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We compute reconstructions of 4D and 5D fast-ion phase-space distribution functions in fusion plasmas from synthetic projections of these functions. The fast-ion phase-space distribution functions originating from neutral beam injection (NBI) at TCV and Wendelstein 7-X (W7-X) at full, half, and one-third injection energies can be distinguished and particle densities of each component inferred based on 20 synthetic spectra of projected velocities at TCV and 680 at W7-X. Further, we demonstrate that an expansion into a basis of slowing-down distribution functions is equivalent to regularization using slowing-down physics as prior information. Using this technique in a Tikhonov formulation, we infer the particle density fractions for each NBI energy for each NBI beam from synthetic measurements, resulting in six unknowns at TCV and 24 unknowns at W7-X. Additionally, we show that installing 40 LOS in each of 17 ports at W7-X, providing full beam coverage and almost full angle coverage, produces the highest quality reconstructions.

The pin-based pointwise energy slowing-down method (PSM) has been refined through eliminating the approximation for using the pre-tabulated collision probability during the slowing-down calculation. A collision probability table is generated by assuming that material composition and temperature are constant in the fuel pellet using the collision probability method (CPM). Refined PSM (PSM-CPM), which calculates the collision probability in the isolated fuel pellet during the slowing-down calculation using CPM, can consider nonuniform material and temperature distribution. For the methods, the extensive comparative analysis is performed with problems representing various possible conditions in a light water reactor (LWR) design. Conditions are categorized with the geometry, material distribution, temperature profile in the fuel pellet, and burnup. With test problems, PSMs (PSM and PSM-CPM) have been compared with conventional methods based on the equivalence theory. With overall calculation results, PSMs show the accuracy in the eigenvalue with differences in the order of 100 pcm compared to the reference results. There was no noticeable difference in the multigroup cross sections, reaction rates, and pin power distributions. However, PSM-CPM maintains the accuracy in the calculation of the fuel temperature coefficient under the condition with 200% power and nonuniform temperature distribution in the fuel pellet. PSM shows the difference in the eigenvalue in the order of 2,000 pcm for the fictitious pin-cell problem with highly steep temperature profiles and material compositions, but PSM-CPM shows the difference in the eigenvalue within 100 pcm.

Changing amplitude of the seasonal cycle of atmospheric CO2 (SCA) in the northern hemisphere is an emerging carbon cycle property. Mauna Loa (MLO) station (20°N, 156°W), which has the longest continuous northern hemisphere CO2 record, shows an increasing SCA before the 1980s (p <.01), followed by no significant change thereafter. We analyzed the potential driving factors of SCA slowing‐down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric transport model. We found that slowing‐down of SCA at MLO is primarily explained by response of net biome productivity (NBP) to climate change, and by changes in atmospheric circulations. Through NBP, climate change increases SCA at MLO before the 1980s and decreases it afterwards. The effect of climate change on the slowing‐down of SCA at MLO is mainly exerted by intensified drought stress acting to offset the acceleration driven by CO2 fertilization. This challenges the view that CO2 fertilization is the dominant cause of emergent SCA trends at northern sites south of 40°N. The contribution of agricultural intensification on the deceleration of SCA at MLO was elusive according to land–atmosphere CO2 flux estimated by DGVMs and atmospheric inversions. Our results also show the necessity to adequately account for changing circulation patterns in understanding carbon cycle dynamics observed from atmospheric observations and in using these observations to benchmark DGVMs. [ABSTRACT FROM AUTHOR]