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During detachment, a buffer of neutral atoms and molecules builds up between the target and the ionising plasma. Collisions between the plasma and the molecules play an important role in the detachment process. Studies of plasma-molecular kinetics indicate that the gas temperature is increased during detachment for a wide range of conditions on the MAST-U and TCV tokamaks. This is related to an increased $\mathrm{D}_2$ lifetime during detachment, leading to more plasma-molecule collisions that raise the molecular temperature. Such collisions subsequently result in significant power and momentum losses to the divertor plasma during detachment. Using a simplified inference, these losses are estimated using the rotational temperature, neutral pressure and ionisation front position. Significant power losses (about $10\%$ of $P_{SOL}$) and dominant momentum losses (majority of the upstream pressure) from plasma-molecule collisions are inferred experimentally in long-legged, strongly baffled, detached divertors (MAST-U Super-X divertor), consistent with SOLPS-ITER simulations. The vibrational distribution obtained is compared with an Eirene-like collisional-radiative model setup, indicating some qualitative agreements and disagreements, potentially highlighting model gaps. These interpretations highlight the importance of plasma-molecular collisions, leading to power and momentum losses during detachment. Our analysis and reduced modelling of these processes provide further insights into detachment control observations, the workings of long-legged divertors and divertor power balance.

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics, and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review delves into how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been harnessed to tackle CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic., Comment: 29 pages, 11 figures

2D electron density profiles obtained from coherence imaging spectroscopy in different MAST-U divertor conditions are compared. The data includes variations of strike point position, core electron density, and heating power. The improved performance of the long-legged divertors results in a lower electron density and particle flux at the target compared to configurations with smaller strike point major radius, while also being characterized by lower temperatures and deeper detachment. Comparisons against SOLPS simulations generally show good agreement in profile shape along and across the separatrix. The peaking of the electron density downstream of the detachment front is associated with significant neutral drag acting on the plasma flow.

Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors, as these outliers are particularly challenging to manage during operation. Incorporating planning information -- such as known data about users' future behavior or scheduled events -- has the potential to significantly enhance the accuracy and specificity of forecasts. Although there have been attempts to integrate such expected future behavior, these efforts consistently rely on conventional regression models to process this information. These models often lack the flexibility and capability to effectively incorporate both dynamic, forward-looking contextual inputs and historical data. To address this challenge, we conceptualize this combined forecasting and regression challenge as a sequence-to-sequence modeling problem and demonstrate, with three distinct models, that our contextually enhanced transformer models excel in this task. By leveraging schedule-based contextual information from the Swiss railway traction network, our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption. Specifically, enhancing the transformer models with contextual information resulted in an average reduction of mean absolute error by 40.6\% , whereas other state-of-the-art methods did not demonstrate any significant improvement., Comment: 22 pages, 8 figures and tables, journal paper

Bayesian priors are investigated for detecting targets of known spectral signature (but unknown strength) in cluttered backgrounds. A specific problem is the construction (or ``sculpting'') of a Bayesian prior that uniformly outperforms its non-Bayesian counterpart, the nominally sub-optimal but widely used Generalized Likelihood Ratio Test (GLRT)., Comment: 11 pages, 7 figures

The physical mechanisms regulating the power sharing at the outer targets of L-mode double-null (DN) configurations are investigated using nonlinear, flux-driven, three-dimensional two-fluid simulations. Scans of parameters that regulate the turbulent level, such as the plasma resistivity and the magnetic imbalance, reveal that the power asymmetry in DN configurations is determined by the combined effects of diamagnetic drift, turbulence, and geometrical factor. Leveraging these observations, an analytical theory-based scaling law for the power-sharing asymmetry is derived and compared with nonlinear simulations. These comparisons indicate that the scaling law effectively captures the trends observed in simulations. Validation with experimental data from TCV DN discharges demonstrates agreement of the scaling law with the experimental results., Comment: 30 pages, 9 figures

Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises, for example, due to unpublished data and/or source code and the sensitivity of ML training conditions. Although different solutions have been proposed to address this issue, such as using ML platforms, the level of reproducibility in ML-driven research remains unsatisfactory. Therefore, in this article, we discuss the reproducibility of ML-driven research with three main aims: (i) identifying the barriers to reproducibility when applying ML in research as well as categorize the barriers to different types of reproducibility (description, code, data, and experiment reproducibility), (ii) discussing potential drivers such as tools, practices, and interventions that support ML reproducibility, as well as distinguish between technology-driven drivers, procedural drivers, and drivers related to awareness and education, and (iii) mapping the drivers to the barriers. With this work, we hope to provide insights and to contribute to the decision-making process regarding the adoption of different solutions to support ML reproducibility., Comment: Pre-print of submission for the AI Magazine - comments to this pre-print are very welcome

2D profiles of electron density and neutral temperature are inferred from multi-delay Coherence Imaging Spectroscopy data of divertor plasmas using a non-linear inversion technique. The inference is based on imaging the spectral line-broadening of Balmer lines and can differentiate between the Doppler and Stark broadening components by measuring the fringe contrast at multiple interferometric delays simultaneously. The model has been applied to images generated from simulated density profiles to evaluate its performance. Typical mean absolute errors of 30 percent are achieved, which are consistent with Monte Carlo uncertainty propagation accounting for noise, uncertainties in the calibrations, and in the model inputs. The analysis has been tested on experimental data from the MAST-U Super-X divertor, where it infers typical electron densities of 2-3 $10^{19}$ m$^{-3}$ and neutral temperatures of 0-2 eV during beam-heated L-mode discharges. The results are shown to be in reasonable agreement with the other available diagnostics.

Pumped-storage hydropower plants (PSH) actively participate in grid power-frequency control and therefore often operate under dynamic conditions, which results in rapidly varying system states. Predicting these dynamically changing states is essential for comprehending the underlying sensor and machine conditions. This understanding aids in detecting anomalies and faults, ensuring the reliable operation of the connected power grid, and in identifying faulty and miscalibrated sensors. PSH are complex, highly interconnected systems encompassing electrical and hydraulic subsystems, each characterized by their respective underlying networks that can individually be represented as graphs. To take advantage of this relational inductive bias, graph neural networks (GNNs) have been separately applied to state forecasting tasks in the individual subsystems, but without considering their interdependencies. In PSH, however, these subsystems depend on the same control input, making their operations highly interdependent and interconnected. Consequently, hydraulic and electrical sensor data should be fused across PSH subsystems to improve state forecasting accuracy. This approach has not been explored in GNN literature yet because many available PSH graphs are limited to their respective subsystem boundaries, which makes the method unsuitable to be applied directly. In this work, we introduce the application of spectral-temporal graph neural networks, which leverage self-attention mechanisms to concurrently capture and learn meaningful subsystem interdependencies and the dynamic patterns observed in electric and hydraulic sensors. Our method effectively fuses data from the PSH's subsystems by operating on a unified, system-wide graph, learned directly from the data, This approach leads to demonstrably improved state forecasting performance and enhanced generalizability., Comment: 11 pages, 8 figures, conference

Studying the process of divertor detachment and the associated complex interplay of plasma dynamics and atomic physics processes is of utmost importance for future fusion reactors. Whilst simplified analytical models exist to interpret the general features of detachment, they are limited in their predictive power, and complex 2D or even 3D codes are generally required to provide a self-consistent picture of the divertor. As an intermediate step, 1D models of the Scrape-Off Layer (SOL) can be particularly insightful as the dynamics are greatly simplified, while still self-consistently including various source and sink terms at play, as well as additional important effects such as flows. These codes can be used to shed light on the physics at play, to perform fast parameter scans, or to interpret experiments. In this paper, we introduce the SPLEND1D (Simulator of PLasma ENabling Detachment in 1D) code: a fast and versatile 1D SOL model. We present in detail the model that is implemented in SPLEND1D. We then employ the code to explore various elements of detachment physics for parameters typical of the Tokamak \`a Configuration Variable (TCV), including the atomic physics and other processes behind power and momentum losses, and explore the various hypotheses and free parameters of the model.

