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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli . It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads— Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
DOE OSTI2024

Femtoscopic correlations of identical charged pions and kaons in $pp$ collisions at $\sqrt{s}$ = 13 TeV with event-shape selection

Collective behavior has been observed in high-energy heavy-ion collisions for several decades. Collectivity is driven by the high particle multiplicities that are produced in these collisions. At the CERN Large Hadron Collider (LHC), features of collectivity have also been seen in high-multiplicity proton-proton collisions that can attain particle multiplicities comparable to peripheral Pb-Pb collisions. One of the possible signatures of collective behavior is the decrease of femtoscopic radii extracted from pion and kaon pairs emitted from high-multiplicity collisions with increasing pair transverse momentum. This decrease can be described in terms of an approximate transverse mass scaling. In the present work, femtoscopic analyses are carried out by the ALICE Collaboration on charged pion and kaon pairs produced in pp collisions at $\sqrt{s}$=13TeV from the LHC to study possible collectivity in pp collisions. The event-shape analysis method based on transverse sphericity is used to select for spherical versus jetlike events, and the effects of this selection on the femtoscopic radii for both charged pion and kaon pairs are studied. This is the first time this selection method has been applied to charged kaon pairs. An approximate transverse-mass scaling of the radii is found in all multiplicity ranges studied when the difference in the Lorentz boost for pions and kaons is taken into account. This observation does not support the hypothesis of collective expansion of hot and dense matter that should only occur in high-multiplicity events. A possible alternate explanation of the present results is based on a scenario of common emission conditions for pions and kaons in pp collisions for the multiplicity ranges studied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Untangling the sequence of events during the S 2 → S 3 transition in photosystem II and implications for the water oxidation mechanism

In oxygenic photosynthesis, light-driven oxidation of water to molecular oxygen is carried out by the oxygen-evolving complex (OEC) in photosystem II (PS II). Recently, we reported the room-temperature structures of PS II in the four (semi)stable S-states, S 1 , S 2 , S 3 , and S 0 , showing that a water molecule is inserted during the S 2 → S 3 transition, as a new bridging O(H)-ligand between Mn1 and Ca. To understand the sequence of events leading to the formation of this last stable intermediate state before O 2 formation, we recorded diffraction and Mn X-ray emission spectroscopy (XES) data at several time points during the S 2 → S 3 transition. At the electron acceptor site, changes due to the two-electron redox chemistry at the quinones, Q A and Q B , are observed. At the donor site, tyrosine Y Z and His190 H-bonded to it move by 50 µs after the second flash, and Glu189 moves away from Ca. This is followed by Mn1 and Mn4 moving apart, and the insertion of O X (H) at the open coordination site of Mn1. This water, possibly a ligand of Ca, could be supplied via a “water wheel”-like arrangement of five waters next to the OEC that is connected by a large channel to the bulk solvent. XES spectra show that Mn oxidation (τ of ∼350 µs) during the S 2 → S 3 transition mirrors the appearance of O X electron density. This indicates that the oxidation state change and the insertion of water as a bridging atom between Mn1 and Ca are highly correlated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Progress Towards Interpretable Machine Learning-based Disruption Predictors Across Tokamaks

In this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently missing for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. Thanks to the univariate analysis on the features used in such data-driven applications on both tokamaks, we are able to statistically highlight differences in the dominant disruption precursors: JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge cooling mechanisms that destabilize dangerous MHD modes. Even though the analyzed datasets are characterized by such intrinsic differences, we show how data-driven algorithms trained on one device can be used to predict and interpret disruptive scenarios on the other. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be used to explain a disruptive behavior on another device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention.

Yang, Hung-Chia (ORCID:0000000161439904)↗
DOE OSTI2022

Preface: special issues on photosystem II

Here, the appearance of oxygenic photosynthesis 3–4 billion years ago is arguably one of the most important evolutionary developments which radically transformed both the biotic and abiotic landscapes of Earth. The presence of molecular oxygen enabled the evolution of complex lifeforms requiring large amounts of chemical energy which could only be produced by aerobic respiration. Additionally, molecular oxygen remodeled the geochemistry of the planet during its transition from a reducing to an oxidizing atmosphere during the Great Oxidation Event. Central to these processes is Photosystem II (PS II), the light energy-driven, water-plastoquinone oxidoreductase which initially evolved in cyanobacteria and subsequently was incorporated, along with Photosystem I (PS I), the cytochrome b 6 f complex and the Ndh complex, into all algae and higher plants by the process of endosymbiosis. This membrane protein complex releases molecular oxygen and protons as the byproducts of water splitting and produces plastoquinol, which provides reducing equivalents that are used sequentially by the cytochrome b 6 /f complex, PS I, and ultimately, as NADPH + H + , by the Calvin–Benson–Bassham cycle. Additionally, the protons released by PS II (and those pumped by the cytochrome b 6 /f complex and the NDH complex (during cyclic electron transfer)) form the proton motive force that drives ATP synthesis via the ATP Synthase, thus providing the chemical energy source for carbon fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Wildfire and power grid nexus in a changing climate

