable nonmagnetic alternative with competitive thermoelectric performance. In this work, we perform a systematic first-principles study of the inverse Heusler compounds Sc 2 RuZ (Z = Si, Ge, Sn), focusing on their structural, electronic, mechanical, and thermodynamic-thermoelectric properties. Density Functional Theory (DFT) was employed to compute optimized lattice structures and band dispersion, while dynamical stability was assessed via phonon calculations. Thermoelectric transport coefficients, including Seebeck coefficient, electrical conductivity, and thermal conductivity, were estimated using the semiclassical Boltzmann transport theory within the constant relaxation time approximation. Our results show that all Sc 2 RuZ compounds are thermodynamically stable semiconductors with indirect band gaps of 0.12–0.16 eV and exhibit high elastic moduli, especially Sc 2 RuSn, which demonstrates superior stiffness and incompressibility. Importantly, all compounds display promising room-temperature thermoelectric characteristics, including high Seebeck coefficients and power factors. These findings reveal that Sc 2 RuZ alloys represent a rare class of stable, nonmagnetic inverse Heusler semiconductors with intrinsic thermoelectric potential at room temperature, unlike many existing Heusler systems optimized for spintronics or high-temperature operation. This work expands the known design space for Heusler-based thermoelectrics and offers a theoretical basis for experimental realization of efficient, low-temperature, nonmagnetic thermoelectric materials.

alloys↗

DOE OSTI2026

Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells

Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems remains a challenge. In this work, we train an MLIP to describe hydrated Nafion ionomers and platinum catalysts, which are important components of fuel cells, by constructing a diverse training set to describe the bulk polymer and interfacial catalyst–polymer interactions well. We use our trained MLIP to study the properties of the platinum–Nafion system, including polymer structure, proton mobility in a bulk Nafion polymer and near a platinum-Nafion interface, and reactions near and far from the interface, finding excellent results for structure and reactions contained within our training set. Transport seems to be well described, with both vehicular transport and Grotthuss hopping captured, although converged calculations of diffusivities were not computed because they require calculations of tens of nanoseconds that are challenging with current state-of-the-art MLIPs. The combined insights that this model provides can be leveraged to optimize fuel cell performance, and the approach can be applied to other chemical processes and devices where structure, transport, and reactivity all contribute to the overall observed performance.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Harnessing the Power of AI: Status and Expansion of Current Domestic Transport Security Through Flexible Embedded Hardware

As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗

DOE OSTI2026

Towards a Unified Low-Cost Flow Plate, Flow-Field, PTL Solution for Proton Exchange Membrane Electrolyzers

Proton exchange membrane (PEM) water electrolysis is a highly efficient method for hydrogen production. Research cells typically consist of one proton exchange membrane, two catalyst layers, two porous transport layers, two flow-field plates, and two endplates. In commercial systems, the machined flow-field plates that are employed in research cells are typically replaced by stamped parts or open mesh material solutions to reduce manufacturing cost at scale. Nonetheless, the cell contains about 8 total interfaces: bipolar plate / flow plate material / porous transport medium / electrode / membrane / electrode / porous transport medium / flow plate material / bipolar plate. All these materials and interfaces need to be optimized for maximum performance and efficiency. Reducing the amount of interfaces by combining individual cell components directly benefits the fabrication cost (by reducing the parts count and the needs for surface coatings) and the electrochemical performance (by reducing ohmic losses). We have designed a novel PEM electrolysis cell with a piece of channeled titanium felt functioning as both the anode flow-field and the PTL, referred to as the channeled diffusion layer (CDL). The pores of the felt facilitate both in-plane and through-plane diffusion, ensuring maximum catalyst utilization while also minimizing mass transport loss. The titanium felt can be mass manufactured with existing stamping and forming methods and is therefore a promising candidate to reduce the capital cost of PEM electrolyzers whilst improving hydrogen production efficiency. Experiments conducted with 3mg IrOx/cm2 loading MEAs have shown a approximately 40% boost in peak current by implementing the CDL design. Low catalyst-loading MEAs are being tested in ongoing experiments and their results will be discussed and compared.

08 HYDROGEN↗

DOE OSTI2026

Radiation-front position control with scenario adjustments during the first tungsten divertor operation on KSTAR

This study reports the first experimental demonstration of a radiation front-based detachment control system during the initial tungsten divertor campaign in KSTAR. The system employs real-time infrared video bolometry with tomographic reconstruction to track the inboard radiation-front position, which served as a feedback variable for impurity seeding. In a baseline scenario without pre-D 2 fueling, tungsten accumulation triggered core radiation surges and repetitive H–L back transitions. In contrast, with pre-D 2 fueling, detachment was stabilized by maintaining higher edge-localized mode frequency, suppressing core tungsten buildup, and reducing sputtering, thereby enabling sustained feedback control. The suitability of the inboard front position as a detachment control metric was confirmed by its correlation with the degree of detachment: with D 2 fueling the relation exhibited a broad hysteresis loop characteristic of momentum-loss-dominated detachment, whereas with N 2 fueling, it became narrow and nearly vertical, consistent with radiation-loss-dominated pinning. These results provide a proof-of-principle for radiation-front-based detachment control, demonstrating its viability as a control approach. At the same time, these results clarify that the detachment outcomes were further conditioned by impurity transport characteristics of tungsten divertor operation in KSTAR, providing guidance for future scenario optimization and controller development in tungsten devices.

KSTAR↗

DOE OSTI2025

Development and Evaluation of Embrittlement Resistant Alloys for Advanced LWR Cladding

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

J-TEXT achievements in turbulence and transport in support of future device/reactor

Following the reconstruction of the TEXT tokamak at Huazhong University of Science and Technology in China, renamed as J-TEXT, a plethora of experimental and theoretical investigations has been conducted to elucidate the intricacies of turbulent transport within the tokamak configuration. These endeavors encompass not only the J-TEXT device's experimental advancements but also delve into critical issues pertinent to the optimization of future fusion devices and reactors. Here, the research includes topics on the suppression of turbulence, flow drive and damping, density limit, non-local transport, intrinsic toroidal flow, turbulence and flow with magnetic islands, turbulent transport in the stochastic layer, and turbulence and zonal flow with energetic particles or helium ash. Several important achievements have been made in the last few years, which will be further elaborated upon in this comprehensive review.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