L-mode negative triangularity (NT) operation is a promising alternative to the positive triangularity (PT) H-mode as a high-confinement ELM-free operational regime. In this work, two TCV L-mode lower single null Ohmic discharges with opposite triangularity $\delta \simeq \pm 0.3$ are investigated using SOLPS-ITER modelling. The main focus is the exploration of the reasons behind the experimentally observed feature of NT plasmas being more difficult to detach than similar PT experiments. SOLPS-ITER simulations are performed assuming the same anomalous diffusivity for particles $D_n^{AN}$ and energy $\kappa_{e/i}^{AN}$ in PT and NT. Nonetheless, the results clearly show dissimilar transport and accumulation of neutral particles in the scrape-off layer (SOL) of the two configurations, which consequently gives rise to different ionization sources for the plasma and produces different poloidal and cross-field fluxes. Simulations also recover the experimental feature of the outer target being hotter in the NT scenario (with $T_{e, NT} \gtrsim 5 \, \mathrm{eV}$) than in the PT counterpart., Comment: 18 pages, 8 figures

Exhausting power from the hot fusion core to the plasma-facing components is one of the biggest challenges in fusion energy. The MAST Upgrade tokamak uniquely integrates strong containment of neutrals within the exhaust area (divertor) with extreme divertor shaping capability. By systematically altering the divertor shape, this study shows the strongest evidence to date that long-legged divertors with a high magnetic field gradient (total flux expansion) deliver key power exhaust benefits without adversely impacting the hot fusion core. These benefits are already achieved with relatively modest geometry adjustments that are more feasible to integrate in reactor designs. Benefits include reduced target heat loads and improved access to, and stability of, a neutral gas buffer that 'shields' the target and enhances power exhaust (detachment). Analysis and model comparisons shows these benefits are obtained by combining multiple shaping aspects: long-legged divertors have expanded plasma-neutral interaction volume that drive reductions in particle and power loads, while total flux expansion enhances detachment access and stability. Containing the neutrals in the exhaust area with physical structures further augments these shaping benefits. These results demonstrate strategic variation in the divertor geometry and magnetic topology is a potential solution to one of fusion's biggest challenges: power exhaust.

This experimental study presents an in-depth investigation of the performance of the MAST-U Super-X divertor during NBI-heated operation (up to 2.5 MW) focussing on volumetric ion sources and sinks as well as power losses during detachment. The particle balance and power loss analysis revealed the crucial role of Molecular Activated Recombination and Dissociation (MAR and MAD) ion sinks in divertor particle and power balance, which remain pronounced in the change from ohmic to higher power (NBI heated) L-mode conditions. The importance of MAR and MAD remains with double the absorbed NBI heating. MAD results in significant power dissipation (up to $\sim 20 \%$ of $P_{SOL}$), mostly in the cold ($T_e < 5$ eV) detached region. Theoretical and experimental evidence is found for the potential contribution of $D^-$ to MAR and MAD, which warrants further study. These results suggest that MAR and MAD can be relevant in higher power conditions than the ohmic conditions studied previously. Post-processing reactor-scale simulations shows that MAR and MAD can play a significant role in divertor physics and synthetic diagnostic signals of reactor-scale devices, which are currently underestimated in exhaust simulations. This raises implications for the accuracy of reactor-scale divertor simulations of particularly tightly baffled (alternative) divertor configurations.

Plasma edge simulations with the SOLPS-ITER code are performed to study the influence of divertor baffles on nitrogen-seeded detachment in TCV single-null, L-mode discharges. Scans of nitrogen seeding rate are conducted in both baffled and unbaffled TCV divertors, where the nitrogen seeding with baffles is found to yield lower target temperatures and heat fluxes than with baffles-only and with seeding-only. The cumulative effects of baffles and seeding on target parameters are explained by the two-point model. The divertor neutral density and neutral compression increase with baffles, due to lower divertor to main chamber neutral conductance, as explained by a schematic neutral transport model with baffles. The nitrogen retention, defined as the ratio of average nitrogen nuclei density in divertor and main chamber, increases with the seeding rate if baffled, and remains constant if unbaffled. At the same outboard mid-plane separatrix plasma density, the nitrogen retention with baffles is lower than the unbaffled retention at low seeding levels and is higher at high seeding levels, which is explained by the changes of nitrogen ion and neutral transport with baffles and seeding. The baffled carbon retention is higher than the unbaffled retention due to lower divertor to main chamber carbon neutral conductance. Baffles increase the divertor radiation. The predicted trends of target parameters, the distribution of neutrals and radiations are well supported by TCV experiments, though discrepancies in the absolute values remain. The simulations yield an overall colder and denser divertor, consistent with previous SOLPS-ITER simulations of Ohmically heated L-modes in TCV. The successful comparison of simulation and experiment, together with the understanding gained from the neutral transport model, increases the confidence in the SOLPS simulations for the next TCV divertor upgrade.