Global wildfire events have had increasingly severe impacts in recent years, particularly in the western USA, driven by extreme fire-weather conditions, fuel accumulation and multiple ignition sources. Wildfires sparked by power lines tend to be larger and more destructive, as they often occur during high winds, which accelerate the spread of fires. Moreover, efforts to contain wildfires frequently result in power outages, causing considerable economic disruption. Here, in this Review, we examine wildfire risks related to power-line-induced ignitions, infrastructure damage, climate-induced environmental impacts, grid operational risks, real-time grid management risks, vegetation management risks, and financial and funding risks in the context of a changing climate and their interdependence with power grid infrastructures. We then explore the resilience of power grids under wildfire threats, looking at risk analysis, prediction and mitigation strategies. The Review also shares practical insights and experiences in the USA to inform researchers, policymakers and industry professionals.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Graph Convolutional Network for Active Distribution System Anomaly Detection Considering Measurement Spatial-Temporal Correlations

The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

active distribution system↗
DOE OSTI2020

FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019)

Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R2 = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Dissipation Enables Strongly Detuning-Dependent Interference in Pulsed Dynamical Decoupling

One of the defining features of pulsed dynamical decoupling is its suppression of a driven qubit's sensitivity to static detuning errors between the drive field and qubit resonance. In this paper, we show that dissipation, in the form of excited-state decay, can change this behavior entirely, producing an interference signal with a strong detuning dependence. This signal arises from decay during driven evolution and relies on coherence retained by the qubit after such a decay event. We develop analytical and numerical models that capture the underlying mechanism and observe this same dissipation-induced detuning dependence experimentally in both a free-space strontium atom interferometer and a superconducting transmon qubit system. We also use this dissipation-induced detuning dependence as the basis for a new spectroscopic technique called Dissipative Carr-Purcell Spectroscopy (DCPS) and compare it with a traditional Ramsey sequence. Our results establish a regime of pulsed dynamical decoupling in which dissipation reshapes, rather than merely degrades, coherent control, and we expect these dynamics to be relevant to a wide range of quantum systems.

DeRose, Kenneth [Northwestern U.; Fermilab] (ORCID↗
DOE OSTI2022

The Press and Pulse of Climate Change: Extreme Events in the Colorado River Basin

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate-related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate-change-driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long-term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Development of SAM Code Capabilities for Safety Analysis of GCR Air-ingress Events

Air-ingress following a depressurized loss-of-forced-cooling (DLOFC) event is a challenging, multiphysics safety scenario for High-Temperature Gas-Cooled Reactors (HTGRs), involving coupled gas composition transport, buoyancy-driven flow redistribution, graphite oxidation, and structural heat-up. Despite its importance — air ingress is a key scenario identified in the PIRT process for the HTGRs — existing system-level safety codes have lacked the integrated capability to simulate the complete event sequence with high confidence. This report documents the development, validation, and demonstration of three new capabilities in the SAM code to address this gap: (1) a multi-component gas mixture flow model with binary diffusion to track the helium-air composition and its effect on system density and flow; (2) a 0-D graphite oxidation model based on the Roes correlation, including oxygen consumption and exothermic heat release; and (3) an isentropic critical flow model for accurate representation of primary system depressurization through a break. These capabilities are validated against two benchmark experiments. The NSTF heavy-gas ingress experiment validates the multi-component flow model: SAM correctly reproduces the rapid buoyancydriven flow stagnation and subsequent natural circulation recovery driven by composition-dependent density changes. The NACOK graphite oxidation experiment validates the oxidation model: SAM predicts a bottom-level graphite weight loss of 25%, in close agreement with the measured 24%, and reproduces the strong axial nonuniformity and block-geometry dependence of oxidation, at a level comparable to the SPECTRA and TINTE codes. The validated capabilities are then exercised together in an integrated, reactor-scale simulation of a DLOFC air-ingress transient in a simplified HTR-PM pebble-bed reactor. In a single calculation spanning approximately 8 days, SAM reproduces the complete accident sequence: rapid depressurization, densityand diffusion-driven air ingress over ˜15 hours, onset of buoyancy-driven natural circulation, exothermic graphite oxidation with a peak fuel temperature at ˜62 hours, and eventual passive cooldown. These results demonstrate that SAM now provides the nuclear community with a preliminarily validated, modern systemlevel tool for HTGR air-ingress safety analysis, filling a recognized capability gap. Future extensions to broaden species tracking, improve oxidation chemistry, and refine the reactor model are discussed.

Yang, Gang↗
DOE OSTI2020

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗
DOE OSTI2025

Quadrupolar density structures in driven magnetic reconnection experiments with a guide field

Magnetic reconnection is a ubiquitous process in plasma physics, driving rapid and energetic events such as coronal mass ejections. Reconnection between magnetic fields with arbitrary shear can be decomposed into an anti-parallel reconnecting component and a non-reconnecting guide-field component, which is parallel to the reconnecting electric field. This guide field modifies the structure of the reconnection layer and the reconnection rate. We present results from experiments on the MAIZE pulsed-power generator (500 kA peak current, 200 ns rise time), which use two exploding wire arrays, tilted in opposite directions, to embed a guide field in the plasma flows with a relative strength b≡B g /B rec =0, 0.4, or 1. The reconnection layers in these experiments have widths that are less than the ion skin depth, d i =c/ω pi , indicating the importance of the Hall term, which generates a distinctive quadrupolar magnetic field structure along the separatrices of the reconnection layer. Using laser imaging interferometry, we observe quadrupolar structures in the line-integrated electron density, consistent with the interaction of the embedded guide field with the quadrupolar Hall field. Our measurements extend over much larger length scales (40d i ) at higher β (∼1) than previous experiments, providing an insight into the global structure of the reconnection layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks

Here in this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