Perturbative transport experiments in magnetically confined plasmas have shown, for more than 20 years, that the injection of cold pulses at the plasma edge can trigger the fast increase of core temperature. Because no single standard local transport model tried to date has been able to reproduce satisfactorily all the observed temporal behavior in the experiments, these transient transport phenomena feature prominently as an open question in the community and as a challenge for predictive capabilities in tokamak burning plasmas, such as ITER and SPARC. For the first time after more than two decades of experimental evidence, this Thesis resolves this long-standing enigma in plasma transport, by modeling of experiments conducted on the Alcator C-Mod and DIII-D tokamaks. Predictive integrated simulations with the Trapped Gyro Landau Fluid (TGLF) quasilinear transport model demonstrate that the increase of core temperature in some regimes, and lack thereof in other regimes, can be explained by a change in dominant linear micro-instability in the plasma core. The effect of major radius, electron density and plasma current on the cold pulse are well captured by TGLF, including the relative change in position of the temperature flex point as current density changes. Linear stability analysis of simulated density and current scans in Alcator C-Mod reveals a competition between trapped electron and ion temperature gradient modes as the main driver of the core transient response. Measurements of electron density evolution during the cold-pulse propagation in DIII-D are enabled by a high time resolution density profile reflectometer. The density evolution reveals the quick propagation of a pulse from edge to core, which is the mechanism to transiently increase core temperature in low-collisionality plasmas. The work presented in this Thesis demonstrates that the existence of nonlocal heat transport phenomena is not necessary for explaining the behavior and time scales of cold-pulse experiments in tokamak plasmas.

This thesis presents two new techniques for analyzing data from impurity transport experiments in magnetically confined plasmas, with specific applications to the Alcator C-Mod tokamak. The objective in developing these new techniques is to improve the quality of the experimental results used to test simulations of turbulent transport: better characterization of the uncertainty in the experimental results will yield a better test of the simulations. Transport codes are highly sensitive to the gradients of the background temperature and density profiles, so the first half of this thesis presents a new approach to fitting tokamak profiles using nonstationary Gaussian process regression. This powerful technique overcomes many of the shortcomings of previous spline-based data smoothing techniques, and can even handle more complicated cases such as line-integrated measurements, computation of second derivatives, and 2d fitting of spatially- and temporally-resolved measurements. The second half of this thesis focuses on experimental measurements of impurity transport coefficients. It is shown that there are considerable shortcomings in existing point estimates of these quantities. Next, a linearized model of impurity transport data is constructed and used to estimate diagnostic requirements for impurity transport measurements. It is found that spatial resolution is more important than temporal resolution. Finally, a fully Bayesian approach to inferring experimental impurity transport coefficient profiles which overcomes the shortcomings of the previous approaches through use of multimodal nested sampling is developed and benchmarked using synthetic data. These tests reveal that uncertainties in the transport coefficient profiles previously attributed to uncertainties in the temperature and density profiles are in fact entirely explained by changes in the spline knot positions. Appendices are provided describing the extensive work done to determine the derivatives of stationary and nonstationary covariance kernels and the open source software developed as part of this thesis work. The techniques developed here will enable more rigorous benchmarking of turbulent transport simulations, with the ultimate goal of developing a predictive capability.