This paper presents a quantitative validation of SOLPS-ITER simulations against the TCV-X21 reference case and provides insights into the neutral dynamics and ionization source distribution in this scenario. TCV-X21 is a well-diagnosed diverted L-mode sheath-limited plasma scenario in both toroidal field directions, designed specifically for the validation of turbulence codes [D.S. Oliveira, T. Body, et al 2022 Nucl. Fusion 62 096001]. Despite the optimization to reduce the impact of the neutral dynamics, the absence of neutrals in previous turbulence simulations of TCV-X21 was identified as a possible explanation for the disagreements with the experimental data in the divertor region. This motivates the present study with SOLPS-ITER that includes kinetic neutral dynamics via EIRENE. Five new observables are added to the extensive, publicly available TCV-X21 dataset. These are three deuterium Balmer lines in the divertor and neutral pressure in the common and private flux regions. The quantitative agreement metric is combined with the conjugate gradient method to approach the SOLPS-ITER input parameters that return the best overall agreement with the experiment. A proof-of-principle of this method results in a modest improvement in the level-of-agreement; shortcomings of the method and how to improve it are discussed. Alternatively, a scan of the particle and heat diffusion coefficients shows an improvement of 10.4% beyond the agreement level achieved by the gradient method. The result is found for an increased transport coefficient compared to what is usually used for TCV L-mode plasmas, suggesting the need for accurate self-consistent turbulence models for predictive boundary simulations. The simulations indicate that ~65% of the total ionization occurs in the SOL, motivating the inclusion of neutrals in future turbulence simulations towards improved agreement with the experiment.

In recent years, negative triangularity (NT) has emerged as a potential high-confinement L-mode reactor solution. In this work, detachment is investigated using core density ramps in lower single null Ohmic L-mode plasmas across a wide range of upper, lower, and average triangularity (the mean of upper and lower triangularity: $\delta$) in the TCV tokamak. It is universally found that detachment is more difficult to access for NT shaping. The outer divertor leg of discharges with $\delta\approx -0.3$ could not be cooled to below $5~\mathrm{eV}$ through core density ramps alone. The behavior of the upstream plasma and geometrical divertor effects (e.g. a reduced connection length with negative lower triangularity) do not fully explain the challenges in detaching NT plasmas. Langmuir probe measurements of the target heat flux widths ($\lambda_q$) were constant to within 30% across an upper triangularity scan, while the spreading factor $S$ was lower by up to 50% for NT, indicating a generally lower integral Scrape-Off Layer width, $\lambda_{int}$. The line-averaged core density was typically higher for NT discharges for a given fuelling rate, possibly linked to higher particle confinement in NT. Conversely, the divertor neutral pressure and integrated particle fluxes to the targets were typically lower for the same line-averaged density, indicating that NT configurations may be closer to the sheath-limited regime than their PT counterparts, which may explain why NT is more challenging to detach.

Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learning (ML) and artificial intelligence (AI) research. Often, this is the case due to unpublished data and/or source-code, and due to sensitivity to ML training conditions. Although different solutions to address this issue are discussed in the research community such as using ML platforms, the level of reproducibility in ML-driven research is not increasing substantially. Therefore, in this mini survey, we review the literature on reproducibility in ML-driven research with three main aims: (i) reflect on the current situation of ML reproducibility in various research fields, (ii) identify reproducibility issues and barriers that exist in these research fields applying ML, and (iii) identify potential drivers such as tools, practices, and interventions that support ML reproducibility. With this, we hope to contribute to decisions on the viability of different solutions for supporting ML reproducibility., Comment: This research is supported by the Horizon Europe project TIER2 under grant agreement No 101094817

The Super-X Divertor (SXD) is an alternative divertor configuration leveraging total flux expansion at the Outer Strike Point (OSP). While the extended 2-Point Model (2PM) predicts facilitated detachment access and control in the SXD configuration, these attractive features are not always retrieved experimentally. These discrepancies are at least partially explained by the effect of parallel flows which, when self-consistently included in the 2PM, reveal the role of total flux expansion on the pressure balance and weaken the total flux expansion effect on detachment access and control, compared to the original predictions. This new model can partially explain the discrepancies between the 2PM and experiments performed on tokamak \`a configuration variable (TCV), in ohmic L-mode scenarios, which are particularly apparent when scanning the OSP major radius Rt. In core density ramps in lower Single-Null (SN) configuration, the impact of Rt on the CIII emission front movement in the divertor outer leg - used as a proxy for the plasma temperature in the divertor - is substantially weaker than 2PM predictions. Furthermore, in OSP radial sweeps in lower and upper SN configurations, in ohmic L-mode scenarios with a constant core density, the peak parallel particle flux density at the OSP is almost independent of Rt, while the 2PM predicts a linear dependence. Finally, analytical and numerical modeling of parallel flows in the divertor is presented. It is shown that an increase in total flux expansion can favour supersonic flows at the OSP. Parallel flows are also shown to be relevant by analysing SOLPS-ITER simulations of TCV., Comment: This version of the paper is outdated. The most recent, and published, version of the paper can be found at the corresponding Journal / DOI reference

This paper shows first quantitative analysis of the detachment processes in the MAST Upgrade Super-X divertor (SXD). We identify an unprecedented impact of plasma-molecular interactions involving molecular ions (likely $D_2^+$), resulting in strong ion sinks (Molecular Activated Recombination - MAR), leading to a reduction of ion target flux. The MAR ion sinks exceed the divertor ion sources before electron-ion recombination (EIR) starts to occur, suggesting that significant ionisation occurs outside of the divertor chamber. In the EIR region, $T_e \ll 0.2$ eV is observed and MAR remains significant in these deep detached phases. The total ion sink strength demonstrates the capability for particle (ion) exhaust in the Super-X Configuration. Molecular Activated Dissociation (MAD) is the dominant volumetric neutral atom creation process can lead to an electron cooling of 20\% of $P_{SOL}$. The measured total radiative power losses \emph{in the divertor chamber} are consistent with inferred hydrogenic radiative power losses. This suggests that intrinsic divertor impurity radiation, despite the carbon walls, is minor in the divertor chamber. This contrasts previous TCV results, which may be associated with enhanced plasma-neutral interactions and reduced chemical erosion in the detached, tightly baffled SXD. The above observations have also been observed in higher heat flux (narrower SOL width) type I ELMy H-mode discharges. This provides evidence that the characterisation in this paper may be general.

Numerical simulations explore the possibility to test the tightly baffled, long-legged divertor (TBLLD) concept in a future upgrade of the Tokamak \`a configuration variable (TCV). The SOLPS-ITER code package is used to compare the exhaust performance of several TBLLD configurations with existing unbaffled and baffled TCV configurations. The TBLLDs feature a range of radial gaps between the separatrix and the outer leg side walls. All considered TBLLDs are predicted to lead to a denser and colder plasma in front of the targets and improve the power handling by factors of 2-3 compared to the present, baffled divertor and by up to a factor of 12 compared to the original, unbaffled configuration. The improved TBLLD performance is mainly due to a better neutral confinement with improved plasma-neutral interactions in the divertor region. Both power handling capability and neutral confinement increases when reducing the radial gap. The core compatibility of TBLLDs with nitrogen seeding is also evaluated and the detachment window with acceptable core pollution for the proposed TBLLDs is explored, showing a reduction of required upstream impurity concentration up to 18% to achieve the detachment with thinner radial gap.

