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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Tidal Disruptions of Main Sequence Stars - II. Simulation Methodology and Stellar Mass Dependence of the Character of Full Tidal Disruptions

This paper introduces a series of papers presenting a quantitative theory for the tidal disruption of main-sequence stars by supermassive black holes. Using fully general relativistic hydrodynamics simulations and MESA-model initial conditions, we explore the pericenter-dependence of tidal disruption properties for eight stellar masses (0.15 < M_/M<10)and six black hole masses( ́10<MM105 105BH7). We present here the results most relevant to observations. The effects of internal stellar structure and relativity decouple for both the disruption cross section and the characteristic energy width of the debris. Moreover, the full disruption cross section is almost independent of Må for Må/Me3. Independent of Må, relativistic effects increase the critical pericenter distance for full disruption events by up to a factor of ∼3 relative to the Newtonian prediction. The probability of a direct capture is also independent of Må; at MBH/Me5×106this probability is equal to the probability of a complete disruption. The breadth of the debris energy distribution ΔE can differ from the standard estimate by factors of 0.35−2, depending on Må and MBH, implying a corresponding change(∝(ΔE)−3/2)in the characteristic mass-return timescale. We provide analytic forms, suitable for use in both event rate estimates and parameter inference, to describe all these trends. For partial disruptions, we find a nearly universal relation between the star’s angular momentum and the fraction of Må remaining. Within the “empty loss-cone” regime, partial disruptions must precede full disruptions. These partial disruptions can drastically affect the rate and appearance of subsequent total disruptions

Taeho Ryu↗
DOE OSTI2021

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. Lastly, a real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Plasma steering to avoid disruptions in ITER and tokamak power plants

Steering tokamak plasmas is commonly viewed as a way to avoid disruptions and runaway electrons. Plasma steering sounds as safe as driving to work but will be shown to more closely resemble driving at high speed through a dense fog on an icy road. The long time required to terminate an ITER discharge compared to time over which dangers can be foreseen is analogous to driving in a dense fog. Here, the difficulty of regaining plasma control if it is lost resembles driving on an icy road. Disruptions and runaways are associated with three issues—a solution to one tends to complicate the solution to the other two: loss of plasma position control, excessive heat deposition, and wall melting due to runaway electrons. All three risks must be addressed for ITER to achieve its mission and essentially eliminated before tokamak power plants can be deployed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Data for Creating Yellow Seed Camelina sativa with Enhanced Oil Accumulation by CRISPR-Mediated Disruption of Transparent Testa 8

Camelina ( Camelina sativa L.), a hexaploid member of the Brassicaceae family, is an emerging oilseed crop being developed to meet the increasing demand for plant oils as biofuel feedstocks. In other Brassicas, high oil content can be associated with a yellow seed phenotype, which is unknown for camelina. We sought to create yellow seed camelina using CRISPR/Cas9 technology to disrupt its Transparent Testa 8 (TT8) transcription factor genes and to evaluate the resulting seed phenotype. We identified three TT8 genes, one in each of the three camelina subgenomes, and obtained independent CsTT8 lines containing frameshift edits. Disruption of TT8 caused seed coat colour to change from brown to yellow reflecting their reduced flavonoid accumulation of up to 44%, and the loss of a well-organized seed coat mucilage layer. Transcriptomic analysis of CsTT8-edited seeds revealed significantly increased expression of the lipid-related transcription factors LEC1, LEC2, FUS3, and WRI1 and their downstream fatty acid synthesis-related targets. These changes caused metabolic remodelling with increased fatty acid synthesis rates and corresponding increases in total fatty acid (TFA) accumulation from 32.4% to as high as 38.0% of seed weight, and TAG yield by more than 21% without significant changes in starch or protein levels compared to parental line. These data highlight the effectiveness of CRISPR in creating novel enhanced-oil germplasm in camelina. The resulting lines may directly contribute to future net-zero carbon energy production or be combined with other traits to produce desired lipid-derived bioproducts at high yields.

