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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Numerical minimization of neoclassical poloidal viscosity for supersonic equilibria in tokamak geometry

Extensive experimental evidence has shown that the presence of poloidal flow in tokamaks can dramatically improve transport properties. However, theory indicates that poloidal flows are damped by poloidal viscosity, thus necessitating external drivers, such as neutral beam injection or radio frequency heating. In this work, ideal magnetohydrodynamic equilibria are calculated via the FORTRAN code FLOW [Guazzotto et al., Phys. Plasmas 11, 604 (2004)] and a postprocessor is used to estimate the neoclassical poloidal viscosity. The equilibrium inputs, which correspond to intuitive physical quantities, are then numerically optimized to reduce a viscosity figure of merit. We present supersonic equilibria in tokamak geometry with minimized neoclassical poloidal viscosities for various velocity free function inputs, plasma aspect ratios, and collisionality regimes. Benchmarks are made against an analytic theory as well as a classical expression of poloidal viscosity. Numerical confirmation of the analytic theory is obtained in the high aspect ratio and high collisionality limit. Good agreement is also seen near the plasma core and edge, with discrepancies arising in the intermediate region. Outside of these limits, rotation input function profiles are found that provide ∼order of magnitude improvements over the analytic theory, with additional progress being made toward predictions for tokamak-relevant equilibria.

Physics↗
DOE OSTI2024

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Mining experimental magnetized liner inertial fusion data: Trends in stagnation morphology

In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Experimental methods to test photoionized plasma models in emission

The expanding foil photoionized plasma platform was developed on the Z-machine at Sandia National Laboratories to produce terrestrial photoionized plasmas at some of the same conditions as those observed in astrophysical black hole accretion disks. The specific scientific goals of the platform are motivated by long-standing astrophysical puzzles related to accreting black hole systems. Ultimately, the experimental goal is to inform the supersolar Fe abundance problem by testing photoionized plasma models in emission with at-parameter laboratory data. High quality high resolution emission data with absolute intensity calibration from a laboratory photoionized plasma have never been collected prior to the experiments described here, providing broader motivation. A robust test of the model predictions requires the successful production of a laboratory photoionized plasma along with measurements of the supporting model inputs including the radiation drive spectrum used to create the photoionized plasma, the electron density, and the temperature. The measured absolute spectral radiance in emission collected from the independently diagnosed plasma can be compared against model calculations performed using the measured inputs. In this paper, we focus on the experimental platform and the model inputs, specifically detailing the methodology used to measure the plasma conditions. The moderate electron temperature of ∼41 eV ± 15 eV and electron density of ∼1e19 ± 1.6e18 e−/cm3 and the ∼3e12 W/cm2 x-ray irradiance at the sample confirm that photoionization dominates the plasma ionization and spectra.

Cho, P. B. (ORCID:0000000291632905)↗
DOE OSTI2025

Efficient generation and extreme compression of multidimensional solitary states in molecular gas-filled hollow-core fibers driven by picosecond Yb lasers

We present an in-depth study on the impact of spatiotemporal Raman enhancement in molecular gas-filled hollow-core fibers (HCFs), demonstrating the efficient generation and post-compression of multidimensional solitary states (MDSS). Through different experimental scenarios—employing large-core HCFs filled with molecular gases (N 2 and N 2 O) and driven by high energy, sub-picosecond and picosecond Fourier transform-limited ytterbium laser pulses—this work leverages multimode propagation and enhanced spatiotemporal interactions to achieve significant spectral broadening and asymmetric redshift, contrasting sharply with self-phase modulation. Our findings reveal that, beyond the regime of maximum nonadiabatic molecular alignment, spatiotemporal nonlinear enhancement primarily governs spectral broadening for input pulse durations up to 1 ps. The process shows limited sensitivity to input pulse duration and the two investigated molecular gases (N 2 and N 2 O), with only subtle differences in broadening arising from their distinct Raman spectroscopic properties. Furthermore, post-compression of MDSS was achieved in various cases. Notably, using 7 mJ, 1 ps laser pulses, we generated 22 fs pulses with a 47% energy conversion efficiency of the input pulse energy. These results position MDSS as a powerful platform for generating high-energy, ultrashort pulses with tunable wavelengths, offering a robust solution for applications such as high harmonic generation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Magnetized liner inertial fusion platform development to assess performance scaling with drive parameters

