On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence

Objective: This study (MEasurement Reactions In Trials) aimed to produce recommendations on how best to minimize bias from measurement reactivity (MR) in randomized controlled trials of interventions to improve health. Study design and setting: The MERIT study consisted of: (1) an updated systematic review that examined whether measuring participants had effects on participants’ health-related behaviors, relative to no-measurement controls, and three rapid reviews to identify: (i) existing guidance on MR; (ii) existing systematic reviews of studies that have quantified the effects of measurement on behavioral or affective outcomes; and (iii) studies that have investigated the effects of objective measurements of behavior on health-related behavior; (2) a Delphi study to identify the scope of the recommendations; and (3) an expert workshop in October 2018 to discuss potential recommendations in groups. Results: Fourteen recommendations were produced by the expert group to: (1) identify whether bias is likely to be a problem for a trial; (2) decide whether to collect data about whether bias is likely to be a problem; (3) design trials to minimize the likelihood of this bias. Conclusion: These recommendations raise awareness of how and where taking measurements can produce bias in trials, and are thus helpful for trial design