Plasma-molecular interactions generate molecular ions which react with the plasma and contribute to detachment through molecular activated recombination (MAR), reducing the ion target flux, and molecular activated dissociation (MAD), both of which create excited atoms. Hydrogenic emission from these atoms have been detected experimentally in detached TCV, JET and MAST-U deuterium plasmas. The TCV findings, however, were in disagreement with SOLPS-ITER simulations for deuterium indicating a molecular ion density ($D_2^+$) that was insufficient to lead to significant hydrogenic emission, which was attributed to underestimates of the molecular charge exchange rate ($D_2 + D^+ \rightarrow D_2^+ + D$) for deuterium (obtained by rescaling the hydrogen rates by their isotope mass). In this work, we have performed new SOLPS-ITER simulations with the default rate setup and a modified rate setup where ion isotope mass rescaling was disabled. This increased the $D_2^+$ content by $> \times 100$. By disabling ion isotope mass rescaling: 1) the total ion sinks are more than doubled due to the inclusion of MAR; 2) the additional MAR causes the ion target flux to roll-over during detachment; 3) the total $D\alpha$ emission in the divertor increases during deep detachment by roughly a factor four; 4) the neutral atom density in the divertor is doubled due to MAD, leading to a 50\% increase in neutral pressure; 5) total hydrogenic power loss is increased by up to 60\% due to MAD. These differences result in an improved agreement between the experiment and the simulations in terms of spectroscopic measurements, ion source/sink inferences and the occurrence of an ion target flux roll-over.

In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To do that, we present Uptrendz, a multi-domain recommendation platform that can be customized to provide real-time recommendations in an API-centric way. We demonstrate (i) how to set up a real-time movie recommender using the popular MovieLens-100k dataset, and (ii) how to simultaneously support multiple application domains based on the use-case of recommendations in entrepreneurial start-up founding. For that, we differentiate between domains on the item- and system-level. We believe that our demonstration shows a convenient way to adapt, deploy and evaluate a recommender system in an API-centric way. The source-code and documentation that demonstrates how to utilize the configured Uptrendz API is available on GitHub., Comment: ECIR 2023 demo paper

The problem of composite hypothesis testing is considered in the context of Bayesian detection of weak target signals in cluttered backgrounds. (A specific example is the detection of sub-pixel targets in multispectral imagery.) In this model, the target strength (call it $a$) is an unknown parameter, and that lack of knowledge can be addressed by incorporating a prior over possible parameter values. The performance of the detector depends on the choice of prior, and -- with the motivation of enabling better performance at low target abundances -- a family of priors are investigated in which singular weight is associated with the $a\to 0$ limit. Careful treatment of this limiting process leads to a situation in which components of the prior with infinitesimal weight have nontrivial effects. Similar claims have been made for homeopathic medicines., Comment: 4 pages, 1 figure

Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the applicability of recommender systems for data and algorithm sharing. To fill this gap, we identify six recommendation scenarios for supporting data and algorithm sharing, where four of these scenarios substantially differ from the traditional recommendation scenarios in e-commerce applications. We evaluate these recommendation scenarios using a novel dataset based on interaction data of the OpenML data and algorithm sharing platform, which we also provide for the scientific community. Specifically, we investigate three types of recommendation approaches, namely popularity-, collaboration-, and content-based recommendations. We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios. Plus, the recommendation accuracy strongly depends on the specific scenario, e.g., algorithm recommendations for users are a more difficult problem than algorithm recommendations for datasets. Finally, the content-based approach generates the least popularity-biased recommendations that cover the most datasets and algorithms., Comment: Accepted to the DataEconomy Workshop at CoNEXT'22

