Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “delay errors.”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2022

Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays

Abstract Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16 × 12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5% root mean square deviations from the ideal image require approximately 5 μ s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than 10 −3 , depending on the strength of the feedback.

Prasad, Nitin (ORCID:0000000180880164)↗
DOE OSTI2020

Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States: Preprint

This work describes the development of a process based and open source balance of system cost model which provides the capability to evaluate both existing and novel offshore wind technologies. Individual design and installation steps are represented with bottom-up engineering models that compute the times and costs associated with the process; furthermore, operational constraints are assigned to each process so that delays due to weather and presence of marine mammals may be accounted for in the overall project timeline. The model structure, assumptions, inputs, and results are vetted with industry partners and compared against actual projects for validation; installation times are shown to agree with real data with relative errors of less than 35\%. Case studies are presented to demonstrate the functionality of the model. First, individual vessel efficiencies are computed for varying numbers of installation vessels and weather time series to show the diminishing returns of more than two feeder barges. Then, array cable capital costs and installation times are determined for a representative project with different turbine sizes. This quantifies the cost benefit tradeoffs of decreasing number of turbines, increased turbine spacing, and fewer turbine terminations and shows a net cost savings. These results demonstrate that the BOS model features the accuracy, functionality, and accessibility to serve as the foundation for a wide range of analyses to identify cost reduction potentials for offshore wind energy in the United States.

17 WIND ENERGY↗
DOE OSTI2020

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dynamic motion trajectory control with nanoradian accuracy for multi-element X-ray optical systems via laser interferometry

The past decades have witnessed the development of new X-ray beam sources with brightness growing at a rate surpassing Moore’s law. Current and upcoming diffraction limited and fully coherent X-ray beam sources, including multi-bend achromat based synchrotron sources and high repetition rate X-ray free electron lasers, puts increasingly stringent requirements on stability and accuracy of X-ray optics systems. Parasitic motion errors at sub-micro radian scale in beam transport and beam conditioning optics can lead to significant loss of coherence and brightness delivered from source to experiment. To address this challenge, we incorporated optical metrology based on interferometric length and angle sensing and real-time correction as part of the X-ray optics motion control system. A prototype X-ray optics system was constructed following the optical layout of a tunable X-ray cavity. On-line interferometric metrology enabled dynamical feedback to a motion control system to track and compensate for motion errors. The system achieved sub-microradian scale performance, as multiple optical elements are synchronously and continuously adjusted. This first proof of principle measurement demonstrated both the potential and necessity of incorporating optical metrology as part of the motion control architecture for large scale X-ray optical systems such as monochromators, delay lines, and in particular, X-ray cavity systems to enable the next generation cavity-based X-ray free electron lasers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

VERA-Grizzly Ex-Core Calculations: Watts Bar Unit 1 Cycles 1-2

The critical structures that comprise light-water reactor (LWR) nuclear power plants are subjected to operating environments that can challenge their integrity. Structures in close proximity to the reactor core, such as the reactor pressure vessel (RPV) and the biological shield wall, are subjected to high levels of radiation emanating from the core, as well as elevated temperatures. As the US fleet of operating LWRs ages, the effects of these operating environments on the integrity of these structures must be considered to ensure their continued safe operation. Extending the lifetime of commercial reactors and maintaining the aging reactor fleet require accurate prediction of the exposure of ex-core components to neutron and photon radiation. In particular, concrete degradation studies must be performed to evaluate the safety and long-term operation of reactors with lifetime extensions. The concrete reactor bioshield is important for providing radiological protection during operation and must last for the entire lifetime of the reactor. Recent interest in lifetime extensions furthers the need to accurately simulate concrete material degradation in the reactor bioshield. As a result of this need, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded this study to couple its tools, Virtual Environment for Reactor Applications (VERA) and Grizzly. VERA allows users to set up models to calculate time-dependent and fully coupled solutions (with thermal feedback) for ex-core quantities of interest such as vessel and coupon fluence and detector responses for multiple statepoints and cycles. Grizzly is a finite-element application based on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework that is used to enable aging materials calculations. This report highlights the work performed to calculate the fluence in the vessel and concrete for Watts Bar Nuclear Plant Unit 1 (WBN1) Cycles 1 and 2. The fluences obtained from VERA were successfully transferred to Grizzly using a Python script. Four simulations were run with Grizzly: (1) the Mazars model with the initial Young’s modulus being the instantaneous modulus, (2) the Mazars model with the initial Young’s modulus being the delayed modulus, (3) the Mazars model with the initial Young’s modulus being the delayed modulus with the addition of the effects of micro-damage caused by irradiation, and (4) the Mazars model with the initial Young’s modulus being the instantaneous modulus, and with the addition of micro-damage and creep. Details regarding the methods used to obtain the fluence and the statistical errors associated with the VERA Monte Carlo Shift calculations are discussed in greater detail in this report. The results obtained from the four Grizzly models are also presented in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Automatically generated model for light alkene combustion