Biofuels↗
DOE OSTI2024

Impact of breech geometry and propellant flow on the release of large pellets for the ITER disruption mitigation system

Studies have been performed on the release mechanism for large pellets using high pressure gas in a shattered pellet injector. Typically, pellets are dislodged from the cryogenic surface and accelerated down a barrel using high pressure gas delivered by a fast-acting propellant valve. The pellets impact an angled surface which shatters the pellet into many small fragments before entering the plasma. This technique was initially demonstrated on DIII-D (Commaux et al 2016 Nucl. Fusion 56 046007) and is now deployed on JET, KSTAR, ASDEX-Upgrade, and other tokamaks around the world in support of ITER's disruption mitigation system design and physics basis. The large hydrogen, 28.5 mm diameter, 2 length-to-diameter ratio, pellets foreseen for ITER SPI operation have low material strength and low heat of sublimation, which cause the pellets to be fragile and highly reactive to the impact of warm propellant gas. Due to the size of the pellets, significantly more propellant gas is required to dislodge and accelerate them. This creates a potentially significant propellant gas removal issue as 2–6 bar-L of gas is expected to be required for release and speed control. The research presented in this paper is an in-depth exploration of the parameters that are keys to reliable pellet release and speed control. Computational fluid dynamics (CFD) modeling of propellant flows through various breech designs was conducted to determine the force generated on the back surface of a pellet. These simulations assumed the use of the ORNL designed flyer plate valve. CFD modeling combined with experimental measurements provide adequate insight to determine a path to an optimal valve and breech design for ITER SPI pellet release and speed control while minimizing propellant gas usage.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The rebrightening of a ROSAT -selected tidal disruption event: repeated weak partial disruption flares from a quiescent galaxy?

The ROSAT-selected tidal disruption event (TDE) candidate RX J133157.6-324319.7 (J1331) was detected in 1993 as a bright [0.2–2 keV flux of (1.0 ± 0.1) × 10 -12 erg s -1 cm -2 ], ultra-soft (kT = 0.11 ± 0.03 keV) X-ray flare from a quiescent galaxy (z = 0.051 89). During its fifth all-sky survey (eRASS5) in 2022, Spectrum-Roentgen-Gamma (SRG)/ eROSITA detected the repeated flaring of J1331, where it had rebrightened to an observed 0.2–2 keV flux of (6.0 ± 0.7) × 10 -13 erg s -1 cm -2 , with spectral properties (kT = 0.115 ± 0.007 keV) consistent with the ROSAT-observed flare ~30 yr earlier. In this work, we report on X-ray, ultraviolet, optical, and radio observations of this system. During a pointed XMM observation ~17 d after the eRASS5 detection, J1331 was not detected in the 0.2–2 keV band, constraining the 0.2–2 keV flux to have decayed by a factor of ≳40 over this period. Given the extremely low probability (~5 × 10 -6 ) of observing two independent full TDEs from the same galaxy over a 30 yr period, we consider the variability seen in J1331 to be likely caused by two partial TDEs involving a star on an elliptical orbit around a black hole. J1331-like flares show faster rise and decay time-scales [$\mathcal {O}(\mathrm{d})$] compared to standard TDE candidates, with negligible ongoing accretion at late times post-disruption between outbursts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Role of peracetic acid on the disruption of lignin packing structure and its consequence on lignin depolymerisation

Peracetic acid (PAA) is an effective oxidant capable of solubilising lignin and efficiently depolymerising it to selective phenolic compounds; however, the specific role by which PAA initiates the depolymerisation is still elusive. Herein, interaction between PAA and the lignin macromolecule and the consequent structural changes of the latter were studied by characterising the lignin packing structure and its associated changes during the oxidation process. While the lignin packing structure and its changes associated with the PAA-mediated oxidation were probed by X-ray diffraction, the impact of the PAA on different chemical functionalities present in the lignin structure was established by 13 C and 1 H– 13 C heteronuclear single-quantum coherence nuclear magnetic resonance (NMR) spectroscopy. Further, combining the NMR spectroscopy results, and the product distribution, we conclude that the predominant reaction pathway for the oxidative depolymerisation of lignin with PAA is the Baeyer–Villiger oxidation of the ketone formed by the oxidation of the benzylic hydroxyl group adjacent to the β-O-4 linkage. The experimental evidence provided herein corroborated that PAA instigates oxygen insertion to the lignin macromolecule resulting in disruption of its packing structures and facilitates depolymerisation. We also investigated various metal oxide and mixed metal oxide catalysts to identify effective catalysts that further enhance the efficiency of PAA-mediated depolymerisation of lignin and produce selective monomeric phenolic compounds. Techno-economic analysis was also conducted to identify the key parameters associated with this oxidative process that need to be considered for possible commercial application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2020

