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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Scale‐Adaptive Urban Hydrologic Framework: Incorporating Network‐Level Storm Drainage Pipes Representation

Abstract Below‐ground urban stormwater networks (BUSNs) significantly influence urban flood dynamics, yet their representation at the watershed or larger scales remains challenging. We introduce a scalable urban hydrologic framework that centers on a novel network‐level BUSN representation, balancing the needs for physical basis, parameter parsimony, and computational efficiency. Our framework conceptualizes an urban watershed into four interacting zones: hillslopes (natural), storm‐sewersheds (urban), a sub‐network channel (tributaries), and a main channel. We develop an innovative Graph Theory‐based algorithm to derive network‐level BUSN parameters from publicly available datasets, enabling efficient, scalable parameterization. We demonstrate this framework's applicability at nine representative watersheds in the Houston metropolitan region, USA, with urban imperviousness ranging from 0% to 64% and drainage areas ranging from 24 to 302 . Our model achieves satisfying computational efficiency, completing hourly time step simulations for 18 years in less than 5 sec per watershed on a standard PC. Validation against observed daily streamflow confirms that the model can capture small‐to‐large flood peaks and seasonal and annual water balance over these watersheds. Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. Furthermore, analysis reveals nonlinear relationships between BUSNs' designed capacities and flood reduction effects. Our approach bridges the gap between detailed hydraulic and large‐scale hydrologic models, providing a valuable tool for urban flood prediction and management across broader spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Zheng et al. (2025), "AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry"

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties, but current models inadequately represent these complexities. This dataset contains the observational and modeling data from Zheng et al. (2025), which described a process-based modeling framework that couples soil solution chemistry with microbial carbon cycling reactions to study the impacts of soil salinization. This conceptual model is implemented numerically into the open-source geochemical program PHREEQC 3.0 (Parkhurst and Appelo, 2013). This dataset consists of: - Figure2_AquaMEND_salinity_buffer: Contains model simulation outputs to assess the impact of three different cation exchange and surface complexation processes on salinity buffering (Fig. 2 from Zheng et al. 2025). - Figure3_Salinity_function: Contains salinity function fitting for literature data (Fig. 3 from Zheng et al. 2025). - Figure4_AquaMEND_microbial_mechanisms: Contains model simulation outputs for testing various microbial process-based hypotheses related to soil salinization, including microbial mortality, carbon use efficiency (CUE), extracellular enzyme activity, and other microbial mechanisms (Fig. 4 from Zheng et al. 2025). - Figure5_AquaMEND_Redox: Contains on model simulation outputs to evaluate shifts among key redox processes, such as aerobic respiration, sulfate reduction, and methanogenesis (Fig.5 from Zheng et al. 2025). - Figure6_AquaMEND_sorption: Contains on model simulation outputs for investigating the effects of salinity on dissolved organic matter (DOM) sorption and desorption processes (Fig. 6 from Zheng et al. 2025). - Figure7_AquaMEND_process_couple: Contains on model simulation outputs for exploring coupled biotic-abiotic processes and their interactions (Fig. 7 from Zheng et al. 2025). - data: Includes datasets used to develop salinity response functions and evaluate salinity buffering capacity. Datasets for MEND model calibration. - database: Contains the `.dat` file required by PHREEQC for model execution. - README.md: A Markdown plain text file describing the computational tools and directories. Files are a mixture of plain text CSV (comma-separated value) and plain text *.dat files written by the model; no special software is required to read them.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL SALINIT↗
DOE OSTI2025

Advances in Modeling Capabilities for Critical Mineral Separation Technologies: A PrOMMiS Overview

This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

critical minerals and materials↗
DOE OSTI2026

Desorption of Phosphate from Iron-Bearing Soil Minerals by a Plant Secondary Metabolite

In soils, phosphorus readily adsorbs to the surfaces of ubiquitous iron (oxyhydr)oxide minerals, rendering it less accessible to plants and microorganisms. Plants have a number of strategies to access iron, among them the secretion of redox-active metabolites from their roots. Although these strategies likely increase the bioavailability of surface-adsorbed phosphorus through reductive dissolution, their effect on phosphorus cycling has not yet been investigated. We tested the ability of fraxetin, a coumarin-type redox-active metabolite produced by the model plant Arabidopsis thaliana and other dicotyledon plant species, to reductively solubilize phosphate from the surface of ferrihydrite. Our findings show that, at low and neutral pH, fraxetin increased aqueous phosphate concentrations under both oxic and anoxic conditions; at high pH, it was only effective in anoxic experiments. Additionally, a combination of liquid chromatography–mass spectrometry and spectroscopic methods demonstrated substantial fraxetin adsorption to the mineral surface but showed that iron reduction did not alter the mineral structure or change the nature of the chemical environment of the phosphorus atom over short time scales. These results provide evidence that the secretion of redox-active metabolites from roots is likely to be an effective phosphorus acquisition tactic in iron-rich soils.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Open‐Air Combustion Synthesis with Rapid Plasma Processing of Large‐Area Transparent Conducting Oxides