A baffled Snowflake Minus Low-Field Side (SF-LFS) is geometrically-optimised in TCV, increasing divertor neutral pressure, to evaluate the roles of divertor closure (comparing with an unbaffled SF-LFS) and magnetic geometry (comparing with a baffled Single Null, SN) in power exhaust and core-divertor compatibility. Ohmically-heated L-mode discharges in deuterium, with a line-averaged core density of approximately 4x10(19) m-3, are seeded with nitrogen to approach detached conditions. Baffles in the SF-LFS configuration are found to reduce the peak outer target heat flux by up to 23%, without significantly affecting the location of the inter-null radiation region or the core-divertor compatibility. When compared to the baffled SN, the baffled SF-LFS exhibits a reduction in outer target heat flux by up to 66% and the ability to balance the strike-point distribution of heat flux. These benefits are less significant with N2 seeding, with similar peak target quantities (such as heat flux, electron temperature and ion flux) and divertor radiated power. Despite a radiating region located farther from the confined plasma for the SF-LFS than the baffled SN, no change in core confinement is observed. Core effective charge even indicates an increase in core impurity penetration for the SF-LFS. These experiments constitute a good reference for detailed model validations and extrapolations, exploring important physics such as core impurity shielding and the dependence of divertor cross-field transport on magnetic geometry.

Nitrogen seeded detachment has been achieved in the Tokamak a Configuration Variable (TCV) in advanced divertor configurations (ADCs), namely X-divertor and X-point target, with and without baffles in H-mode plasmas with high core confinement. Both ADCs show a remarkable reduction in the inter-ELM particle and heat fluxes to the target compared to the standard divertor configuration. 95-98% of the peak heat flux to the target is mitigated as a synergetic effect of ADCs, baffling, and nitrogen seeded detachment. The effect of divertor geometry and baffles on core-divertor compatibility is investigated in detail. The power balance in these experiments is also investigated to explore the physics behind the observed reduction in heat fluxes in the ADCs.

In-situ, two-dimensional (2D) Langmuir probe measurements across a large part of the TCV divertor are reported in L-mode discharges with and without divertor baffles. This provides detailed insights into time averaged profiles, particle fluxes, and fluctuations behavior in different divertor regimes. The presence of the baffles is shown to substantially increase the divertor neutral pressure for a given upstream density and to facilitate the access to detachment, an effect that increases with plasma current. The detailed, 2D probe measurements allow for a divertor particle balance, including ion flux contributions from parallel flows and ExB drifts. The poloidal flux contribution from the latter is often comparable or even larger than the former, such that the divertor parallel flow direction reverses in some conditions, pointing away from the target. In most conditions, the integrated particle flux at the outer target can be predominantly ascribed to ionization along the outer divertor leg, consistent with a closed-box approximation of the divertor. The exception is a strongly detached divertor, achieved here only with baffles, where the total poloidal ion flux even decreases towards the outer target, indicative of significant plasma recombination. The most striking observation from relative density fluctuation measurements along the outer divertor leg is the transition from poloidally uniform fluctuation levels in attached conditions to fluctuations strongly peaking near the X-point when approaching detachment., Comment: Added reference

A new Gas Puff Imaging (GPI) diagnostic has been installed on the TCV tokamak, providing two-dimensional insights into Scrape-Off-Layer (SOL) turbulence dynamics above, at and below the magnetic X-point. A detailed study in L-mode, attached, lower single-null discharges shows that statistical properties have little poloidal variations, while vast differences are present in the 2D behaviour of intermittent filaments. Strongly elongated filaments, just above the X-point and in the divertor far-SOL, show a good consistency in shape and dynamics with field-line tracing from filaments at the outboard midplane, highlighting their connection. In the near-SOL of the outer divertor leg, shortlived, high frequency and more circular (diameter $\sim$15 sound Larmour radii) filaments are observed. These divertor-localised filaments appear born radially at the position of maximum density and display a radially outward motion with velocity $\approx$400\,m/s that is comparable to radial velocities of upstream-connected filaments. Conversely, in these discharges ($B\times\nabla B$ pointing away from the divertor), these divertor filaments' poloidal velocities differ strongly from those of upstream-connected filaments. The importance of divertor-localised filaments upon radial transport and profile broadening is explored using filament statistics and in-situ kinetic profile measurements along the divertor leg. This provides evidence that these filaments contribute significantly to electron density profile broadening in the divertor., Comment: This is the version of the article before peer review or editing, as submitted by an author to IOPScience Nuclear Fusion. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. 15 pages, 15 figures