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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Modelling the limiter ramp-up of WEST for addressing the future challenges of ITER

This paper presents a joint experimental and numerical investigation into the physics of long limited plasma ramp-up in tokamaks with tungsten (W) first walls, a critical phase for ITER operation. The comparison between the average plasma quantities simulated using the SolEdge-HDG code and the measurements taken during three successive WEST discharges after boronisation shows how challenging it is to predict this phase. While simulations reproduce the general trends at the midplane, with reasonable match in density profiles, they consistently underestimate core temperatures possibly due to too large perpendicular heat conductivity. On the contrary, at the high field side (HFS) limiter, simulations overestimate the measured quantities, and highlights the limitation of using Bohm boundary conditions at grazing magnetic angles. Experimental measurements reveal that the boron layer is rapidly eroded, on a timescale comparable to a single ITER discharge. The subsequent transition from a boron-coated to a tungsten wall increases recycling and significantly degrades the core plasma, reducing the electron temperature by nearly half due to W contamination, despite wall parameters remaining stable. Furthermore, comparisons with Langmuir probes, bolometry, reflectometry, and spectroscopy indicate that the experimental far scrape-Off layer (SOL) is significantly wider than simulated. This wide SOL implies that boron erosion extends along the entire HFS limiter rather than being confined to the contact point. This work highlights some characteristics of the plasma during this phase of the discharge and emphasizes the current modelling issues that need to be resolved in order to obtain reliable predictions concerning the ITER ramp-up.

ITER↗
DOE OSTI2025

Thermal-Fluid and Thermal-Structural Response of the T-Tube Modular Divertor to Spatiotemporally Varying Heat Loads

Tungsten (W) is the leading candidate for divertor target plates because of its high melting point (>3000°C), thermal conductivity, and ultimate tensile stress. While W and its alloys are the only solid materials that can survive the high heat fluxes incident on the divertor, W’s low-ductility high ductile-to-brittle transition temperature of ~600°C and relatively low recrystallization temperature (RT) of ~1300°C pose structural (among other) challenges. The objective of this work is to estimate the thermal-fluid and thermal-structural performance of the helium (He)-cooled T-tube divertor, which was originally developed by the Advanced Reactor Innovation and Evaluation Study (ARIES) using numerical simulations. Here, predictions of temperature distributions across the plasma-facing structural component and surface pressures from computational fluid dynamics simulations are used to determine stress distributions using commercial structural finite element modeling software over a range of fusion-relevant conditions. The maximum allowable incident heat fluxes are determined based on the temperature limits imposed by the ITER elastic Structural Design Criteria for In-vessel Components (SDC-IC) and the maximum RT over a range of He mass flow rates and presented in the form of performance design charts. Our recent work found that thermal- structural criteria accounting for the low ductility of W in a finger-type modular divertor constrain the maximum incident heat fluxes to values well below the ITER specifications, and those based on considering only the RT demonstrate that integrated thermal-fluid and elastic structural performance evaluation are required for accurate assessment of divertor performance. This novel analysis of the T-tube considers how nonuniform and transient incident heat fluxes affect its thermal-fluid and thermal-structural performance, as well as the effect of volumetric heating, which can be as great as 27% of the power incident on the divertor surface. The W tile of the T-tube, with its relatively large plasma-facing area of ~15 cm 2 , will likely experience significant spatial variations in incident heat flux. This work therefore assesses whether steady-state incident heat flux profiles with a peak of 10 MW/m 2 and maximum heat flux gradients of 200 MW/m 2 per m exceed the structural limits imposed by the ITER elastic SDC-IC and the maximum RT over a range of fusion-relevant conditions. The effect of transient heat fluxes typical of plasma detachment and reattachment from the target plate due, for example, to gas injection are also evaluated

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Numerical error analysis of SOLPS-ITER simulations of EAST