Tidal Disruptions of Main-sequence Stars. II. Simulation Methodology and Stellar Mass Dependence of the Character of Full Tidal Disruptions

This is the second in a series of papers presenting the results of fully general relativistic simulations of stellar tidal disruptions in which the stars' initial states are realistic main-sequence models. In the first paper, we gave an overview of this program and discussed the principal observational implications of our work. Here we describe our calculational method, which includes a new method for calculating fully relativistic stellar self-gravity, and provide details about the outcomes of full disruptions, focusing on the stellar mass dependence of the outcomes for a black hole of mass 106 M(⊙). We consider eight different stellar masses, from 0.15 M(⊙) to 10 M(⊙). We find that, relative to the traditional order-of-magnitude estimate r t, the physical tidal radius of low-mass stars (M(*) ≲ 0.7 M(⊙)) is larger by tens of percent, while for high-mass stars (M(*) ≲ 1 M(⊙)) it is smaller by a factor of 2–2.5. The traditional estimate of the range of energies found in the debris is ≈1.4× too large for low-mass stars, but is a factor of ~2 too small for high-mass stars; in addition, the energy distribution for high-mass stars has significant wings. For all stars undergoing tidal encounters, we find that mass loss continues for many stellar vibration times because the black hole's tidal gravity competes with the instantaneous stellar gravity at the star's surface until the star has reached a distance from the black hole ~O(10)r(t).

Taeho Ryu↗
NASA NTRS1986

Catastrophic disruption in the solar system - Asteroid collisional history, origin of Hirayama families and disruption of small satellites

The process of collisional catastrophic disruption has played a significantly role in structuring the solar system. Diverse populations of bodies such as the asteroid belt, small satellites of Jupiter and Saturn and perhaps even the rings of Saturn have been created or substantially changed by catastrophic distruption. Understanding the outcome of large scale impacts is essential to learning about the early history of the solar system in the asteroid zone and the reason why a planet failed to form there.

Davis, D. R.↗
DOE OSTI2023

Disruption event characterization and forecasting in tokamaks

Disruption prediction and avoidance is a critical need for next-step tokamaks, such as ITER. Disruption Event Characterization and Forecasting (DECAF) research fully automates analysis of tokamak data to determine chains of events that lead to disruptions and to forecast their evolution allowing sufficient time for mitigation or complete avoidance of the disruption. Disruption event chains related to local rotating or global magnetohydrodynamic (MHD) modes and vertical instability are examined with warnings issued for many off-normal physics events, including density limits, plasma dynamics, confinement transitions, and profile variations. Along with Greenwald density limit evaluation, a local radiative island power balance theory is evaluated and compared to the observation of island growth. Automated decomposition and analysis of rotating tearing modes produce physical event chains leading to disruptions. A total MHD state warning model comprised of 15 separate criteria produces a disruption forecast about 180 ms before a standard locked mode detector warning. Single DECAF event analyses have begun on KSTAR, MAST, and NSTX/-U databases with thousands of shot seconds of device operation using from 0.5 to 1 × 10 6 tested sample times per device. An initial multi-device database comparison illustrates a highly important result that plasma disruptivity does not need to increase as βN increases. Global MHD instabilities, such as resistive wall modes (RWMs), can give the briefest time period of warning before disruption compared to other physics events. In an NSTX database with unstable RWMs, the mode onset, loss of boundary and current control, and disruption event warnings are found in all cases and vertical displacement events are found in 91% of cases. An initial time-dependent reduced physics model of kinetic RWM stabilization created to forecast the disruption chain predicts instability 84% of the time for experimentally unstable cases with a relatively low false positive rate. Instances of the disruption event chain analysis illustrate dynamics including H–L back transitions for rotating MHD and global RWM triggering events. Furthermore, disruption warnings are issued with sufficient time before the disruption (on transport timescales) to potentially allow active profile control for disruption avoidance, active mode control, or mitigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Simulated Disruptions of the Quasi-Biennial Oscillation

