GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad-Shafranov equation.

The force-balanced state of magnetically confined plasmas heated up to 100 million degrees Celsius must be sustained long enough to achieve a burning-plasma state, such as in the case of ITER, a fusion reactor that promises a net energy gain. This force balance between the Lorentz force and the pressure gradient force, known as a plasma equilibrium, can be theoretically portrayed together with Maxwell's equations as plasmas are collections of charged particles. Nevertheless, identifying the plasma equilibrium in real time is challenging owing to its free-boundary and ill-posed conditions, which conventionally involves iterative numerical approach with a certain degree of subjective human decisions such as including or excluding certain magnetic measurements to achieve numerical convergence on the solution as well as to avoid unphysical solutions. Here, we introduce GS-DeepNet, which learns plasma equilibria through solely unsupervised learning, without using traditional numerical algorithms. GS-DeepNet includes two neural networks and teaches itself. One neural network generates a possible candidate of an equilibrium following Maxwell's equations and is taught by the other network satisfying the force balance under the equilibrium. Measurements constrain both networks. Our GS-DeepNet achieves reliable equilibria with uncertainties in contrast with existing methods, leading to possible better control of fusion-grade plasmas.

Design of Non-Incendive High-Voltage Liquid Electrolyte Formulation for Safe Lithium-Ion Batteries.

Battery safety has an ever-increasing significance and is required for consumer's safety. The high flammability of traditional organic liquid electrolyte, which consists of ethylene carbonate and highly flammable linear carbonate, is one of the major reasons for thermal runaway and battery fire events. Replacement of flammable liquid electrolyte with non-incendive one is urgently needed for safe lithium-ion batteries. A fluorinated linear sulfate paired with 1 m LiPF6 was developed and evaluated as a solvent of non-incendive liquid electrolyte for a use in high-voltage (4.4 V) and high-temperature (45 °C) LiNi0.82 Mn0.07 Co0.11 O2 (NCM811) chemistry-based lithium-ion batteries. Non-incendive liquid electrolyte containing sulfate with two trifluoroethyl groups exhibited superior anodic and thermal stability and promoted cathode-electrolyte and anode-electrolyte interfacial stability, compared to flammable traditional electrolyte. Non-incendive electrolyte showed markedly improved 300 cycle performance of an industrial graphite‖NCM811 lithium-ion pouch cell with a nominal capacity of 730 mAh under harsh conditions, and high safety of 10 V overcharge abuse tolerance, from which safe and high-performing high-energy lithium-ion batteries and battery-powered electric vehicles and energy storage system are anticipated.

Bayesian inference of spatially resolved Zeff profiles from line integrated bremsstrahlung spectra.

In nuclear fusion research, the effective ion charge Zeff, which characterizes the overall content of impurities, can be experimentally derived from the plasma electron-ion bremsstrahlung, given the electron density ne and temperature Te. At Wendelstein 7-X, a multichannel near-infrared spectrometer is installed to collect the plasma bremsstrahlung along 27 lines of sight covering more than half the plasma cross section, which provides information on Zeff over the entire plasma radius. To infer spatially resolved Zeff profiles, a Bayesian model is developed in the Minerva framework. Zeff, ne, and Te profiles are modeled as Gaussian processes, whose smoothness is determined by hyperparameters. These profiles are transformed to fields in Cartesian coordinates, given the poloidal magnetic flux surfaces calculated by the variational moments equilibrium code. Given all these physical quantities, the model predicts line-of-sight integrals of near-infrared bremsstrahlung spectra. The model includes the predictive (forward) models of the interferometer, Thomson scattering system, and visible and near-infrared spectrometers. Given the observations of all these diagnostics, the posterior probability distribution of Zeff profiles is calculated and shown as an inference solution. The smoothness (gradient) of the profiles is optimally chosen by Bayesian Occam's razor. Furthermore, wall reflections can significantly pollute the measurements of the plasma bremsstrahlung, which leads to over-estimation of Zeff values in the edge region. In the first results presented in this work, this problem does not appear, and the posterior samples of Zeff profiles are overall plausible and consistent with Zeff values inferred, given the data from the single-channel visible spectrometer.

Bayesian modeling of microwave radiometer calibration on the example of the Wendelstein 7-X electron cyclotron emission diagnostic.