Abstract Plasma edge simulations with codes like SOLPS-ITER are widely employed to interpret fusion experiments. However, numerical errors appearing in such simulations are rarely investigated, despite their potential large impact on simulation results. These errors consist of the statistical error and the bias, both resulting from the finite number of employed EIRENE Monte Carlo particles and incomplete convergence, and the discretization error due to the finite resolution of the computational grids. In this contribution, the resulting numerical errors on simulations of pure deuterium and neon seeded H-mode EAST discharges are examined. The statistical error can be kept small compared to other numerical error contributions by averaging the plasma profiles. This allows investigating the bias and discretization errors using Richardson extrapolation. It is shown that grid refinement and the number of employed Monte Carlo particles have the largest influence on the result, in agreement with similar studies of an ITER deuterium case. For the first time, numerical error bars on the entire simulated target profiles are determined showing that the largest numerical error is 17.9%, mainly due to the plasma grid discretization. On top, also numerical errors on simulated neutral pressures are investigated in detail, for which the statistical error is dominant. The analysis demonstrates which setup is needed to keep numerical errors limited: the SOLPS-ITER averaging procedure should be employed including enough EIRENE particles, and the involved grids should be sufficiently refined to reduce discretization errors.

Boeyaert, Dieter (ORCID:0000000309208660)↗
DOE OSTI2025

Feasibility of fusion plasma burn control via real-time, sub-divertor neutral gas isotopic and compositional analysis

The ability to provide fusion burn control without requiring physical access through the first wall and fuel breeding blankets, would be vital for any future, magnetically confined fusion power reactor. A multi-sensor, fusion fuel cycle exhaust, neutral gas analysis system on JET, capable of delivering real time data, and accessing only the sub-divertor region, provides an excellent example of such capability. Optimized for and operated during the deuterium–tritium experimental campaigns 2 and 3 (DTE2, DTE3), it is proving valuable for planning to explore fusion reactor burn control in ITER with a comparable diagnostic system called the Diagnostic Residual Gas Analyzer (DRGA). This paper aims to show feasibility of developing model-based controllers for ITER and next generation, reactor-relevant devices, by building both on the empirical experience in JET-DTE2, and on the already emerging experience on developing such models specifically for ITER. The paper begins with a specific use-case from JET-DTE2, pertaining to the observed sensitivity of the fusion neutron yield on the concentration of isotopic helium-3 ( 3 He), with data from one of the high-performance DT shots exhibited with emphasis on the 3 He measurement via the sub-divertor. Then, a first model is developed and then explored with simulations that aim to discover how well the controllers in the model react to either insufficient levels of 3 He or excessive levels of 3 He. The simulations then explore potential impact from a delay in the measurement (or the response) that would be comparable to the ∼1 s, conductance limited response for the ITER DRGA system, currently in its final design. The simulations show that control is feasible, and that its effectiveness is not significantly impacted by such delay.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Progress towards edge-localized mode suppression via magnetic perturbations in hydrogen plasmas

Abstract The suppression of edge-localized modes (ELMs) by applying resonant magnetic perturbations (RMPs) is well studied in low collisionality deuterium plasmas as a measure to reduce transient divertor heat loads. However, ELM suppression has yet to be demonstrated in non-nuclear fuels such as hydrogen and hydrogen + helium mixtures which are the main ion species to be used in the ITER pre-fusion power operation (PFPO) phase. For the first time, attempts have been made to access ELM suppression with RMPs in ITER-like low collisionality hydrogen plasmas at DIII-D and ASDEX Upgrade. The DIII-D experiments focused on operation with injected power slightly above the L–H power threshold similar to the expected conditions in the ITER PFPO phase with limited external heating power. The RMPs were found to trigger H–L backtransitions, which is shown to be avoided by reducing the L–H power threshold by diluting the plasma with helium. The additional helium combined with a larger measured neutral density of hydrogen inside the separatrix compared to ELM suppressed deuterium plasmas precluded access to a pedestal top density below the known RMP-ELM suppression threshold. At ASDEX Upgrade, RMP-ELM suppression has been achieved when the concentration of 1 H in the hydrogen isotope mix is below 40 % . While all known access criteria for RMP-ELM suppression were met above this threshold, full ELM suppression was replaced by strong mitigation. The most prominent difference between the hydrogen and deuterium plasmas was a change of turbulence characteristics in the pedestal where Doppler reflectometry measurements suggest a significant reduction of turbulence even at small hydrogen concentrations. In conclusion, these experiments not only identify issues that may prevent access to RMP-ELM suppression in the ITER PFPO phase, but also highlight missing physics in our current understanding of RMP-ELM suppression such as potentially the role of turbulence in the pedestal gradient region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Simulation of pellet ELM triggering in low-collisionality, ITER-like discharges

