2 results
Search Results
Now showing 1 - 2 of 2
- ItemEffect of Fans’ Placement on the Indoor Thermal Environment of Typical Tunnel-Ventilated Multi-Floor Pig Buildings Using Numerical Simulation(Basel : MDPI AG, 2022) Wang, Xiaoshuai; Cao, Mengbing; Hu, Feiyue; Yi, Qianying; Amon, Thomas; Janke, David; Xie, Tian; Zhang, Guoqiang; Wang, KaiyingAn increasing number of large pig farms are being built in multi-floor pig buildings (MFPBs) in China. Currently, the ventilation system of MFPB varies greatly and lacks common standards. This work aims to compare the ventilation performance of three popular MFPB types with different placement of fans using the Computational Fluid Dynamics (CFD) technique. After being validated with field-measured data, the CFD models were extended to simulate the air velocity, air temperature, humidity, and effective temperature of the three MFPBs. The simulation results showed that the ventilation rate of the building with outflowing openings in the endwall and fans installed on the top of the shaft was approximately 25% less than the two buildings with fans installed on each floor. The ventilation rate of each floor increased from the first to the top floor for both buildings with a shaft, while no significant difference was observed in the building without a shaft. Increasing the shaft’s width could mitigate the variation in the ventilation rate of each floor. The effective temperature distribution at the animal level was consistent with the air velocity distribution. Therefore, in terms of the indoor environmental condition, the fans were recommended to be installed separately on each floor.
- ItemCharting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics(Cambridge : RSC Publ., 2021) Zhu, Taishan; He, Ran; Gong, Sheng; Xie, Tian; Gorai, Prashun; Nielsch, Kornelius; Grossman, Jeffrey C.Thermoelectric power generation represents a promising approach to utilize waste heat. The most effective thermoelectric materials exhibit low thermal conductivity κ. However, less than 5% out of about 105 synthesized inorganic materials are documented with their κ values, while for the remaining 95% κ values are missing and challenging to predict. In this work, by combining graph neural networks and random forest approaches, we predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database, and chart the structural chemistry of κ into extended van-Arkel triangles. Together with the newly developed κ map and our theoretical tool, we identify rare-earth chalcogenides as promising candidates, of which we measured ZT exceeding 1.0. We note that the κ chart can be further explored, and our computational and analytical tools are applicable generally for materials informatics.