This paper reports about a novel approach to the absolute intensity calibration of an electron cyclotron emission (ECE) spectroscopy system. Typically, an ECE radiometer consists of tens of separated frequency channels corresponding to different plasma locations. An absolute calibration of the overall diagnostic including near plasma optics and transmission line is achieved with blackbody sources at LN2 temperature and room temperature via a hot/cold calibration mirror unit. As the thermal emission of the calibration source is typically a few thousand times lower than the receiver noise temperature, coherent averaging over several hours is required to get a sufficient signal to noise ratio. A forward model suitable for any radiometer calibration using the hot/cold method and a periodic switch between them has been developed and used to extract the voltage difference between the hot and cold temperature source via Bayesian analysis. In contrast to the classical analysis which evaluates only the reference temperatures, the forward model takes into account intermediate effective temperatures caused by the finite beam width and thus uses all available data optimally. This allows the evaluation of weak channels where a classical analysis would not be feasible, is statistically rigorous, and provides a measurement of the beam width. By using a variance scaling factor, a model sensitive adaptation of the absolute uncertainties can be implemented, which will be used for the combined diagnostic Bayesian modeling analysis.

Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time.

A Bayesian with Gaussian process-based numerical method to impute a few missing magnetic signals caused by impaired magnetic probes during tokamak operations is developed such that the real-time reconstruction of magnetic equilibria, whose performance strongly depends on the measured magnetic signals and their intactness, is affected minimally. Likelihood of the Bayesian model constructed with Maxwell's equations, specifically Gauss's law for magnetism and Ampère's law, results in an infinite number of solutions if two or more magnetic signals are missing. This undesirable characteristic of the Bayesian model is remediated by coupling the model with the Gaussian process. Our proposed numerical method infers nine non-consecutive missing magnetic signals correctly in less than 1 ms suitable for the real-time reconstruction of magnetic equilibria during tokamak operations.

Bayesian modelling of the emission spectrum of the Joint European Torus Lithium Beam Emission Spectroscopy system.

A Bayesian model of the emission spectrum of the JET lithium beam has been developed to infer the intensity of the Li I (2p-2s) line radiation and associated uncertainties. The detected spectrum for each channel of the lithium beam emission spectroscopy system is here modelled by a single Li line modified by an instrumental function, Bremsstrahlung background, instrumental offset, and interference filter curve. Both the instrumental function and the interference filter curve are modelled with non-parametric Gaussian processes. All free parameters of the model, the intensities of the Li line, Bremsstrahlung background, and instrumental offset, are inferred using Bayesian probability theory with a Gaussian likelihood for photon statistics and electronic background noise. The prior distributions of the free parameters are chosen as Gaussians. Given these assumptions, the intensity of the Li line and corresponding uncertainties are analytically available using a Bayesian linear inversion technique. The proposed approach makes it possible to extract the intensity of Li line without doing a separate background subtraction through modulation of the Li beam.

Comparison of the inhibitory effect of gonadotropin releasing hormone (GnRH) agonist, selective estrogen receptor modulator (SERM), antiprogesterone on myoma cell proliferation in vitro.

Uterine myomas are the most common gynecologic tumor in women of reproductive age. Treatment options of uterine myomas consist of surgical, medical and interventional therapy such as uterine artery embolization or myolysis. Given that it is the most common type of tumor in women of reproductive age, the treatment of uterine myomas must prioritize uterine conservation. There are several drugs for medical treatment of uterine myoma such as gonadotropin releasing hormone (GnRH) agonist, selective estrogen receptor modulator (SERM) and antiprogesterone. The objective of this study was to compare the effect of GnRH agonist, SERM, and antiprogesterone in the treatment of uterine myomas in vitro. The effect of drugs was evaluated through the cell viability assay in cultured leiomyoma cells, western blot analysis of proliferating cell nuclear antigen (PCNA), and BCL-2 protein expression. As a result, mifepristone single-treated group represents the most significant reduction in myoma cell viability and proliferation. When pretreated with leuprolide acetate, raloxifene shows more significant reduction in myoma cell viability and proliferation than mifepristone. This study suggests one of the possible mechanisms how medications act on uterine myoma, especially at the molecular level.