3D nonlinear, as well as 2D linear M3D-C1 simulations are used to model ELM triggering by small pellets in DIII-D discharges in the ITER relevant, peeling-limited pedestal stability regime. A critical pellet size threshold is found in both experiment and modeling depending on pedestal conditions, pellet velocity and injection direction. Using radial injection at the outboard midplane, the threshold is determined by M3D-C1 for multiple time slices of a DIII-D low-collisionality discharge that has pellet ELM triggering. Experimental observations show that a larger pellet size than the standard 1.3 mm diameter is necessary for ELM triggering; 1.8 mm pellets triggered several ELMs in cases where a smaller pellet failed. The M3D-C1 simulations are in good agreement with these observations. While the 2D linear simulations give insight into the change of growth rates for various toroidal modes with pellet size, the 3D nonlinear simulations apply a pellet ablation model that mimics the actual injection with good match to the experiment. The 3D nonlinear simulation confirms the pellet ELM triggering for a pellet size larger than the threshold found by the linear simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin [Fermilab] (ORCID:0000000157000288↗
DOE OSTI2025

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2025

wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation

As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as model synthesis, are now becoming limiting factors in the rapid iteration of designs. To reduce these emerging constraints, multiple efforts are being launched toward designing an ML-based surrogate model that estimates resource usage of synthesized accelerator architectures. This model would reduce the design iteration time, especially when designing within a set of given hardware constraints. This approach shows considerable potential, but as it stands, the effort is early and would benefit from coordination and standardization to assist future work as it emerges. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of more than 100,000 fully connected neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. In addition to the resource utilization and latency data provided, the dataset includes generated artifacts and log files for many of the synthesized neural networks, in order to support future research in ML-based code generation. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, as well as the average performance across a subset of the dataset. We measure the performance of a given predictor model through multiple metrics, including $R^2$ score and SMAPE on regression tasks, as well as inference time to further characterize the estimator under test. Additionally, we introduce the latency/utilization inference graph neural network (lui-gnn), a surrogate model that uses a graph neural network to represent input architectures in the form of a directed graph. This graph representation allows for a diverse set of model architectures to all be effectively handled by a surrogate model. We present the architecture and performance of the model, as evaluated by the new proposed benchmark, including SMAPE, $R^2$ score, and inference times, and find that lui-gnn generally predicts latency and utilization for the 75\% quantile within several percent of the synthesized resources on the synthetic test dataset, indicating that this approach of estimating resource and latency via a surrogate models has promise and warrants further research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Assessment of the burning-plasma operational space in ITER by using a control-oriented core-SOL-divertor model

In future tokamaks, the control of burning plasmas will require careful regulation of the plasma density and temperature. Along with the design of effective burn-control systems, understanding how the fusion power varies in the density-temperature space is vital for the operation of fusion power plants. Here in this work, the steady-state operational space of ITER is studied using a control-oriented core-plasma model coupled to a two-point model of the scrape-off-layer (SOL) and divertor regions. The two models are coupled through the exchange of input-output parameters. The deuterium and tritium recycling from the wall are output parameters of the SOL-divertor model that are used as input parameters in the core-plasma density balance. Furthermore, the separatrix temperature, which is an output parameter of the SOL-divertor model, is incorporated into the radial core-plasma temperature profiles. Therefore, the temperature-dependent power balance of the plasma core is intimately linked to the SOL-divertor model. Both the power entering the SOL from the core, as determined by the core-plasma power balance, and the separatrix density, as dictated by the core-plasma density balance, are input parameters to the SOL-divertor model. They are control knobs in the SOL-divertor model that can be regulated using the core-plasma actuators: auxiliary power and pellet injection. There are various operational limitations, such as the saturation of the aforementioned actuators, that will prevent ITER from accessing certain high-fusion plasma regimes. The achievable tritium concentration in the fueling lines and the maximum sustainable heat load on the divertor will impose further restrictions. By accounting for these limitations, the ITER operational space is computed based on the coupled core-SOL-divertor model and visualized using Plasma Operation Contour (POPCON) plots that map performance metrics, such as the fusion to auxiliary power ratio, over the density-temperature space. Comparisons are drawn between plasmas with different recycling, confinement, and SOL-divertor conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Iterative hybrid manufacture of a titanium alloy component