The Quasi-Biennial Oscillation has exhibited remarkable stability over the observational record—until a well-documented 2015/16 disruption and an emerging disruption in 2020/21. The possibility that disruptions are more frequent in a changing climate is important to consider, as the QBO affects predictability, stratospheric composition, and surface weather. However, this possibility is challenging to assess for a variety of reasons. For instance, the 2015/16 disruption has been attributed to anomalous easterly momentum flux from extratropical waves. By comparison, the 2020/21 disruption involves anomalous westerly forcing, less likely to originate from the same mechanism. We present a rich variety of QBO disruptions that spontaneously arise in integrations of the hightop NASA GISS Model E2.2. The disruptions loosely fall into several categories, some of which are analogous to the 2015/16 disruption and the 2020 disruption, as well as a previously undocumented possible disruption in 1988. Several factors appear to influence QBO disruptions in the model: natural variability, climate change, tropical SSTs, volcanic eruptions, and model physics/tuning. Although QBO representation is an ongoing challenge for models, the results point to a model-independent framework for assessment of disruptions.

Quasi-Biennial Oscillation↗
DOE OSTI2023

Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

Abstract The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.

plasma instabilities↗
DOE OSTI2020

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. Here, the explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

ARC disruption physics and strategy

Commonwealth Fusion Systems (CFS) plans to operate a tokamak power plant called ARC in the early 2030s. Tokamak plasmas have stability limits that, if crossed, lead to a rapid termination of the plasma, referred to as a disruption. Disruptions pose a melt risk to the first wall resulting from thermal and non-thermal particle heat fluxes, and an electromagnetic loading risk on all metal components within the equilibrium coils. A comprehensive set of models is used herein to provide an assessment of both mitigated and unmitigated ARC disruption loads. A preliminary massive gas injection system is baselined and a runaway electron mitigation coil option is proposed to close possible gaps in the baseline. It is predicted that all ARC disruption loads are within a factor of 2 of the disruption loads in SPARC, a tokamak presently under construction by CFS, and therefore SPARC provides an opportunity to calibrate models, test solutions and inform the design of ARC. The goal for ARC is disruption-free operation, however, the pragmatic design target is to withstand one mitigated disruption per day, and to restart the plasma following mitigation in tens of seconds without interrupting the power output. Unmitigated disruptions must be rare, and experience with unmitigated disruption impacts in SPARC will better define what rare means. The implications of this strategy for plasma disruptivity and disruption prediction are discussed, and operating the ARC scenario on SPARC is expected to refine the ARC final design and operational plan.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2020

Tidal Disruptions of Main Sequence Stars - I. Observable Quantities and their Dependence on Stellar and Black Hole Mass

This paper introduces a series of papers presenting a quantitative theory for the tidal disruption of main-sequence stars by supermassive black holes. Using fully general relativistic hydrodynamics simulations and MESA-model initial conditions, we explore the pericenter-dependence of tidal disruption properties for eight stellar masses(MM0.1510)and six black hole masses( MM105 105BH7). We present here the results most relevant to observations. The effects of internal stellar structure and relativity decouple for both the disruption cross section and the characteristic energy width of the debris. Moreover, the full disruption cross section is almost independent of Må for Må/Me3. Independent of Må, relativistic effects increase the critical pericenter distance for full disruption events by up to a factor of∼3 relative to the Newtonian prediction. The probability of a direct capture is also independent of Må; at MBH/Me;5×106 this probability is equal to the probability of a complete disruption. The breadth of the debris energy distribution ΔE can differ from the standard estimate by factors of0.35−2, depending on Må and MBH, implying a corresponding change(∝(ΔE)−3/2)in the characteristic mass-return timescale. We provide analytic forms, suitable for use in both event rate estimates and parameter inference, to describe all these trends. For partial disruptions, we find a nearly universal relation between the star’s angular momentum and the fraction of Må remaining. Within the “empty loss-cone” regime, partial disruptions must precede full disruptions. These partial disruptions can drastically affect the rate and appearance of subsequent total disruptions.

Taeho Ryu↗