A vacuum-free, high-throughput synthesis of indium tin oxide (ITO) via Combustion Oxidation with Rapid Plasma Processing (CORP) utilizes a solution-based exothermic combustion reaction to generate the oxide with tunable control of either amorphous or crystalline phases. A subsequent open-air, forming gas plasma treatment is used to introduce oxygen vacancies and promote crystallization. Here, the evolution of the oxide structure is elucidated by extended X-ray absorption spectroscopy fine structure analysis. Using CORP, fabrication of 300 cm 2 of ITO possessing a champion sheet resistance of 38 Ω sq. −1 , visible transmission of 89%, conductivity stability for over 250 days, roughness < 2nm, and Haacke figure of merit (%T 550nm 10 /R s ) of 0.012 Ω −1 is achieved. Cost modeling of CORP demonstrates up to a 67% reduction in price for TCOs using fully continuous, in-line unit operations compared with vacuum sputtering. The work shows a path toward a low-cost, vacuum-free manufacturing method for TCOs at commercial scales.

42 ENGINEERING↗
DOE OSTI2025

Scheduler Modeling of IBR Plants for Providing Ancillary Services [SWR-25-16]

Inverter-based resources (IBRs) have been integral components of modern power systems and their capability in providing grid services have been widely studied. To promote the deployment of IBR grid services in real utility operation, this software proposes a scheduler model for IBRs. The energy and reserve are co-optimized in day-ahead, various ancillary services, including operating reserve provision, peak load reduction, voltage regulation and power factor control are integrated in the model.

Wang, Xiaofei [National Renewable Energy Laborator↗
DOE OSTI2025

Complexity Reduction Methods for Large-Scale Spatially Explicit Biofuels Network Design

The size and complexity of energy system optimization models have increased significantly in recent years, driven by the availability of high-resolution spatial data. We present complexity reduction and solution methods that enable us to efficiently represent high-resolution spatial data in the network design of large-scale energy systems. We aim to reduce the size and enhance the computational efficiency of network design models without sacrificing solution accuracy. Specifically, we first present how to aggregate highly granular data into larger resolutions without averaging out their specific properties through a composite-curve-based approach and then develop a method to linearly represent these curves. Second, we utilize a general clustering method to determine groups of geographically proximate biomass fields and establish a single transportation arc for all of them, reducing the number of transportation-related variables while maintaining an accurate representation of the system. Finally, we introduce a two-step algorithm that decomposes large-scale network design problems into two smaller, more manageable subproblems. We demonstrate the application of our methods using a case study of switchgrass-to-biofuels network design in the eight states of the U.S. Midwest, using realistic and highly explicit spatial data.

09 BIOMASS FUELS↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2025

Perturbative model for the saturation of energetic-particle-driven modes limited by self-generated zonal modes

We present a simplified energy-conserving approach to incorporate wave–wave nonlinear effects within the framework commonly used to describe wave–particle nonlinearities. In particular, the effects of zonal mode (ZM) generation on the determination of the saturation amplitude of energetic particle (EP)-driven Alfvénic instabilities is studied. The model assumes that the zonal perturbations grow at a rate twice that of the original (pump) wave, consistent with a beat-driven (or force-driven) generation mechanism. The evolution and saturation of the mode amplitude are investigated both analytically and numerically within our reduced model assumptions, in both the collisionless and scattering-dominated regimes. These studies underscore the crucial role of sources and sinks in capturing the impact and the role of beat-driven zonal perturbations on mode evolution. In the realistic case of saturation set by sources and sinks, we discuss the role of a finite amplitude ZM in reducing microturbulent particle scattering, thus limiting the energy source for the resonant mode. We then discuss comparisons between the model’s predictions and simulation results. The model reproduces key features observed in gyrokinetic simulations as the reduction in saturated mode amplitude and the onset of wave–wave nonlinear effects as functions of mode growth rate and amplitude. Thanks to its simplicity, it can be readily implemented into codes based on reduced models, thereby improving their predictive capability for strongly driven instabilities.

