Field experiment on coalmine heat disaster governance using cold source from surface water

Regarding the lack of cold source for underground cooling systems from either mine inflow or return air, field experiments were taken in a high temperature deep coal mine with abundant cold source from surface water. Taking Sanhejian coal mine as an example, this paper introduced the technology scheme of heat disaster governance using surface water cold source. The paper presents the basics of this field experiment at the beginning, following by the design and site layout of the cooling system including the analysis and calculation of cold source. Numerical calculation method is also applied based on the operation parameters to simulate the influence to the surface river ecosystem. The results suggest that the temperature of surface water shall be lower than 34 ℃ after heat exchange, and when more cooling capacities are needed in the future, increasing the water flow is more favorable than increasing the cooling range of water, which is better for the ecological environment protection,

Performance and Optimization of Air Source Heat Pump Water Heater with Cyclic Heating

A new type of microchannel condenser applied in the air source heat pump water heater(ASHPWH)with cyclic heating was proposed in this study.The operating performance of the ASHPWH was frst tested.Then,the structure of the microchannel condenser was optimized with the implement of vortex generators.Finally,a numerical model of the ASHPWH was established and the optimized microchannel condenser was studied.The experimental results showed that the average coefficient of performance(COP)of the 1HP(735 W)ASHPWH reached 3.48.In addition,the optimized microchannel condenser could be matched with a 3 HP(2430W)ASHPWH with an average heating capacity of 10.30 kW,and achieving an average COP of 4.24,14.6%higher than the limit value in the national standard.

Finite element simulation of three-dimensional temperature field in underwater welding

Mathematical models of three-dimensional temperature fields in underwater welding with moving heat sources are built. Double ellipsoid Gauss model is proposed as heat sources models. Several factors which affect the temperature fields of underwater welding are analyzed. Water has little influence on thermal efftciency. Water convection coefftcient varies with the temperature difference between the water and the workpiece , and water convection makes molten pool freeze quickly. With the increase of water depth, the dimensions of heat sources model should be reduced as arc shrinks. Finite element technology is used to solve mathematical models. ANSYS software is used as finite element tool, and ANSYS Parametric Design Language is used to develop subprograms for loading the moving heat sources and the various convection coefftcients. Experiment results show that computational results by using double ellipsoid Gauss heat sources model accord well with the experimental results.

Data-driven sensitivity analysis and electricity consumption prediction for water source heat pump system using limited information

The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the prediction model can be developed. However, it is very difficult to get detailed information about existing/old buildings (information-poor buildings), it is a big challenge to predict the energy consumption accurately by limited information. This study aims to predict the electricity consumption of the water source heat pump system of an office building based on meteorological data. The key variables are selected by error analysis and sensitivity analysis, and the effects of each variable on the models’ prediction performance can be obtained. Besides, the prediction models are established by support vector regression algorithm and trained by the local meteorological data. The results show that the positive and negative variables can be identified, and these positive variables are responsible for more than 70% of the total importance. Moreover, the root mean square error falls to 4.6044 from 7.8227 and the relative square error falls to 0.1494 from 0.4313 when the negative inputs are removed. And the errors reduce further to 4.1160 and 0.1194 by parameter optimization.