Light alkenes are common combustion intermediates for a variety of fuels. Therefore, understanding their oxidation and pyrolysis chemistry is key to building detailed mechanisms for heavier fuels. This work was focused on the development and evaluation of a detailed kinetic mechanism suitable for the combustion of light alkenes up to C 4 without the use of tuned parameters, instead, the parameter values come from first principles or direct measurements. The generated mechanism accurately estimates the laminar burning velocity (S u ) and ignition delay time (IDT) of light alkenes available in the literature, which represent fundamental combustion properties at a wide range of conditions. Because each parameter is thought to have a physically realistic value, not tuned to these measurements, the new model could be used as a sub-mechanism in models for other applications. The reaction network was generated with the open-source Reaction Mechanism Generator (RMG) software. Sensitivity analyses were performed under wide ranges of temperatures and pressures, allowing for the identification of the most impactful species and reactions. Based on these, a comprehensive thermochemistry database, including calculations on 550 molecules performed in this work at the CBS-QB3 level of theory, and a kinetic library, including theoretically-derived reaction rates retrieved from the literature, were built and used in the mechanism generation. The developed mechanism was compared against several existing detailed kinetic mechanisms for ethene, propene, 1-butene, 2-butene, and isobutene. Here, the newly generated model is the most accurate among the ones analyzed, in terms of fractional bias and normalized mean square error. Hence, this new model was used to analyze the chemistry of alkene combustion. Key rate coefficients were compared, to identify the cause of deviations between the models and possible areas for further improvements.

09 BIOMASS FUELS↗
DOE OSTI2023

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters

Accurately predicting the criticality of an experiment before interacting with the experimental components is very important for criticality safety. Radiation transport software can be utilized to calculate the effective neutron multiplication factor of a nuclear system. Because of the integral nature of the effective neutron multiplication factor, the value calculated contains various sources of nuclear-data induced uncertainty. The sensitivity analysis and data assimilation technique presented in this paper exhibit one possible method of identifying and reducing the effective neutron multiplication factor nuclear-data induced uncertainty. The results presented in this work show that it is possible to use relative sensitivity coefficients of the prompt neutron decay constant and the effective delayed neutron fraction to 239 Pu nuclear data to reduce nuclear-data induced uncertainties in the effective neutron multiplication factor. This work has been utilized by members of the Los Alamos National Laboratory project EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) for optimally designing a new experiment, which will be used to reduce compensating errors in 239 Pu nuclear data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Toroidal injection angle dependence of EC assisted plasma initiation at DIII-D

An experimental scan of the electron cyclotron waves (EC) toroidal injection angle in plasma breakdown is performed at the DIII-D tokamak. The second harmonic, extraordinary mode EC is used for the study. The dependence of n e and T e on the EC injection angle cannot be conclusively drawn from this study due to the large error bars in the n e and T e measurements. On the other hand, high T e data points are observed in some discharges which can be explained by nonlinear heating. The D α emission measurement shows a clear relation between the breakdown time and the injection angle. An experimental investigation of the cause of the dependence of breakdown delay on the EC injection angle suggests that when the injection angle is oblique, the EC heating after the reflection at the inboard wall may become ineffective and cause the breakdown delay even when the EC heating before and directly upon injection remains effective. A preliminary run of the heat and transport balance code DYON indicates that the obtained dataset is suitable for a quantitative validation of EC absorption models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Liouvillian dynamics of the open Schwinger model: String breaking and kinetic dissipation in a thermal medium

