Event‐integrated fluxes of protons and alpha particles in solar‐flare‐associated particle events during solar cycle 21 (1976–1986) are determined from data obtained by detectors on board the IMP‐7 and IMP‐8 satellites. Sixty‐three solar particle events with proton fluence (E > 10 MeV) > 107 cm−2 were identified from October 1972 to March 1987. The average omnidirectional flux of protons with kinetic energy > 10 MeV for cycle 21, 64 cm−2s−1, is lower than the corresponding number for cycle 20 (92 cm−2s−1) based on satellite data and for the cycle 19 (378 cm−2s−1) based on lunar sample data. Six large events contributed 70% of the total proton fluence during solar cycle 21. Several events in early 1981 with high proton fluences could account for much of the high 56Co radioactivities observed in the small‐sized Salem meteorite. The event‐averaged alpha‐particle to proton ratio in the energy interval 1–10 MeV/nucleon varies from 0.006 to 0.05, with an average value of 0.02 for the whole cycle. The events during solar cycle 21 are characterized by softer spectra for both protons and alpha particles compared to those in earlier solar cycles. No definitive correlation exists between cycle‐averaged solar flare proton fluxes and peak sunspot numbers. A comparison with long‐term (million year) averaged data for these parameters, obtained from lunar sample data, shows that the contemporary solar flare proton spectra are characterized by softer spectra (lower R0 values). A similar comparison cannot be made for the mean long‐term averaged flux, as the contemporary average suffers from uncertainty due to statistics of single events. For this reason, the solar energetic particle activity for the last two solar cycles cannot be considered as representative of average solar particle activity over longer time periods.
Data-driven models were constructed for the Productivity, CO 2 emission, and Si content for an operational Blast furnace using evolutionary approaches that involved two recent strategies based upon bi-objective genetic Programming and neural nets evolving through Genetic Algorithms. The models were utilized to compute the optimum tradeoff between the level of CO 2 emission and productivity at different Si levels, using a Predator-Prey Genetic Algorithm, well tested for computing the Pareto-optimality. The results were pitted against some similar calculations performed with commercial softwares and also compared with the results of thermodynamics-based analytical models.
Tempering is a critical step in Alnico alloy processing, yet the effects of tempering on microstructure have not been well studied. Here we report these effects, and in particular the effects on the Cu-Ni bridges. Energy-dispersive X-ray spectroscopy (EDS) maps and line scans show that tempering changes the elemental distribution in the Cu-Ni bridges, but not the morphology and distribution of Cu-bridges. The Cu concentration in the Cu-Ni bridges increases after tempering while other element concentrations decrease, especially Ni and Al. Furthermore, tempering sharpens the Cu bridge boundaries. These effects are primarily related to the large 2C44/(C11-C12) ratio for Cu, largest of all elements in Alnico. In addition, the Ni-Cu loops around the α1 phases become inconspicuous with tempering. The diffusion of Fe and Co to the α1 phase during tempering, which increases the difference of saturation magnetization between the α1 and α2 phases, is observed by EDS. In summary, α1, α2 and Cu-bridges are concentrated with their major elements during tempering which improves the magnetic properties. The formation of these features formed through elemental diffusion is discussed via energy theories.
Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.