Magnetized liner inertial fusion (MagLIF) experiments have demonstrated fusion-relevant ion temperatures up to 3.1 keV and thermonuclear production of up to 1.1 × 1013 deuterium–deuterium neutrons. This performance was enabled through platform development that provided increases in applied magnetic field, coupled preheat energy, and drive current. Advanced coil designs with internal reinforcement enabled an increase from 10 to 20 T. An improved laser pulse shape, beam smoothing, and thinner laser entrance foils increased preheat energy coupling from less than 1 to 2.3 kJ. A redesign of the final transmission line and load region increased peak load current from 16 to 20 MA. The wider range of input parameters was leveraged to study target performance trends with preheat energy, applied magnetic field, and peak load current. Ion temperature and neutron yield generally followed trends in two-dimensional clean Lasnex calculations. Stagnation performance improved with peak load current when other input parameters were also increased such that convergence was maintained. This dataset suggests that reducing convergence to less than 30 would improve predictability of target performance. Lasnex was used to identify a simulation-optimized scaling path, which suggests 10+ kJ of fusion yield is possible on the Z facility with achievable input parameters. This path also indicates >10 MJ could be generated through volume burn on a future facility with a path to high yield (>200 MJ) using cryogenic dense fuel layers. The newly developed MagLIF platform enables exploration of both this simulation optimized scaling path and a recently developed similarity-scaling path.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Behavioral resilience via dynamic circuit firing homeostasis

Homeostatic regulation ensures stable neural circuit output under changing conditions. We find that in Drosophila larvae, either presynaptic weakening due to perturbation of transmitter release or postsynaptic weakening due to perturbation of glutamate receptors at synapses between motor neuron (MN) and muscle has little impact on locomotion, suggesting a nonsynaptic compensatory mechanism. In vivo imaging shows that five different forms of synaptic weakening increase the duration of activity bouts in type I MNs. Strikingly, this compensation is input selective: occurring only in the tonic type Ib MN, not the phasic type Is MN that innervates the same muscle. Moreover, an inhibitory class of central pre-MNs that innervates the tonic—but not phasic—input decreases in activity. The adjustment in activity occurs remarkably quickly: within minutes of synapse perturbation. We propose that MN firing is dynamically regulated by two coordinated mechanisms: a cell-autonomous adjustment of MN excitability and a circuit adjustment of inhibitory central drive. The input selectivity of this process suggests homeostatic adjustment to maintain tonic drive but hold constant the phasic drive that organizes locomotory wave patterns.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2022

Spatiotemporally resolved measurements of electric field around a piezoelectric transformer using electric-field induced second harmonic (E-FISH) generation

When a piezoelectric transformer (PT) is actuated at its second harmonic frequency by a low input voltage, the generated electric field at the distal end can be sufficient to breakdown the surrounding gas, making them attractive power sources for non-equilibrium plasma generation. Understanding the potential and electric fields produced in the surrounding medium by the PT is important for effectively designing and using PT plasma devices. In this work, the spatiotemporally resolved characteristics of the electric field generated by a PT operating in open air have been investigated using the femtosecond electric field-induced second harmonic generation (E-FISH) method. Here, electric field components were determined by simultaneously conducting E-FISH measurements with the incident laser polarized in two orthogonal directions relative to the PT crystal. Results of this work demonstrate the spatial distribution of electric field around the PT’s output distal end and how it evolves as a function of time. Notably, the strongest electric field appears on the face of the PT’s distal surface, near the top and bottom edges and decreases by approximately 70% over 3 mm. The time delay between the PT’s input voltage and measured electric field indicates that there is an about 0.45 phase difference between the PT’s input voltage and output signal.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Emulation of the calculations of final r -process abundance patterns with a neural network