Here, this paper describes an iterative hybrid (additive + subtractive) manufacturing approach for a titanium alloy (Ti6Al4V) part using a laser hotwire directed energy deposition system (LHWDED) and a traditional four-axis milling machine tool. The term iterative hybrid manufacturing is used to described hybrid manufacturing where the additive and subtractive operations occur in multiple stages rather than sequentially. It is currently common to produce an entire part by sequential hybrid manufacturing by additively manufacturing (AM) an entire preform geometry that then requires post processing by another machine tool to create final part features. By contrast, a part produced by iterative hybrid manufacturing (IHM) does not produce the entire preform geometry in a single AM process. Instead, a portion of the entire preform geometry is manufactured by an AM process, then that portion is transferred to another machine tool which creates features in that portion, and then that machined portion is transferred back to the AM machine to complete another AM process. There is no limit to the number of iterations that an IHM process can have. IHM offers several advantages over sequential hybrid manufacturing such as the use of shorter and stiffer subtractive tooling, better access to part geometries that require subtractive processes, and the separation of the AM heat source from the subtractive machine tool. A titanium alloy demonstration part was successfully manufactured by IHM with three iterations using a shared pallet system between the AM machine tool and the subtractive machine tool.

Hybrid manufacturing↗
DOE OSTI2021

The role of edge plasma parameters in H-mode density limit on the JET-ILW

A study of a dataset of JET H-mode plasma with the Be/W ITER-like wall (JET-ILW) shows that reaching the edge MHD ballooning limit leads to confinement degradation. However, unlike JET plasmas with a carbon wall (JET-C), the JET-ILW plasmas stay in a marginal dithering phase for a relatively long period, associated with a higher (≈20%) H-mode density limit (HDL) than JET-C equivalents. This suggests that ITER could be operated in H-mode with higher density than the scaling based on carbon wall devices, but likely with a dithering phase plasma with lower confinement. A new, reliable estimator for JET E r, min has been derived by combining HRTS measurements of pedestal gradient and edge-SOL decay lengths. JET radial E r ETB wells are observed in the range of -15 to -60 kV m -1 in high performance H-modes, consistent with previous CXRS results in ASDEX Upgrade. The results imply that a higher positive E × B shear in the near SOL plays a role in sustaining a marginal phase in JET-ILW which leads to a higher HDL than that in JET-C. The results of the JET-ILW dataset show agreement with the Goldston finite collisionality HD model for SOL broadening at high collisionality. A hypothesis for the dithering H-mode phase is proposed: as n e,SOL increases, ν *,SOL increases, SOL broadens, E r shear decreases, triggers L-mode; n e drops, ν *,SOL decreases, SOL becomes narrower, and E r shear increases, triggering H-mode, resulting in a cycle of H–L–H- oscillations. For burning plasma devices, such as ITER, operating just below the MHD limit for the dithering phase could be a promising regime for maximising core density, and fusion performance while minimising plasma-material interaction. The oscillatory signal during the dithering phase could be used as a precursor of undesirable plasma performance for control purposes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Results from the last DD and DT JET campaigns in the framework of the EUROfusion Tokamak Exploitation Work Package activity