Understanding the dynamics of bound state formation is one of the fundamental questions in confining quantum field theories such as Quantum Chromodynamics (QCD). One hadronization mechanism that has garnered significant attention is the breaking of a string initially connecting a fermion and an antifermion. Deepening our understanding of real-time string-breaking dynamics with simpler, lower dimensional models like the Schwinger model can improve our understanding of the hadronization process in QCD and other confining systems found in condensed matter and statistical systems. Here in this paper, we consider the string-breaking dynamics within the Schwinger model and investigate its modification inside a thermal medium, treating the Schwinger model as an open quantum system coupled to a thermal environment. Within the regime of weak coupling between the system and environment, the real-time evolution of the system can be described by a Lindblad evolution equation. We analyze the Liouvillian gaps of this Lindblad equation and the time dependence of the system’s von Neumann entropy. We observe that the late-time relaxation rate decreases as the environment correlation length increases. Moreover, when the environment correlation length is infinite, the system exhibits two steady states, one in each of the sectors with definite charge-conjugation-parity (CP) quantum numbers. For parameter regimes where an initial string breaks in vacuum, we observe a delay of the string breaking in the medium, due to kinetic dissipation effects. Conversely, in regimes where an initial string remains intact in vacuum time evolution, we observe string breaking (melting) in the thermal medium. We further discuss how the Liouvillian dynamics of the open Schwinger model can be simulated on quantum computers and provide an estimate of the associated Trotter errors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Numerical Simulations of Orographic Convection across Multiple Gray Zones

Idealized simulations are used to determine the sensitivity of moist orographic convection to horizontal grid spacing Δ h . In simulated mechanically (MECH) and thermally (THERM) forced convection over an isolated ridge, Δ h is varied systematically over both the deep-convection (Δ h ~ 10 –1 km) and turbulence (Δ h ~ 1 km–100 m) gray zones. To aid physical interpretation, a new parcel-based bulk entrainment/detrainment diagnosis for horizontally heterogeneous flows is developed. Within the deep-convection gray zone, the Δ h sensitivity is dominated by differences in parameterized versus explicit convection; the former initiates convection too far upstream of the ridge (MECH) and too early in the diurnal heating cycle (THERM). These errors stem in part from a large underprediction of parameterized entrainment and detrainment. Within the turbulence gray zone, sensitivities to Δh arise from the representation of both subcloud- and cloud-layer turbulence. As Δ h is decreased, MECH exhibits stronger cloud-layer entrainment to enhance the convective mass flux M co , while THERM exhibits stronger detrainment to suppress Mco and delay convection initiation. The latter is reinforced by increased subcloud turbulence at smaller Δ h , which leads to drying and diffusion of the central updraft responsible for initiating moist convection. Numerical convergence to a robust solution occurs only in THERM, which develops a fully turbulent flow with a resolved inertial subrange (for Δ h ≤ 250 m). In MECH, by contrast, turbulent transition occurs within the orographic cloud, the details of which depend on both physical location and Δ h .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predissociation-based measurements of bond dissociation energies: US 2 , OUS, and USe

The uranium-containing molecules US 2 , OUS, and USe have been investigated using a pulsed laser ablation supersonic beam molecular source with time-of-flight mass spectrometric detection. Spectra have been recorded using the resonant two-photon ionization method over the spectroscopic range from 277 to 238 nm. These species have a myriad of excited electronic states in this spectroscopic region, leading to spectra that are highly congested and appear quasicontinuous. Sharp predissociation thresholds are observed, allowing precise bond dissociation energies to be measured. In the case of the triatomic molecules, it was necessary to use one laser for excitation and a delayed laser for ionization in order to observe a sharp predissociation threshold that allowed a precise bond dissociation energy to be measured. The resulting thermochemical values are D 0 (SU-S) = 4.910 ± 0.003 eV, D 0 (OU-S) = 5.035 ± 0.004 eV, and D 0 (USe) = 4.609 ± 0.009 eV. These results provide the first measurement of D 0 (USe) and reduce the error limits in the previous values of D 0 (SU-S) and D 0 (OU-S) by a factor of more than 70.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A control-oriented combustion model framework for compression ignition engines operating on low-reactivity fuel