Alfvén eigenmodes↗
DOE OSTI2025

Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020

With the surge in electric vehicle (EV) adoption and the need for extended driving ranges, optimizing energy efficiency, particularly through thermal management, is critical, especially in extreme weather. Managing the substantial energy needed for cabin climate control and battery temperature regulation can increase energy demands by over 50 %, severely limiting range. This study conducts a comparative analysis of thermal management systems (TMS) in three popular EV vehicles, 2020 Chevrolet Bolt, 2019 Nissan Leaf Plus, and 2020 Tesla Model 3, evaluating their distinct TMS configurations and performance under varied weather conditions. Using both numerical simulations and experimental data collected on a controlled test bench at Argonne National Laboratory, we assess how TMS architecture and operational modes influence energy consumption and range. A comprehensive TMS model was developed, integrating cabin and battery thermal sub-models in the Autonomie software platform, to simulate temperature fluctuations and range impacts. Cabin climate was modeled using a mono-zonal approach, while battery cell temperature distribution was estimated through a 2D nodal structure. Each vehicle's distinct TMS setup was evaluated: the Chevrolet Bolt and Tesla Model 3 use a dual evaporator vapor compression cycle with a PTC heater for the cabin and a coolant loop for battery thermal management; the Nissan Leaf Plus employs a heat pump with a PTC heater for the cabin and air-cooling for the battery. Tests conducted at ambient temperatures of 35°C, 22°C, -7°C, and -18°C reveal significant differences in energy use and range reduction across both configurations and conditions. At 35°C, the Tesla Model 3, Chevrolet Bolt, and Nissan Leaf Plus have a range reduction of 8%, 9%, and 13%, respectively, due to air conditioning. In winter, heating technology is paramount; at -7°C, the Nissan Leaf's heat pump configuration achieves a lower range reduction (19.3%) compared to the Tesla and Chevrolet Bolt PTC heaters, which reduce range by 28.3% and 31%, respectively. Further, this study provides valuable insights for automotive engineers, EV technology researchers, and thermal management system designers aiming to enhance electric vehicle performance by understanding how different weather conditions and TMS architectures impact energy consumption and driving range.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Single-Molecule Conductance through Hybrid Radially and Linearly π-Conjugated Macromolecules Reveals an Unusual Intramolecular π-Interaction

We describe the design, synthesis, and single-molecule junction conductance of π-electron molecules bearing both radial and linear π-conjugation pathways, whereby cycloparaphenylene (CPP) radial cores are π-extended linearly with aryl alkyne substituents as models for previously reported CPP-arylene ethynylene conjugated polymers. Although radially and linearly conjugated molecules have been studied previously in isolation as junction-bridging molecular electronic units, this is the first study to examine molecules where both topologies are operative. Our results reveal that the presence of radial CPP components within the junction-spanning pathway leads to a reduction in the conductance of the backbone compared to model linear phenyl substituents. Through tight-binding and DFT-based calculations, we attribute this conductance change to intramolecular van der Waals (vdW) interactions between the CPP ring and the junction-spanning arylene-ethynylene molecular backbone. These interactions induce changes in the dihedral angles of the backbone, leading to a reduced overlap of π orbitals within the molecular junction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Implementation and evaluation of multi-dual mode counter-current chromatography in the CUP Modeler software

Counter-current chromatography (CCC) is a separation technique that utilizes immiscible solvent pairs as stationary and mobile phases, which imparts numerous benefits compared to solid-liquid chromatography including the ability to treat either the more-dense or less-dense solvent layer as the mobile phase. Multi-dual mode (MDM) is a CCC elution mode capable of improving the separation of closely eluting compounds by alternating upper- and lower-layer solvent flows in opposing directions within the same separation. While some effort has been made to model MDM, implementation of these models in experimental design has yet to be widely adopted. Accordingly, we further developed our previously published cell utilized partitioning (CUP) model to include MDM predictions with CCC and packaged the full suite of CUP modeling capabilities into a user-friendly, open-source tool called the CUP Modeler. The mathematical model for MDM CCC was derived and validated with experimental separation of ethyl guaiacol (EG) and ethyl phenol (EP), two compounds that co-elute in our previously demonstrated reductive catalytic fractionation (RCF) lignin monomer isolation method. The developed MDM model provided insights into the effect of multiple operating parameters - including stationary phase retention, flow rate, column efficiency, feed concentration ratio, selectivity factor, and solute distribution ratios - on the separation yields, productivity, and purities. Our model agreed with prevailing understanding of MDM but also revealed new insights including that the ideal distribution ratios for co-eluting solutes to be separated by MDM is between 1.1 and 1.5, with the lower value ideally close to 1.25. Overall, this work provides fundamental insights for MDM process design and enables broader adoption of general liquid-liquid chromatography with a new, open-source user-friendly interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗
DOE OSTI2025

