Search

• Three 1.6 m (Length) × 1.6 m (Width) diesel oil pool fire tests were carried out on two URTS in hot summer. • Shafts exhausted out of much smoke, but backflow occurred farther away from the fire sources. • Temperatures/velocities under the ceiling decayed longitudinally with the power index law. • The measured mass flow rates out of unit shafts were in range of 0.31 ∼ 1.76 kg/s.m2. • Dimensionless number Ri′ was adopted to assess the smoke flow under the shafts. Shallow-buried urban road tunnels with shafts (URTS) have been operated in large cities of China, and successfully released the traffic congestion. Fire smoke is expected to exhaust out of one-side shaft groups driven by its own thermal buoyancy. However, full-scale test data of the fire smoke spreading in such tunnel are still lacked, and the prediction models needs to be verified. In hot summer of year 2020, three full-scale experiments were carried out on two URTS. A 1.6 m (length) × 1.6 m (width) iron pan was placed on ground with diesel oil used as the fire source. Testing systems were arranged under the ceiling, on top openings of shafts and at bottoms of shafts. Lots of smoke were rapidly exhausted out of the shafts closer to the fire sources, but backflow occurred at bottoms of those shafts farther from the fire sources being not conducive to smoke extraction. Other three results of full-scale fire experiments conducted on one URTS in cold winter are provided here for comparison. The findings are that both the ceiling smoke temperatures and the smoke spreading velocities decayed longitudinally well with the exponential function, and the downstream temperature attenuation coefficient k 2 were in range of 0.042 ∼ 0.188 being smaller than that of the upstream, also the hotter the weather, the smaller the k 2 ; maximum values on top openings of the shafts were measured to be 47 °C in temperature, 5 m/s in velocity and 180 ppm in CO concentration; mass flow rates of the unit shafts were measured to decrease away from the fire sources that were 0.30 ∼ 1.77 kg/(s·m2) in Fires 1 and 2, and 0.46 ∼ 1.27 kg/(s·m2) in Fire 3, also the prediction models were confirmed not to be accurate enough; number R i ′ differed among all unit shafts of Shaft 6#, and were about 13.5 ∼ 15.2 in Fire 1, but smaller to be about 1.4 in Fire 2. [ABSTRACT FROM AUTHOR]

A comfortable indoor environment plays an important role in improving students' learning efficiency and health. How to optimize the design of primary and secondary school education buildings to achieve a comfortable indoor environment, considering both energy and cost is a considerable challenge. This paper proposes a two-stage multi-objective optimization method based on a meta-model to obtain the optimal design scheme for primary and secondary school education buildings, based on daylighting, thermal comfort, energy savings and economy. The method has two stages: building envelope optimization and building generation system optimization. In the stage of building envelope optimization, an artificial neural network (ANN) model coupling optimization algorithm is used to optimize the building envelope. The performances of the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm are compared in the optimization process. In the stage of building generation system optimization, the design optimization of the building photovoltaic generation system is studied. Finally, the effectiveness of the two-stage optimization method is verified by a typical teaching building in Nanjing. The results show that the optimal scheme set of building envelope design can be obtained by this optimization method, and the optimal tilt angle and azimuth angle of the photovoltaic generation system are 30° and 210°, respectively. The minimum payback periods of the photovoltaic generation system are 11.75 years and 9.32 years under the policy of selling electricity that is not permitted and selling electricity that is permitted, respectively. • A multi-objective optimization method based on the meta-model is presented. • Optimization considering daylighting, thermal comfort, energy saving and economy. • A sensitivity analysis method based on regression method is used. • The performance of the NSGA-II and MOPSO algorithm is compared. • The payback period of photovoltaic system under different policies is optimized. [ABSTRACT FROM AUTHOR]