Andrew D. Maris

Andrew D. Maris

Plasma physicist, data scientist, and PhD candidate at the MIT Plasma Science and Fusion Center.

Talks and presentations

Correlation of the L-mode density limit with edge collisionality (Invited)

October 07, 2024

Talk, 66th Annual Meeting of the APS Division of Plasma Physics, Atlanta, Georgia

A novel, multi-machine database study (Alcator C-Mod, AUG, DIII-D, and TCV) identifies a two-variable, dimensionless stability boundary for the disruptive, L-mode density limit (LDL). This power law, with effective collisionality in the edge (ν*,edge) as the leading term, predicts the LDL with significantly higher accuracy than the widely-utilized Greenwald limit. With density being such a critical lever for tokamak performance, there has historically been a wide interest in understanding the operational boundary imposed by disruptive LDL events. This boundary is commonly estimated with the Greenwald limit, although it is widely accepted that the disruptive boundary is set by the plasma edge, not necessarily bulk plasma quantities. Additionally, input power dependencies have been reported, but vary significantly between studies. In this study, we create a multi-machine database of over 150 LDL events assembled from both carbon- (DIII-D, TCV) and metal-wall devices (Alcator C-Mod, AUG), with 3000+ additional non-LDL discharges for comparison. We find statistical/machine learning models utilizing edge density and temperature from Thomson Scattering achieve significantly higher LDL prediction performance than the Greenwald fraction. This is true for Greenwald-like scalings with line-averaged density, edge density, and with an input power scaling. Additionally, we identify a two-variable, dimensionless, stability boundary that retains the accuracy of the far more sophisticated neural network model. This analytic stability boundary is dominated by the contribution of the effective collisionality in the plasma edge. Our study demonstrates that LDLs occur at high edge collisionality, which bodes well for burning plasmas with naturally low edge collisionality due to self-heating. In the near-term, this collisionality boundary could also be readily measured for active density limit avoidance.

Correlation of the L-mode density limit with edge collisionality

September 03, 2024

Talk, IAEA 3rd Technical Meeting on Plasma Disruptions and their Mitigation, ITER Headquarters, France

A novel database study of the L-mode Density Limit (LDL) in metal- and carbon-wall devices (Alcator C-Mod, AUG, DIII-D, and TCV) identifies a two-variable, dimensionless stability boundary that predicts the LDL with significantly higher accuracy than the widely-utilized Greenwald limit. Historically, there has been broad interest in understanding the operational boundary imposed by the disruptive LDL because density is a critical lever for fusion performance. In this study, we create a multi-machine database of over 150 LDL events with 3000+ non-LDL discharges for evaluating the True and False Positive Rate. We find that data-driven models involving edge density and temperature measurements achieve significantly higher LDL prediction performance than the Greenwald fraction. Additionally, we utilize a Support Vector Machine to identify an analytic, dimensionless, stability boundary that retains the accuracy of the more sophisticated models, such as a Neural Network and Random Forest. The boundary is dominated by the effective collisionality in the plasma edge. This finding suggests that burning plasmas, with naturally low edge collisionality due to self-heating, may be able to achieve super-Greenwald densities. Additionally, in current and “next step” devices such as ITER, this collisionality boundary can also be deployed for active density limit avoidance.

Distilling the density limit in tokamaks using data-driven techniques

January 22, 2024

Guest seminar, AstroAI Journal Club - Harvard & Smithsonian Center for Astrophyics, Cambridge, MA

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

The impact of disruptions on the economics of a tokamak power plant

March 14, 2023

Guest seminar, MIT Energy Intiative - Fusion Study Team, Cambridge, MA

Tokamaks are often considered a leading candidate for near term, cost-effective fusion energy, but these devices are susceptible to sudden loss of confinement events called “disruptions.” The threat of disruptions has garnered serious attention in research and development for the next generation of burning plasma experiments, such as ITER, but has received little treatment in economic studies of magnetic fusion energy. In this talk, we present a model for quantifying the effect of disruptions on the cost of electricity produced by a tokamak power plant (TPP). We outline the various ways disruptions increase costs and decrease revenues, introduce metrics to quantify these effects, and add them to a Levelized Cost of Electricity (LCOE) model. Additionally, we identify several rate-limiting repair steps and introduce a classification system of disruption types based on the time to return to operations. We demonstrate how the LCOE model can be used to find the cost of electricity and requirements for disruption handling of a TPP, and we further highlight where future research can have a strong impact in neutralizing the “showstopping” potential of disruptions