A methodology for decay heat characterization in molten salt reactors

Accurate decay heat prediction in molten salt reactors (MSRs) faces dual challenges: complex operational uncertainties and the need for interpretable models compatible with engineering workflows. This work presents a hybrid machine learning and segmented polynomial methodology that addresses both requirements through three key innovations. First, a modular data architecture encodes MSR-specific operational parameters (power density: 1-100 W cm -3 , humidity: 0-0.1 wt %, air ingress: 0-0.1 mol %) with uncertainty-aware temporal discretization spanning 15 orders of magnitude. Second, region-optimized machine learning models achieve 92.3 % root mean square error (RMSE) reduction over conventional polynomials while maintaining physical interpretability through automated piecewise equation generation. Third, dual front-end interfaces accelerate safety analyses — a Jupyter environment enables researchers to explore 10,000+ parameter combinations via interactive widgets, while a Streamlit web application reduces design iteration cycles through production-grade visualization tools. Operational deployment demonstrates prediction times of only a couple hundred milliseconds for 10 4 years decay profiles, enabling real-time optimization of spent fuel container designs.

42 - ENGINEERING↗
DOE OSTI2026

Circular economy pathways for decarbonizing aluminum and steel automotive body sheet components in the United States

Decarbonizing vehicle production is essential to reducing automotive sector emissions. This study quantifies greenhouse gas (GHG) emissions from aluminum and steel auto-body sheet components produced in the US. It evaluates the effectiveness of circular economy (CE) strategies (greater closed-loop recycling of pre-consumer scrap, post-consumer scrap, and increased manufacturing yields) to reduce supply chain emissions across different process technology and electricity grid decarbonization pathways. We combine dynamic material flow analysis (2025–2050) with cradle-to-gate life-cycle modeling to assess production emissions and the potential reductions associated with the CE strategies under frozen, moderate, and aggressive technology and grid decarbonization scenarios. Current emissions intensities are estimated at approximately 12.3 kg.CO₂eq/kg of aluminum and 4.3 kg.CO₂eq/kg of steel sheet embedded in the vehicle. Under the frozen decarbonization scenario and current levels of circularity, annual emissions attributable to US aluminum and steel auto-body sheet supply chains could rise by 54 % and 18 % respectively by 2050. Rapid deployment of the CE strategies can cut these annual emissions in 2050 by 52 % for aluminum and 44 % for steel. However, scrap quality constraints lead to saturation points, limiting these benefits unless addressed. Aggressive deployment of low-carbon production technologies and a low-carbon grid reduces the relative benefit of implementing the CE strategies; however, even under the aggressive technology and grid decarbonization scenario, the CE strategies reduce annual emissions by a further 23 %-54 % by 2050. These findings highlight the urgent need to integrate CE strategies into the sheet metal supply chain to support decarbonization efforts and help meet climate targets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Thermal stress-induced depolarization compensation in wide-bandwidth, high-energy, high-repetition-rate multi-slab laser amplifiers

We present an innovative design for a two-head, gas-cooled multi-slab high-energy, high-repetition-rate amplifier aimed at mitigating thermally induced depolarization in a wide-bandwidth neodymium-doped glass gain medium. This architecture employs two quartz rotators (QRs) with opposite-handedness, strategically positioned within each multi-slab amplifier head, to enhance depolarization compensation. Theoretical modeling of this amplifier configuration demonstrates a 20× reduction in depolarization losses for a 70 mm beam operating at the central wavelength, compared to conventional approaches that utilize a single QR positioned between the amplifier heads. In addition, for a wide bandwidth source, the integration of QRs with opposite-handedness yields a 9× improvement in depolarization losses at the spectral extremes compared to the use of two QRs exhibiting the same optical handedness in both amplifier heads.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Effect of rotation on negative triangularity plasmas in DIII-D

At the DIII-D tokamak, plasmas with negative triangularity (NT) have been found to have similar energy confinement times as H-mode plasmas with positive triangularity. These NT plasmas also have a stable edge that inhibits H-mode, lacks dangerous edge transients, and has naturally low impurity confinement. For these reasons, NT plasmas have the potential to be a transformative reactor scenario. To determine the importance of strong co-current neutral beam torque for NT performance at DIII-D, scans of neutral beam torque at constant neutral beam power have been performed. These scans have shown that the energy confinement time for a basic, inductive NT scenario (q 95 ≈ 2.8) decreases by 16% when torque is reduced to reactor levels. This reduction in confinement can be replicated with gyrofluid modeling that isolates the role of E x B shear in the change in transport. In a separate set of discharges with lower plasma current (q 95 ≈ 2.5), no significant correlation between confinement time and rotation was found. Throughout this investigation, excellent MHD stability was observed, even with very low toroidal rotation. Further, low impurity confinement was found at multiple rotation levels. Throughout the campaign, rotation near the edge was counter-current, consistent with the measurement of counter-current intrinsic edge rotation, and these observations agree with NT results from the TCV tokamak.

confinement↗