This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the r-process). An emulator is built using a feed-forward artificial neural network (ANN). We train the ANN with nuclear data and relative abundance patterns. We take as input the β-decay half-lives and the one-neutron separation energy of the nuclei in the rare-earth region. The output is the final isotopic abundance pattern. In this work, we focus on the nuclear data and abundance patterns in the rare-earth region to reduce the dimension of the input and output space. We show that the ANN can capture the effect of the changes in the nuclear physics inputs on the final r-process abundance pattern in the adopted astrophysical conditions. We employ the deep ensemble method to quantify the prediction uncertainty of the neural network emulator. The emulator achieves a speed-up by a factor of about 20 000 in obtaining a final abundance pattern in the rare-earth region. The emulator may be utilized in statistical analyses such as uncertainty quantification, inverse problems, and sensitivity analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Characterization and controllability of radiated power via extrinsic impurity seeding in strongly negative triangularity plasmas in DIII-D

Experiments with extrinsic impurity seeding in strongly negative triangularity shapes in DIII-D achieved radiated power fractions (relative to input power) of up to ≈85% total radiation and ≈55% core radiation in steady-operating conditions. The relationship between core and total radiation was sensitive to impurity species and input power. Attempts to reach higher radiation levels via higher impurity flows resulted in radiative collapse disruptions. Nitrogen, neon, argon, and krypton were tested. Injection was by gas puffing, usually controlled by feeding back real-time estimates for core or total radiating fractions ($P_\textrm{rad} / P_\textrm{input}$). Argon and krypton were controlled by feeding back total radiating fraction, whereas neon was controlled by feeding back the core radiating fraction, due to the lower efficiency of neon as a divertor radiator. Nitrogen flows were pre-programmed. Reasonable total $P_\textrm{rad}$ control target following was achieved with argon or krypton. Control was more challenging with the neon/core radiation configuration, which was more prone to slow response and overshooting of the control target. Poor particle removal contributed to the control challenge: neon particle inventory within the last closed flux surface was roughly constant for up to 1 s (the longest duration tested) after neon injection was halted. However, separate experiments with laser-blow off of non-recycling impurities measured a short impurity confinement time, on the scale of the energy confinement time of ∼100 ms. Modeling with the Aurora impurity transport simulation matched experimental neon density profiles with full recycling (R = 1.0) and weak pumping but predicted rapid decreases in neon inventory if pumping were increased or recycling decreased. This indicates that changes outside the confined plasma (adding a well-placed pump) would improve controllability for all highly recycling species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Novel plasma actuator for mitigation of dynamic stall

A novel plasma actuator, the Linear Counter-flow using a Point Embedded Electrode (LCPEE), is developed for the prevention of dynamic stall for a sinusoidal pitching movement between α = 4° and α = 18°. The LCPEE is implemented on a NACA0012 airfoil and tested at a Reynolds Number of Re c = 2 × 105 and reduced frequency of k = π/16. Prior investigations using a standard linear actuator showed that the exposed electrode introduced perturbations passively which delayed dynamic stall when the actuator was off. For the LCPEE actuator, when turned off, there is least passive delay. When the LCPEE is turned on at St f = 50, the dynamic stall is prevented for the sinusoidal pitching motion of the airfoil. The LCPEE actuator is also tested for the same sinusoidal motion between α = 6° and α = 20°. Four cases are considered for the higher α range of motion: actuator off, actuator on at St f = 50 with a sinusoidal input waveform, actuator on at St f = 50 with a triangular input waveform, and actuator on at Stf = 100 with a sinusoidal input waveform. Specifically for the last case, experiment shows no flow reversal demonstrating the efficacy of LCPEE in controlling the dynamic stall. The effects of LCPEE on the flow energy distribution have also been studied by using proper orthogonal decomposition (POD) method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Wavelet flow for extragalactic foreground simulations

Extragalactic foregrounds in cosmic microwave background (CMB) observations are both a source of cosmological and astrophysical information and a nuisance to the CMB. Effective field-level modeling that captures their non-Gaussian statistical distributions is increasingly important for optimal information extraction, particularly given the low-noise observations from current and upcoming experiments. Here, we explore the use of Wavelet Flow (WF) models to tackle the novel task of modeling the field-level probability distributions of multi-component CMB secondaries and foregrounds. Specifically, we jointly train correlated CMB lensing convergence (κ) and cosmic infrared background (CIB) maps with a WF model and obtain a network that statistically recovers the input to high accuracy — the trained network generates samples of κ and CIB fields whose average power spectra are within a few percent of the inputs across all scales, and whose Minkowski functionals are similarly accurate compared to the inputs. Leveraging the multiscale architecture of these models, we fine-tune both the model parameters and the priors at each scale independently, optimizing performance across different resolutions. These results demonstrate that WF models can accurately simulate correlated components of CMB secondaries, supporting improved analysis of cosmological data. Our code and trained models can be found on this GitHub repo.