JET, the only tokamak capable of operating with deuterium–tritium (D–T) fuel (since TFTR was shutdown in 1999), has provided essential experimental data to support ITER and DEMO design and operation. Within the EUROfusion Tokamak Exploitation Work Package, JET completed its final campaigns (2022–2023), culminating in the third D–T campaign (DTE3). These experiments addressed key challenges in plasma scenarios, exhaust control, and tritium management under reactor-relevant conditions. Significant progress was achieved in demonstrating ITER-like integrated scenarios with impurity seeding, achieving partial divertor detachment and high confinement ($H_{98}(y,2)$ ≈ 0.85) at 3 MA in D–T plasmas. Advanced exhaust regimes such as quasi-continuous exhaust (QCE) and X-point radiator (XPR) were successfully achieved first in D–D and then extended to D–T operation, confirming their relevance for mixed isotope operation. Operational milestones included a new world record of 69 MJ fusion energy in tritium-rich hybrid plasmas and long-pulse H-mode operation up to 60 s, contributing with unique data to the CICLOP database. Physics studies focused on peeling-limited pedestals in support of ITER and improved understanding of edge stability and impurity screening in metallic environments. Extensive usage of the shattered pellet injector (SPI) on JET provided critical information for the design of the ITER disruption mitigation system (DMS). Real-time control systems for D/T ratio control and plasma exhaust were deployed and demonstrated in D–D and D–T, while energetic particle physics investigations unfolded the role of fast ions in turbulence suppression mechanisms. Comprehensive tritium retention studies using gas balance method, post-mortem analysis, and ITER-relevant laser induced desorption spectroscopy (LIDS) diagnostics provided essential input for tritium accountancy strategies. These results are validating the ITER operational concepts, inform DEMO design, and deliver critical experience in nuclear operation and scenario integration.

disruptions↗
DOE OSTI2023

L-BFGS Class Implementation in C++

This report presents a header-only C++ class implementation of the Limited-memory BroydenFletcher-Goldfarb-Shanno (L-BFGS) algorithm. The L-BFGS method is a general purpose quasi-Netwon optimization method that builds an approximation of the descent direction from consecutive iterate and gradient vectors. The limited-memory aspect stems from the replacement of the N × N approximation matrix of the original BFGS method with M vectors of length N. An example usage of the class is included along with the reference source code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Feasibility of raised inner strike point equilibria scenario in ITER for detritiation from beryllium co-deposits

Abstract In ITER, tritium retention primarily occurs through co-deposition with beryllium. To avoid exceeding the strict tritium inventory limit, efficient tritium recovery techniques are essential. Baking is the ITER baseline for tritium recovery, but its effectiveness in removing tritium from thick beryllium layers is limited. A raised strike point scenario is considered an alternative method for removing tritium from the ITER inner vertical divertor target by heating components via plasma flux. This paper presents SOLPS-ITER code simulations conducted under various conditions, assessing the divertor performance and tritium outgassing of the raised strike point scenario. As the strike point is raised, recycled neutrals are not efficiently baffled by the dome and scrape-off layer, significantly changing the neutral trajectory and ionization source distribution. This improves detachment accessibility but worsens core-edge compatibility compared to the baseline scenario. However, in the partially detached condition, the impact of raising the strike point, perpendicular transport, and q 95 on target heat flux is not significant, as it primarily scales with the input power. Target heat flux is translated to target surface temperature using a simplified heat transfer model that considers the 3D target monoblock geometry and active cooling condition, excluding Be layer thermal properties. For partially detached divertor conditions, the bulk tungsten monoblock surface temperature remains below the baking temperature, which is insufficient for efficient tritium outgassing under the actively cooled ITER divertor condition. However, considering the potential thermal contact resistance between the beryllium and tungsten layers, which may significantly impact temperature distribution, the temperature of the beryllium layer can be raised to a level sufficient for efficient tritium outgassing. Therefore, the raised strike point scenario can be considered as an alternative in-vessel tritium removal technique.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Correlation of the L-mode density limit with edge collisionality

The "density limit'' is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the widely-used Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling - in its remarkable simplicity - may not capture the full complexity of the disruptive density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and identify alternative stability metrics. We find that a two-parameter dimensionless boundary in the plasma edge, $\nu_{*\rm, edge}^{\rm limit} = 3.0 \beta_{T,{\rm edge}}^{-0.4}$, achieves significantly higher accuracy (true negative rate of 97.7\% at a true positive rate of 95\%) than the Greenwald limit (true negative rate 86.1\% at a true positive rate of 95\%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). The collisionality boundary presented here can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