Andrew D. Maris

Andrew D. Maris

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

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Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Chaos in the quantum Duffing oscillator in the semiclassical regime under parametrized dissipation

Physical Review E, 2021

We study the quantum dissipative Duffinng oscillator across a range of system sizes and environ- mental couplings under varying semiclassical approximations. Remarkably, we find that a parametrically invariant meta- attractor emerges at a specific length scale and noise-added classical models deviate strongly from quantum dynamics below this scale. Our findings also generalize the previous surprising result that classically regular orbits can have the greatest quantum-classical differences in the semiclassical regime. In particular, we show that the dynamical growth of quantum-classical differences is not determined by the degree of classical chaos.

Recommended citation: Maris, Andrew D., Bibek Pokharel, Sharan Ganjam Seshachallam, Moses ZR Misplon, and Arjendu K. Pattanayak. "Chaos in the quantum Duffing oscillator in the semiclassical regime under parametrized dissipation." Physical Review E 104, no. 2 (2021). https://journals.aps.org/pre/abstract/10.1103/PhysRevE.104.024206

Investigating Boosted Decision Trees as a Guide for Inertial Confinement Fusion Design

Physics of Plasmas, 2023

Inertially confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modelling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. In this paper, we have applied the Gradient Boosted Decision Tree (GBDT) machine learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While this data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.

Recommended citation: Maris, Andrew D., Shahab F. Khan, Michael M. Pokornik, J. Luc Peterson, Kelli D. Humbird, and Steven W. Haan. "Investigating boosted decision trees as a guide for inertial confinement fusion design." Physics of Plasmas 30, no. 4 (2023): 042713. https://pubs.aip.org/aip/pop/article/30/4/042713/2882225/Investigating-boosted-decision-trees-as-a-guide

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

Fusion Science and Technology, 2023

Tokamaks are often considered to be 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 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 paper, 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.

Recommended citation: Maris, Andrew D., Allen Wang, Cristina Rea, Robert Granetz, and Earl Marmar. "The impact of disruptions on the economics of a tokamak power plant." Fusion Science and Technology (2023).

Correlation of the L-mode density limit with edge collisionality

Nuclear Fusion, 2024

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 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 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 compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, achieves significantly higher accuracy (false positive rate (FPR) of 2.3% at a true positive rate (TPR) of 95%) of predicting density limit disruptions than the Greenwald limit (FPR of 13.4% at a TPR of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

Recommended citation: Maris, Andrew D., Cristina Rea, Alessandro Pau, Wenhui Hu, Bingjia Xiao, Robert Granetz, Earl Marmar, the EUROfusion Tokamak Exploitation team, the Alcator C-Mod team, the ASDEX Upgrade team, the DIII-D Team, the EAST Team, and the TCV team. "Correlation of the L-mode density limit with edge collisionality." Nuclear Fusion (2024).

talks

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

Published:

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

Distilling the density limit in tokamaks using data-driven techniques

Published:

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.

Correlation of the L-mode density limit with edge collisionality

Published:

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.

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

Published:

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.

teaching

Splash High School Outreach Teacher

Lecture, Splash, Massachusetts Institute of Technology, 2020

  • Lead the creation of a lesson plan for Color, Light, and Perception, a class about the psychology, physics, and philosophy of color
  • Co-taught two fully booked virtual classes of 30 students

Kaufman Teaching Certificate Program

Teaching certificate, Teaching and Learning Lab, Massachusetts Institute of Technology, 2024

  • Studied modern pedagogical methods (ex. backwards course design, active learning, utilizing generative AI) in semester-long program from MIT Teaching and Learning Lab
  • Practiced techniques by delivering two “microteaching” sessions and crafting a mock syllabus for course “Data science and machine learning methods in the physical sciences”