cosmological simulations↗
DOE OSTI2021

High yield polar direct drive fusion neutron sources at the National Ignition Facility

Polar direct drive neutron source experiments were performed at the National Ignition Facility showing substantial improvement in total neutron yield and efficiency of conversion of laser energy to fusion output. Plastic capsules 3–4 mm in diameter were filled with 1.5 mg/cc of deuterium–tritium (DT) fuel and imploded with laser beam pointing and defocus designed to compensate for polar asymmetry introduced by the facility beam entrance angles. Radiation-hydrodynamics simulations were employed to optimize the multi-dimensional laser and target parameter space, within facility and target fabrication constraints. Ensembles of 1D simulations tuned to match the outputs of early shots in the series were used to design subsequent shots in the series. This allowed the later shots to be designed based on empirically motivated sensitivities to laser and target input parameters, while eliminating the need to explicitly model phenomena such as hydrodynamic instabilities and nonlinear laser–plasma interactions. Additionally, one experiment with a 3.0 mm diameter CH capsule produced 13.6 kJ (4.81 × 10 15 DT neutrons) from a laser input below the NIF optics damage threshold at 585 kJ, 328 TW. Two experiments with 4.0 mm capsules produced 31.3 and 33.6 kJ of fusion output (1.11 × 10 16 and 1.19 × 10 16 DT neutrons) with 1.10 MJ, 390 TW and 1.26 MJ, 425 TW of laser input, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Abstract Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q , toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Power and isotope effects in the ITER baseline scenario with tungsten and tungsten-equivalent radiators in DIII-D

Abstract Experiments in DIII-D document the ITER Baseline Scenario (IBS) at q 95 ∼ 3 and P IN / P LH ∼ 1–2, in both deuterium and hydrogen utilizing Kr and Xe as Tungsten-equivalent radiators. The power threshold for H-mode operation ( P LH ) was determined experimentally without added impurities and found to be about a factor of two higher than the scaling law. In recent IBS experiments in deuterium, intrinsic levels of metals such as Tungsten (W) or molybdenum and inconel are present that reduce the pedestal pressure by 20%–25%. A complete radiative collapse of deuterium IBS plasmas occurs at W core concentrations C W = 10 −5 . Simulations show that for core temperatures expected for ITER, the plasmas would not have a radiative collapse at C W = 1 × 10 −5 , moreover Q = 8–10 would still be achieved for C W up to 3 × 10 −5 . In contrast to deuterium, the IBS in hydrogen is not affected by intrinsic high-Z impurities, indicating that hydrogen H-modes in ITER may not inform the D-T phase with respect to W accumulation and discharge survival. Compared to deuterium, the pedestal pressure in hydrogen is ∼25% lower, with much higher ELM frequency of 150 Hz, decreasing with input power. Krypton was injected in a matrix scan of input power and impurity flow in IBS hydrogen discharges. Krypton impurity density profiles in hydrogen are similar to deuterium plasmas, but at Kr flows that are 2–3 times higher for the same input power. Krypton is transported into the core and affects the whole radius; at the highest injection rates a radiative collapse occurs at core radiation fractions of 0.3–0.35, consistent with the expected maximum W radiation fraction for ITER core plasmas. Comparing the results with previous International Tokamak Physics Activity database studies of the IBS confirms that at higher radiation fraction due to high-Z impurities, a drop in H 98 of >10% is observed. On the other hand, the results using Kr as a W-equivalent radiator indicate that metal (W) devices at lower core temperatures than ITER may provide overly pessimistic performance extrapolations to ITER for deuterium-tritium operation. The new DIII-D results support a more attractive option for the ITER Research Plan with a short hydrogen phase for system commissioning, transitioning to deuterium operations as soon as possible to provide relevant conditions for deuterium-tritium operations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