With popularity of heating metering system, system can be adjusted according to the change of load, and the resistance coefficient of pipe network change. Optimal identification of variable resistance coefficient is an effective means to understand the real-time operation of heating network. We present an optimal identification method for variable resistance coefficient of heat supply network based on flow observation data. Genetic algorithm is used to solve the problem. Relative error of the identification results is less than 5% in practical verification example. It shows that the method can obtain variable resistance coefficient of heating network with high accuracy when only flow observation data is available. It can provide guidance for the operation and regulation of heating systems.

To understand fantastic multiferroicity of CoCr₂O₄, a Heisenberg spin model on three-dimensional spinel lattice was constructed. In the model, we considered multiple magnetic interactions including nearest-neighbor A-A, A-B, B-B couplings as well as next-nearest-neighbor interaction among B sites. Monte Carlo simulation is employed to obtain magnetoelectric quantities. Influences of exchange couplings on magnetoelectric behaviors are emphatically investigated under rotation of magnetic field. It is found that macroscopic magnetization and polarization stem mainly from contributions of B1 ions. With varying exchange couplings of A-A interaction and next-nearest-neighboring B-B interaction among B-site ions, B-site ions exhibit prominent magnetoelectric responses, while A-site ions remain unchanged. It indicates distinct differences between symmetry and magnetic interaction environment for A-site and B-site ions in spinel lattice.