This work focuses on zero-dimensional modeling of the heat release rate in a compression ignition engine operating on gasoline-like fuels. Due to the properties of gasoline, such as high volatility and longer ignition delay than diesel, the injection strategies can vary significantly from the operation with conventional diesel fuel. Different injection strategies are commonly used to achieve varying degrees of in-cylinder stratification in order to shape the combustion event and maximize efficiency. The proposed zero-dimensional combustion model was developed to account for the different stages in combustion caused by the fuel stratification. As the ignition delay model is an integral part of the entire combustion process and significantly affects the prediction accuracy, special attention has been paid to local phenomena influencing ignition delay. A one-dimensional spray model by Musculus and Kattke was employed in conjunction with a Lagrangian tracking approach in order to estimate the local air–fuel ratio within the spray tip, as a proxy for reactivity. The local air–fuel ratio, in-cylinder temperature and pressure were used in an integral fashion to estimate the ignition delay. Heat release rates were modeled using first-order non-linear differential equations. The proposed combustion model was validated against experimental data of a heavy-duty compression ignition engine with up to three injection events at mostly 1038 r/min and 14 bar brake mean effective pressure. Further validation of the model was carried out at other engine loads and speeds. Model prediction errors in CA50 of less than 1 °CA across all conditions were found. Modeling results of other combustion metrics such as combustion duration and indicated mean effective pressure are also highly satisfactory. In addition, the model has been shown to be capable of estimating the ringing intensity for most conditions.

Pamminger, Michael↗
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Complementary dispersive mirror pair produced in one coating run based on desired non-uniformity

We report a novel one-coating-run method for producing an octave-spanning complementary dispersive mirror (DM) pair. The anti-phase group delay dispersion (GDD) oscillations are realized by two mirrors of the DM pair due to the certain thickness difference. Both mirrors are deposited within a single coating run enabled by the non-uniformity of the ion beam sputtering coating plant, which is obtained by tuning the distance between the source target and coating substrates. Since the DM pair is produced in a single deposition run, the GDD performance is more robust against deposition errors than that of the conventional complementary DM pair, in which two separated coating runs are necessary. Moreover, the new DM pair is compatible for both laser polarizations under the same angle of incidence, which could effectively reduce the difficulties of alignment for their implementation in laser systems than the double angle DM pair. The new DM pair is successfully applied to compress pulses from a Ti: Sapphire laser system down to 4.26 fs in pulse duration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Non-disruptive error field measurement in DIII-D low safety factor plasmas and projection to ITER

Previous experiments in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion 62 126007) introduced a method to identify intrinsic error fields (EFs) in tokamaks with minimal disruption risk by promptly healing driven magnetic islands during the conventional 'compass scan'. This paper presents recent experimental and numerical advancements in extending this approach to low q 95 plasmas, and projects its applicability to ITER. Non-disruptive EF measurement is achieved at q 95 = 4.5 and 3.9 without any initial EF correction (EFC) by reducing the time between the occurrence of the locked mode (LM) and control action to 10 ms and increasing the density 50%–100%. However, 50% correction of the intrinsic EF is required to achieve island healing at q 95 = 3.2 with 10 ms delay for the control action. Nonlinear two-fluid modeling with the TM1 code reproduces the DIII-D experimental observations, indicating that promptly turning off the 3D coil current reduces both magnetic island width and electromagnetic force, while raising the density increases plasma viscosity, facilitating magnetic island healing. The simulations show that for scenarios with q 95 = 3.2, lowering the control action time to 5 ms will lead to island healing without EFC. TM1 simulations are extended to future ITER scenarios with 5 MA and 7.5 MA plasma currents, predicting the dependence of required density rise on action time and EF amplitude. These simulations indicate that, benefiting from the much longer resistive time, island healing can be successfully achieved in ITER when taking control action 100–500 ms after a LM occurrence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