The thermophysical properties, including composition, thermodynamic properties, transport coefficients and net emission coefficients, of thermal plasmas formed from pure iso-C4 perfluoronitrile C4F7N and C4F7N–CO2 mixtures are calculated for temperatures from 300 to 30 000 K and pressures from 0.1 to 20 atm. These gases have received much attention as alternatives to SF6 for use in circuit breakers, due to the low global warming potential and good dielectric properties of C4F7N. Since the parameters of the large molecules formed in the dissociation of C4F7N are unavailable, the partition function and enthalpy of formation were calculated using computational chemistry methods. From the equilibrium composition calculations, it was found that when C4F7N is mixed with CO2, CO2 can capture C atoms from C4F7N, producing CO, since the system consisting of small molecules such as CF4 and CO has lower energy at room temperature. This is in agreement with previous experimental results, which show that CO dominates the decomposition products of C4F7N–CO2 mixtures; it could limit the repeated breaking performance of C4F7N. From the point of view of chemical stability, the mixing ratio of CO2 should therefore be chosen carefully. Through comparison with common arc quenching gases (including SF6, CF3I and C5F10O), it is found that for the temperature range for which electrical conductivity remains low, pure C4F7N has similar ρCp (product of mass density and specific heat) properties to SF6, and higher radiative emission coefficient, properties that are correlated with good arc extinguishing capability. For C4F7N–CO2 mixtures, the electrical conductivity is very close to that of SF6 while the ρCp peak at 7000 K caused by decomposition of CO implies inferior interruption capability to that of SF6. The calculated properties will be useful in arc simulations.
Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine-based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine-based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.
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