Wenju Hu

Dr. Wenju Hu


Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2017 in Procedia Engineering
Verified
Journal Article
Open access
WHWenju Hu
Wenju Hu
MCMo-Ning Chang
Mo-Ning Chang
YGYan Gao
Yan Gao
Qunli ZhangQunli Zhang
Qunli Zhang
LYLing-Yan Yang
Ling-Yan Yang
DLDe-Ying Li
De-Ying Li

23 August 2017 in Journal of Heat Transfer
Verified
Journal Article
Closed access
PDF file
LZLianying Zhang
Lianying Zhang
YLYuanyuan Liu
Yuanyuan Liu
YWYuan Wang
Yuan Wang
Liwen JinLiwen Jin
Liwen Jin
Qunli ZhangQunli Zhang
Qunli Zhang
WHWenju Hu
Wenju Hu

25 December 2018 in Applied Sciences
Verified
Journal Article
Open access
PDF file
WHWenju Hu
Wenju Hu
JPJia Peng
Jia Peng
JNJinzhe Nie
Jinzhe Nie
GYGao Yan
Gao Yan
Qunli ZhangQunli Zhang
Qunli Zhang
The hot-wall heat exchanger (HWHE) has been widely used in thermal engineering fields such as ceiling radiant heating/cooling, refrigerator condenser, solar heat collection, and high-temperature heat recovery. However, the numerical simulation normally used for heat transfer prediction in HWHE is usually not as convenient as the analytic solutions in engineering applications. In this paper, a new heat transfer mathematical model of HWHE-based on analytic solutions was developed, which could be much faster to obtain the heat transfer properties of HWHE. The proposed model was validated under four conditions with literature values, which showed that the deviations of heat flux are 2.53%, 0.99%, 2.12%, and 1.96%, indicating its accuracy is satisfied. The model was then used to analyze the thermal property of HWHE. The results show the thermal resistance caused by panel with heat convection and conduction accounts for 96.54% of HWHE thermal resistance, and the thermal resistance caused by heat convection on the surface of panel is 74.43%. The analyzation results also show that adding aluminum foil around pipes could decrease HWHE thermal resistance by 5.11%. Besides, the influence of pipe diameters, pipe distance, pipe heat conductivity, side wall heat conductivity, and convective heat transfer coefficient on the heat transfer performance of HWHE was analyzed. The research in this paper can be used for fast prediction and optimization of heat transfer in HWHE.

20 July 2022 in Energies
Verified
Journal Article
Open access
PDF file
WHWenju Hu
Wenju Hu
XZXin Zhang
Xin Zhang
Refrigerant maldistribution severely deteriorates the heat transfer performance of a microchannel evaporator. Compared with the refrigerant distribution among flat tubes along the header, refrigerant distribution among microchannels in the flat tube in the airflow direction has barely been paid attention. In this paper, a heat transfer mathematical model of a microchannel evaporator’s flat tube composed of vertically placed parallel microchannels in the airflow direction was developed. The Refrigerant distribution among the microchannels was evaluated and its influence on heat transfer between air and refrigerant was analyzed. The results showed that the refrigerant distribution and heat transfer performance between air and refrigerant were interrelated and interacted with each other. The temperature of the air leaving the microchannel evaporator changed along the microchannel because of uneven refrigerant distribution among the microchannels, and the air temperature difference between air leaving out of the bottom and the top of the evaporator was approximately 2.13 °C. Ignoring the heat transfer from adjacent microchannels will lead to a small heat transfer deviation for the flat tube; thus, heat transfer among microchannels can be neglected.

30 June 2026 in Drones
Verified
Journal Article
Open access
PDF file
YZYingran Zhao
Yingran Zhao
WHWenju Hu
Wenju Hu
To tackle the dynamic assignment problem of unmanned aerial vehicle (UAV) swarms, a merged coordination-optimized pigeon-inspired optimization (MCOPIO) algorithm based on the pigeon-inspired optimization (PIO) algorithm is proposed in this paper. The algorithm disrupts the original pigeon distribution via random grouping and performs mutual learning and optimization within the new groups. After dynamic optimization, the underperforming pigeons are discarded, and the flock is reorganized. Subsequently, the two stages of the basic PIO are integrated through a dynamic factor. These improvements overcome the limitations of the basic PIO algorithm, such as insufficient global search capability, poor stability, and disconnection between the two algorithm stages. Comparative experiments are conducted with the state-of-the-art intelligent computing algorithms, such as the basic PIO, particle swarm optimization (PSO), genetic algorithm (GA), and improved consensus-based bundle algorithm (ICBBA), the comparative results verify the feasibility and effectiveness of our improved PIO for UAV swarm dynamic task allocation.